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abstract
the study of combinatorial optimization problems with a submodular objective has attracted
much attention in recent years. such problems are important in both theory and practice because
their objective functions are very general. obtaining further improvements for many submodular
maximization problems boils down to finding better algorithms for optimizing a relaxation of
them known as the multilinear extension.
in this work we present an algorithm for optimizing the multilinear relaxation whose guarantee improves over the guarantee of the best previous algorithm (which was given by ene
and nguyen (2016)). moreover, our algorithm is based on a new technique which is, arguably,
simpler and more natural for the problem at hand. in a nutshell, previous algorithms for this
problem rely on symmetry properties which are natural only in the absence of a constraint. our
technique avoids the need to resort to such properties, and thus, seems to be a better fit for
constrained problems. | 8 |
abstract
numerous variants of self-organizing maps (soms) have been proposed in the literature, including
those which also possess an underlying structure, and in some cases, this structure itself can be defined
by the user although the concepts of growing the som and updating it have been studied, the whole
issue of using a self-organizing adaptive data structure (ads) to further enhance the properties of the
underlying som, has been unexplored. in an earlier work, we impose an arbitrary, user-defined, tree-like
topology onto the codebooks, which consequently enforced a neighborhood phenomenon and the so-called
tree-based bubble of activity (boa). in this paper, we consider how the underlying tree itself can be
rendered dynamic and adaptively transformed. to do this, we present methods by which a som with
an underlying binary search tree (bst) structure can be adaptively re-structured using conditional
rotations (conrot). these rotations on the nodes of the tree are local, can be done in constant time,
and performed so as to decrease the weighted path length (wpl) of the entire tree. in doing this, we
introduce the pioneering concept referred to as neural promotion, where neurons gain prominence in the
neural network (nn) as their significance increases. we are not aware of any research which deals with
the issue of neural promotion. the advantages of such a scheme is that the user need not be aware of any
of the topological peculiarities of the stochastic data distribution. rather, the algorithm, referred to as
the ttosom with conditional rotations (ttoconrot), converges in such a manner that the neurons
are ultimately placed in the input space so as to represent its stochastic distribution, and additionally,
the neighborhood properties of the neurons suit the best bst that represents the data. these properties
have been confirmed by our experimental results on a variety of data sets. we submit that all of these
concepts are both novel and of a pioneering sort. | 9 |
abstract— we consider the problem of steering a system with
unknown, stochastic dynamics to satisfy a rich, temporallylayered task given as a signal temporal logic formula. we
represent the system as a markov decision process in which
the states are built from a partition of the statespace and
the transition probabilities are unknown. we present provably
convergent reinforcement learning algorithms to maximize the
probability of satisfying a given formula and to maximize the
average expected robustness, i.e., a measure of how strongly
the formula is satisfied. we demonstrate via a pair of robot
navigation simulation case studies that reinforcement learning
with robustness maximization performs better than probability
maximization in terms of both probability of satisfaction and
expected robustness. | 3 |
abstract. we present high performance implementations of the qr and the singular value decomposition of a batch of small matrices hosted on the gpu with applications in the compression
of hierarchical matrices. the one-sided jacobi algorithm is used for its simplicity and inherent
parallelism as a building block for the svd of low rank blocks using randomized methods. we
implement multiple kernels based on the level of the gpu memory hierarchy in which the matrices can reside and show substantial speedups against streamed cusolver svds. the resulting
batched routine is a key component of hierarchical matrix compression, opening up opportunities
to perform h-matrix arithmetic efficiently on gpus. | 8 |
abstract
skew bridges are common in highways and railway lines when non perpendicular crossings are encountered. the structural effect of skewness is an additional
torsion on the bridge deck which may have a considerable effect, making its
analysis and design more complex. in this paper, an analytical model following
3d beam theory is firstly derived in order to evaluate the dynamic response
of skew bridges under moving loads. following, a simplified 2d model is also
considered which includes only vertical beam bending. the natural frequencies,
eigenmodes and orthogonality relationships are determined from the boundary
conditions. the dynamic response is determined in time domain by using the
“exact” integration. both models are validated through some numerical examples by comparing with the results obtained by 3d fe models. a parametric
study is performed with the simplified model in order to identify parameters
that significantly influence the vertical dynamic response of the skew bridge under traffic loads. the results show that the grade of skewness has an important
influence on the vertical displacement, but hardly on the vertical acceleration of
the bridge. the torsional stiffness really has effect on the vertical displacement
when the skew angle is large. the span length reduces the skewness effect on
the dynamic behavior of the skew bridge.
keywords: skew bridge, bridge modelling, modal analysis, moving load | 5 |
abstract
in recent years crowdsourcing has become the method of choice for gathering labeled training data
for learning algorithms. standard approaches to crowdsourcing view the process of acquiring labeled
data separately from the process of learning a classifier from the gathered data. this can give rise to
computational and statistical challenges. for example, in most cases there are no known computationally
efficient learning algorithms that are robust to the high level of noise that exists in crowdsourced data,
and efforts to eliminate noise through voting often require a large number of queries per example.
in this paper, we show how by interleaving the process of labeling and learning, we can attain computational efficiency with much less overhead in the labeling cost. in particular, we consider the realizable
setting where there exists a true target function in f and consider a pool of labelers. when a noticeable
fraction of the labelers are perfect, and the rest behave arbitrarily, we show that any f that can be efficiently learned in the traditional realizable pac model can be learned in a computationally efficient
manner by querying the crowd, despite high amounts of noise in the responses. moreover, we show that
this can be done while each labeler only labels a constant number of examples and the number of labels
requested per example, on average, is a constant. when no perfect labelers exist, a related task is to find
a set of the labelers which are good but not perfect. we show that we can identify all good labelers, when
at least the majority of labelers are good. | 8 |
abstract
a novel method to identify trampoline skills using a single video camera is proposed herein. conventional computer vision techniques are used for identification, estimation, and tracking of the gymnast’s body
in a video recording of the routine. for each frame, an open source convolutional neural network is used to
estimate the pose of the athlete’s body. body orientation and joint angle estimates are extracted from these
pose estimates. the trajectories of these angle estimates over time are compared with those of labelled reference skills. a nearest neighbour classifier utilising a mean squared error distance metric is used to identify
the skill performed. a dataset containing 714 skill examples with 20 distinct skills performed by adult male
and female gymnasts was recorded and used for evaluation of the system. the system was found to achieve
a skill identification accuracy of 80.7% for the dataset. | 1 |
abstract machines involved:
the net m of df a’s ms , ma , . . . with state set q = { q, . . . } (states are drawn as circular nodes); the pilot df a p with m-state set r = { i0 , i1 , . . . } (states are drawn as
rectangular nodes); and the dp da a to be next defined. as said, the dp da stores in
the stack the series of m-states entered during the computation, enriched with additional
information used in the parsing steps. moreover, the m-states are interleaved with terminal or nonterminal grammar symbols. the current m-state, i.e., the one on top of stack,
determines the next move: either a shift that scans the next token, or a reduction of a topmost stack segment (also called reduction handle) to a nonterminal identified by a final
candidate included in the current m-state. the absence of shift-reduce conflicts makes
the choice between shift and reduction operations deterministic. similarly, the absence of
reduce-reduce conflicts allows the parser to uniquely identify the final state of a machine.
however, this leaves open the problem to determine the stack segment to be reduced. for
that two designs will be presented: the first uses a finite pushdown alphabet; the second
uses unbounded integer pointers and, strictly speaking, no longer qualifies as a pushdown
automaton.
first, we specify the pushdown stack alphabet. since for a given net m there are finitely
many different candidates, the number of m-states is bounded and the number of candidates
in any m-state is also bounded by cmax = |q| × ( |σ| + 1 ). the dp da stack elements | 6 |
abstract. we present an i/o-efficient algorithm for computing similarity joins based on locality-sensitive hashing (lsh). in contrast to
the filtering methods commonly suggested our method has provable subquadratic dependency on the data size. further, in contrast to straightforward implementations of known lsh-based algorithms on external memory, our approach is able to take significant advantage of the available
internal memory: whereas the time complexity of classical algorithms
includes a factor of n ρ , where ρ is a parameter of the lsh used, the i/o
complexity of our algorithm merely includes a factor (n/m )ρ , where n
is the data size and m is the size of internal memory. our algorithm is
randomized and outputs the correct result with high probability. it is a
simple, recursive, cache-oblivious procedure, and we believe that it will
be useful also in other computational settings such as parallel computation. | 8 |
abstract. let a be a commutative ring, and let a be a weakly proregular
ideal in a. (if a is noetherian then any ideal in it is weakly proregular.)
suppose m is a compact generator of the category of cohomologically a-torsion
complexes. we prove that the derived double centralizer of m is isomorphic
to the a-adic completion of a. the proof relies on the mgm equivalence from
[psy] and on derived morita equivalence. our result extends earlier work of
dwyer-greenlees-iyengar [dgi] and efimov [ef]. | 0 |
abstract
though traditional algorithms could be embedded
into neural architectures with the proposed principle of (xiao, 2017), the variables that only occur
in the condition of branch could not be updated
as a special case. to tackle this issue, we multiply
the conditioned branches with dirac symbol (i.e.
1x>0 ), then approximate dirac symbol with the
continuous functions (e.g. 1 − e−α|x| ). in this
way, the gradients of condition-specific variables
could be worked out in the back-propagation process, approximately, making a fully functioned
neural graph. within our novel principle, we propose the neural decision tree (ndt), which takes
simplified neural networks as decision function
in each branch and employs complex neural networks to generate the output in each leaf. extensive experiments verify our theoretical analysis
and demonstrate the effectiveness of our model. | 9 |
abstract
adams’ extension of parsing expression grammars enables specifying indentation sensitivity using
two non-standard grammar constructs — indentation by a binary relation and alignment. this
paper proposes a step-by-step transformation of well-formed adams’ grammars for elimination
of the alignment construct from the grammar. the idea that alignment could be avoided was
suggested by adams but no process for achieving this aim has been described before.
1998 acm subject classification d.3.1 formal definitions and theory; d.3.4 processors; f.4.2
grammars and other rewriting systems
keywords and phrases parsing expression grammars, indentation, grammar transformation | 6 |
abstraction. | 6 |
abstract
the coalitional manipulation problem has been studied extensively in the literature for many
voting rules. however, most studies have focused on the complete information setting, wherein
the manipulators know the votes of the non-manipulators. while this assumption is reasonable for purposes of showing intractability, it is unrealistic for algorithmic considerations. in
most real-world scenarios, it is impractical to assume that the manipulators to have accurate
knowledge of all the other votes. in this work, we investigate manipulation with incomplete
information. in our framework, the manipulators know a partial order for each voter that is
consistent with the true preference of that voter. in this setting, we formulate three natural
computational notions of manipulation, namely weak, opportunistic, and strong manipulation.
we say that an extension of a partial order is viable if there exists a manipulative vote for that extension. we propose the following notions of manipulation when manipulators have incomplete
information about the votes of other voters.
1. w eak m anipulation: the manipulators seek to vote in a way that makes their preferred
candidate win in at least one extension of the partial votes of the non-manipulators.
2. o pportunistic m anipulation: the manipulators seek to vote in a way that makes
their preferred candidate win in every viable extension of the partial votes of the nonmanipulators.
3. s trong m anipulation: the manipulators seek to vote in a way that makes their preferred
candidate win in every extension of the partial votes of the non-manipulators.
we consider several scenarios for which the traditional manipulation problems are easy (for
instance, borda with a single manipulator). for many of them, the corresponding manipulative
questions that we propose turn out to be computationally intractable. our hardness results often
hold even when very little information is missing, or in other words, even when the instances are
very close to the complete information setting. our results show that the impact of paucity of
information on the computational complexity of manipulation crucially depends on the notion
of manipulation under consideration. our overall conclusion is that computational hardness
continues to be a valid obstruction to manipulation, in the context of a more realistic model. | 8 |
abstract
we propose a simple approach which, given
distributed computing resources, can nearly
achieve the accuracy of k-nn prediction, while
matching (or improving) the faster prediction
time of 1-nn. the approach consists of aggregating denoised 1-nn predictors over a small
number of distributed subsamples. we show,
both theoretically and experimentally, that
small subsample sizes suffice to attain similar
performance as k-nn, without sacrificing the
computational efficiency of 1-nn. | 10 |
abstract. we consider graded artinian complete intersection algebras a =
c[x0 , . . . , xm ]/i with i generated by homogeneous forms of degree d ≥ 2. we
show that the general multiplication by a linear form µl : ad−1 → ad is
injective. we prove that the weak lefschetz property for holds for any c.i.
algebra a as above with d = 2 and m ≤ 4, previously known for m ≤ 3. | 0 |
abstract
recurrent neural networks (rnns), particularly long
short-term memory (lstm), have gained much attention in
automatic speech recognition (asr). although some successful stories have been reported, training rnns remains
highly challenging, especially with limited training data. recent research found that a well-trained model can be used as
a teacher to train other child models, by using the predictions
generated by the teacher model as supervision. this knowledge transfer learning has been employed to train simple
neural nets with a complex one, so that the final performance
can reach a level that is infeasible to obtain by regular training. in this paper, we employ the knowledge transfer learning
approach to train rnns (precisely lstm) using a deep neural network (dnn) model as the teacher. this is different
from most of the existing research on knowledge transfer
learning, since the teacher (dnn) is assumed to be weaker
than the child (rnn); however, our experiments on an asr
task showed that it works fairly well: without applying any
tricks on the learning scheme, this approach can train rnns
successfully even with limited training data.
index terms— recurrent neural network, long shortterm memory, knowledge transfer learning, automatic speech
recognition
1. introduction
deep learning has gained significant success in a wide range
of applications, for example, automatic speech recognition
(asr) [1]. a powerful deep learning model that has been
reported effective in asr is the recurrent neural network
(rnn), e.g., [2, 3, 4]. an obvious advantage of rnns compared to conventional deep neural networks (dnns) is that
rnns can model long-term temporal properties and thus are
suitable for modeling speech signals.
a simple training method for rnns is the backpropagation through time algorithm [5]. this first-order approach,
this work was supported by the national natural science foundation of
china under grant no. 61371136 and the mestdc phd foundation project
no. 20130002120011. this paper was also supported by huilan ltd. and
sinovoice. | 9 |
abstract— this paper considers the problem of implementing
a previously proposed distributed direct coupling quantum
observer for a closed linear quantum system. by modifying the
form of the previously proposed observer, the paper proposes
a possible experimental implementation of the observer plant
system using a non-degenerate parametric amplifier and a
chain of optical cavities which are coupled together via optical
interconnections. it is shown that the distributed observer
converges to a consensus in a time averaged sense in which an
output of each element of the observer estimates the specified
output of the quantum plant. | 3 |
abstract. using valuation rings and valued fields as examples, we
discuss in which ways the notions of “topological ifs attractor” and
“fractal space” can be generalized to cover more general settings. | 0 |
abstract
we consider the problem of reconstructing an unknown bounded function u defined on a domain x ⊂
rd from noiseless or noisy samples of u at n points (xi )i=1,...,n . we measure the reconstruction error in a
norm l2 (x, dρ) for some given probability measure dρ. given a linear space vm with dim(vm ) = m ≤ n,
we study in general terms the weighted least-squares approximations from the spaces vm based on
independent random samples. it is well known that least-squares approximations can be inaccurate and
unstable when m is too close to n, even in the noiseless case. recent results from [4, 5] have shown the
interest of using weighted least squares for reducing the number n of samples that is needed to achieve an
accuracy comparable to that of best approximation in vm , compared to standard least squares as studied
in [3]. the contribution of the present paper is twofold. from the theoretical perspective, we establish
results in expectation and in probability for weighted least squares in general approximation spaces vm .
these results show that for an optimal choice of sampling measure dµ and weight w, which depends on the
space vm and on the measure dρ, stability and optimal accuracy are achieved under the mild condition
that n scales linearly with m up to an additional logarithmic factor. in contrast to [3], the present
analysis covers cases where the function u and its approximants from vm are unbounded, which might
occur for instance in the relevant case where x = rd and dρ is the gaussian measure. from the numerical
perspective, we propose a sampling method which allows one to generate independent and identically
distributed samples from the optimal measure dµ. this method becomes of interest in the multivariate
setting where dµ is generally not of tensor product type. we illustrate this for particular examples of
approximation spaces vm of polynomial type, where the domain x is allowed to be unbounded and high
or even infinite dimensional, motivated by certain applications to parametric and stochastic pdes. | 10 |
abstract
in this paper, we explore different ways to extend a recurrent neural network
(rnn) to a deep rnn. we start by arguing that the concept of depth in an rnn
is not as clear as it is in feedforward neural networks. by carefully analyzing
and understanding the architecture of an rnn, however, we find three points of
an rnn which may be made deeper; (1) input-to-hidden function, (2) hidden-tohidden transition and (3) hidden-to-output function. based on this observation, we
propose two novel architectures of a deep rnn which are orthogonal to an earlier
attempt of stacking multiple recurrent layers to build a deep rnn (schmidhuber, 1992; el hihi and bengio, 1996). we provide an alternative interpretation
of these deep rnns using a novel framework based on neural operators. the
proposed deep rnns are empirically evaluated on the tasks of polyphonic music
prediction and language modeling. the experimental result supports our claim
that the proposed deep rnns benefit from the depth and outperform the conventional, shallow rnns. | 9 |
abstract—today’s hpc applications are producing extremely large amounts of data, such that data storage and
analysis are becoming more challenging for scientific research.
in this work, we design a new error-controlled lossy compression algorithm for large-scale scientific data. our key
contribution is significantly improving the prediction hitting
rate (or prediction accuracy) for each data point based on
its nearby data values along multiple dimensions. we derive
a series of multilayer prediction formulas and their unified
formula in the context of data compression. one serious
challenge is that the data prediction has to be performed based
on the preceding decompressed values during the compression
in order to guarantee the error bounds, which may degrade
the prediction accuracy in turn. we explore the best layer
for the prediction by considering the impact of compression
errors on the prediction accuracy. moreover, we propose an
adaptive error-controlled quantization encoder, which can further improve the prediction hitting rate considerably. the data
size can be reduced significantly after performing the variablelength encoding because of the uneven distribution produced by
our quantization encoder. we evaluate the new compressor on
production scientific data sets and compare it with many other
state-of-the-art compressors: gzip, fpzip, zfp, sz-1.1, and
isabela. experiments show that our compressor is the best
in class, especially with regard to compression factors (or bitrates) and compression errors (including rmse, nrmse, and
psnr). our solution is better than the second-best solution
by more than a 2x increase in the compression factor and
3.8x reduction in the normalized root mean squared error on
average, with reasonable error bounds and user-desired bitrates. | 7 |
abstract
in this paper we consider the problem of detecting a change in the parameters of an autoregressive process, where the moments of the innovation process
do not necessarily exist. an empirical likelihood ratio test for the existence
of a change point is proposed and its asymptotic properties are studied. in
contrast to other work on change point tests using empirical likelihood, we do
not assume knowledge of the location of the change point. in particular, we
prove that the maximizer of the empirical likelihood is a consistent estimator
for the parameters of the autoregressive model in the case of no change point
and derive the limiting distribution of the corresponding test statistic under
the null hypothesis. we also establish consistency of the new test. a nice
feature of the method consists in the fact that the resulting test is asymptotically distribution free and does not require an estimate of the long run
variance. the asymptotic properties of the test are investigated by means of | 10 |
abstract | 6 |
abstract
pandemic influenza has the epidemic potential to kill millions of people. while various preventive measures exist (i.a., vaccination and school
closures), deciding on strategies that lead to their most effective and efficient use, remains challenging. to this end, individual-based epidemiological models are essential to assist decision makers in determining the
best strategy to curve epidemic spread. however, individual-based models are computationally intensive and therefore it is pivotal to identify
the optimal strategy using a minimal amount of model evaluations. additionally, as epidemiological modeling experiments need to be planned,
a computational budget needs to be specified a priori. consequently, we
present a new sampling method to optimize the evaluation of preventive
strategies using fixed budget best-arm identification algorithms. we use
epidemiological modeling theory to derive knowledge about the reward
distribution which we exploit using bayesian best-arm identification algorithms (i.e., top-two thompson sampling and bayesgap). we evaluate
these algorithms in a realistic experimental setting and demonstrate that
it is possible to identify the optimal strategy using only a limited number
of model evaluations, i.e., 2-to-3 times faster compared to the uniform
sampling method, the predominant technique used for epidemiological
decision making in the literature. finally, we contribute and evaluate a
statistic for top-two thompson sampling to inform the decision makers
about the confidence of an arm recommendation. | 2 |
abstract
this paper deals with feature selection procedures for spatial point processes intensity estimation. we consider regularized versions of estimating
equations based on campbell theorem derived from two classical functions:
poisson likelihood and logistic regression likelihood. we provide general conditions on the spatial point processes and on penalty functions which ensure
consistency, sparsity and asymptotic normality. we discuss the numerical
implementation and assess finite sample properties in a simulation study. finally, an application to tropical forestry datasets illustrates the use of the
proposed methods. | 10 |
abstract—wireless networked control systems (wncs) are
composed of spatially distributed sensors, actuators, and controllers communicating through wireless networks instead of
conventional point-to-point wired connections. due to their main
benefits in the reduction of deployment and maintenance costs,
large flexibility and possible enhancement of safety, wncs are
becoming a fundamental infrastructure technology for critical control systems in automotive electrical systems, avionics
control systems, building management systems, and industrial
automation systems. the main challenge in wncs is to jointly
design the communication and control systems considering their
tight interaction to improve the control performance and the
network lifetime. in this survey, we make an exhaustive review
of the literature on wireless network design and optimization for
wncs. first, we discuss what we call the critical interactive
variables including sampling period, message delay, message
dropout, and network energy consumption. the mutual effects of
these communication and control variables motivate their joint
tuning. we discuss the effect of controllable wireless network
parameters at all layers of the communication protocols on the
probability distribution of these interactive variables. we also
review the current wireless network standardization for wncs
and their corresponding methodology for adapting the network
parameters. moreover, we discuss the analysis and design of
control systems taking into account the effect of the interactive
variables on the control system performance. finally, we present
the state-of-the-art wireless network design and optimization for
wncs, while highlighting the tradeoff between the achievable
performance and complexity of various approaches. we conclude
the survey by highlighting major research issues and identifying
future research directions.
index terms—wireless networked control systems, wireless
sensor and actuator networks, joint design, delay, reliability,
sampling rate, network lifetime, optimization. | 3 |
abstract. in this article, the projectivity of a finitely generated
flat module of a commutative ring is studied through its exterior
powers and invariant factors. consequently, the related results of
endo, vasconcelos, wiegand, cox-rush and puninski-rothmaler
on the projectivity of f.g. flat modules are generalized. | 0 |
abstract | 2 |
abstract—this paper studies optimal tracking performance
issues for multi-input-multi-output linear time-invariant systems
under networked control with limited bandwidth and additive
colored white gaussian noise channel. the tracking performance
is measured by control input energy and the energy of the error
signal between the output of the system and the reference signal
with respect to a brownian motion random process. this paper
focuses on two kinds of network parameters, the basic network
parameter-bandwidth and the additive colored white gaussian
noise, and studies the tracking performance limitation problem.
the best attainable tracking performance is obtained, and the
impact of limited bandwidth and additive colored white gaussian
noise of the communication channel on the attainable tracking
performance is revealed. it is shown that the optimal tracking
performance depends on nonminimum phase zeros, gain at all
frequencies and their directions unitary vector of the given plant,
as well as the limited bandwidth and additive colored white
gaussian noise of the communication channel. the simulation
results are finally given to illustrate the theoretical results.
index terms—networked control systems, bandwidth, additive
colored white gaussian noise, performance limitation. | 3 |
abstract
a group of order pn (p prime) has an indecomposable polynomial
invariant of degree at least pn−1 if and only if the group has a cyclic
subgroup of index at most p or it is isomorphic to the elementary
abelian group of order 8 or the heisenberg group of order 27.
keywords: polynomial invariants, degree bounds, zero-sum sequences | 4 |
abstract. secret sharing is a cryptographic discipline in which the goal is
to distribute information about a secret over a set of participants in such a
way that only specific authorized combinations of participants together can
reconstruct the secret. thus, secret sharing schemes are systems of variables
in which it is very clearly specified which subsets have information about
the secret. as such, they provide perfect model systems for information
decompositions. however, following this intuition too far leads to an information
decomposition with negative partial information terms, which are difficult to
interpret. one possible explanation is that the partial information lattice
proposed by williams and beer is incomplete and has to be extended to
incorporate terms corresponding to higher order redundancy. these results
put bounds on information decompositions that follow the partial information
framework, and they hint at where the partial information lattice needs to be
improved. | 7 |
abstract
permutation polynomials over finite fields are an interesting subject due to their important
applications in the areas of mathematics and engineering. in this paper we investigate the trinomial
f (x) = x(p−1)q+1 + xpq − xq+(p−1) over the finite field fq2 , where p is an odd prime and q =
pk with k being a positive integer. it is shown that when p = 3 or 5, f (x) is a permutation
trinomial of fq2 if and only if k is even. this property is also true for more general class of
polynomials g(x) = x(q+1)l+(p−1)q+1 + x(q+1)l+pq − x(q+1)l+q+(p−1) , where l is a nonnegative integer
and gcd(2l + p, q − 1) = 1. moreover, we also show that for p = 5 the permutation trinomials f (x)
proposed here are new in the sense that they are not multiplicative equivalent to previously known
ones of similar form.
index terms finite fields, permutation polynomials, trinomials, niho exponents, multiplicative inequivalent.
ams 94b15, 11t71 | 7 |
abstracts/definitions) to improve the answering ability of a
model. marino et al. (2017) explicitly incorporate knowledge graphs into an image classification
model. xu et al. (2016) created a recall mechanism into a standard lstm cell that retrieves pieces
of external knowledge encoded by a single representation for a conversation model. concurrently,
dhingra et al. (2017) exploit linguistic knowledge using mage-grus, an adapation of grus to
handle graphs, however, external knowledge has to be present in form of triples. the main difference to our approach is that we incorporate external knowledge in free text form on the word level
prior to processing the task at hand which constitutes a more flexible setup. ahn et al. (2016) exploit knowledge base facts about mentioned entities for neural language models. bahdanau et al.
(2017) and long et al. (2017) create word embeddings on-the-fly by reading word definitions prior
to processing the task at hand. pilehvar et al. (2017) seamlessly incorporate information about word
senses into their representations before solving the downstream nlu task, which is similar. we go
one step further by seamlessly integrating all kinds of fine-grained assertions about concepts that
might be relevant for the task at hand.
another important aspect of our approach is the notion of dynamically updating wordrepresentations. tracking and updating concepts, entities or sentences with dynamic memories is
a very active research direction (kumar et al., 2016; henaff et al., 2017; ji et al., 2017; kobayashi
et al., 2017). however, those works typically focus on particular tasks whereas our approach is taskagnostic and most importantly allows for the integration of external background knowledge. other
related work includes storing temporary information in weight matrices instead of explicit neural
activations (such as word representations) as a biologically more plausible alternative. | 2 |
abstraction: at the highest level, the software architecture models the
m. bujorianu and m. fisher (eds.):
workshop on formal methods for aerospace (fma)
eptcs 20, 2010, pp. 80–87, doi:10.4204/eptcs.20.9 | 6 |
abstract. we prove that abelian subgroups of the outer automorphism group of a free group are quasiisometrically embedded. our proof uses recent developments in the theory of train track maps by feighnhandel. as an application, we prove the rank conjecture for out(fn ). | 4 |
abstract
catroid is a free and open source visual programming language,
programming environment, image manipulation program, and
website. catroid allows casual and first-time users starting from
age eight to develop their own animations and games solely using
their android phones or tablets. catroid also allows to wirelessly
control external hardware such as lego mindstorms robots via
bluetooth, bluetooth arduino boards, as well as parrot’s popular
and inexpensive ar.drone quadcopters via wifi. | 6 |
abstract
the map-reduce computing framework rose to prominence with datasets of such size that dozens
of machines on a single cluster were needed for individual jobs. as datasets approach the exabyte
scale, a single job may need distributed processing not only on multiple machines, but on multiple
clusters. we consider a scheduling problem to minimize weighted average completion time of n
jobs on m distributed clusters of parallel machines. in keeping with the scale of the problems
motivating this work, we assume that (1) each job is divided into m “subjobs” and (2) distinct
subjobs of a given job may be processed concurrently.
when each cluster is a single machine, this is the np-hard concurrent open shop problem. a
clear limitation of such a model is that a serial processing assumption sidesteps the issue of how
different tasks of a given subjob might be processed in parallel. our algorithms explicitly model
clusters as pools of resources and effectively overcome this issue.
under a variety of parameter settings, we develop two constant factor approximation algorithms for this problem. the first algorithm uses an lp relaxation tailored to this problem
from prior work. this lp-based algorithm provides strong performance guarantees. our second
algorithm exploits a surprisingly simple mapping to the special case of one machine per cluster.
this mapping-based algorithm is combinatorial and extremely fast. these are the first constant
factor approximations for this problem.
remark - a shorter version of this paper (one that omitted several proofs) appeared in the
proceedings of the 2016 european symposium on algorithms.
1998 acm subject classification f.2.2 nonnumerical algorithms and problems
keywords and phrases approximation algorithms, distributed computing, machine scheduling,
lp relaxations, primal-dual algorithms
digital object identifier 10.4230/lipics.esa.2016.234 | 8 |
abstract
the propagation of sound in a shallow water environment is characterized by boundary reflections from the sea surface and sea floor.
these reflections result in multiple (indirect) sound propagation
paths, which can degrade the performance of passive sound source
localization methods. this paper proposes the use of convolutional
neural networks (cnns) for the localization of sources of broadband acoustic radiated noise (such as motor vessels) in shallow
water multipath environments. it is shown that cnns operating
on cepstrogram and generalized cross-correlogram inputs are able
to more reliably estimate the instantaneous range and bearing of
transiting motor vessels when the source localization performance
of conventional passive ranging methods is degraded. the ensuing
improvement in source localization performance is demonstrated
using real data collected during an at-sea experiment.
index terms— source localization, doa estimation, convolutional neural networks, passive sonar, reverberation
1. introduction
sound source localization plays an important role in array signal processing with wide applications in communication, sonar and robotics
systems [1]. it is a focal topic in the scientific literature on acoustic array signal processing with a continuing challenge being acoustic source localization in the presence of interfering multipath arrivals [2, 3, 4]. in practice, conventional passive narrowband sonar
array methods involve frequency-domain beamforming of the outputs of hydrophone elements in a receiving array to detect weak signals, resolve closely-spaced sources, and estimate the direction of
a sound source. typically, 10-100 sensors form a linear array with
a uniform interelement spacing of half a wavelength at the array’s
design frequency. however, this narrowband approach has application over a limited band of frequencies. the upper limit is set by
the design frequency, above which grating lobes form due to spatial
aliasing, leading to ambiguous source directions. the lower limit is
set one octave below the design frequency because at lower frequencies the directivity of the array is much reduced as the beamwidths
broaden.
an alternative approach to sound source localization is to measure the time difference of arrival (tdoa) of the signal at an array of spatially distributed receivers [5, 6, 7, 8], allowing the instantaneous position of the source to be estimated. the accuracy of
the source position estimates is found to be sensitive to any uncertainty in the sensor positions [9]. furthermore, reverberation has an
adverse effect on time delay estimation, which negatively impacts
∗ work | 7 |
abstract
we consider the ransac algorithm in the context of subspace recovery and subspace clustering. we derive some theory and perform some numerical experiments. we also draw some
correspondences with the methods of hardt and moitra (2013) and chen and lerman (2009b). | 10 |
abstraction of a physical sensing device
b
located at position p and running program p . module m is the collection
of methods that the sensor makes available for internal and for external usage. typically this collection of methods may be interpreted as the library of
functions of the tiny operating system installed in the sensor. sensors may only
broadcast values to its neighborhood sensors. radius rt defines the transmitting
power of a sensor and specifies the border of communication: a circle centered
at position p (the position of the sensor) with radius rt . likewise, radius rs
defines the sensing capability of the sensor, meaning that a sensor may only
read values inside the circle centered at position p with radius rs .
values h~v ip define the field of measures that may be sensed. a value consists
of a tuple ~v denoting the strength of the measure at a given position p of the
plane. values are managed by the environment; in csn there are no primitives for manipulating values, besides reading (sensing) values. we assume that
the environment inserts these values in the network and update its contents.
networks are combined using the parallel composition operator | .
1 | 2 |
abstract—filtering and smoothing algorithms for linear
discrete-time state-space models with skew-t-distributed measurement noise are presented. the presented algorithms use a
variational bayes based posterior approximation with coupled
location and skewness variables to reduce the error caused by
the variational approximation. although the variational update
is done suboptimally, our simulations show that the proposed
method gives a more accurate approximation of the posterior
covariance matrix than an earlier proposed variational algorithm.
consequently, the novel filter and smoother outperform the
earlier proposed robust filter and smoother and other existing
low-complexity alternatives in accuracy and speed. we present
both simulations and tests based on real-world navigation data,
in particular gps data in an urban area, to demonstrate the
performance of the novel methods. moreover, the extension of the
proposed algorithms to cover the case where the distribution of
the measurement noise is multivariate skew-t is outlined. finally,
the paper presents a study of theoretical performance bounds for
the proposed algorithms. | 3 |
abstract
in this paper, a discrete-time multi-agent system is presented which is formulated in terms of the
delta operator. the proposed multi-agent system can unify discrete-time and continuous-time multi-agent
systems. in a multi-agent network, in practice, the communication among agents is acted upon by various
factors. the communication network among faulty agents may cause link failures, which is modeled
by randomly switching graphs. first, we show that the delta representation of discrete-time multi-agent
system reaches consensus in mean (in probability and almost surely) if the expected graph is strongly
connected. the results induce that the continuous-time multi-agent system with random networks can
also reach consensus in the same sense. second, the influence of faulty agents on consensus value is
quantified under original network. by using matrix perturbation theory, the error bound is also presented
in this paper. finally, a simulation example is provided to demonstrate the effectiveness of our theoretical
results.
index terms
consensus, multi-agent systems, delta operator, link failures, error bound. | 3 |
abstract
we give sufficient identifiability conditions for estimating mixing proportions in two-component mixtures of skew normal distributions with one known component. we consider the
univariate case as well as two multivariate extensions: a multivariate skew normal distribution (msn) by azzalini and dalla valle (1996) and the canonical fundamental skew normal
distribution (cfusn) by arellano-valle and genton (2005). the characteristic function of
the cfusn distribution is additionally derived. | 10 |
abstract: the shear strength and stick-slip behavior of a rough rock joint are analyzed using the | 5 |
abstract
for σ an orientable surface of finite topological type having genus
at least 3 (possibly closed or possibly with any number of punctures
or boundary components), we show that the mapping class group
m od(σ) has no faithful linear representation in any dimension over
any field of positive characteristic. | 4 |
abstract. in this work we propose a heuristic algorithm for the layout optimization for disks installed in
a rotating circular container. this is a unequal circle packing problem with additional balance constraints.
it proved to be an np-hard problem, which justifies heuristics methods for its resolution in larger instances.
the main feature of our heuristic is based on the selection of the next circle to be placed inside the container
according to the position of the system’s center of mass. our approach has been tested on a series of
instances up to 55 circles and compared with the literature. computational results show good performance
in terms of solution quality and computational time for the proposed algorithm.
keywords: packing problem, layout optimization problem, nonidentical circles, heuristic algorithm | 5 |
abstract
a rack on [n] can be thought of as a set of maps (fx )x∈[n] , where each
fx is a permutation of [n] such that f(x)fy = fy−1 fx fy for all x and y. in
2013, blackburn showed that the number of isomorphism classes of racks
2
2
on [n] is at least 2(1/4−o(1))n and at most 2(c+o(1))n , where c ≈ 1.557;
2
in this paper we improve the upper bound to 2(1/4+o(1))n , matching the
lower bound. the proof involves considering racks as loopless, edge-coloured
directed multigraphs on [n], where we have an edge of colour y between x
and z if and only if (x)fy = z, and applying various combinatorial tools. | 4 |
abstract
this paper discusses minimum distance estimation method in the linear regression
model with dependent errors which are strongly mixing. the regression parameters are
estimated through the minimum distance estimation method, and asymptotic distributional properties of the estimators are discussed. a simulation study compares the
performance of the minimum distance estimator with other well celebrated estimator.
this simulation study shows the superiority of the minimum distance estimator over
another estimator. koulmde (r package) which was used for the simulation study is
available online. see section 4 for the detail. | 10 |
abstract. programs that process data that reside in files are widely
used in varied domains, such as banking, healthcare, and web-traffic analysis. precise static analysis of these programs in the context of software
verification and transformation tasks is a challenging problem. our key
insight is that static analysis of file-processing programs can be made
more useful if knowledge of the input file formats of these programs is
made available to the analysis. we propose a generic framework that
is able to perform any given underlying abstract interpretation on the
program, while restricting the attention of the analysis to program paths
that are potentially feasible when the program’s input conforms to the
given file format specification. we describe an implementation of our approach, and present empirical results using real and realistic programs
that show how our approach enables novel verification and transformation tasks, and also improves the precision of standard analysis problems. | 6 |
abstract
integer-forcing source coding has been proposed as a low-complexity method for compression of distributed correlated
gaussian sources. in this scheme, each encoder quantizes its observation using the same fine lattice and reduces the result
modulo a coarse lattice. rather than directly recovering the individual quantized signals, the decoder first recovers a full-rank
set of judiciously chosen integer linear combinations of the quantized signals, and then inverts it. it has been observed that the
method works very well for “most” but not all source covariance matrices. the present work quantifies the measure of bad
covariance matrices by studying the probability that integer-forcing source coding fails as a function of the allocated rate, where
the probability is with respect to a random orthonormal transformation that is applied to the sources prior to quantization. for
the important case where the signals to be compressed correspond to the antenna inputs of relays in an i.i.d. rayleigh fading
environment, this orthonormal transformation can be viewed as being performed by nature. hence, the results provide performance
guarantees for distributed source coding via integer forcing in this scenario. | 7 |
abstract
the classical-input quantum-output (cq) wiretap channel is a communication model involving a classical sender x, a legitimate quantum receiver b, and a quantum eavesdropper e.
the goal of a private communication protocol that uses such a channel is for the sender x to
transmit a message in such a way that the legitimate receiver b can decode it reliably, while the
eavesdropper e learns essentially nothing about which message was transmitted. the ε-oneshot private capacity of a cq wiretap channel is equal to the maximum number of bits that can
be transmitted over the channel, such that the privacy error is no larger than ε ∈ (0, 1). the
present paper provides a lower bound on the ε-one-shot private classical capacity, by exploiting
the recently developed techniques of anshu, devabathini, jain, and warsi, called position-based
coding and convex splitting. the lower bound is equal to a difference of the hypothesis testing
mutual information between x and b and the “alternate” smooth max-information between x
and e. the one-shot lower bound then leads to a non-trivial lower bound on the second-order
coding rate for private classical communication over a memoryless cq wiretap channel. | 7 |
abstract—the world is connected through the internet. as the
abundance of internet users connected into the web and the
popularity of cloud computing research, the need of artificial
intelligence (ai) is demanding. in this research, genetic algorithm (ga) as ai optimization method through natural selection
and genetic evolution is utilized. there are many applications of
ga such as web mining, load balancing, routing, and scheduling
or web service selection. hence, it is a challenging task to discover whether the code mainly server side and web based language
technology affects the performance of ga. travelling salesperson problem (tsp) as non polynomial-hard (np-hard) problem
is provided to be a problem domain to be solved by ga. while
many scientists prefer python in ga implementation, another
popular high-level interpreter programming language such as
php (php hypertext preprocessor) and ruby were benchmarked. line of codes, file sizes, and performances based on ga
implementation and runtime were found varies among these programming languages. based on the result, the use of ruby in ga
implementation is recommended.
keywords—tsp; genetic algorithm; web-programming language | 6 |
abstract—smartphone applications designed to track human
motion in combination with wearable sensors, e.g., during physical exercising, raised huge attention recently. commonly, they
provide quantitative services, such as personalized training instructions or the counting of distances. but qualitative monitoring
and assessment is still missing, e.g., to detect malpositions, to
prevent injuries, or to optimize training success.
we address this issue by presenting a concept for qualitative
as well as generic assessment of recurrent human motion by
processing multi-dimensional, continuous time series tracked
with motion sensors. therefore, our segmentation procedure
extracts individual events of specific length and we propose
expressive features to accomplish a qualitative motion assessment
by supervised classification. we verified our approach within
a comprehensive study encompassing 27 athletes undertaking
different body weight exercises. we are able to recognize six
different exercise types with a success rate of 100% and to assess
them qualitatively with an average success rate of 99.3%.
keywords—motion assessment; activity recognition; physical
exercises; segmentation | 1 |
abstractions for high-performance remote data access,
mechanisms for scalable data replication, cataloging with rich semantic and syntactic information, data discovery, distributed monitoring, and web-based portals for using the system.
keywords—climate modeling, data management, earth system
grid (esg), grid computing. | 5 |
abstract—modern dense flash memory devices operate at
very low error rates, which require powerful error correcting
coding (ecc) techniques. an emerging class of graph-based ecc
techniques that has broad applications is the class of spatiallycoupled (sc) codes, where a block code is partitioned into
components that are then rewired multiple times to construct
an sc code. here, our focus is on sc codes with the underlying
circulant-based structure. in this paper, we present a three-stage
approach for the design of high performance non-binary sc (nbsc) codes optimized for practical flash channels; we aim at
minimizing the number of detrimental general absorbing sets of
type two (gasts) in the graph of the designed nb-sc code. in
the first stage, we deploy a novel partitioning mechanism, called
the optimal overlap partitioning, which acts on the protograph of
the sc code to produce optimal partitioning corresponding to
the smallest number of detrimental objects. in the second stage,
we apply a new circulant power optimizer to further reduce the
number of detrimental gasts. in the third stage, we use the
weight consistency matrix framework to manipulate edge weights
to eliminate as many as possible of the gasts that remain in
the nb-sc code after the first two stages (that operate on the
unlabeled graph of the code). simulation results reveal that nbsc codes designed using our approach outperform state-of-theart nb-sc codes when used over flash channels. | 7 |
abstract. let g be a finite group and, for a prime p, let s be a sylow p-subgroup of g. a
character χ of g is called sylp -regular if the restriction of χ to s is the character of the regular
representation of s. if, in addition, χ vanishes at all elements of order divisible by p, χ is said
to be steinberg-like. for every finite simple group g we determine all primes p for which g
admits a steinberg-like character, except for alternating groups in characteristic 2. moreover,
we determine all primes for which g has a projective f g-module of dimension |s|, where f is
an algebraically closed field of characteristic p. | 4 |
abstract
we present bounded dynamic (but observer-free) output feedback laws
that achieve global stabilization of equilibrium profiles of the partial
differential equation (pde) model of a simplified, age-structured
chemostat model. the chemostat pde state is positive-valued, which
means that our global stabilization is established in the positive orthant of
a particular function space—a rather non-standard situation, for which we
develop non-standard tools. our feedback laws do not employ any of the
(distributed) parametric knowledge of the model. moreover, we provide a
family of highly unconventional control lyapunov functionals (clfs) for
the age-structured chemostat pde model. two kinds of feedback
stabilizers are provided: stabilizers with continuously adjusted input and
sampled-data stabilizers. the results are based on the transformation of the
first-order hyperbolic partial differential equation to an ordinary
differential equation (one-dimensional) and an integral delay equation
(infinite-dimensional). novel stability results for integral delay equations
are also provided; the results are of independent interest and allow the
explicit construction of the clf for the age-structured chemostat model. | 3 |
abstract
this paper studies a pursuit-evasion problem involving a single pursuer and a single evader, where we are interested in developing
a pursuit strategy that doesn’t require continuous, or even periodic, information about the position of the evader. we propose
a self-triggered control strategy that allows the pursuer to sample the evader’s position autonomously, while satisfying desired
performance metric of evader capture. the work in this paper builds on the previously proposed self-triggered pursuit strategy
which guarantees capture of the evader in finite time with a finite number of evader samples. however, this algorithm relied
on the unrealistic assumption that the evader’s exact position was available to the pursuer. instead, we extend our previous
framework to develop an algorithm which allows for uncertainties in sampling the information about the evader, and derive
tolerable upper-bounds on the error such that the pursuer can guarantee capture of the evader. in addition, we outline the
advantages of retaining the evader’s history in improving the current estimate of the true location of the evader that can be
used to capture the evader with even less samples. our approach is in sharp contrast to the existing works in literature and
our results ensure capture without sacrificing any performance in terms of guaranteed time-to-capture, as compared to classic
algorithms that assume continuous availability of information.
key words: pursuit-evasion; self-triggered control; sampled-data control; set-valued analysis | 3 |
abstract. in this article we explore an algorithm for diffeomorphic
random sampling of nonuniform probability distributions on riemannian manifolds. the algorithm is based on optimal information transport
(oit)—an analogue of optimal mass transport (omt). our framework
uses the deep geometric connections between the fisher-rao metric on
the space of probability densities and the right-invariant information
metric on the group of diffeomorphisms. the resulting sampling algorithm is a promising alternative to omt, in particular as our formulation is semi-explicit, free of the nonlinear monge–ampere equation.
compared to markov chain monte carlo methods, we expect our algorithm to stand up well when a large number of samples from a low
dimensional nonuniform distribution is needed.
keywords: density matching, information geometry, fisher–rao metric, optimal transport, image registration, diffeomorphism groups, random sampling
msc2010: 58e50, 49q10, 58e10 | 10 |
abstract
this paper presents the development of an adaptive algebraic multiscale solver for
compressible flow (c-ams) in heterogeneous porous media. similar to the recently
developed ams for incompressible (linear) flows [wang et al., jcp, 2014], c-ams
operates by defining primal and dual-coarse blocks on top of the fine-scale grid.
these coarse grids facilitate the construction of a conservative (finite volume) coarsescale system and the computation of local basis functions, respectively. however,
unlike the incompressible (elliptic) case, the choice of equations to solve for basis
functions in compressible problems is not trivial. therefore, several basis function
formulations (incompressible and compressible, with and without accumulation)
are considered in order to construct an efficient multiscale prolongation operator.
as for the restriction operator, c-ams allows for both multiscale finite volume
(msfv) and finite element (msfe) methods. finally, in order to resolve highfrequency errors, fine-scale (pre- and post-) smoother stages are employed. in order
to reduce computational expense, the c-ams operators (prolongation, restriction,
and smoothers) are updated adaptively. in addition to this, the linear system in the
newton-raphson loop is infrequently updated. systematic numerical experiments
are performed to determine the effect of the various options, outlined above, on the
c-ams convergence behaviour. an efficient c-ams strategy for heterogeneous 3d
compressible problems is developed based on overall cpu times. finally, c-ams is
compared against an industrial-grade algebraic multigrid (amg) solver. results
of this comparison illustrate that the c-ams is quite efficient as a nonlinear solver,
even when iterated to machine accuracy.
key words: multiscale methods, compressible flows, heterogeneous porous media,
scalable linear solvers, multiscale finite volume method, multiscale finite element
method, iterative multiscale methods, algebraic multiscale methods. | 5 |
abstract
numerical simulation of compressible fluid flows is performed using the euler equations. they include the scalar advection equation for the density, the
vector advection equation for the velocity and a given pressure dependence on
the density. an approximate solution of an initial–boundary value problem is
calculated using the finite element approximation in space. the fully implicit
two–level scheme is used for discretization in time. numerical implementation
is based on newton’s method. the main attention is paid to fulfilling conservation laws for the mass and total mechanical energy for the discrete formulation.
two–level schemes of splitting by physical processes are employed for numerical
solving problems of barotropic fluid flows. for a transition from one time level
to the next one, an iterative process is used, where at each iteration the linearized scheme is implemented via solving individual problems for the density
and velocity. possibilities of the proposed schemes are illustrated by numerical
results for a two–dimensional model problem with density perturbations.
keywords: compressible fluids, the euler system, barotropic fluid, finite
element method, conservation laws, two–level schemes, decoupling scheme | 5 |
abstract. formal concept analysis and its associated conceptual structures have been used to support exploratory search through conceptual
navigation. relational concept analysis (rca) is an extension of formal concept analysis to process relational datasets. rca and its multiple interconnected structures represent good candidates to support exploratory search in relational datasets, as they are enabling navigation
within a structure as well as between the connected structures. however,
building the entire structures does not present an efficient solution to
explore a small localised area of the dataset, for instance to retrieve the
closest alternatives to a given query. in these cases, generating only a
concept and its neighbour concepts at each navigation step appears as
a less costly alternative. in this paper, we propose an algorithm to compute a concept and its neighbourhood in extended concept lattices. the
concepts are generated directly from the relational context family, and
possess both formal and relational attributes. the algorithm takes into
account two rca scaling operators. we illustrate it on an example.
keywords: relational concept analysis, formal concept analysis, ondemand generation | 2 |
abstract
a research frontier has emerged in scientific computation, wherein discretisation error
is regarded as a source of epistemic uncertainty that can be modelled. this raises several
statistical challenges, including the design of statistical methods that enable the coherent
propagation of probabilities through a (possibly deterministic) computational work-flow,
in order to assess the impact of discretisation error on the computer output. this paper
examines the case for probabilistic numerical methods in routine statistical computation.
our focus is on numerical integration, where a probabilistic integrator is equipped with a full
distribution over its output that reflects the fact that the integrand has been discretised. our
main technical contribution is to establish, for the first time, rates of posterior contraction for
one such method. several substantial applications are provided for illustration and critical
evaluation, including examples from statistical modelling, computer graphics and a computer
model for an oil reservoir. | 10 |
abstract and
talk (mcdonnell et al., 2017).
10
imagenet32 4x
imagenet32 10x
imagenet32 15x
cifar-100 4x
cifar-100 10x
cifar-10
svhn
mnist
imagenet single crop
imagenet multi-crop
bwn on imagenet | 9 |
abstract
today’s javascript applications are composed of scripts from different origins that are loaded at
run time. as not all of these origins are equally trusted, the execution of these scripts should
be isolated from one another. however, some scripts must access the application state and some
may be allowed to change it, while preserving the confidentiality and integrity constraints of the
application.
this paper presents design and implementation of decentjs, a language-embedded sandbox
for full javascript. it enables scripts to run in a configurable degree of isolation with fine-grained
access control. it provides a transactional scope in which effects are logged for review by the
access control policy. after inspection of the log, effects can be committed to the application
state or rolled back.
the implementation relies on javascript proxies to guarantee full interposition for the full
language and for all code, including dynamically loaded scripts and code injected via eval. its
only restriction is that scripts must be compliant with javascript’s strict mode.
1998 acm subject classification d.4.6 security and protection
keywords and phrases javascript, sandbox, proxy | 6 |
abstract
let n (n) denote the number of isomorphism types of groups of order n. we
consider the integers n that are products of at most 4 not necessarily distinct primes
and exhibit formulas for n (n) for such n. | 4 |
abstract
we consider the action of an irreducible outer automorphism φ on the
closure of culler–vogtmann outer space. this action has north-south
dynamics and so, under iteration, points converge exponentially to [t+φ ].
for each n ≥ 3, we give a family of outer automorphisms φk ∈
out(fn ) such that as, k goes to infinity, the rate of convergence of φk
goes to infinity while the rate of convergence of φ−1
goes to one. even
k
if we only require the rate of convergence of φk to remain bounded away
from one, no such family can be constructed when n < 3.
this family also provides an explicit example of a property described
by handel and mosher: that there is no uniform upper bound on the
distance between the axes of an automorphism and its inverse. | 4 |
abstract—distribution grid is the medium and low voltage part
of a large power system. structurally, the majority of distribution
networks operate radially, such that energized lines form a
collection of trees, i.e. forest, with a substation being at the root of
any tree. the operational topology/forest may change from time to
time, however tracking these changes, even though important for
the distribution grid operation and control, is hindered by limited
real-time monitoring. this paper develops a learning framework
to reconstruct radial operational structure of the distribution
grid from synchronized voltage measurements in the grid subject
to the exogenous fluctuations in nodal power consumption. to
detect operational lines our learning algorithm uses conditional
independence tests for continuous random variables that is
applicable to a wide class of probability distributions of the nodal
consumption and gaussian injections in particular. moreover, our
algorithm applies to the practical case of unbalanced three-phase
power flow. algorithm performance is validated on ac power flow
simulations over ieee distribution grid test cases.
keywords—distribution networks, power flow, unbalanced threephase, graphical models, conditional independence, computational
complexity | 3 |
abstract. let k be a discretly henselian field whose residue field is separably
closed. answering a question raised by g. prasad, we show that a semisimple k–
group g is quasi-split if and only if it quasi–splits after a finite tamely ramified
extension of k. | 4 |
abstract
the idea that there are any large-scale trends in the evolution of biological organisms is
highly controversial. it is commonly believed, for example, that there is a large-scale trend in
evolution towards increasing complexity, but empirical and theoretical arguments undermine
this belief. natural selection results in organisms that are well adapted to their local environments, but it is not clear how local adaptation can produce a global trend. in this paper, i
present a simple computational model, in which local adaptation to a randomly changing
environment results in a global trend towards increasing evolutionary versatility. in this
model, for evolutionary versatility to increase without bound, the environment must be
highly dynamic. the model also shows that unbounded evolutionary versatility implies an
accelerating evolutionary pace. i believe that unbounded increase in evolutionary versatility
is a large-scale trend in evolution. i discuss some of the testable predictions about organismal
evolution that are suggested by the model. | 5 |
abstract—machine learning models are frequently used to
solve complex security problems, as well as to make decisions in sensitive situations like guiding autonomous vehicles
or predicting financial market behaviors. previous efforts have
shown that numerous machine learning models were vulnerable
to adversarial manipulations of their inputs taking the form
of adversarial samples. such inputs are crafted by adding
carefully selected perturbations to legitimate inputs so as to
force the machine learning model to misbehave, for instance
by outputting a wrong class if the machine learning task of
interest is classification. in fact, to the best of our knowledge,
all previous work on adversarial samples crafting for neural
network considered models used to solve classification tasks,
most frequently in computer vision applications. in this paper,
we contribute to the field of adversarial machine learning by
investigating adversarial input sequences for recurrent neural
networks processing sequential data. we show that the classes
of algorithms introduced previously to craft adversarial samples
misclassified by feed-forward neural networks can be adapted
to recurrent neural networks. in a experiment, we show that
adversaries can craft adversarial sequences misleading both
categorical and sequential recurrent neural networks. | 9 |
abstract. we give a characterization for asymptotic dimension growth. we
apply it to cat(0) cube complexes of finite dimension, giving an alternative proof
of n. wright’s result on their finite asymptotic dimension. we also apply our new
characterization to geodesic coarse median spaces of finite rank and establish
that they have subexponential asymptotic dimension growth. this strengthens a
recent result of j. s̆pakula and n. wright. | 4 |
abstract
we consider the problem of density estimation on riemannian manifolds. density
estimation on manifolds has many applications in fluid-mechanics, optics and
plasma physics and it appears often when dealing with angular variables (such as
used in protein folding, robot limbs, gene-expression) and in general directional
statistics. in spite of the multitude of algorithms available for density estimation in
the euclidean spaces rn that scale to large n (e.g. normalizing flows, kernel methods and variational approximations), most of these methods are not immediately
suitable for density estimation in more general riemannian manifolds. we revisit
techniques related to homeomorphisms from differential geometry for projecting
densities to sub-manifolds and use it to generalize the idea of normalizing flows to
more general riemannian manifolds. the resulting algorithm is scalable, simple
to implement and suitable for use with automatic differentiation. we demonstrate
concrete examples of this method on the n-sphere sn .
in recent years, there has been much interest in applying variational inference techniques to learning
large scale probabilistic models in various domains, such as images and text [1, 2, 3, 4, 5, 6].
one of the main issues in variational inference is finding the best approximation to an intractable
posterior distribution of interest by searching through a class of known probability distributions.
the class of approximations used is often limited, e.g., mean-field approximations, implying that
no solution is ever able to resemble the true posterior distribution. this is a widely raised objection
to variational methods, in that unlike mcmc, the true posterior distribution may not be recovered
even in the asymptotic regime. to address this problem, recent work on normalizing flows [7],
inverse autoregressive flows [8], and others [9, 10] (referred collectively as normalizing flows),
focused on developing scalable methods of constructing arbitrarily complex and flexible approximate
posteriors from simple distributions using transformations parameterized by neural networks, which
gives these models universal approximation capability in the asymptotic regime. in all of these works,
the distributions of interest are restricted to be defined over high dimensional euclidean spaces.
there are many other distributions defined over special homeomorphisms of euclidean spaces that are
of interest in statistics, such as beta and dirichlet (n-simplex); norm-truncated gaussian (n-ball);
wrapped cauchy and von-misses fisher (n-sphere), which find little applicability in variational
inference with large scale probabilistic models due to the limitations related to density complexity
and gradient computation [11, 12, 13, 14]. many such distributions are unimodal and generating
complicated distributions from them would require creating mixture densities or using auxiliary
random variables. mixture methods require further knowledge or tuning, e.g. number of mixture
components necessary, and a heavy computational burden on the gradient computation in general,
e.g. with quantile functions [15]. further, mode complexity increases only linearly with mixtures as
opposed to exponential increase with normalizing flows. conditioning on auxiliary variables [16] on
the other hand constrains the use of the created distribution, due to the need for integrating out the
auxiliary factors in certain scenarios. in all of these methods, computation of low-variance gradients
is difficult due to the fact that simulation of random variables cannot be in general reparameterized
(e.g. rejection sampling [17]). in this work, we present methods that generalizes previous work on
improving variational inference in rn using normalizing flows to riemannian manifolds of interest
such as spheres sn , tori tn and their product topologies with rn , like infinite cylinders. | 10 |
abstract
we show that memcapacitive (memory capacitive) systems can be used as synapses in artificial neural
networks. as an example of our approach, we discuss the architecture of an integrate-and-fire neural
network based on memcapacitive synapses. moreover, we demonstrate that the spike-timing-dependent
plasticity can be simply realized with some of these devices. memcapacitive synapses are a low-energy
alternative to memristive synapses for neuromorphic computation. | 9 |
abstract
let g be an n-node simple directed planar graph with nonnegative edge weights. we
study the fundamental problems of computing (1) a global cut of g with minimum weight
and (2) a cycle of g with minimum weight. the best previously known algorithm for the
former problem, running in o(n log3 n) time, can be obtained from the algorithm of łacki,
˛
nussbaum, sankowski, and wulff-nilsen for single-source all-sinks maximum flows. the
best previously known result for the latter problem is the o(n log3 n)-time algorithm of
wulff-nilsen. by exploiting duality between the two problems in planar graphs, we solve
both problems in o(n log n log log n) time via a divide-and-conquer algorithm that finds a
shortest non-degenerate cycle. the kernel of our result is an o(n log log n)-time algorithm
for computing noncrossing shortest paths among nodes well ordered on a common face
of a directed plane graph, which is extended from the algorithm of italiano, nussbaum,
sankowski, and wulff-nilsen for an undirected plane graph. | 8 |
abstract
a shortest-path algorithm finds a path containing the minimal cost between two vertices in a
graph. a plethora of shortest-path algorithms is studied in the literature that span across multiple
disciplines. this paper presents a survey of shortest-path algorithms based on a taxonomy that is
introduced in the paper. one dimension of this taxonomy is the various flavors of the shortest-path
problem. there is no one general algorithm that is capable of solving all variants of the shortest-path
problem due to the space and time complexities associated with each algorithm. other important
dimensions of the taxonomy include whether the shortest-path algorithm operates over a static or
a dynamic graph, whether the shortest-path algorithm produces exact or approximate answers, and
whether the objective of the shortest-path algorithm is to achieve time-dependence or is to only be
goal directed. this survey studies and classifies shortest-path algorithms according to the proposed
taxonomy. the survey also presents the challenges and proposed solutions associated with each
category in the taxonomy. | 8 |
abstract— we present an event-triggered control strategy
for stabilizing a scalar, continuous-time, time-invariant, linear
system over a digital communication channel having bounded
delay, and in the presence of bounded system disturbance. we
propose an encoding-decoding scheme, and determine lower
bounds on the packet size and on the information transmission
rate which are sufficient for stabilization. we show that for
small values of the delay, the timing information implicit in
the triggering events is enough to stabilize the system with any
positive rate. in contrast, when the delay increases beyond a
critical threshold, the timing information alone is not enough to
stabilize the system and the transmission rate begins to increase.
finally, large values of the delay require transmission rates
higher than what prescribed by the classic data-rate theorem.
the results are numerically validated using a linearized model
of an inverted pendulum.
index terms— control under communication constraints,
event-triggered control, quantized control | 3 |
abstract. we define tate-betti and tate-bass invariants for modules over a commutative noetherian local ring r. then we show
the periodicity of these invariants provided that r is a hypersurface. in case r is also gorenstein, we see that a finitely generated
r-module m and its matlis dual have the same tate-betti and
tate-bass numbers. | 0 |
abstract—this paper studies the performance of sparse regression codes for lossy compression with the squared-error distortion
criterion. in a sparse regression code, codewords are linear
combinations of subsets of columns of a design matrix. it is shown
that with minimum-distance encoding, sparse regression codes
achieve the shannon rate-distortion function for i.i.d. gaussian
sources r∗ (d) as well as the optimal excess-distortion exponent.
this completes a previous result which showed that r∗ (d) and
the optimal exponent were achievable for distortions below a
certain threshold. the proof of the rate-distortion result is based
on the second moment method, a popular technique to show
that a non-negative random variable x is strictly positive with
high probability. in our context, x is the number of codewords
within target distortion d of the source sequence. we first identify
the reason behind the failure of the standard second moment
method for certain distortions, and illustrate the different failure
modes via a stylized example. we then use a refinement of the
second moment method to show that r∗ (d) is achievable for
all distortion values. finally, the refinement technique is applied
to suen’s correlation inequality to prove the achievability of the
optimal gaussian excess-distortion exponent.
index terms—lossy compression, sparse superposition codes,
rate-distortion function, gaussian source, error exponent, second
moment method, large deviations | 7 |
abstract. motivated by the common academic problem of allocating papers to
referees for conference reviewing we propose a novel mechanism for solving the
assignment problem when we have a two sided matching problem with preferences from one side (the agents/reviewers) over the other side (the objects/papers)
and both sides have capacity constraints. the assignment problem is a fundamental problem in both computer science and economics with application in many
areas including task and resource allocation. we draw inspiration from multicriteria decision making and voting and use order weighted averages (owas)
to propose a novel and flexible class of algorithms for the assignment problem.
we show an algorithm for finding an σ -owa assignment in polynomial time, in
contrast to the np-hardness of finding an egalitarian assignment. inspired by this
setting we observe an interesting connection between our model and the classic
proportional multi-winner election problem in social choice. | 2 |
abstract
we propose a new estimator of a discrete monotone probability
mass function with known flat regions. we analyse its asymptotic
properties and compare its performance to the grenander estimator
and to the monotone rearrangement estimator. | 10 |
abstract— we consider the problem of dense depth prediction
from a sparse set of depth measurements and a single rgb
image. since depth estimation from monocular images alone is
inherently ambiguous and unreliable, to attain a higher level of
robustness and accuracy, we introduce additional sparse depth
samples, which are either acquired with a low-resolution depth
sensor or computed via visual simultaneous localization and
mapping (slam) algorithms. we propose the use of a single
deep regression network to learn directly from the rgb-d raw
data, and explore the impact of number of depth samples on
prediction accuracy. our experiments show that, compared to
using only rgb images, the addition of 100 spatially random
depth samples reduces the prediction root-mean-square error
by 50% on the nyu-depth-v2 indoor dataset. it also boosts
the percentage of reliable prediction from 59% to 92% on
the kitti dataset. we demonstrate two applications of the
proposed algorithm: a plug-in module in slam to convert
sparse maps to dense maps, and super-resolution for lidars.
software2 and video demonstration3 are publicly available. | 2 |
abstract
estimating the entropy based on data is one of the prototypical problems in distribution
property testing and estimation. for estimating the shannon entropy of a distribution on s
elements with independent samples, [pan04] showed that the sample complexity is sublinear in
s, and [vv11a] showed that consistent estimation of shannon entropy is possible if and only
if the sample size n far exceeds logs s . in this paper we consider the problem of estimating the
entropy rate of a stationary reversible markov chain with s states from a sample path of n
observations. we show that
(a) as long as the markov chain mixes not too slowly, i.e., the relaxation time is
s2
at most o( lns3 s ), consistent estimation is achievable when n ≫ log
s.
(b) as long as the markov chain has some slight dependency, i.e., the relaxation
2
s2
time is at least 1 + ω( ln√ss ), consistent estimation is impossible when n . log
s.
2 | 10 |
abstract
the paper investigates the computational problem of predicting rna secondary
structures. the general belief is that allowing pseudoknots makes the problem hard.
existing polynomial-time algorithms are heuristic algorithms with no performance guarantee and can only handle limited types of pseudoknots. in this paper we initiate the
study of predicting rna secondary structures with a maximum number of stacking
pairs while allowing arbitrary pseudoknots. we obtain two approximation algorithms
with worst-case approximation ratios of 1/2 and 1/3 for planar and general secondary
structures, respectively. for an rna sequence of n bases, the approximation algorithm
for planar secondary structures runs in o(n3 ) time while that for the general case runs
in linear time. furthermore, we prove that allowing pseudoknots makes it np-hard to
maximize the number of stacking pairs in a planar secondary structure. this result is in
contrast with the recent np-hard results on psuedoknots which are based on optimizing
some general and complicated energy functions. | 5 |
abstract
in this paper, we further develop the approach, originating in [26], to “computation-friendly”
statistical estimation via convex programming.our focus is on estimating a linear or quadratic
form of an unknown “signal,” known to belong to a given convex compact set, via noisy indirect
observations of the signal. classical theoretical results on the subject deal with precisely stated
statistical models and aim at designing statistical inferences and quantifying their performance in
a closed analytic form. in contrast to this traditional (highly instructive) descriptive framework,
the approach we promote here can be qualified as operational – the estimation routines and their
risks are not available “in a closed form,” but are yielded by an efficient computation. all we
know in advance is that under favorable circumstances the risk of the resulting estimate, whether
high or low, is provably near-optimal under the circumstances. as a compensation for the lack of
“explanatory power,” this approach is applicable to a much wider family of observation schemes
than those where “closed form descriptive analysis” is possible.
we discuss applications of this approach to classical problems of estimating linear forms of parameters of sub-gaussian distribution and quadratic forms of partameters of gaussian and discrete
distributions. the performance of the constructed estimates is illustrated by computation experiments in which we compare the risks of the constructed estimates with (numerical) lower bounds
for corresponding minimax risks for randomly sampled estimation problems. | 10 |
abstract
we study nonconvex finite-sum problems and analyze stochastic variance reduced gradient
(svrg) methods for them. svrg and related methods have recently surged into prominence for
convex optimization given their edge over stochastic gradient descent (sgd); but their theoretical analysis almost exclusively assumes convexity. in contrast, we prove non-asymptotic rates
of convergence (to stationary points) of svrg for nonconvex optimization, and show that it is
provably faster than sgd and gradient descent. we also analyze a subclass of nonconvex problems on which svrg attains linear convergence to the global optimum. we extend our analysis
to mini-batch variants of svrg, showing (theoretical) linear speedup due to mini-batching in
parallel settings. | 9 |
abstract
a template-based generic programming approach was presented in a previous paper [19]
that separates the development effort of programming a physical model from that of computing
additional quantities, such as derivatives, needed for embedded analysis algorithms. in this
paper, we describe the implementation details for using the template-based generic programming
approach for simulation and analysis of partial differential equations (pdes). we detail several
of the hurdles that we have encountered, and some of the software infrastructure developed
to overcome them. we end with a demonstration where we present shape optimization and
uncertainty quantification results for a 3d pde application. | 5 |
abstract
blomer and naewe [bn09] modified the randomized sieving algorithm of ajtai, kumar and
sivakumar [aks01] to solve the shortest vector problem (svp). the algorithm starts with
n = 2o(n) randomly chosen vectors in the lattice and employs a sieving procedure to iteratively
obtain shorter vectors in the lattice. the running time of the sieving procedure is quadratic in
n.
we study this problem for the special but important case of the ℓ∞ norm. we give a new
sieving procedure that runs in time linear in n , thereby significantly improving the running time
of the algorithm for svp in the ℓ∞ norm. as in [aks02, bn09], we also extend this algorithm
to obtain significantly faster algorithms for approximate versions of the shortest vector problem
and the closest vector problem (cvp) in the ℓ∞ norm.
we also show that the heuristic sieving algorithms of nguyen and vidick [nv08] and wang
et.al. [wltb11] can also be analyzed in the ℓ∞ norm. the main technical contribution in
this part is to calculate the expected volume of intersection of a unit ball centred at origin and
another ball of a different radius centred at a uniformly random point on the boundary of the
unit ball. this might be of independent interest. | 8 |
abstract. we present a method for computing the table of marks of a direct product
of finite groups. in contrast to the character table of a direct product of two finite groups,
its table of marks is not simply the kronecker product of the tables of marks of the two
groups. based on a decomposition of the inclusion order on the subgroup lattice of a
direct product as a relation product of three smaller partial orders, we describe the table
of marks of the direct product essentially as a matrix product of three class incidence
matrices. each of these matrices is in turn described as a sparse block diagonal matrix.
as an application, we use a variant of this matrix product to construct a ghost ring
and a mark homomorphism for the rational double burnside algebra of the symmetric
group s3 . | 4 |
abstract
if x, y, z denote sets of random variables, two different data sources may contain
samples from px,y and py,z , respectively. we argue that causal inference can help
inferring properties of the ‘unobserved joint distributions’ px,y,z or px,z . the properties
may be conditional independences (as in ‘integrative causal inference’) or also quantitative
statements about dependences.
more generally, we define a learning scenario where the input is a subset of variables
and the label is some statistical property of that subset. sets of jointly observed variables
define the training points, while unobserved sets are possible test points. to solve this
learning task, we infer, as an intermediate step, a causal model from the observations that
then entails properties of unobserved sets. accordingly, we can define the vc dimension
of a class of causal models and derive generalization bounds for the predictions.
here, causal inference becomes more modest and better accessible to empirical tests
than usual: rather than trying to find a causal hypothesis that is ‘true’ (which is a problematic term when it is unclear how to define interventions) a causal hypothesis is useful
whenever it correctly predicts statistical properties of unobserved joint distributions.
within such a ‘pragmatic’ application of causal inference, some popular heuristic approaches become justified in retrospect. it is, for instance, allowed to infer dags from
partial correlations instead of conditional independences if the dags are only used to
predict partial correlations.
i hypothesize that our pragmatic view on causality may even cover the usual meaning in terms of interventions and sketch why predicting the impact of interventions can
sometimes also be phrased as a task of the above type. | 10 |
abstract
the constrained lcs problem asks one to find a longest common subsequence of two input strings
a and b with some constraints. the str-ic-lcs problem is a variant of the constrained lcs
problem, where the solution must include a given constraint string c as a substring. given two strings
a and b of respective lengths m and n , and a constraint string c of length at most min{m, n },
the best known algorithm for the str-ic-lcs problem, proposed by deorowicz (inf. process. lett.,
11:423–426, 2012), runs in o(m n ) time. in this work, we present an o(mn + nm )-time solution to
the str-ic-lcs problem, where m and n denote the sizes of the run-length encodings of a and b,
respectively. since m ≤ m and n ≤ n always hold, our algorithm is always as fast as deorowicz’s
algorithm, and is faster when input strings are compressible via rle. | 8 |
abstract
let (r, m) be a noetherian local ring, e the injective hull of k = r/m and m ◦ =
homr (m, e) the matlis dual of the r-module m. if the canonical monomorphism ϕ :
m → m ◦◦ is surjective, m is known to be called (matlis-)reflexive. with the help of the
bass numbers µ(p, m) = dimκ(p) (homr (r/p, m)p ) of m with respect to p we show: m
is reflexive if and only if µ(p, m) = µ(p, m ◦◦ ) for all p ∈ spec(r). from this it follows
for every r-module m: if there exists a monomorphism m ◦◦ ֒→ m or an epimorphism
m ։ m ◦◦ , then m is already reflexive.
key words: matlis-reflexive modules, bass numbers, associated prime ideals, torsion modules, cotorsion modules.
mathematics subject classification (2010): 13b35, 13c11, 13e10. | 0 |
abstract
the ability to use inexpensive, noninvasive sensors to accurately classify flying insects would have significant
implications for entomological research, and allow for the development of many useful applications in vector
control for both medical and agricultural entomology. given this, the last sixty years have seen many research
efforts on this task. to date, however, none of this research has had a lasting impact. in this work, we explain
this lack of progress. we attribute the stagnation on this problem to several factors, including the use of acoustic
sensing devices, the overreliance on the single feature of wingbeat frequency, and the attempts to learn complex
models with relatively little data. in contrast, we show that pseudo-acoustic optical sensors can produce vastly
superior data, that we can exploit additional features, both intrinsic and extrinsic to the insect’s flight behavior,
and that a bayesian classification approach allows us to efficiently learn classification models that are very
robust to overfitting. we demonstrate our findings with large scale experiments that dwarf all previous works
combined, as measured by the number of insects and the number of species considered. | 5 |
abstract | 8 |
abstract. in the context of the genome-wide association studies (gwas), one has to solve
long sequences of generalized least-squares problems; such a task has two limiting factors:
execution time –often in the range of days or weeks– and data management –data sets in
the order of terabytes. we present an algorithm that obviates both issues. by pipelining
the computation, and thanks to a sophisticated transfer strategy, we stream data from hard
disk to main memory to gpus and achieve sustained peak performance; with respect to a
highly-optimized cpu implementation, our algorithm shows a speedup of 2.6x. moreover, the
approach lends itself to multiple gpus and attains almost perfect scalability. when using 4
gpus, we observe speedups of 9x over the aforementioned implementation, and 488x over a
widespread biology library.
keywords: gwas, generalized least-squares, computational biology, out-of-core computation, high-performance, multiple gpus, data transfer, multibuffering, streaming, big data | 5 |
abstract. finite rank median spaces are a simultaneous generalisation
of finite dimensional cat(0) cube complexes and real trees. if γ is an
irreducible lattice in a product of rank one simple lie groups, we show
that every action of γ on a complete, finite rank median space has
a global fixed point. this is in sharp contrast with the behaviour of
actions on infinite rank median spaces.
the fixed point property is obtained as corollary to a superrigidity
result; the latter holds for irreducible lattices in arbitrary products of
compactly generated groups.
we exploit roller compactifications of median spaces; these were introduced in [fio17a] and generalise a well-known construction in the
case of cube complexes. we provide a reduced 1-cohomology class that
detects group actions with a finite orbit in the roller compactification.
even for cat(0) cube complexes, only second bounded cohomology
classes were known with this property, due to [cfi16]. as a corollary,
we observe that, in gromov’s density model, random groups at low density do not have shalom’s property hf d . | 4 |
abstract—in this paper, we compare the performance of two
main mimo techniques, beamforming and multiplexing, in the
terahertz (thz) band. the main problem with the thz band is
its huge propagation loss, which is caused by the tremendous
signal attenuation due to molecule absorption of the electromagnetic wave. to overcome the path loss issue, massive mimo
has been suggested to be employed in the network and is expected
to provide tbps for a distance within a few meters. in this
context, beamforming is studied recently as the main technique
to take advantage of mimo in thz and overcome the very
high path loss with the assumption that the thz communication
channel is line-of-sight (los) and there are not significant
multipath rays. on the other hand, recent studies also showed
that the well-known absorbed energy by molecules can be reradiated immediately in the same frequency. such re-radiated
signal is correlated with the main signal and can provide rich
scattering paths for the communication channel. this means that
a significant mimo multiplexing gain can be achieved even in a
los scenario for the thz band. our simulation results reveal a
surprising observation that the mimo multiplexing could be
a better choice than the mimo beamforming under certain
conditions in thz communications. | 7 |
abstract. an algorithm to decide the emptiness of a regular type expression with set operators given a set of parameterised type definitions
is presented. the algorithm can also be used to decide the equivalence
of two regular type expressions and the inclusion of one regular type expression in another. the algorithm strictly generalises previous work in
that tuple distributivity is not assumed and set operators are permitted
in type expressions.
keywords: type, emptiness, prescriptive type | 6 |
abstract—the allan variance (av) is a widely used quantity
in areas focusing on error measurement as well as in the general
analysis of variance for autocorrelated processes in domains
such as engineering and, more specifically, metrology. the form
of this quantity is widely used to detect noise patterns and
indications of stability within signals. however, the properties of
this quantity are not known for commonly occurring processes
whose covariance structure is non-stationary and, in these cases,
an erroneous interpretation of the av could lead to misleading
conclusions. this paper generalizes the theoretical form of the
av to some non-stationary processes while at the same time
being valid also for weakly stationary processes. some simulation
examples show how this new form can help to understand the
processes for which the av is able to distinguish these from the
stationary cases and hence allow for a better interpretation of
this quantity in applied cases.
index terms—metrology, sensor calibration, bias-instability,
longitudinal studies, haar wavelet variance, heteroscedasticity. | 10 |
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