Datasets:

jordyvl

/

arxiv_dataset_prep

like 0

1110.0981

The Fermi surface (FS) nesting properties of URu$_2$Si$_2$ are analyzed with particular focus on their implication for the mysterious hidden order phase. We show that there exist two Fermi surfaces that exhibit a strong nesting at the antiferromagnetic wavevector, $\boldsymbol{Q}_0$=(0,\,0,\,1). The corresponding energy dispersions fulfill the relation $\epsilon_{1}(\boldsymbol{k})$=$- \epsilon_{2} (\boldsymbol{k}\pm \boldsymbol{Q}_0)$ at eight FS hotspot lines. The spin-orbital characters of the involved $5f$ states are {\it distinct} ($j_z$=$\pm$5/2 {\it vs.} $\pm$3/2) and hence the degenerate Dirac crossings are symmetry protected in the nonmagnetic normal state. Dynamical symmetry breaking through an Ising-like spin and orbital excitation mode with $\Delta j_z$=$\pm$1 induces a hybridization of the two states, causing substantial FS gapping. Concomitant spin and orbital currents in the uranium planes give rise to a rotational symmetry breaking.

cond-mat.str-el

Strongly Correlated Electrons

6,979Strongly Correlated Electrons

1903.01931

In this paper, we propose Orthogonal Generative Adversarial Networks (O-GANs). We decompose the network of discriminator orthogonally and add an extra loss into the objective of common GANs, which can enforce discriminator become an effective encoder. The same extra loss can be embedded into any kind of GANs and there is almost no increase in computation. Furthermore, we discuss the principle of our method, which is relative to the fully-exploiting of the remaining degrees of freedom of discriminator. As we know, our solution is the simplest approach to train a generative adversarial network with auto-encoding ability.

cs.CV

cs.LG

Computer Vision and Pattern Recognition;Machine Learning

1,593Computer Vision and Pattern Recognition;Machine Learning

1205.0382

Living cell signaling systems include multistep biochemical signaling reaction cascades (BSCs) comprising modifications of molecular signaling proteins. Substantial data on BSCs have been accumulated in the field of molecular biology and the analysis of signaling systems requires qualitative evaluation. However, quantification of the information and channel capacity of BSCs has not been focused on from the perspective of information theory. In the current study, we aimed to derive basic equations for describing the channel capacity and information density of BSCs using the fluctuation theorem. From the results, channel capacity and information density can be described using the average entropy production rate when the signaling system is minimally redundant. The channel capacity could actually be calculated for the mitogen-activated protein kinase BSC when it was minimally redundant. This quantitative method of examination is applicable to the quantitative analysis of BSCs.

q-bio.MN

Molecular Networks

4,646Molecular Networks

2007.08934

In this paper, we propose offline and online adaptive enrichment algorithms for the generalized multiscale approximation of a mixed finite element method with velocity elimination to solve the subsurface flow problem in high-contrast and heterogeneous porous media. We give the theoretical analysis for the convergence of these two adaptive methods, which shows that sufficient initial basis functions (belong to the offline space) leads to a faster convergence rate. A series of numerical examples are provided to highlight the performance of both these two adaptive methods and also validate the theoretical analysis. Both offline and online adaptive methods are effective that can reduce the relative error substantially. In addition, the online adaptive method generally performs better than the offline adaptive method as online basis functions contain important global information such as distant effects that cannot be captured by offline basis functions. The numerical results also show that with a suitable initial multiscale space that includes all offline basis functions corresponding to relative smaller eigenvalues of local spectral decompositions in the offline stage, the convergence rate of the online enrichment is independent of the permeability contrast.

math.NA

cs.NA

Numerical Analysis;Numerical Analysis

5,059Numerical Analysis;Numerical Analysis

2312.14327

Abbreviation expansion is a strategy used to speed up communication by limiting the amount of typing and using a language model to suggest expansions. Here we look at personalizing a Large Language Model's (LLM) suggestions based on prior conversations to enhance the relevance of predictions, particularly when the user data is small (~1000 samples). Specifically, we compare fine-tuning, prompt-tuning, and retrieval augmented generation of expanded text suggestions for abbreviated inputs. Our case study with a deployed 8B parameter LLM on a real user living with ALS, and experiments on movie character personalization indicates that (1) customization may be necessary in some scenarios and prompt-tuning generalizes well to those, (2) fine-tuning on in-domain data (with as few as 600 samples) still shows some gains, however (3) retrieval augmented few-shot selection also outperforms fine-tuning. (4) Parameter efficient tuning allows for efficient and scalable personalization. For prompt-tuning, we also find that initializing the learned "soft-prompts" to user relevant concept tokens leads to higher accuracy than random initialization.

cs.CL

Computation and Language

1,168Computation and Language

1506.06823

We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities.

physics.data-an

stat.ME

Data Analysis, Statistics and Probability;Methodology

1,894Data Analysis, Statistics and Probability;Methodology

1607.06853

Present laboratory limits on the coupling strength of anomalous pseudoscalar and axial interactions are many orders of magnitude weaker than their scalar and vector analogs. Here we investigate two mechanisms which can circumvent this suppression and thereby lead to improved limits.

hep-ph

nucl-th

High Energy Physics - Phenomenology;Nuclear Theory

3,240High Energy Physics - Phenomenology;Nuclear Theory

1904.02680

Quantum resource theories have been widely studied to systematically characterize the non-classicality of quantum systems. Most resource theories focus on quantum states and study their interconversions. Although quantum channels are generally used as the tool for state manipulation, such a manipulation capability can be naturally regarded as a generalized quantum resource, leading to an open research direction in the resource theories of quantum channels. Various resource-theoretic properties of channels have been investigated, however, without treating channels themselves as operational resources that can also be manipulated and converted. In this Letter, we address this problem by first proposing a general resource framework for quantum channels and introducing resource monotones based on general distance quantifiers of channels. We study the interplay between channel and state resource theories by relating resource monotones of a quantum channel to its manipulation power of the state resource. Regarding channels as operational resources, we introduce asymptotic channel distillation and dilution, the most important tasks in an operational resource theory, and show how to bound the conversion rates with channel resource monotones. Finally, we apply our results to quantum coherence as an example and introduce the coherence of channels, which characterizes the coherence generation ability of channels. We consider asymptotic channel distillation and dilution with maximally incoherent operations and find the theory asymptotically irreversible, in contrast to the asymptotic reversibility of the coherence of states.

quant-ph

Quantum Physics

5,985Quantum Physics

1006.1939

A quasi-tree is a geodesic metric space quasi-isometric to a tree. We give a general construction of many actions of groups on quasi-trees. The groups we can handle include non-elementary (relatively) hyperbolic groups, rank 1 CAT(0) groups, mapping class groups and Out(Fn). As an application, we show that mapping class groups act on finite products of {\delta}-hyperbolic spaces so that orbit maps are quasi-isometric embeddings. We prove that mapping class groups have finite asymptotic dimension.

math.GR

math.GT

Group Theory;Geometric Topology

2,950Group Theory;Geometric Topology

1511.06775

Darwin's theory of evolution - as introduced in game theory by Maynard Smith - is not the only important evolutionary aspect in a evolutionary dynamics, since complex interdependencies, competition, and growth should be modeled by, for example, reactive aspects. In the ultimatum game the reciprocity and the fifty-fifty partition seems to be a deviation from rational behavior of the players under the light of the Nash equilibrium concept.Such equilibrium emerges, for example, from the punishment of the responder who generally tends to refuse unfair proposals. In the iterated version of the game, the proposers are able to improve their proposals by adding a value thus making fairer proposals. Such evolutionary aspects are not properly Darwinian-motivated, but they are endowed with a fundamental aspect: they reflect their actions according to value of the offers. Recently, a reactive version of the ultimatum game where acceptance occurs with fixed probability was proposed. In this paper, we aim at exploring this reactive version of the ultimatum game where the acceptance by players depends on the offer. In order to do so, we analyze two situations: (i) mean field and (ii) we consider players inserted within the networks with arbitrary coordination. We then show that the reactive aspect, here studied, thus far not analyzed in the evolutionary game theory literature can unveil an essential feature for the convergence to fifty-fifty split. Moreover we also analyze populations under four different polices ranging from a highly conservative to a moderate one, with respect to decision in changing the proposal based on acceptations. We show that the idea of gaining less more times added to the reciprocity of the players is highly relevant to the "healthy" societies population bargaining concept.

physics.soc-ph

Physics and Society

5,463Physics and Society

2103.02918

Let $A$ be a Noetherian flat $K[t]$-algebra, $h$ an integer and let $N$ be a graded $K[t]$-module, we introduce and study "$N$-fiber-full up to $h$" $A$-modules. We prove that an $A$-module $M$ is $N$-fiber-full up to $h$ if and only if $\mathrm{Ext}^i_A(M, N)$ is flat over $K[t]$ for all $i\le h-1$. And we show some applications of this result extending the recent result on squarefree Gr\"obner degenerations by Conca and Varbaro.

math.AC

Commutative Algebra

1,107Commutative Algebra

2009.13831

In this paper, we treat the problem of testing for normality as a binary classification problem and construct a feedforward neural network that can successfully detect normal distributions by inspecting small samples from them. The numerical experiments conducted on small samples with no more than 100 elements indicated that the neural network which we trained was more accurate and far more powerful than the most frequently used and most powerful standard tests of normality: Shapiro-Wilk, Anderson-Darling, Lilliefors and Jarque-Berra, as well as the kernel tests of goodness-of-fit. The neural network had the AUROC score of almost 1, which corresponds to the perfect binary classifier. Additionally, the network's accuracy was higher than 96% on a set of larger samples with 250-1000 elements. Since the normality of data is an assumption of numerous techniques for analysis and inference, the neural network constructed in this study has a very high potential for use in everyday practice of statistics, data analysis and machine learning in both science and industry.

stat.ML

cs.LG

Machine Learning;Machine Learning;Methodology

4,183Machine Learning;Machine Learning;Methodology

1903.07576

In this paper we study the existence and linear stability of almost periodic solutions for a NLS equation on the circle with external parameters. Starting from the seminal result of Bourgain (2005) on the quintic NLS, we propose a novel approach allowing to prove in a unified framework the persistence of finite and infinite dimensional invariant tori, which are the support of the desired solutions. The persistence result is given through a rather abstract "counter-term theorem" `a la Herman, directly in the original elliptic variables without passing to action-angle ones. Our framework allows us to find "many more" almost periodic solutions with respect to the existing literature and consider also non-translation invariant PDEs.

math.AP

Analysis of PDEs

205Analysis of PDEs

0903.2938

A semimetal-insulator transition in the Hubbard model on the honeycomb lattice is studied by using the dynamical mean field theory. Electrons in the honeycomb lattice resemble the Dirac electron liquid and for weak interactions the system is semimetal. With increasing the local interaction a semimetal-insulator transition occurs. We find a nonanalytical structure of the phase transition which consists of a first-order transition line ending in a second-order transition point and high-temperature crossover line. A phase separation of semimetal and insulator occurs at low temperatures. Maxwell construction is performed to determine the first order transition line. The phase diagram is also presented.

cond-mat.str-el

Strongly Correlated Electrons

6,979Strongly Correlated Electrons

2303.16252

Task-oriented dialog systems empower users to accomplish their goals by facilitating intuitive and expressive natural language interactions. State-of-the-art approaches in task-oriented dialog systems formulate the problem as a conditional sequence generation task and fine-tune pre-trained causal language models in the supervised setting. This requires labeled training data for each new domain or task, and acquiring such data is prohibitively laborious and expensive, thus making it a bottleneck for scaling systems to a wide range of domains. To overcome this challenge, we introduce a novel Zero-Shot generalizable end-to-end Task-oriented Dialog system, ZS-ToD, that leverages domain schemas to allow for robust generalization to unseen domains and exploits effective summarization of the dialog history. We employ GPT-2 as a backbone model and introduce a two-step training process where the goal of the first step is to learn the general structure of the dialog data and the second step optimizes the response generation as well as intermediate outputs, such as dialog state and system actions. As opposed to state-of-the-art systems that are trained to fulfill certain intents in the given domains and memorize task-specific conversational patterns, ZS-ToD learns generic task-completion skills by comprehending domain semantics via domain schemas and generalizing to unseen domains seamlessly. We conduct an extensive experimental evaluation on SGD and SGD-X datasets that span up to 20 unique domains and ZS-ToD outperforms state-of-the-art systems on key metrics, with an improvement of +17% on joint goal accuracy and +5 on inform. Additionally, we present a detailed ablation study to demonstrate the effectiveness of the proposed components and training mechanism

cs.CL

cs.LG

Computation and Language;Machine Learning

1,237Computation and Language;Machine Learning

physics/0603205

We propose a mechanism for the low frequency electromagnetic emissions and other electromagnetic phenomena which have been associated with earthquakes. The mechanism combines the critical earthquake concept and the concept of crust acting as a charging electric battery under increasing stress. The electric charges are released by activation of dormant charge carriers in the oxygen anion sublattice, called peroxy bonds or positive hole pairs (PHP), where a PHP represents an $O_3X/^{OO}\backslash YO_3$ with $X,Y = Si^{4+}, Al^{3+}...$, i.e. an $O^-$ in a matrix of $O^{2-}$ of silicates. We propose that PHP are activated by plastic deformations during the slow cooperative build-up of stress and the increasingly correlated damage culminating in a large ``critical'' earthquake. Recent laboratory experiments indeed show that stressed rocks form electric batteries which can release their charge when a conducting path closes the equivalent electric circuit. We conjecture that the intermittent and erratic occurrences of EM signals are a consequence of the progressive build-up of the battery charges in the Earth crust and their erratic release when crack networks are percolating throughout the stressed rock volumes, providing a conductive pathway for the battery currents to discharge. EM signals are thus expected close to the rupture, either slightly before or after, that is, when percolation is most favored.

physics.geo-ph

physics.gen-ph