Miracle-dz/UniFAR-FTUs huggingface dataset:
Biases in Inverse Ising Estimates of Near-Critical Behaviour

Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and they may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer to criticality than one would expect from the data. Data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging data set from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world data sets.
Sublinear Algorithms for TSP via Path Covers

We study sublinear time algorithms for the traveling salesman problem (TSP). First, we focus on the closely related maximum path cover problem, which asks for a collection of vertex disjoint paths that include the maximum number of edges. We show that for any fixed $\epsilon>0$, there is an algorithm that $(1/2 - \epsilon)$-approximates the maximum path cover size of an $n$-vertex graph in $\widetilde{O}(n)$ time. This improves upon a $(3/8-\epsilon)$-approximate $\widetilde{O}(n \sqrt{n})$-time algorithm of Chen, Kannan, and Khanna [ICALP'20]. Equipped with our path cover algorithm, we give $\widetilde{O}(n)$ time algorithms that estimate the cost of graphic TSP and $(1, 2)$-TSP up to factors of $1.83$ and $(1.5+\epsilon)$, respectively. Our algorithm for graphic TSP improves over a $1.92$-approximate $\widetilde{O}(n)$ time algorithm due to [CHK ICALP'20, Behnezhad FOCS'21]. Our algorithm for $(1,2)$-TSP improves over a folklore $(1.75 + \epsilon)$-approximate $\widetilde{O}(n)$-time algorithm, as well as a $(1.625+\epsilon)$-approximate $\widetilde{O}(n\sqrt{n})$-time algorithm of [CHK ICALP'20]. Our analysis of the running time uses connections to parallel algorithms and is information-theoretically optimal up to poly log $n$ factors. Additionally, we show that our approximation guarantees for path cover and $(1,2)$-TSP hit a natural barrier: We show better approximations require better sublinear time algorithms for the well-studied maximum matching problem.
Language-Informed Transfer Learning for Embodied Household Activities

For service robots to become general-purpose in everyday household environments, they need not only a large library of primitive skills, but also the ability to quickly learn novel tasks specified by users. Fine-tuning neural networks on a variety of downstream tasks has been successful in many vision and language domains, but research is still limited on transfer learning between diverse long-horizon tasks. We propose that, compared to reinforcement learning for a new household activity from scratch, home robots can benefit from transferring the value and policy networks trained for similar tasks. We evaluate this idea in the BEHAVIOR simulation benchmark which includes a large number of household activities and a set of action primitives. For easy mapping between state spaces of different tasks, we provide a text-based representation and leverage language models to produce a common embedding space. The results show that the selection of similar source activities can be informed by the semantic similarity of state and goal descriptions with the target task. We further analyze the results and discuss ways to overcome the problem of catastrophic forgetting.
Excitation of Multiperiodic Kink Motions in Solar Flare Loops: Possible Application to Quasiperiodic Pulsations

Magnetohydrodynamic (MHD) waves are often invoked to interpret quasiperiodic pulsations (QPPs) in solar flares. We study the response of a straight flare loop to a kink-like velocity perturbation using three-dimensional MHD simulations and forward model the microwave emissions using the fast gyrosynchrotron code. Kink motions with two periodicities are simultaneously generated, with the long-period component (P L = 57 s) being attributed to the radial fundamental kink mode and the short-period component (P S = 5.8 s) to the first leaky kink mode. Forward modeling results show that the two-periodic oscillations are detectable in the microwave intensities for some lines of sight. Increasing the beam size to (1″)2 does not wipe out the microwave oscillations. We propose that the first leaky kink mode is a promising candidate mechanism to account for short-period QPPs. Radio telescopes with high spatial resolutions can help distinguish between this new mechanism and such customary interpretations as sausage modes.
Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency

With the boom of cryptocurrency and its concomitant financial risk concerns, detecting fraudulent behaviors and associated malicious addresses has been drawing significant research effort. Most existing studies, however, rely on the full history features or full-fledged address transaction networks, both of which are unavailable in the problem of early malicious address detection and therefore failing them for the task. To detect fraudulent behaviors of malicious addresses in the early stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor. Specifically, in addition to the general address features, we propose Asset Transfer Paths and corresponding path graphs to characterize early transaction patterns. Furthermore, since transaction patterns change rapidly in the early stage, we propose Evolve Path Encoder LSTM and Evolve Path Graph GCN to encode asset transfer path and path graph under an evolving structure setting. Hierarchical Survival Predictor then predicts addresses' labels with high scalability and efficiency. We investigate the effectiveness and generalizability of Evolve Path Tracer on three real-world malicious address datasets. Our experimental results demonstrate that Evolve Path Tracer outperforms the state-of-the-art methods. Extensive scalability experiments demonstrate the model's adaptivity under a dynamic prediction setting.
Assymmetry of the α-laser emitted in far-field radiation

The main feature of a semiconductor (alpha) -laser is the codirectional small angle Bragg-reflection in a medium with gain. Within the framework of the coupled wave formalism for Bragg reflection an asymmetrical filtering characteristic occurs in dependence on the coupling constant, the ground gain and the gain coupling coefficient. The asymmetric far- field distribution of (alpha) -lasers is comparable with that filtering process.
New upper limits on low-frequency radio emission from isolated neutron stars with LOFAR

Neutron stars that show X-ray and $\gamma$-ray pulsed emission must, somewhere in the magnetosphere, generate electron-positron pairs. Such pairs are also required for radio emission, but then why do a number of these sources appear radio quiet? Here, we carried out a deep radio search towards four such neutron stars that are isolated X-ray/$\gamma$-ray pulsars but for which no radio pulsations have been detected yet. These sources are 1RXS J141256.0+792204 (Calvera), PSR J1958+2846, PSR J1932+1916 and SGR J1907+0919. Searching at lower radio frequencies, where the radio beam is thought to be wider, increases the chances of detecting these sources, compared to the earlier higher-frequency searches. We thus carried a search for periodic and single-pulse radio emission with the LOFAR radio telescope at 150 MHz. We used the known periods, and searched a wide range of dispersion measures, as the distances are not well constrained. We did not detect pulsed emission from any of the four sources. However, we put very constraining upper limits on the radio flux density at 150 MHz, of $\lesssim$1.4 mJy.
On the monotonicity of the stopping boundary for time-inhomogeneous optimal stopping problems

We consider a class of time-inhomogeneous optimal stopping problems and we provide sufficient conditions on the data of the problem that guarantee monotonicity of the optimal stopping boundary. In our setting, time-inhomogeneity stems not only from the reward function but, in particular, from the time dependence of the drift coefficient of the one-dimensional stochastic differential equation (SDE) which drives the stopping problem. In order to obtain our results, we mostly employ probabilistic arguments: we use a comparison principle between solutions of the SDE computed at different starting times, and martingale methods of optimal stopping theory. We also show a variant of the main theorem, which weakens one of the assumptions and additionally relies on the renowned connection between optimal stopping and free-boundary problems.
Magnetic phase diagram of the breathing-kagome antiferromagnet 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Nd</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:msub><mml:mi>BWO</mml:mi><mml:mn>9</mml:mn></mml:msub></mml:mrow></mml:math>

The highly-frustrated rare-earth based magnet Nd$_3$BWO$_9$ is a promising candidate in the search for proximate spin liquid physics. We present a thorough investigation on single crystals of this material using bulk and microscopic techniques. Magnetization data reveal a fractional magnetization plateau for three different investigated field directions. The magnetic phase diagram is mapped out from calorimetric data and exhibits several domes of magnetic order below 0.3 K. Propagation vectors for all ordered phases are presented. The results suggest complex ordering in this material, and unveil the existence of a commensuration transition of the propagation vector at zero magnetic field. A scenario where interplane exchange interactions are essential to a magnetic model of Nd$_3$BWO$_9$ is discussed.
Concentration in Gossip Opinion Dynamics over Random Graphs

We study concentration inequalities in gossip opinion dynamics over random graphs. In the model, a network is generated from a random graph model with independent edges, and agents interact pairwise randomly over the network. During the process, regular agents average neighbors' opinions and then update, whereas stubborn agents do not change opinions. To approximate the original process, we introduce a gossip model over an expected graph, obtained by averaging all possible networks generated from the random graph model. Using concentration inequalities, we derive high-probability bounds for the distance between the expected final opinion vectors over the random graph and over the expected graph. Leveraging matrix perturbation results, we show how such concentration can help study the effect of network structure on the expected final opinions in two cases: (i) When the influence of stubborn agents is large, the expected final opinions polarize and are close to stubborn agents' opinions. (ii) When the influence of stubborn agents is small, the expected final opinions are close to each other. With the help of concentration inequalities for Markov chains, we obtain high-probability bounds for the distance between time-averaged opinions and the expected final opinions over the expected graph. In simulation, we validate the theoretical findings, and study a gossip model over a stochastic block model that has community structure.
The effect of Pd doped in Ru layer on the interlayer coupling in Co/RuPd multilayers

2.0nmCo/tnmRu1−xPdx multilayers with x=0, 0.05, 0.08, 0.24, 0.39 and 0.48 were prepared by magnetron sputtering. The spacer layer thickness of both Ru (before doping Pd) and RuPd (after doping Pd) varies from 0.2nm to 1.6nm. Two effects have been investigated: (1) the dependence of the interlayer coupling on the thickness of Ru1−xPdx as a function of x and (2) the dependence of the interlayer coupling on Pd doping density, x, as a function of thickness of Ru1−xPdx. Our results indicate that the interlayer coupling is strongly dependent on the doping density and the spacer layer thickness. The saturation field Hs increases when very low concentration of Pd doped in the Ru layers and a suitable spacer thickness are adopted.
Mutation Testing of Deep Reinforcement Learning Based on Real Faults

Testing Deep Learning (DL) systems is a complex task as they do not behave like traditional systems would, notably because of their stochastic nature. Nonetheless, being able to adapt existing testing techniques such as Mutation Testing (MT) to DL settings would greatly improve their potential verifiability. While some efforts have been made to extend MT to the Supervised Learning paradigm, little work has gone into extending it to Reinforcement Learning (RL) which is also an important component of the DL ecosystem but behaves very differently from SL. This paper builds on the existing approach of MT in order to propose a framework, RLMutation, for MT applied to RL. Notably, we use existing taxonomies of faults to build a set of mutation operators relevant to RL and use a simple heuristic to generate test cases for RL. This allows us to compare different mutation killing definitions based on existing approaches, as well as to analyze the behavior of the obtained mutation operators and their potential combinations called Higher Order Mutation(s) (HOM). We show that the design choice of the mutation killing definition can affect whether or not a mutation is killed as well as the generated test cases. Moreover, we found that even with a relatively small number of test cases and operators we manage to generate HOM with interesting properties which can enhance testing capability in RL systems.
Skorohod's topologies on path space

We introduce the path space over a general metrisable space. Elements of this space are paths, which are pairs consisting of a closed subset of the real line and a cadlag function that is deﬁned on that subset and takes values in the metrisable space. We equip the space of all paths with topologies that generalise Skorohod’s J1 and M1 topologies, prove that these topologies are Polish, and derive compactness criteria. The central idea is that the closed graph (in case of the J1 topology) and the ﬁlled-in graph (in case of the M1 topology) of a path can naturally be viewed as totally ordered compact sets. We deﬁne a variant of the Hausdorﬀ metric that measures the distance between two compact sets, each of which is equipped with a total order. We show that the topology generated by this metric is Polish and derive a compactness criterion. Specialising to closed or ﬁlled-in graphs then yields Skorohod’s J1 and M1 topologies, generalised to functions that need not all be deﬁned on the same domain. MSC 2020. To be ﬁlled in.
Incremental Dead State Detection in Logarithmic Time

Identifying live and dead states in an abstract transition system is a recurring problem in formal verification; for example, it arises in our recent work on efficiently deciding regex constraints in SMT. However, state-of-the-art graph algorithms for maintaining reachability information incrementally (that is, as states are visited and before the entire state space is explored) assume that new edges can be added from any state at any time, whereas in many applications, outgoing edges are added from each state as it is explored. To formalize the latter situation, we propose guided incremental digraphs (GIDs), incremental graphs which support labeling closed states (states which will not receive further outgoing edges). Our main result is that dead state detection in GIDs is solvable in $O(\log m)$ amortized time per edge for $m$ edges, improving upon $O(\sqrt{m})$ per edge due to Bender, Fineman, Gilbert, and Tarjan (BFGT) for general incremental directed graphs. We introduce two algorithms for GIDs: one establishing the logarithmic time bound, and a second algorithm to explore a lazy heuristics-based approach. To enable an apples-to-apples experimental comparison, we implemented both algorithms, two simpler baselines, and the state-of-the-art BFGT baseline using a common directed graph interface in Rust. Our evaluation shows $110$-$530$x speedups over BFGT for the largest input graphs over a range of graph classes, random graphs, and graphs arising from regex benchmarks.
Decentralized model-free reinforcement learning in stochastic games with average-reward objective

We propose the first model-free algorithm that achieves low regret performance for decentralized learning in two-player zero-sum tabular stochastic games with infinite-horizon average-reward objective. In decentralized learning, the learning agent controls only one player and tries to achieve low regret performances against an arbitrary opponent. This contrasts with centralized learning where the agent tries to approximate the Nash equilibrium by controlling both players. In our infinite-horizon undiscounted setting, additional structure assumptions is needed to provide good behaviors of learning processes : here we assume for every strategy of the opponent, the agent has a way to go from any state to any other. This assumption is the analogous to the"communicating"assumption in the MDP setting. We show that our Decentralized Optimistic Nash Q-Learning (DONQ-learning) algorithm achieves both sublinear high probability regret of order $T^{3/4}$ and sublinear expected regret of order $T^{2/3}$. Moreover, our algorithm enjoys a low computational complexity and low memory space requirement compared to the previous works of (Wei et al. 2017) and (Jafarnia-Jahromi et al. 2021) in the same setting.
Surface acoustic wave generation and detection in the quantum paraelectric regime of 
SrTiO3
-based heterostructures

Strontium titanate (STO), apart from being a ubiquitous substrate for complex-oxide heterostructures, possesses a multitude of strongly-coupled electronic and mechanical properties. Surface acoustic wave (SAW) generation and detection offers insight into electromechanical couplings that are sensitive to quantum paraelectricity and other structural phase transitions. Propagating SAWs can interact with STO-based electronic nanostructures, in particular LaAlO$_3$/SrTiO$_3$ (LAO/STO). Here we report generation and detection of SAW within LAO/STO heterointerfaces at cryogenic temperatures ($T\ge$~2 K) using superconducting interdigitated transducers (IDTs). The temperature dependence shows an increase in the SAWs quality factor that saturates at $T\approx 8 $ K. The effect of backgate tuning on the SAW resonance frequency shows the possible acoustic coupling with the ferroelastic domain wall evolution. This method of generating SAWs provides a pathway towards dynamic tuning of ferroelastic domain structures, which are expected to influence electronic properties of complex-oxide nanostructures. Devices which incorporate SAWs may in turn help to elucidate the role of ferroelastic domain structures in mediating electronic behavior.
Generalized Tur\'an problem with bounded matching number

For a graph T and a set of graphs H , let ex( n, T, H ) denote the maximum number of copies of T in an n -vertex H -free graph. Recently, Alon and Frankl (arXiv2210.15076) determined the exact value of ex( n, K 2 , { K k +1 , M s +1 } ), where K k +1 and M s +1 are complete graph on k + 1 vertices and matching of size s + 1, respectively. Soon after, Gerbner (arXiv2211.03272) continued the study by extending K k +1 to general ﬁxed graph H . In this paper, we continue the study of the function ex( n, T, { H, M s +1 } ) when T = K r for r ≥ 3. We determine the exact value of ex( n, K r , { K k +1 , M s +1 } ) and give the value of ex( n, K r , { H, M s +1 } ) for general H with an error term O (1).
Kawahara equation and modified Kawahara equation with time dependent coefficients: symmetry analysis and generalized 
‐expansion method

In this paper, variable coefficients Kawahara equation (VCKE) and variable coefficients modified Kawahara equation (VCMKE), which arise in modeling of various physical phenomena, are studied by Lie group analysis. The similarity reductions and exact solutions are derived by determining the complete sets of point symmetries of these equations. Moreover, some exact analytic solutions are considered by the power series method. Further, a generalized ‐expansion method is applied to VCKE and VCMKE for constructing some new exact solutions. As a result, hyperbolic function solutions, trigonometric function solutions and some rational function solutions with parameters are furnished. Copyright © 2012 John Wiley & Sons, Ltd.
Simultaneous diagonalization via congruence of $m$ real symmetric matrices and its implications in optimization

Let $\{C_1, C_2, \ldots, C_m\},~m\ge2$ be a collection of $n\times n$ real symmetric matrices. The objective of the paper is to offer an algorithm that finds a common congruence matrix $R$ such that $R^TC_iR$ is real diagonal for every $C_i;$ or reports none of such kind. The problem, referred to as the simultaneously diagonalization via congruence (SDC in short), seems to be of pure linear algebra at first glance. However, for quadratically constrained quadratic programming (QCQP), if the quadratic forms are SDC, their joint range set is a closed convex polyhedral cone, which opens the possibility to extend the classical $\mathcal{S}$-lemma for more than two symmetric matrices. In addition, under the SDC assumption of quadratic forms, QCQP can be recast in separable forms which is usually easier to tackle. It is thus important to have a standard procedure for determining whether or not the SDC property holds for the underlined quadratic optimization problem. Our result solves a long standing problem posed by Hiriart-Urruty in 2007.
Normal Reference Attention and Defective Feature Perception Network for Surface Defect Detection

In the context of industrial product quality control, visual defect detection plays a crucial role, emphasizing the importance of developing a high-performing defect detection model. Thus, it is imperative to propose an exceptional defect detection model that can ensure the reliable and accurate detection of defects. In this article, we introduce a novel unsupervised reconstruction-based method, called the normal reference attention and defective feature perception network (NDP-Net), which accurately detects various textured defects. Unlike most reconstruction-based methods, our NDP-Net first employs an encoding module that extracts multiscale discriminative features of the surface textures, which is augmented with the discriminative defect ability by proposed artificial defects and a novel pixel-level defect perception loss. Subsequently, a novel reference-based attention module (RBAM) is proposed to leverage the normal reference features to repair the defective ones and restrain the reconstruction of the defects. Finally, a novel multiscale defect segmentation module (MSDSM) is introduced for precise defect detection and segmentation. Exhaustive experimental results obtained from benchmark datasets, including public MVTec AD and DAGM, showcase the exceptional performance and efficiency of the proposed method. Furthermore, we successfully implemented the proposed method in practical industrial settings, demonstrating its efficacy in real-world applications.