Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games

This work tackles the complexities of multi-player scenarios in \emph{unknown games}, where the primary challenge lies in navigating the uncertainty of the environment through bandit feedback alongside strategic decision-making. We introduce Thompson Sampling (TS)-based algorithms that exploit the information of opponents' actions and reward structures, leading to a substantial reduction in experimental budgets -- achieving over tenfold improvements compared to conventional approaches. Notably, our algorithms demonstrate that, given specific reward structures, the regret bound depends logarithmically on the total action space, significantly alleviating the curse of multi-player. Furthermore, we unveil the \emph{Optimism-then-NoRegret} (OTN) framework, a pioneering methodology that seamlessly incorporates our advancements with established algorithms, showcasing its utility in practical scenarios such as traffic routing and radar sensing in the real world.
Characterizing Role Models in Software Practitioners' Career: An Interview Study

A role model is a person who serves as an example for others to follow, especially in terms of values, behavior, achievements, and personal characteristics. In this paper, authors study how role models influence software practitioners careers, an aspect not studied in the literature before. By means of this study, authors aim to understand if there are any salient role model archetypes and what characteristics are valued by participants in their role models. To do so, authors use a thematic coding approach to analyze the data collected from interviewing ten Latin American software practitioners. Findings reveal that role models were perceived as sources of knowledge, yet the majority of participants, regardless of their career stage, displayed a stronger interest in the human side and the moral values that their role models embodied. This study also shows that any practitioner can be viewed as a role model.
Optimal Bayesian stepped-wedge cluster randomised trial designs for binary outcome data

Under a generalised estimating equation analysis approach, approximate design theory is used to determine Bayesian D-optimal designs. For two examples, considering simple exchangeable and exponential decay correlation structures, we compare the efficiency of identified optimal designs to balanced stepped-wedge designs and corresponding stepped-wedge designs determined by optimising using a normal approximation approach. The dependence of the Bayesian D-optimal designs on the assumed correlation structure is explored; for the considered settings, smaller decay in the correlation between outcomes across time periods, along with larger values of the intra-cluster correlation, leads to designs closer to a balanced design being optimal. Unlike for normal data, it is shown that the optimal design need not be centro-symmetric in the binary outcome case. The efficiency of the Bayesian D-optimal design relative to a balanced design can be large, but situations are demonstrated in which the advantages are small. Similarly, the optimal design from a normal approximation approach is often not much less efficient than the Bayesian D-optimal design. Bayesian D-optimal designs can be readily identified for stepped-wedge cluster randomised trials with binary outcome data. In certain circumstances, principally ones with strong time period effects, they will indicate that a design unlikely to have been identified by previous methods may be substantially more efficient. However, they require a larger number of assumptions than existing optimal designs, and in many situations existing theory under a normal approximation will provide an easier means of identifying an efficient design for binary outcome data.
Quickest Detection of False Data Injection Attack in Distributed Process Tracking

This paper addresses the problem of detecting false data injection (FDI) attacks in a distributed network without a fusion center, represented by a connected graph among multiple agent nodes. Each agent node is equipped with a sensor, and uses a Kalman consensus information filter (KCIF) to track a discrete time global process with linear dynamics and additive Gaussian noise. The state estimate of the global process at any sensor is computed from the local observation history and the information received by that agent node from its neighbors. At an unknown time, an attacker starts altering the local observation of one agent node. In the Bayesian setting where there is a known prior distribution of the attack beginning instant, we formulate a Bayesian quickest change detection (QCD) problem for FDI detection in order to minimize the mean detection delay subject to a false alarm probability constraint. While it is well-known that the optimal Bayesian QCD rule involves checking the Shriyaev's statistic against a threshold, we demonstrate how to compute the Shriyaev's statistic at each node in a recursive fashion given our non-i.i. d. observations. Next, we consider non-Bayesian QCD where the attack begins at an arbitrary and unknown time, and the detector seeks to minimize the worst case detection delay subject to a constraint on the mean time to false alarm and probability of misidentification. We use the multiple hypothesis sequential probability ratio test for attack detection and identification at each sensor. For unknown attack strategy, we use the window-limited generalized likelihood ratio (WL-GLR) algorithm to solve the QCD problem. Numerical results demonstrate the performances and trade-offs of the proposed algorithms.
Electrical Behavior Association Mining for Household ShortTerm Energy Consumption Forecasting

Accurate household short-term energy consumption forecasting (STECF) is crucial for home energy management, but it is technically challenging, due to highly random behaviors of individual residential users. To improve the accuracy of STECF on a day-ahead scale, this paper proposes an novel STECF methodology that leverages association mining in electrical behaviors. First, a probabilistic association quantifying and discovering method is proposed to model the pairwise behaviors association and generate associated clusters. Then, a convolutional neural network-gated recurrent unit (CNN-GRU) based forecasting is provided to explore the temporal correlation and enhance accuracy. The testing results demonstrate that this methodology yields a significant enhancement in the STECF.
Myricetin promotes peripheral nerve regeneration in rat model of sciatic nerve injury via regulation of BDNFAkt/ GSK-3β/mTOR signalling pathway

Purpose: To investigate the effects of myricetin on peripheral nerve regeneration in sciatic nerve crush injury model. Methods: Separate groups of rats were administered myricetin at 25, 50 or 100 mg/kg body weight/day for 2 weeks. Functional recovery following sciatic nerve injury was assessed by foot position and walking track analyses, measurement of mechanical hyperalgesia, and withdrawal reflex latency (WRL). Results: Myricetin treatment resulted in significantly enhanced recovery of sensorimotor functions as evidenced by increased scores in functional analysis tests. Myricetin treatment remarkably elevated brain derived neurotrophic factor (BDNF) expression, and also enhanced activation of Akt and mTORc1, reflecting up-regulation of PI3K/Akt/mTORC1 signalling involved in nerve regeneration. Conclusion: Myricetin enhances functional recovery and nerve regeneration in rats. These findings suggest that myricetin is a potent neuroprotective agent with potential for the management of peripheral nerve injury. Keywords: Glycogen synthase kinase 3β, Mammalian target of rapamycin (mTOR), Myricetin, Nerve regeneration
System-level Impact of Non-Ideal Program-Time of Charge Trap Flash (CTF) on Deep Neural Network

Learning of deep neural networks (DNN) using Resistive Processing Unit (RPU) architecture is energy-efficient as it utilizes dedicated neuromorphic hardware and stochastic computation of weight updates for in-memory computing. Charge Trap Flash (CTF) devices can implement RPU-based weight updates in DNNs. However, prior work has shown that the weight updates (V_T) in CTF-based RPU are impacted by the non-ideal program time of CTF. The non-ideal program time is affected by two factors of CTF. Firstly, the effects of the number of input pulses (N) or pulse width (pw), and secondly, the gap between successive update pulses (t_gap) used for the stochastic computation of weight updates. Therefore, the impact of this non-ideal program time must be studied for neural network training simulations. In this study, Firstly, we propose a pulse-train design compensation technique to reduce the total error caused by non-ideal program time of CTF and stochastic variance of a network. Secondly, we simulate RPU-based DNN with non-ideal program time of CTF on MNIST and Fashion-MNIST datasets. We find that for larger N (~1000), learning performance approaches the ideal (software-level) training level and, therefore, is not much impacted by the choice of t_gap used to implement RPU-based weight updates. However, for lower N (<500), learning performance depends on T_gap of the pulses. Finally, we also performed an ablation study to isolate the causal factor of the improved learning performance. We conclude that the lower noise level in the weight updates is the most likely significant factor to improve the learning performance of DNN. Thus, our study attempts to compensate for the error caused by non-ideal program time and standardize the pulse length (N) and pulse gap (t_gap) specifications for CTF-based RPUs for accurate system-level on-chip training.
Phase coexistence in the Non-reciprocal Cahn-Hilliard model

We establish the criterion for the phase coexistence in a mixture of nonreciprocally interacting scalar densities. For an arbitrary number of components the active pressure exists for a specific class of interactions, and when the free energy receives no contribution from cross couplings between spatial gradients of two different species. In this case, the pressure can be used to determine phase equilibrium, i.e. to construct binodals, and the active mixture can be mapped to a passive system with an effective free energy. For general interfacial tension, the pressure changes discontinuously across a flat interface which assumes the form of an active Laplace pressure in two dimensions.
Revisiting Stochastic Realization Theory using Functional Itô Calculus

This paper considers the problem of constructing finite-dimensional state space realizations for stochastic processes that can be represented as the outputs of a certain type of a causal system driven by a continuous semimartingale input process. The main assumption is that the output process is infinitely differentiable, where the notion of differentiability comes from the functional It\^o calculus introduced by Dupire as a causal (nonanticipative) counterpart to Malliavin's stochastic calculus of variations. The proposed approach builds on the ideas of Hijab, who had considered the case of processes driven by a Brownian motion, and makes contact with the realization theory of deterministic systems based on formal power series and Chen-Fliess functional expansions.
Data Reconstruction Attacks and Defenses: A Systematic Evaluation

Reconstruction attacks and defenses are essential in understanding the data leakage problem in machine learning. However, prior work has centered around empirical observations of gradient inversion attacks, lacks theoretical groundings, and was unable to disentangle the usefulness of defending methods versus the computational limitation of attacking methods. In this work, we propose a strong reconstruction attack in the setting of federated learning. The attack reconstructs intermediate features and nicely integrates with and outperforms most of the previous methods. On this stronger attack, we thoroughly investigate both theoretically and empirically the effect of the most common defense methods. Our findings suggest that among various defense mechanisms, such as gradient clipping, dropout, additive noise, local aggregation, etc., gradient pruning emerges as the most effective strategy to defend against state-of-the-art attacks.
Asymptotic stability for $n$-dimensional isentropic compressible MHD equations without magnetic diffusion

Whether the global well-posedness of strong solutions of $n$-dimensional compressible isentropic magnetohydrodynamic (MHD for short) equations without magnetic diffusion holds true or not remains an challenging open problem, even for the small initial data. In recent years, stared from the pioneer work by Wu and Wu [Adv. Math. 310 (2017), 759--888], much more attention has been paid to the system when the magnetic field near an equilibrium state (the background magnetic field for short). In particular, when the background magnetic field satisfies the Diophantine condition (see (1.3) for details), Wu and Zhai [Math. Models Methods Appl. Sci. 33 (2023), no. 13, 2629--2656] established the decay estimates and asymptotic stability for smooth solutions of the 3D compressible isentropic MHD system without magnetic diffusion in $H^{4r+7}(\mathbb{T}^3)$ with $r>2$ by exploiting a wave structure. In this paper, a new dissipative mechanism is found out and applied so that we can improve the spaces where the decay estimates and asymptotic stability of solutions are taking place by Wu and Zhai. More precisely, we establish the decay estimates of solutions in $H^{r+1}(\mathbb{T}^n)$ and asymptotic stability result in $H^{\left(3r+3\right)^+}(\mathbb{T}^n)$ for any dimensional periodic domain $\mathbb{T}^n$ with $n\geq 2$ and $r>n-1$. Our results provide an approach for establishing the decay estimates and asymptotic stability in the Sobolev spaces with much lower regularity and uniform dimension, which can be used to study many other related models such as the compressible non-isentropic MHD system without magnetic diffusion and so on.
Seasons's Greetings by AD

We use Algorithmic Differentiation (AD) to implement type-generic tangent and adjoint versions of $$ y=\sum_{i=0}^{n-1} x_{2 i} \cdot x_{2 i+1} $$ in C++. We run an instantiation for char-arithmetic and we print the gradient at $(101~77~114~114~32~121~109~88~115~97)^T$ to std::cout, yielding the output ``Merry Xmas''. Similar instantiations of type-generic second-order tangent and second-order adjoint versions of $$ y=\frac{1}{6} \cdot \sum_{i=0}^{n-1} x^3_{i} $$ yield ``Happy 2024'' at $(72~97~112~112~121~32~50~48~50~52)^T. $ Prepend a sufficiently large number of zeros to the input vector to explore the varying run times of the different derivative codes. The entire source code can be found on https://github.com/un110076/SeasonsGreetings.
Strong coupling in molecular systems: a simple predictor employing routine optical measurements

We provide a simple method that enables readily acquired experimental data to be used to predict whether or not a candidate molecular material may exhibit strong coupling. Specifically, we explore the relationship between the hybrid molecular/photonic (polaritonic) states and the bulk optical response of the molecular material. For a given material this approach enables a prediction of the maximum extent of strong coupling (vacuum Rabi splitting), irrespective of the nature of the confined light field. We provide formulae for the upper limit of the splitting in terms of the molar absorption coefficient, the attenuation coefficient, the extinction coefficient (imaginary part of the refractive index) and the absorbance. To illustrate this approach we provide a number of examples, we also discuss some of the limitations of our approach.
LogicPrpBank: A Corpus for Logical Implication and Equivalence

Logic reasoning has been critically needed in problem-solving and decision-making. Although Language Models (LMs) have demonstrated capabilities of handling multiple reasoning tasks (e.g., commonsense reasoning), their ability to reason complex mathematical problems, specifically propositional logic, remains largely underexplored. This lack of exploration can be attributed to the limited availability of annotated corpora. Here, we present a well-labeled propositional logic corpus, LogicPrpBank, containing 7093 Propositional Logic Statements (PLSs) across six mathematical subjects, to study a brand-new task of reasoning logical implication and equivalence. We benchmark LogicPrpBank with widely-used LMs to show that our corpus offers a useful resource for this challenging task and there is ample room for model improvement.
Reg-NF: Efficient Registration of Implicit Surfaces within Neural Fields

Neural fields, coordinate-based neural networks, have recently gained popularity for implicitly representing a scene. In contrast to classical methods that are based on explicit representations such as point clouds, neural fields provide a continuous scene representation able to represent 3D geometry and appearance in a way which is compact and ideal for robotics applications. However, limited prior methods have investigated registering multiple neural fields by directly utilising these continuous implicit representations. In this paper, we present Reg-NF, a neural fields-based registration that optimises for the relative 6-DoF transformation between two arbitrary neural fields, even if those two fields have different scale factors. Key components of Reg-NF include a bidirectional registration loss, multi-view surface sampling, and utilisation of volumetric signed distance functions (SDFs). We showcase our approach on a new neural field dataset for evaluating registration problems. We provide an exhaustive set of experiments and ablation studies to identify the performance of our approach, while also discussing limitations to provide future direction to the research community on open challenges in utilizing neural fields in unconstrained environments.
Three-state $p$-SOS models on binary Cayley trees

We consider a version of the solid-on-solid model on the Cayley tree of order two in which vertices carry spins of value $0,1$ or $2$ and the pairwise interaction of neighboring vertices is given by their spin difference to the power $p>0$. We exhibit all translation-invariant splitting Gibbs measures (TISGMs) of the model and demonstrate the existence of up to seven such measures, depending on the parameters. We further establish general conditions for extremality and non-extremality of TISGMs in the set of all Gibbs measures and use them to examine selected TISGMs for a small and a large $p$. Notably, our analysis reveals that extremality properties are similar for large $p$ compared to the case $p=1$, a case that has been explored already in previous work. However, for the small $p$, certain measures that were consistently non-extremal for $p=1$ do exhibit transitions between extremality and non-extremality.
Computing the EHZ capacity is NP-hard

The Ekeland-Hofer-Zehnder capacity (EHZ capacity) is a fundamental symplectic invariant of convex bodies. We show that computing the EHZ capacity of polytopes is NP-hard. For this we reduce the feedback arc set problem in bipartite tournaments to computing the EHZ capacity of simplices.
HyperMagNet: A Magnetic Laplacian based Hypergraph Neural Network

In data science, hypergraphs are natural models for data exhibiting multi-way relations, whereas graphs only capture pairwise. Nonetheless, many proposed hypergraph neural networks effectively reduce hypergraphs to undirected graphs via symmetrized matrix representations, potentially losing important information. We propose an alternative approach to hypergraph neural networks in which the hypergraph is represented as a non-reversible Markov chain. We use this Markov chain to construct a complex Hermitian Laplacian matrix - the magnetic Laplacian - which serves as the input to our proposed hypergraph neural network. We study HyperMagNet for the task of node classification, and demonstrate its effectiveness over graph-reduction based hypergraph neural networks.
Towards Precision Cardiovascular Analysis in Zebrafish: The ZACAF Paradigm

Quantifying cardiovascular parameters like ejection fraction in zebrafish as a host of biological investigations has been extensively studied. Since current manual monitoring techniques are time-consuming and fallible, several image processing frameworks have been proposed to automate the process. Most of these works rely on supervised deep-learning architectures. However, supervised methods tend to be overfitted on their training dataset. This means that applying the same framework to new data with different imaging setups and mutant types can severely decrease performance. We have developed a Zebrafish Automatic Cardiovascular Assessment Framework (ZACAF) to quantify the cardiac function in zebrafish. In this work, we further applied data augmentation, Transfer Learning (TL), and Test Time Augmentation (TTA) to ZACAF to improve the performance for the quantification of cardiovascular function quantification in zebrafish. This strategy can be integrated with the available frameworks to aid other researchers. We demonstrate that using TL, even with a constrained dataset, the model can be refined to accommodate a novel microscope setup, encompassing diverse mutant types and accommodating various video recording protocols. Additionally, as users engage in successive rounds of TL, the model is anticipated to undergo substantial enhancements in both generalizability and accuracy. Finally, we applied this approach to assess the cardiovascular function in nrap mutant zebrafish, a model of cardiomyopathy.
InfoNet: Neural Estimation of Mutual Information without Test-Time Optimization

Estimating mutual correlations between random variables or data streams is essential for intelligent behavior and decision-making. As a fundamental quantity for measuring statistical relationships, mutual information has been extensively studied and utilized for its generality and equitability. However, existing methods often lack the efficiency needed for real-time applications, such as test-time optimization of a neural network, or the differentiability required for end-to-end learning, like histograms. We introduce a neural network called InfoNet, which directly outputs mutual information estimations of data streams by leveraging the attention mechanism and the computational efficiency of deep learning infrastructures. By maximizing a dual formulation of mutual information through large-scale simulated training, our approach circumvents time-consuming test-time optimization and offers generalization ability. We evaluate the effectiveness and generalization of our proposed mutual information estimation scheme on various families of distributions and applications. Our results demonstrate that InfoNet and its training process provide a graceful efficiency-accuracy trade-off and order-preserving properties. We will make the code and models available as a comprehensive toolbox to facilitate studies in different fields requiring real-time mutual information estimation.
Spin-orbit torque in single-molecule junctions from ab initio

The use of electric fields applied across magnetic heterojunctions that lack spatial inversion symmetry has been previously proposed as a non-magnetic mean of controlling localized magnetic moments through spin-orbit torques (SOT). The implementation of this concept at the single-molecule level has remained a challenge, however. Here, we present first-principle calculations of SOT in a single-molecule junction under bias and beyond linear response. Employing a self-consistency scheme invoking density functional theory and non-equilibrium Green's function theory, we compute the current-induced SOT. Responding to this torque, a localized magnetic moment can tilt. Within the linear regime our quantitative estimates for the SOT in single-molecule junctions yield values similar to those known for magnetic interfaces. Our findings contribute to an improved microscopic understanding of SOT in single molecules.