Miracle-dz/UniFAR-FTUs huggingface dataset:
Reentrant condensation transition in a model of driven scalar active matter with diffusivity edge

The effect of a diffusivity edge is studied in a system of scalar active matter confined by a periodic potential and driven by an externally applied force. We find that this system shows qualitatively distinct stationary regimes depending on the amplitude of the driving force with respect to the potential barrier. For small driving, the diffusivity edge induces a transition to a condensed phase analogous to the Bose–Einstein-like condensation reported for the nondriven case, which is characterized by a density-independent steady state current. Conversely, large external forces lead to a qualitatively different phase diagram since in this case condensation is only possible beyond a given density threshold, while the associated transition at higher densities is found to be reentrant.
Line Multiview Ideals

We study the following problem in computer vision from the perspective of algebraic geometry: Using $m$ pinhole cameras we take $m$ pictures of a line in $\mathbb P^3$. This produces $m$ lines in $\mathbb P^2$ and the question is which $m$-tuples of lines can arise that way. We are interested in polynomial equations and therefore study the complex Zariski closure of all such tuples of lines. The resulting algebraic variety is a subvariety of $(\mathbb P^2)^m$ and is called line multiview variety. In this article, we study its ideal. We show that for generic cameras the ideal is generated by $3\times 3$-minors of a specific matrix. We also compute Gr\"obner bases and discuss to what extent our results carry over to the non-generic case.
Decision-Oriented Learning with Differentiable Submodular Maximization for Vehicle Routing Problem

We study the problem of learning a function that maps context observations (input) to parameters of a submodular function (output). Our motivating case study is a specific type of vehicle routing problem, in which a team of Unmanned Ground Vehicles (UGVs) can serve as mobile charging stations to recharge a team of Unmanned Ground Vehicles (UAVs) that execute persistent monitoring tasks. We want to learn the mapping from observations of UAV task routes and wind field to the parameters of a submodular objective function, which describes the distribution of landing positions of the UAVs. Traditionally, such a learning problem is solved independently as a prediction phase without considering the downstream task optimization phase. However, the loss function used in prediction may be misaligned with our final goal, i.e., a good routing decision. Good performance in the isolated prediction phase does not necessarily lead to good decisions in the downstream routing task. In this paper, we propose a framework that incorporates task optimization as a differentiable layer in the prediction phase. Our framework allows end-to-end training of the prediction model without using engineered intermediate loss that is targeted only at the prediction performance. In the proposed framework, task optimization (submodular maximization) is made differentiable by introducing stochastic perturbations into deterministic algorithms (i.e., stochastic smoothing). We demonstrate the efficacy of the proposed framework using synthetic data. Experimental results of the mobile charging station routing problem show that the proposed framework can result in better routing decisions, e.g. the average number of UAVs recharged increases, compared to the prediction-optimization separate approach.
Higgs-confinement continuity and matching of Aharonov-Bohm phases

Some gauge theories with a spontaneously broken $U(1)$ symmetry exhibit fractional Aharonov-Bohm (AB) phases around vortices in the Higgs regime. We discuss continuity between confining and Higgs regimes in such gauge theories with fundamental matter fields, focusing on the AB phases. By explicit calculations in relevant lattice models, we demonstrate that the AB phase is smoothly connected between the confining and Higgs regimes, supporting the Higgs-confinement continuity. This result provides new insight into phase structures of gauge theories with superfluidity, such as dense QCD.
Filtering Eye-Tracking Data From an EyeLink 1000: Comparing Heuristic, Savitzky-Golay, IIR and FIR Digital Filters

In a prior report (Raju et al., 2023) we concluded that, if the goal was to preserve events such as saccades, microsaccades, and smooth pursuit in eye-tracking recordings, data with sine wave frequencies less than 75 Hz were the signal and data above 75 Hz were noise. Here, we compare five filters in their ability to preserve signal and remove noise. We compared the proprietary STD and EXTRA heuristic filters provided by our EyeLink 1000 (SR-Research, Ottawa, Canada), a Savitzky-Golay (SG) filter, an infinite impulse response (IIR) filter (low-pass Butterworth), and a finite impulse filter (FIR). For each of the non-heuristic filters, we systematically searched for optimal parameters. Both the IIR and the FIR filters were zero-phase filters. All filters were evaluated on 216 fixation segments (256 samples), from nine subjects. Mean frequency response profiles and amplitude spectra for all five filters are provided. Also, we examined the effect of our filters on a noisy recording. Our FIR filter had the sharpest roll-off of any filter. Therefore, it maintained the signal and removed noise more effectively than any other filter. On this basis, we recommend the use of our FIR filter. We also report on the effect of these filters on temporal autocorrelation.
Time‐inconsistent contract theory

This paper investigates the moral hazard problem in finite horizon with both continuous and lump‐sum payments, involving a time‐inconsistent sophisticated agent and a standard utility maximizer principal: Building upon the so‐called dynamic programming approach in Cvitanić et al. (2018) and the recently available results in Hernández and Possamaï (2023), we present a methodology that covers the previous contracting problem. Our main contribution consists of a characterization of the moral hazard problem faced by the principal. In particular, it shows that under relatively mild technical conditions on the data of the problem, the supremum of the principal's expected utility over a smaller restricted family of contracts is equal to the supremum over all feasible contracts. Nevertheless, this characterization yields, as far as we know, a novel class of control problems that involve the control of a forward Volterra equation via Volterra‐type controls, and infinite‐dimensional stochastic target constraints. Despite the inherent challenges associated with such a problem, we study the solution under three different specifications of utility functions for both the agent and the principal, and draw qualitative implications from the form of the optimal contract. The general case remains the subject of future research. We illustrate some of our results in the context of a project selection contracting problem between an investor and a time‐inconsistent manager.
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!

Sparse Neural Networks (SNNs) have received voluminous attention predominantly due to growing computational and memory footprints of consistently exploding parameter count in large-scale models. Similar to their dense counterparts, recent SNNs generalize just as well and are equipped with numerous favorable benefits (e.g., low complexity, high scalability, and robustness), sometimes even better than the original dense networks. As research effort is focused on developing increasingly sophisticated sparse algorithms, it is startling that a comprehensive benchmark to evaluate the effectiveness of these algorithms has been highly overlooked. In absence of a carefully crafted evaluation benchmark, most if not all, sparse algorithms are evaluated against fairly simple and naive tasks (eg. CIFAR, ImageNet, GLUE, etc.), which can potentially camouflage many advantages as well unexpected predicaments of SNNs. In pursuit of a more general evaluation and unveiling the true potential of sparse algorithms, we introduce"Sparsity May Cry"Benchmark (SMC-Bench), a collection of carefully-curated 4 diverse tasks with 10 datasets, that accounts for capturing a wide range of domain-specific and sophisticated knowledge. Our systemic evaluation of the most representative sparse algorithms reveals an important obscured observation: the state-of-the-art magnitude- and/or gradient-based sparse algorithms seemingly fail to perform on SMC-Bench when applied out-of-the-box, sometimes at significantly trivial sparsity as low as 5%. By incorporating these well-thought and diverse tasks, SMC-Bench is designed to favor and encourage the development of more scalable and generalizable sparse algorithms.
Valence quark, sea, and gluon content of the pion from the parton distribution functions and the electromagnetic form factor

We present a light-front model calculation of the pion parton distribution functions (PDFs) and the pion electromagnetic form factor. The pion state is modeled in terms of light-front wave functions (LFWFs) for the $q\bar q$, $q\bar q q\bar q$, $q\bar q g$, and $q\bar q gg$ components. We design the LFWFs so that the parameters in the longitudinal and transverse momentum space enter separately in the fit of the pion PDFs and the electromagnetic form factor, respectively. We extract the pion PDFs within the xFitter framework using available Drell-Yan and photon-production data. With the obtained parameters in the longitudinal-momentum space, we then fit the available experimental data on the pion electromagnetic form factor to constrain the remaining parameters in the transverse-momentum space. The results for the pion PDFs are compatible with existing extractions and lattice calculations, and the fit to the pion electromagnetic form factor data works quite successfully. The obtained parametrization for the LFWFs marks a step forward towards a unified description of different hadron distribution functions in both the longitudinal- and transverse-momentum space and will be further applied to a phenomenological study of transverse-momentum dependent parton distribution functions and generalized parton distributions.
Semileptonic and Leptonic $B$ Physics at Belle II

Semileptonic decays of $B$ mesons that proceed via $b\rightarrow u$ and $b\rightarrow c$ transitions, as well as rare leptonic $B$ decays, are fertile grounds for probing new physics beyond the Standard Model. The Belle II experiment presents as an excellent opportunity to study such decays, and since beginning operation in 2019, Belle II has already recorded half of the data collected by its predecessor and reached an instantaneous luminosity greater than $4\times 10^{34}$ cm$^{-2}$s$^{-1}$. Belle II expects to explore flavour physics with $B$ and charmed mesons with unprecedented precision, and has already produced multiple world-leading results in this area. We present recent results from Belle II on measurements of the CKM parameters $V_{ub}$ and $V_{cb}$ as well as results for $R(X_{e/\mu})$.
Design and Sampling Characteristics of a New Airborne Aerosol Inlet for Aerosol Measurements in Clouds

AbstractDesign of a new submicron aerosol inlet (SMAI) for airborne sampling of aerosol particles is introduced and its performance characteristics under a range of sampling conditions are presented. Analysis of inlet performance in clear-air and cloud systems shows that submicron aerosols are sampled representatively by the inlet, and in comparison with other types of inlets the SMAI has a relatively minor or nonexistent problem of droplet shatter contamination. The SMAI has a flow-through cone, with a perpendicular subsampling tube inside it. The cone acts as a virtual blunt body and decelerates the velocity directed toward a subsampling tube within the cone, resulting in reduced droplet impaction velocities and negligible artifact particle generation. The use of a perpendicular subsampling tube helps eliminate large shattered droplets from entering the sample volume, though it also results in lowering the aerosol sampling cut size. The SMAI sampling characteristics are determined from computational flu.
Recalcitrance in groups

Motivated by a well-known conjecture of Andrews and Curtis, we consider the question as to how in a given n-generator group G, a given set of n “annihilators” of G, that is, with normal closure all of G, can be transformed by standard moves into a generating n-tuple. The recalcitrance of G is defined to be the least number of elementary standard moves (”elementary M-transformations”) by means of which every annihilating n-tuple of G can be transformed into a generating n-tuple. We show that in the classes of finite and soluble groups, having zero recalcitrance is equivalent to nilpotence, and that a large class of 2-generator soluble groups has recalcitrance at most 3. Some examples and remarks are included.
Many-Body Theory Calculations of Positron Scattering and Annihilation in H_{2}, N_{2}, and CH_{4}.

The recently developed ab initio many-body theory of positron molecule binding [22J. Hofierka et al., Many-body theory of positron binding to polyatomic molecules, Nature (London) 606, 688 (2022)NATUAS0028-083610.1038/s41586-022-04703-3] is combined with the shifted pseudostates method [A. R. Swann and G. F. Gribakin, Model-potential calculations of positron binding, scattering, and annihilation for atoms and small molecules using a Gaussian basis, Phys. Rev. A 101, 022702 (2020)PLRAAN2469-992610.1103/PhysRevA.101. 022702] to calculate positron scattering and annihilation rates on small molecules, namely H_{2}, N_{2}, and CH_{4}. The important effects of positron-molecule correlations are delineated. The method provides uniformly good results for annihilation rates on all the targets, from the simplest (H_{2}, for which only a sole previous calculation agrees with experiment), to larger targets, where high-quality calculations have not been available.
Ada-Grouper: Accelerating Pipeline Parallelism in Preempted Network by Adaptive Group-Scheduling for Micro-Batches

Pipeline parallelism has been demonstrated to be a remarkable approach to improve throughput for training deep neural networks with billions of parameters over heterogeneous clusters. The 1F1B scheduling plan is a widely adopted strategy for memory and performance optimization, which interchanges the forward and backward stage computations of different micro-batches. On the other hand, a common issue in using the 1F1B scheduling is that stage computation is delayed due to the data transfer when network resources are preempted by other tasks, even with the minimum communication between stages. The exclusive access of these network resources cannot be guaranteed in cloud offerings. We present a general scheduling technique to accommodate pipeline parallelism to preempted network environments at the expense of a certain amount of memory pressure. The core concept is to extend 1F1B schedule scheme to kFkB, which groups k micro-batches, and alternately executes k forward and backward computations. We propose Ada-Grouper, an adaptive kFkB scheduler which regularly adjusts the number of group members k to maintain an optimal balance between communication and computation efficiency correspond to changes in a changing network environment under the memory limit. Experimental results demonstrate that our design maintain stable performance for pipeline parallelism, yielding a performance increase of up from 4% to 30%, compared with 1F1B in preempted network scenarios.
Finding the Optimal Currency Composition of Foreign Exchange Reserves with a Quantum Computer

Portfolio optimization is an inseparable part of strategic asset allocation at the Czech National Bank. Quantum computing is a new technology offering algorithms for that problem. The capabilities and limitations of quantum computers with regard to portfolio optimization should therefore be investigated. In this paper, we focus on applications of quantum algorithms to dynamic portfolio optimization based on the Markowitz model. In particular, we compare algorithms for universal gate-based quantum computers (the QAOA, the VQE and Grover adaptive search), single-purpose quantum annealers, the classical exact branch and bound solver and classical heuristic algorithms (simulated annealing and genetic optimization). To run the quantum algorithms we use the IBM Quantum\textsuperscript{TM} gate-based quantum computer. We also employ the quantum annealer offered by D-Wave. We demonstrate portfolio optimization on finding the optimal currency composition of the CNB's FX reserves. A secondary goal of the paper is to provide staff of central banks and other financial market regulators with literature on quantum optimization algorithms, because financial firms are active in finding possible applications of quantum computing.
Improving GAN Training via Feature Space Shrinkage

Due to the outstanding capability for data generation, Generative Adversarial Networks (GANs) have attracted considerable attention in unsupervised learning. However, training GANs is difficult, since the training distribution is dynamic for the discriminator, leading to unstable image representation. In this paper, we address the problem of training GANs from a novel perspective, \emph{i.e.,} robust image classification. Motivated by studies on robust image representation, we propose a simple yet effective module, namely AdaptiveMix, for GANs, which shrinks the regions of training data in the image representation space of the discriminator. Considering it is intractable to directly bound feature space, we propose to construct hard samples and narrow down the feature distance between hard and easy samples. The hard samples are constructed by mixing a pair of training images. We evaluate the effectiveness of our AdaptiveMix with widely-used and state-of-the-art GAN architectures. The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples. We also show that our AdaptiveMix can be further applied to image classification and Out-Of-Distribution (OOD) detection tasks, by equipping it with state-of-the-art methods. Extensive experiments on seven publicly available datasets show that our method effectively boosts the performance of baselines. The code is publicly available at https://github.com/WentianZhang-ML/AdaptiveMix.
Asteroseismic Determination of Stellar Rotation: On Synchronization in the Close Eclipsing Binaries AB Cas and OO Dra

A star’s rotation rate is difficult to estimate without surface inhomogeneities such as dark or bright spots. This paper presents asteroseismic results to determine the rotation rates of δ Sct-type pulsating primary stars in two eclipsing binary systems, AB Cas and OO Dra. After removing the binarity-induced light variations from archival TESS data and carefully examining the combination frequencies, we identified 12 independent frequencies for AB Cas and 11 frequencies for OO Dra, with amplitudes higher than ∼0.3 mmag, as δ Sct-type pulsation frequencies excited in each primary star. The theoretical frequencies for seismic analysis were obtained by fully considering rotation effects. Grid fitting for various stellar properties, such as mass, radius, metallicity, and rotation rate, yielded the best solutions for which the theoretical frequencies and stellar parameters agreed well with the observations. The rotation rate of the AB Cas primary was tightly constrained to 0.81 ± 0.01 day−1 (f rot/f orb = 1.11−0.02+0.01 ), which is slightly faster than the synchronized rotation. In contrast, the rotation rate of 0.63 ± 0.01 day−1 for the OO Dra primary is lower than the synchronous value of approximately 0.81 day−1. Subsynchronous rotation is uncommon in short-period binaries, and its physical mechanism is not yet well understood. Our results show that asteroseismology can be used to measure the rotation rate of fast-rotating δ Sct stars precisely and thus provide a valuable constraint on rotation–orbit synchronization in close binary systems.
Feature Perturbation Augmentation for Reliable Evaluation of Importance Estimators

Post-hoc explanation methods attempt to make the inner workings of deep neural networks more interpretable. However, since a ground truth is in general lacking, local post-hoc interpretability methods, which assign importance scores to input features, are challenging to evaluate. One of the most popular evaluation frameworks is to perturb features deemed important by an interpretability method and to measure the change in prediction accuracy. Intuitively, a large decrease in prediction accuracy would indicate that the explanation has correctly quantified the importance of features with respect to the prediction outcome (e.g., logits). However, the change in the prediction outcome may stem from perturbation artifacts, since perturbed samples in the test dataset are out of distribution (OOD) compared to the training dataset and can therefore potentially disturb the model in an unexpected manner. To overcome this challenge, we propose feature perturbation augmentation (FPA) which creates and adds perturbed images during the model training. Through extensive computational experiments, we demonstrate that FPA makes deep neural networks (DNNs) more robust against perturbations. Furthermore, training DNNs with FPA demonstrate that the sign of importance scores may explain the model more meaningfully than has previously been assumed. Overall, FPA is an intuitive data augmentation technique that improves the evaluation of post-hoc interpretability methods.
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation

Maximum mean discrepancy (MMD) is a particularly useful distance metric for differentially private data generation: when used with finite-dimensional features it allows us to summarize and privatize the data distribution once, which we can repeatedly use during generator training without further privacy loss. An important question in this framework is, then, what features are useful to distinguish between real and synthetic data distributions, and whether those enable us to generate quality synthetic data. This work considers the using the features of $\textit{neural tangent kernels (NTKs)}$, more precisely $\textit{empirical}$ NTKs (e-NTKs). We find that, perhaps surprisingly, the expressiveness of the untrained e-NTK features is comparable to that of the features taken from pre-trained perceptual features using public data. As a result, our method improves the privacy-accuracy trade-off compared to other state-of-the-art methods, without relying on any public data, as demonstrated on several tabular and image benchmark datasets.
Scale invariant bounds for mixing in the Rayleigh-Taylor instability

We study the Rayleigh-Taylor instability for two miscible, incompressible, inviscid fluids. Scale-invariant estimates for the size of the mixing zone and coarsening of internal structures in the fully nonlinear regime are established following techniques introduced for the Saffman-Taylor instability in [10]. These bounds provide optimal scaling laws and reveal the strong role of dissipation in slowing down mixing.
Stochastic Super-Resolution For Gaussian Textures

Super-resolution (SR) is an ill-posed inverse problem which consists in proposing high-resolution images consistent with a given low-resolution one. While most SR algorithms are deterministic, stochastic SR deals with designing a stochastic sampler generating any realistic SR solution. The goal of this paper is to show that stochastic SR is a well-posed and solvable problem when restricting to Gaussian stationary textures. Using Gaussian conditional sampling and exploiting the stationarity assumption, we propose an efficient algorithm based on fast Fourier transform. We also demonstrate the practical relevance of the approach for SR with a reference image. Although limited to stationary microtextures, our approach compares favorably in terms of speed and visual quality to some state of the art methods designed for a larger class of images.