Miracle-dz/UniFAR-FTUs huggingface dataset:
Look More but Care Less in Video Recognition

Existing action recognition methods typically sample a few frames to represent each video to avoid the enormous computation, which often limits the recognition performance. To tackle this problem, we propose Ample and Focal Network (AFNet), which is composed of two branches to utilize more frames but with less computation. Specifically, the Ample Branch takes all input frames to obtain abundant information with condensed computation and provides the guidance for Focal Branch by the proposed Navigation Module; the Focal Branch squeezes the temporal size to only focus on the salient frames at each convolution block; in the end, the results of two branches are adaptively fused to prevent the loss of information. With this design, we can introduce more frames to the network but cost less computation. Besides, we demonstrate AFNet can utilize fewer frames while achieving higher accuracy as the dynamic selection in intermediate features enforces implicit temporal modeling. Further, we show that our method can be extended to reduce spatial redundancy with even less cost. Extensive experiments on five datasets demonstrate the effectiveness and efficiency of our method.
Convergence of the PCTL algorithm for solving the discretized 3T energy equations in RHD problems

For solving the three-temperature linear system arising from the radiation hydrody- namics (RHD) problems, Xu et. proposed a physical-variable based coarsening algebraic two level (PCTL) iterative method and veriﬁed its eﬃciency by numerical experiments. However, there is still a lack of quantitative evaluation of the performance of PCTL algorithm, thus we aim to ﬁll in this blank in this paper. By theoretical analysis, we give an estimation on the convergence factor of the PCTL algorithm and show it is independent of the problem size, which provides a theoretical guarantee for solving large-scale problems. Moreover, we also discuss the factors that aﬀect the eﬃciency of PCTL algorithm and provide a direction for the eﬃcient application of the PCTL algorithm.
Self-Remixing: Unsupervised Speech Separation VIA Separation and Remixing

We present Self-Remixing, a novel self-supervised speech separation method, which refines a pre-trained separation model in an unsupervised manner. Self-Remixing consists of a shuffler module and a solver module, and they grow together through separation and remixing processes. Specifically, the shuffler first separates observed mixtures and makes pseudo-mixtures by shuffling and remixing the separated signals. The solver then separates the pseudo-mixtures and remixes the separated signals back to the observed mixtures. The solver is trained using the observed mixtures as supervision, while the shuffler’s weights are updated by taking the moving average with the solver’s, generating the pseudo-mixtures with fewer distortions. Our experiments demonstrate that Self-Remixing gives better performance over existing remixing-based self-supervised methods with the same or less training costs under unsupervised setup. Self-Remixing also outperforms baselines in semi-supervised domain adaptation, showing effectiveness in multiple setups.
Local gravitational instability of stratified rotating fluids: 3D criteria for gaseous discs

Fragmentation of rotating gaseous systems via gravitational instability is believed to be a crucial mechanism in several astrophysical processes, such as formation of planets in protostellar discs, of molecular clouds in galactic discs, and of stars in molecular clouds. Gravitational instability is fairly well understood for infinitesimally thin discs. However, the thin-disc approximation is not justified in many cases, and it is of general interest to study the gravitational instability of rotating fluids with different degrees of rotation support and stratification. We derive dispersion relations for axisymmetric perturbations, which can be used to study the local gravitational stability at any point of a rotating axisymmetric gaseous system with either barotropic or baroclinic distribution. 3D stability criteria are obtained, which generalize previous results and can be used to determine whether and where a rotating system of given 3D structure is prone to clump formation. For a vertically stratified gaseous disc of thickness hz (defined as containing ≈70% of the mass per unit surface), a sufficient condition for local gravitational instability is $Q_{\rm 3D}\equiv \left(\sqrt{\kappa ^2+\nu ^2}+c_{\rm s}h_z^{-1}\right)/{\sqrt{4\pi G\rho }}<1$, where ρ is the gas volume density, κ the epicycle frequency, cs the sound speed, and $\nu ^2\equiv \rho ^{\prime }_zp^{\prime }_z/\rho ^2$, where $\rho ^{\prime }_z$ and $p^{\prime }_z$ are the vertical gradients of, respectively, gas density and pressure. The combined stabilizing effects of rotation (κ2) and stratification (ν2) are apparent. In unstable discs, the conditions for instability are typically met close to the midplane, where the perturbations that are expected to grow have characteristic radial extent of a few hz.
Fields: Towards Socially Intelligent Spatial Computing

In our everyday life, we intuitively use space to regulate our social interactions. When we want to talk to someone, we approach them; if someone joins the conversation, we adjust our bodies to make space for them. In contrast, devices are not as considerate: they interrupt us, require us to input commands, and compete for our attention. In this paper, we introduce Fields, a design framework for ubiquitous computing that informs the design of connected products with social grace. Inspired by interactionist theories on social interaction, Fields builds on the idea that the physical space we share with computers can be an interface to mediate interactions. It defines a generalized approach to spatial interactions, and a set of interaction patterns that can be adapted to different ubiquitous computing systems. We investigated its value by implementing it in a set of prototypes and evaluating it in a lab setting.
Conservation of energy and momentum for an electromagnetic field propagating into a linear medium from the vacuum

The form of the energy–momentum tensor when a quasimonochromatic ﬁeld propagates into and through an antireﬂection-coated, sourceless, transparent, continuous, linear magneto-dielectric medium, initially at rest in the local frame, remains controversial. The Minkowski energy– momentum tensor is the main component of the electromagnetic conservation law. It has been known for over a century that the electromagnetic conservation law is unsound as evidenced by alternative energy–momentum tensors that have been proposed to ameliorate known physical de-ﬁciencies (violation of conservation of angular and linear momentum) and by the various material energy–momentum tensors and coupling forces that have been introduced to repair or complete the law. The extant resolution is to treat the continuum electromagnetic system as a subsystem and add a phenomenological material subsystem energy–momentum tensor. We show that the four-divergence of the total, electromagnetic plus material, energy–momentum tensor produces an energy continuity theorem in which the two non-zero terms depend on diﬀerent powers of the refractive index n . Then the extant resolution of the Abraham–Minkowski controversy is self-inconsistent.
A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning

Deep neural networks (DNNs), are widely used in many industries such as image recognition, supply chain, medical diagnosis, and autonomous driving. However, prior work has shown the high accuracy of a DNN model does not imply high robustness (i.e., consistent performances on new and future datasets) because the input data and external environment (e.g., software and model configurations) for a deployed model are constantly changing. Hence, ensuring the robustness of deep learning is not an option but a priority to enhance business and consumer confidence. Previous studies mostly focus on the data aspect of model variance. In this article, we systematically summarize DNN robustness issues and formulate them in a holistic view through two important aspects, i.e., data and software configuration variances in DNNs. We also provide a predictive framework to generate representative variances (counterexamples) by considering both data and configurations for robust learning through the lens of search-based optimization.
Low-scale leptogenesis and dark matter in a three-loop radiative seesaw model

We show that three open questions in particle physics and cosmology: the origin of neutrino mass, the identity of dark matter, and the origin of the baryon asymmetry of the universe can be explained simultaneously in the three-loop seesaw model proposed by Krauss, Nasri, and Trodden. We discuss the difficulty of successful leptogenesis with three right-handed neutrinos, and we propose a scenario with four right-handed neutrinos that satisfies all observational constraints. This scenario predicts a sleptonlike particle as light as a few hundreds GeV that can be probed by future collider experiments.
Listing 4-Cycles

In this note we present an algorithm that lists all $4$-cycles in a graph in time $\tilde{O}(\min(n^2,m^{4/3})+t)$ where $t$ is their number. Notably, this separates $4$-cycle listing from triangle-listing, since the latter has a $(\min(n^3,m^{3/2})+t)^{1-o(1)}$ lower bound under the $3$-SUM Conjecture. Our upper bound is conditionally tight because (1) $O(n^2,m^{4/3})$ is the best known bound for detecting if the graph has any $4$-cycle, and (2) it matches a recent $(\min(n^3,m^{3/2})+t)^{1-o(1)}$ $3$-SUM lower bound for enumeration algorithms. The latter lower bound was proved very recently by Abboud, Bringmann, and Fischer [arXiv, 2022] and independently by Jin and Xu [arXiv, 2022]. In an independent work, Jin and Xu [arXiv, 2022] also present an algorithm with the same time bound.
Deep Gaussian Processes for Air Quality Inference

Air pollution kills around 7 million people annually, and approximately 2.4 billion people are exposed to hazardous air pollution. Accurate, fine-grained air quality (AQ) monitoring is essential to control and reduce pollution. However, AQ station deployment is sparse, and thus air quality inference for unmonitored locations is crucial. Conventional interpolation methods fail to learn the complex AQ phenomena. This work demonstrates that Deep Gaussian Process models (DGPs) are a promising model for the task of AQ inference. We implement Doubly Stochastic Variational Inference, a DGP algorithm, and show that it performs comparably to the state-of-the-art models.
Gauge Theory, Ramification, And The Geometric Langlands Program

In the gauge theory approach to the geometric Langlands program, ramification can be described in terms of "surface operators," which are supported on two-dimensional surfaces somewhat as Wilson or 't Hooft operators are supported on curves. We describe the relevant surface operators in N=4 super Yang-Mills theory, and the parameters they depend on, and analyze how S-duality acts on these parameters. Then, after compactifying on a Riemann surface, we show that the hypothesis of S-duality for surface operators leads to a natural extension of the geometric Langlands program for the case of tame ramification. The construction involves an action of the affine Weyl group on the cohomology of the moduli space of Higgs bundles with ramification, and an action of the affine braid group on A-branes or B-branes on this space.
Theory and simulations of angular momentum transfer from swift electrons to spherical nanoparticles in scanning transmission electron microscopy

Electron beams in scanning transmission electron microscopy (STEM) exert forces and torques on study samples, with magnitudes that allow the controlled manipulation of nanoparticles (a technique called electron tweezers). Related theoretical research has mostly focused on the study of forces and linear momentum transfers from swift electrons (like those used in STEM) to nanoparticles. However, theoretical research on the rotational aspects of the interaction would benefit not only the development of electron tweezers, but also other fields within electron microscopy such as electron vortices. Starting from a classical-electrodynamics description, we present a theoretical model, alongside an efficient numerical methodology, to calculate the angular momentum transfer from a STEM swift electron to a spherical nanoparticle. We show simulations of angular momentum transfers to aluminum, gold, and bismuth nanoparticles of different sizes. We found that the transferred angular momentum is always perpendicular to the system's plane of symmetry, displaying a constant direction for all the cases considered. In the simulations, the angular momentum transfer increased with the radius of the nanoparticle, but decreased as the speed of the electron or the impact parameter increased. Also, the electric contribution to the angular momentum transfer dominated over the magnetic one, being comparable only for high electron's speeds (greater than 90% of the speed of light). Additionally, for nanoparticles with 1 nm radius of the studied materials, we found validity criteria for the small-particle approximation (in which the nanoparticle is modeled as an electric point dipole). We believe that these findings contribute to the understanding of rotational aspects present in STEM experiments, and might be useful for further developments in electron tweezers and other electron microscopy related techniques.
Late internal‐shock model for bright X‐ray flares in gamma‐ray burst afterglows and GRB 011121

We explore two possible models which might give rise to bright X-ray flares in gamma-ray burst (GRB) afterglows. One is an external forward-reverse shock model, in which the shock parameters of forward-reverse shocks are taken to be quite different. The other is a so-called 'late internal-shock model', which requires a refreshed unsteady relativistic outflow generated after the prompt gamma-ray emission. In the forward-reverse shock model, after the time t(x) at which the reverse shock crosses the ejecta, the flux declines more slowly than (t(+)/t(x))(-(2+beta)), where t(+) denotes the observer's time and beta is the spectral index of the X-ray emission. In the 'late internal-shock model', decaying slopes much steeper than (t(+)/t(e,+))(-(2+beta)) are possible if the central engine shuts down after t(e,+) and the observed variability time-scale of the X-ray flare is much shorter than tau(e,circle plus).
Higher-order topological state induced by 
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-wave competing orders in high-
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 superconduc

We introduce a two-dimensional Chern insulator in proximity to a $d$-wave pseudogap state of the high-T$_c$ superconducting material as an effective platform to realize the higher order topological system. The proximity-induced $d$-density-wave (DDW) order in the Chern insulator layer serves as an effective mass. The edge states will be fully gapped by this DDW order. In the real space, the sign of the DDW order parameter changes at the system corners due to the $d$-wave factor, leading to the gapless corner states, indicating that this system may be in a higher order topological state. The higher order topology in this coupled system is confirmed based on the calculation of the edge polarization and the quadrupole moment. In the superconducting state where the superconducting order and the DDW order coexist, the Majorana corner states emerge.
Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic

Propaganda is defined as an expression of opinion or action by individuals or groups deliberately designed to influence opinions or actions of other individuals or groups with reference to predetermined ends and this is achieved by means of well-defined rhetorical and psychological devices. Currently, propaganda (or persuasion) techniques have been commonly used on social media to manipulate or mislead social media users. Automatic detection of propaganda techniques from textual, visual, or multimodal content has been studied recently, however, major of such efforts are focused on English language content. In this paper, we propose a shared task on detecting propaganda techniques for Arabic textual content. We have done a pilot annotation of 200 Arabic tweets, which we plan to extend to 2,000 tweets, covering diverse topics. We hope that the shared task will help in building a community for Arabic propaganda detection. The dataset will be made publicly available, which can help in future studies.
Economical realization of phase-covariant devices in arbitrary dimensions (Invited)

We describe a unified framework of phase-covariant multiuser quantum transformations for d-dimensional quantum systems. We derive the optimal phase-covariant cloning and transposition transformations for multiphase states. We show that for some particular relations between the input and output number of copies, they correspond to economical transformations, which can be achieved without the need of auxiliary systems. We prove a relation between the optimal phase-covariant cloning and transposition maps and optimal estimation of multiple phases for equatorial states.
Summarizing Community-based Question-Answer Pairs

Community-based Question Answering (CQA), which allows users to acquire their desired information, has increasingly become an essential component of online services in various domains such as E-commerce, travel, and dining. However, an overwhelming number of CQA pairs makes it difficult for users without particular intent to find useful information spread over CQA pairs. To help users quickly digest the key information, we propose the novel CQA summarization task that aims to create a concise summary from CQA pairs. To this end, we first design a multi-stage data annotation process and create a benchmark dataset, COQASUM, based on the Amazon QA corpus. We then compare a collection of extractive and abstractive summarization methods and establish a strong baseline approach DedupLED for the CQA summarization task. Our experiment further confirms two key challenges, sentence-type transfer and deduplication removal, towards the CQA summarization task. Our data and code are publicly available.
On the Alignment of Group Fairness with Attribute Privacy

Group fairness and privacy are fundamental aspects in designing trustworthy machine learning models. Previous research has highlighted conflicts between group fairness and different privacy notions. We are the first to demonstrate the alignment of group fairness with the specific privacy notion of attribute privacy in a blackbox setting. Attribute privacy, quantified by the resistance to attribute inference attacks (AIAs), requires indistinguishability in the target model's output predictions. Group fairness guarantees this thereby mitigating AIAs and achieving attribute privacy. To demonstrate this, we first introduce AdaptAIA, an enhancement of existing AIAs, tailored for real-world datasets with class imbalances in sensitive attributes. Through theoretical and extensive empirical analyses, we demonstrate the efficacy of two standard group fairness algorithms (i.e., adversarial debiasing and exponentiated gradient descent) against AdaptAIA. Additionally, since using group fairness results in attribute privacy, it acts as a defense against AIAs, which is currently lacking. Overall, we show that group fairness aligns with attribute privacy at no additional cost other than the already existing trade-off with model utility.
On sure early selection of the best subset

The early solution path, which tracks the ﬁrst few variables that enter the model of a selection procedure, is of profound importance to scientiﬁc discoveries. In practice, it is often statistically hopeless to identify all the important features with no false discovery, let alone the intimidating expense of experiments to test their signiﬁcance. Such realistic limitation calls for statistical guarantee for the early discoveries of a model selector. In this paper, we focus on the early solution path of best subset selection (BSS), where the sparsity constraint is set to be lower than the true sparsity. Under a sparse high-dimensional linear model, we establish the suﬃcient and (near) necessary condition for BSS to achieve sure early selection, or equivalently, zero false discovery throughout its early path. Essentially, this condition boils down to a lower bound of the minimum projected signal margin that characterizes the gap of the captured signal strength between sure selection models and those with spurious discoveries. Deﬁned through projection operators, this margin is independent of the restricted eigenvalues of the design, suggesting the robustness of BSS against collinearity. Moreover, our model selection guarantee tolerates reasonable optimization error and thus applies to near best subsets. Finally, to overcome the computational hurdle of BSS under high dimension, we propose the “screen then select” (STS) strategy to reduce dimension for BSS. Our numerical experiments show that the resulting early path exhibits much lower false discovery rate (FDR) than LASSO, MCP and SCAD, especially in the presence of highly correlated design. We also investigate the early paths of the iterative hard thresholding algorithms, which are greedy computational surrogates for BSS, and which yield comparable FDR as our STS procedure.
Some Properties of the Nash Equilibrium in $2 \times 2$ Zero-Sum Games

In this report, some properties of the set of Nash equilibria (NEs) of 2 × 2 zero-sum games are reviewed. In particular, the cardinality of the set of NEs is given in terms of the entries of the payoff matrix. Moreover, closed-form expressions for the NE strategies and the payoff at the NE (the value of the game) are provided in terms of the entries of the payoff matrix. The results presented in this report are not necessarily new knowledge, as they follow from the definition of the NE after some tedious calculations. Nevertheless this synthetic presentation is original in the literature. Key-words: 2× 2 Zero Sum Games, Normal-form Game, Nash Equilibrium This work is supported by the Inria Exploratory Action – Information and Decision Making (IDEM). The author thanks Samir M. Perlaza (NEO) and Alain Jean-Marie (NEO) for insightful discussions. Ke Sun is with the Inria centre at Université Côte d’Azur, Sophia Antipolis, France. Quelques propriétés des équilibres de Nash des jeux 2× 2 à somme nulle Résumé : Dans ce rapport, certaines propriétés de l’ensemble des équilibres de Nash (EN) des jeux 2 × 2 à somme nulle sont étudiés. En particulier, la cardinalité de l’ensemble des EN est donnée en termes des entrées de la matrice du jeu. De plus, des expressions explicites pour les stratégies d’équilibre et la valeur du jeu sont présentées. Ces résultats ne sont pas nécessairement des connaissances nouvelles, car ils découlent de la définition de l’EN après quelques calculs. Néanmoins, cette présentation synthétique est originale dans la littérature. Mots-clés : jeux 2× 2 à somme nulle, jeux en forme normale, et équilibre de Nash. Some Properties of the NE in 2× 2 Zero-Sum Games iii