Miracle-dz/UniFAR-FTUs huggingface dataset:
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes

Despite the significant interest and progress in reinforcement learning (RL) problems with adversarial corruption, current works are either confined to the linear setting or lead to an undesired $\tilde{O}(\sqrt{T}\zeta)$ regret bound, where $T$ is the number of rounds and $\zeta$ is the total amount of corruption. In this paper, we consider the contextual bandit with general function approximation and propose a computationally efficient algorithm to achieve a regret of $\tilde{O}(\sqrt{T}+\zeta)$. The proposed algorithm relies on the recently developed uncertainty-weighted least-squares regression from linear contextual bandit and a new weighted estimator of uncertainty for the general function class. In contrast to the existing analysis that heavily relies on the linear structure, we develop a novel technique to control the sum of weighted uncertainty, thus establishing the final regret bounds. We then generalize our algorithm to the episodic MDP setting and first achieve an additive dependence on the corruption level $\zeta$ in the scenario of general function approximation. Notably, our algorithms achieve regret bounds either nearly match the performance lower bound or improve the existing methods for all the corruption levels and in both known and unknown $\zeta$ cases.
DiffAlign : Few-shot learning using diffusion based synthesis and alignment

We address the problem of few-shot classification where the goal is to learn a classifier from a limited set of samples. While data-driven learning is shown to be effective in various applications, learning from less data still remains challenging. To address this challenge, existing approaches consider various data augmentation techniques for increasing the number of training samples. Pseudo-labeling is commonly used in a few-shot setup, where approximate labels are estimated for a large set of unlabeled images. We propose DiffAlign which focuses on generating images from class labels. Specifically, we leverage the recent success of the generative models (e.g., DALL-E and diffusion models) that can generate realistic images from texts. However, naive learning on synthetic images is not adequate due to the domain gap between real and synthetic images. Thus, we employ a maximum mean discrepancy (MMD) loss to align the synthetic images to the real images minimizing the domain gap. We evaluate our method on the standard few-shot classification benchmarks: CIFAR-FS, FC100, miniImageNet, tieredImageNet and a cross-domain few-shot classification benchmark: miniImageNet to CUB. The proposed approach significantly outperforms the stateof-the-art in both 5-shot and 1-shot setups on these benchmarks. Our approach is also shown to be effective in the zero-shot classification setup
Progressive Multi-view Human Mesh Recovery with Self-Supervision

To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e.g., motion capture, sport analysis) and robustness to single-view ambiguities. Existing solutions typically suffer from poor generalization performance to new settings, largely due to the limited diversity of image/3D-mesh pairs in multi-view training data. To address this shortcoming, people have explored the use of synthetic images. But besides the usual impact of visual gap between rendered and target data, synthetic-data-driven multi-view estimators also suffer from overfitting to the camera viewpoint distribution sampled during training which usually differs from real-world distributions. Tackling both challenges, we propose a novel simulation-based training pipeline for multi-view human mesh recovery, which (a) relies on intermediate 2D representations which are more robust to synthetic-to-real domain gap; (b) leverages learnable calibration and triangulation to adapt to more diversified camera setups; and (c) progressively aggregates multi-view information in a canonical 3D space to remove ambiguities in 2D representations. Through extensive benchmarking, we demonstrate the superiority of the proposed solution especially for unseen in-the-wild scenarios.
On auxiliary latitudes

The auxiliary latitudes are essential tools in cartography. This paper summarizes methods for converting between them with an emphasis on providing full double-precision accuracy. This includes series expansions in the third flattening, where the truncation error is precisely measured and where estimates of the radii of convergence are given. Also new formulas are given for computing the rectifying and authalic latitudes with minimal roundoff error.
Dark Energy Nature in Logarithmic f(R,T) Cosmology

The present research paper is an investigation of dark energy nature of logarithmic $f(R, T)$-gravity cosmology in a flat FLRW space-time universe. We have derived modified Einstein's field equations for the function $f(R, T)=R-16\pi G\alpha\ln(T)$ where $R$ is the Ricci scalar curvature, $T$ is the trace of the stress energy momentum tensor and $\alpha$ is a model parameter. We have solved field equations in the form of two fluid scenario as perfect-fluid and dark-fluid, where dark fluid term is derived in the form of perfect fluid source. We have made an observational constraints on the cosmological parameters $\Omega_{(m)}, \omega^{(de)}$ and $H_{0}$ using $\chi^{2}$ test with observational datasets like Pantheon sample of SNe Ia and $H(z)$. With these constraints we have discussed our model with deceleration parameter $q$, energy parameters $\Omega_{(m)}, \Omega_{(de)}$, EoS parameter $\omega^{(de)}$ etc. Also, we have done Om diagnostic analysis. The derived $f(R, T)$ model shows a quintessence dark energy model $\omega^{(de)}>-1$ and late-time universe approaches to $\Lambda$CDM model.
Understanding Concurrency Vulnerabilities in Linux Kernel

While there is a large body of work on analyzing concurrency related software bugs and developing techniques for detecting and patching them, little attention has been given to concurrency related security vulnerabilities. The two are different in that not all bugs are vulnerabilities: for a bug to be exploitable, there needs be a way for attackers to trigger its execution and cause damage, e.g., by revealing sensitive data or running malicious code. To fill the gap, we conduct the first empirical study of concurrency vulnerabilities reported in the Linux operating system in the past ten years. We focus on analyzing the confirmed vulnerabilities archived in the Common Vulnerabilities and Exposures (CVE) database, which are then categorized into different groups based on bug types, exploit patterns, and patch strategies adopted by developers. We use code snippets to illustrate individual vulnerability types and patch strategies. We also use statistics to illustrate the entire landscape, including the percentage of each vulnerability type. We hope to shed some light on the problem, e.g., concurrency vulnerabilities continue to pose a serious threat to system security, and it is difficult even for kernel developers to analyze and patch them. Therefore, more efforts are needed to develop tools and techniques for analyzing and patching these vulnerabilities.
Shape-dependent effects of dielectrically nonlinear inclusions in heterogeneous media

In this work the electrical response of a mixture composed of dielectrically nonlinear ellipsoids dispersed in a linear matrix is modeled. The inclusions may be randomly oriented. The aim is both to set up a methodology apt to deal with this kind of system and to use it to study the effect of marked nonsphericity of inclusions on the global behavior of a mixture. The results are quite interesting from both these points of view. The method here developed extends the Maxwell-Garnett theory [A Treatise on Electricity and Magnetism (Clarendon, Oxford, 1881)], which deals with dielectrically linear inclusions, and it allows, inter alia, to obtain a closed-form expression for the hypersusceptibility ratio of the mixture to the dispersed inclusions. These latter can range from cylinders to spheres, already present in the literature, to “penny-shaped” particles. The theoretical framework is based on the assumption that the dispersion is very dilute. We were able to show that in a specific case, when oblate partic.
Linear stabilization for a degenerate wave equation in non divergence form with drift

We consider a degenerate wave equation in one dimension, with drift and in presence of a leading operator which is not in divergence form. We impose a homogeneous Dirichlet boundary condition where the degeneracy occurs and a boundary damping at the other endpoint. We provide some conditions for the uniform exponential decay of solutions for the associated Cauchy problem.
Task modules Partitioning, Scheduling and Floorplanning for Partially Dynamically Reconfigurable Systems Based on Modern Heterogeneous FPGAs

Modern field programmable gate array(FPGA) can be partially dynamically reconfigurable with heterogeneous resources distributed on the chip. And FPGA-based partially dynamically reconfigurable system(FPGA-PDRS) can be used to accelerate computing and improve computing flexibility. However, the traditional design of FPGA-PDRS is based on manual design. Implementing the automation of FPGA-PDRS needs to solve the problems of task modules partitioning, scheduling, and floorplanning on heterogeneous resources. Existing works only partly solve problems for the automation process of FPGA-PDRS or model homogeneous resource for FPGA-PDRS. To better solve the problems in the automation process of FPGA-PDRS and narrow the gap between algorithm and application, in this paper, we propose a complete workflow including three parts, pre-processing to generate the list of task modules candidate shapes according to the resources requirements, exploration process to search the solution of task modules partitioning, scheduling, and floorplanning, and post-optimization to improve the success rate of floorplan. Experimental results show that, compared with state-of-the-art work, the proposed complete workflow can improve performance by 18.7\%, reduce communication cost by 8.6\%, on average, with improving the resources reuse rate of the heterogeneous resources on the chip. And based on the solution generated by the exploration process, the post-optimization can improve the success rate of the floorplan by 14\%.
Arithmetic degrees and Zariski dense orbits of cohomologically hyperbolic maps

A dominant rational self-map on a projective variety is called $p$-cohomologically hyperbolic if the $p$-th dynamical degree is strictly larger than other dynamical degrees. For such a map defined over $\overline{\mathbb{Q}}$, we study lower bounds of the arithmetic degrees, existence of points with Zariski dense orbit, and finiteness of preperiodic points. In particular, we prove that, if $f$ is an $1$-cohomologically hyperbolic map on a smooth projective variety, then (1) the arithmetic degree of a $\overline{\mathbb{Q}}$-point with generic $f$-orbit is equal to the first dynamical degree of $f$; and (2) there are $\overline{\mathbb{Q}}$-points with Zariski dense $f$-orbit. Applying our theorem to the recently constructed rational map with transcendental dynamical degree, we confirm that arithmetic degree can be transcendental.
Millisecond X-Ray Variability from an Accreting Neutron Star System

We report the detection with the Proportional Counter Array (PCA) on board the Rossi X-Ray Timing Explorer (RXTE) of millisecond variability in the X-ray emission from the low-mass X-ray binary 4U 1728-34. Pulsations at 363 Hz with amplitudes (rms) of 2.5%-10% are present in six of the eight bursts analyzed to date. The strongest were seen in two successive bursts recorded on 1996 February 16 when the quiescent count rate was near the highest seen by PCA. The pulsations during these bursts show frequency changes of 1.5 Hz during the first few seconds but become effectively coherent as the burst decays. We interpret the 363 Hz pulsations as rotationally induced modulations of inhomogeneous burst emission. This represents the first compelling evidence for a millisecond spin period in a low-mass X-ray binary. Complex, intensity-dependent, millisecond X-ray variability is also present in all the quiescent flux intervals we examined. Most interesting was the behavior as the count rate approached its highest observed level. Two quasi-periodic oscillations (QPOs) were simultaneously observed in the 650-1100 Hz range. Both QPOs increased in frequency together, maintaining a nearly constant frequency separation of about 363 Hz, the spin period inferred from the burst oscillations. This phenomenology is strongly suggestive of the magnetospheric beat frequency model proposed for the horizontal-branch oscillations (HBOs) seen in Z sources. We discuss this and several other possible physical interpretations for the observed X-ray variability.
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization

Nonconvex optimization is central in solving many machine learning problems, in which block-wise structure is commonly encountered. In this work, we propose cyclic block coordinate methods for nonconvex optimization problems with non-asymptotic gradient norm guarantees. Our convergence analysis is based on a gradient Lipschitz condition with respect to a Mahalanobis norm, inspired by a recent progress on cyclic block coordinate methods. In deterministic settings, our convergence guarantee matches the guarantee of (full-gradient) gradient descent, but with the gradient Lipschitz constant being defined w.r. t. ~a Mahalanobis norm. In stochastic settings, we use recursive variance reduction to decrease the per-iteration cost and match the arithmetic operation complexity of current optimal stochastic full-gradient methods, with a unified analysis for both finite-sum and infinite-sum cases. We prove a faster linear convergence result when a Polyak-{\L}ojasiewicz (P{\L}) condition holds. To our knowledge, this work is the first to provide non-asymptotic convergence guarantees -- variance-reduced or not -- for a cyclic block coordinate method in general composite (smooth + nonsmooth) nonconvex settings. Our experimental results demonstrate the efficacy of the proposed cyclic scheme in training deep neural nets.
Global existence for coupled reaction-diffusion equations with a balance law and nonlinearities with non-constant sign

This paper aims to prove the global existence of solutions for coupled reaction diffusion equations with a balance Law and nonlinearities with a non constant sign. The case when one (or both) of the components of the solution is not a priori bounded is treated. Proofs are based on developed Lyapunov techniques.
Scoring rules in survival analysis

Scoring rules promote rational and good decision making and predictions by models, this is increasingly important for automated procedures of `auto-ML'. The Brier score and Log loss are well-established scoring rules for classification and regression and possess the `strict properness' property that encourages optimal predictions. In this paper we survey proposed scoring rules for survival analysis, establish the first clear definition of `(strict) properness' for survival scoring rules, and determine which losses are proper and improper. We prove that commonly utilised scoring rules that are claimed to be proper are in fact improper. We further prove that under a strict set of assumptions a class of scoring rules is strictly proper for, what we term, `approximate' survival losses. We hope these findings encourage further research into robust validation of survival models and promote honest evaluation.
Formal theory: status and outlook

A very brief overview is presented on some of the current most active areas of research in formal aspects of High Energy Physics and Cosmology. Including the recent breakthrough on the black hole information paradox, developments on amplitudes, the bootstrap and swampland programmes as well as progress towards realistic UV complete models of particle physics and cosmology. Perspectives on future contact with observations are discussed emphasising the long term prospect for ultra high frequency gravitational waves to test early universe physics beyond the Standard Model
Spectral Evolution Responsible for the Transition from Positive Lags to Negative Lags in Gamma-Ray Bursts

It is well known that most gamma-ray bursts (GRBs) are dominated by positive spectral lags, while a small fraction of GRBs show negative lags. However, Wei et al. first identified a well-defined transition from positive lags to negative lags in GRB 160625B, and then got robust limits on a possible violation of Lorentz invariance (LIV) based on the observation. Recently, such a transition has been found in three different emission episodes in GRB 190530A by Gunapati et al., which provides us a great opportunity to investigate whether the transition results from LIV-induced observed spectral lags. Our analysis shows that the LIV model cannot be compatible with the current observations, whereas, only the spectral evolution induced spectral lags could responsible for the transition. So, spectral evolution can also explain the positive to negative lag in GRB 190530A.
Toward Robust Graph Semi-Supervised Learning Against Extreme Data Scarcity.

The success of graph neural networks (GNNs) in graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice. When only a few labeled nodes are available, how to improve their robustness is key to achieving replicable and sustainable graph semi-supervised learning. Though self-training is powerful for semi-supervised learning, its application on graph-structured data may fail because 1) larger receptive fields are not leveraged to capture long-range node interactions, which exacerbates the difficulty of propagating feature-label patterns from labeled nodes to unlabeled nodes and 2) limited labeled data makes it challenging to learn well-separated decision boundaries for different node classes without explicitly capturing the underlying semantic structure. To address the challenges of capturing informative structural and semantic knowledge, we propose a new graph data augmentation framework, augmented graph self-training (AGST), which is built with two new (i.e., structural and semantic) augmentation modules on top of a decoupled GST backbone. In this work, we investigate whether this novel framework can learn a robust graph predictive model under the low-data context. We conduct comprehensive evaluations on semi-supervised node classification under different scenarios of limited labeled-node data. The experimental results demonstrate the unique contributions of the novel data augmentation framework for node classification with few labeled data.
Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation

News recommendation is a widely adopted technique to provide personalized news feeds for the user. Recently, pre-trained language models (PLMs) have demonstrated the great capability of natural language understanding and benefited news recommendation via improving news modeling. However, most existing works simply finetune the PLM with the news recommendation task, which may suffer from the known domain shift problem between the pre-training corpus and downstream news texts. Moreover, PLMs usually contain a large volume of parameters and have high computational overhead, which imposes a great burden on low-latency online services. In this paper, we propose Tiny-NewsRec, which can improve both the effectiveness and the efficiency of PLM-based news recommendation. We first design a self-supervised domain-specific post-training method to better adapt the general PLM to the news domain with a contrastive matching task between news titles and news bodies. We further propose a two-stage knowledge distillation method to improve the efficiency of the large PLM-based news recommendation model while maintaining its performance. Multiple teacher models originated from different time steps of our post-training procedure are used to transfer comprehensive knowledge to the student model in both its post-training stage and finetuning stage. Extensive experiments on two real-world datasets validate the effectiveness and efficiency of our method.
Collaboration Promotes Group Resilience in Multi-Agent AI

AI agents need to be robust to unexpected changes in their environment in order to safely operate in real-world scenarios. While some work has been done on this type of robustness in the single-agent case, in this work we introduce the idea that collaboration with other agents can help agents adapt to environment perturbations in multi-agent reinforcement learning settings. We first formalize this notion of resilience of a group of agents. We then empirically evaluate different collaboration protocols and examine their effect on resilience. We see that all of the collaboration approaches considered lead to greater resilience compared to baseline, in line with our hypothesis. We discuss future direction and the general relevance of the concept of resilience introduced in this work.
Robust Relationship Between Mid-latitudes CAPE and Moist Static Energy in Present and Future Simulations

Convective available potential energy (CAPE), a metric associated with severe weather, is expected to increase with warming. Under the most widely-accepted theory, developed for strongly convective regimes, mean CAPE should rise following the Clausius–Clapeyron (C–C) relationship at 6–7%/K. We show here that although the magnitude of CAPE change in high-resolution model output is only slightly underestimated with simple theories, it is insuﬃcient to describe the distributional changes, which has a down-sloping structure and is crucial for impact assessment. A more appropriate framework for understanding CAPE changes uses the tight correlation between CAPE and moist static energy (MSE) surplus. Atmospheric proﬁles develop appreciable CAPE only when MSE surplus becomes positive; beyond this point, CAPE increases as ∼ 25% of the rise in MSE surplus. Because this relationship is robust across climate states, changes in future CAPE distributions can be well-captured by a simple scaling of present-day data using only three parameters.