Miracle-dz/UniFAR-FTUs huggingface dataset:
Combinatorial Approach for Factorization of Variance and Entropy in Spin Systems

We present a simple combinatorial framework for establishing approximate tensorization of variance and entropy in the setting of spin systems (a.k.a. undirected graphical models) based on balanced separators of the underlying graph. Such approximate tensorization results immediately imply as corollaries many important structural properties of the associated Gibbs distribution, in particular rapid mixing of the Glauber dynamics for sampling. We prove approximate tensorization by recursively establishing block factorization of variance and entropy with a small balanced separator of the graph. Our approach goes beyond the classical canonical path method for variance and the recent spectral independence approach, and allows us to obtain new rapid mixing results. As applications of our approach, we show that: 1. On graphs of treewidth $t$, the mixing time of the Glauber dynamics is $n^{O(t)}$, which recovers the recent results of Eppstein and Frishberg with improved exponents and simpler proofs; 2. On bounded-degree planar graphs, strong spatial mixing implies $\tilde{O}(n)$ mixing time of the Glauber dynamics, which gives a faster algorithm than the previous deterministic counting algorithm by Yin and Zhang.
Collision Detection for Multi-Robot Motion Planning with Efficient Quad-Tree Update and Skipping

This paper presents a novel and efficient collision checking approach called Updating and Collision Check Skipping Quad-tree (USQ) for multi-robot motion planning. USQ extends the standard quad-tree data structure through a time-efficient update mechanism, which significantly reduces the total number of collision checks and the collision checking time. In addition, it handles transitions at the quad-tree quadrant boundaries based on worst-case trajectories of agents. These extensions make quad-trees suitable for efficient collision checking in multi-robot motion planning of large robot teams. We evaluate the efficiency of USQ in comparison with Regenerating Quad-tree (RQ) from scratch at each timestep and naive pairwise collision checking across a variety of randomized environments. The results indicate that USQ significantly reduces the number of collision checks and the collision checking time compared to other baselines for different numbers of robots and map sizes. In a 50-robot experiment, USQ accurately detected all collisions, outperforming RQ which has longer run-times and/or misses up to 25% of collisions.
Cumulative Spatial Knowledge Distillation for Vision Transformers

Distilling knowledge from convolutional neural networks (CNNs) is a double-edged sword for vision transformers (ViTs). It boosts the performance since the image-friendly local-inductive bias of CNN helps ViT learn faster and better, but leading to two problems: (1) Network designs of CNN and ViT are completely different, which leads to different semantic levels of intermediate features, making spatial-wise knowledge transfer methods (e.g., feature mimicking) inefficient. (2) Distilling knowledge from CNN limits the network convergence in the later training period since ViT’s capability of integrating global information is suppressed by CNN’s local-inductive-bias supervision.To this end, we present Cumulative Spatial Knowledge Distillation (CSKD). CSKD distills spatial-wise knowledge to all patch tokens of ViT from the corresponding spatial responses of CNN, without introducing intermediate features. Furthermore, CSKD exploits a Cumulative Knowledge Fusion (CKF) module, which introduces the global response of CNN and increasingly emphasizes its importance during the training. Applying CKF leverages CNN’s local inductive bias in the early training period and gives full play to ViT’s global capability in the later one. Extensive experiments and analysis on ImageNet-1k and downstream datasets demonstrate the superiority of our CSKD. Code: https://github.com/Zzzzz1/CSKD
A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System

Terahertz communication networks and intelligent reflecting surfaces exhibit significant potential in advancing wireless networks, particularly within the domain of aerial-based multi-access edge computing systems. These technologies enable efficient offloading of computational tasks from user electronic devices to Unmanned Aerial Vehicles or local execution. For the generation of high-quality task-offloading allocations, conventional numerical optimization methods often struggle to solve challenging combinatorial optimization problems within the limited channel coherence time, thereby failing to respond quickly to dynamic changes in system conditions. To address this challenge, we propose a deep learning-based optimization framework called Iterative Order-Preserving policy Optimization (IOPO), which enables the generation of energy-efficient task-offloading decisions within milliseconds. Unlike exhaustive search methods, IOPO provides continuous updates to the offloading decisions without resorting to exhaustive search, resulting in accelerated convergence and reduced computational complexity, particularly when dealing with complex problems characterized by extensive solution spaces. Experimental results demonstrate that the proposed framework can generate energy-efficient task-offloading decisions within a very short time period, outperforming other benchmark methods.
Quantal Response Equilibrium with a Continuum of Types: Characterization and Nonparametric Identification

Quantal response equilibrium (QRE), a statistical generalization of Nash equilibrium, is a standard benchmark in the analysis of experimental data. Despite its influence, nonparametric characterizations and tests of QRE are unavailable beyond the case of finite games. We address this gap by completely characterizing the set of QRE in a class of binary-action games with a continuum of types. Our characterization provides sharp predictions in settings such as global games, volunteer's dilemma, and the compromise game. Further, we leverage our results to develop nonparametric tests of QRE. As an empirical application, we revisit the experimental data from Carrillo and Palfrey (2009) on the compromise game.
Both Spatial and Frequency Cues Contribute to High-Fidelity Image Inpainting

Deep generative approaches have obtained great success in image inpainting recently. However, most generative inpainting networks suffer from either over-smooth results or aliasing artifacts. The former lacks high-frequency details, while the latter lacks semantic structure. To address this issue, we propose an effective Frequency-Spatial Complementary Network (FSCN) by exploiting rich semantic information in both spatial and frequency domains. Specifically, we introduce an extra Frequency Branch and Frequency Loss on the spatial-based network to impose direct supervision on the frequency information, and propose a Frequency-Spatial Cross-Attention Block (FSCAB) to fuse multi-domain features and combine the corresponding characteristics. With our FSCAB, the inpainting network is capable of capturing frequency information and preserving visual consistency simultaneously. Extensive quantitative and qualitative experiments demonstrate that our inpainting network can effectively achieve superior results, outperforming previous state-of-the-art approaches with significantly fewer parameters and less computation cost. The code will be released soon.
Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR

Understanding the state of changed areas requires that precise information be given about the changes. Thus, detecting different kinds of changes is important for land surface monitoring. SAR sensors are ideal to fulfil this task, because of their all-time and all-weather capabilities, with good accuracy of the acquisition geometry and without effects of atmospheric constituents for amplitude data. In this study, we propose a simplified generalized likelihood ratio ($S_{GLR}$) method assuming that corresponding temporal pixels have the same equivalent number of looks (ENL). Thanks to the denoised data provided by a ratio-based multitemporal SAR image denoising method (RABASAR), we successfully applied this similarity test approach to compute the change areas. A new change magnitude index method and an improved spectral clustering-based change classification method are also developed. In addition, we apply the simplified generalized likelihood ratio to detect the maximum change magnitude time, and the change starting and ending times. Then, we propose to use an adaptation of the REACTIV method to visualize the detection results vividly. The effectiveness of the proposed methods is demonstrated through the processing of simulated and SAR images, and the comparison with classical techniques. In particular, numerical experiments proved that the developed method has good performances in detecting farmland area changes, building area changes, harbour area changes and flooding area changes.
Three-dimensional shipborne inverse synthetic aperture radar imaging based on single receiver

ABSTRACT Three-dimensional (3D) inverse synthetic aperture radar (ISAR) image can provide additional height information compared to conventional 2D ISAR image. However, since the existing 3D ISAR imaging methods concentrate on the interferometric technique, the height resolution depends on a large number of receivers. In this letter, a novel 3D ISAR imaging method is proposed. Instead of using the interferometry between several receivers, the chirp term caused by the shipborne radar’s rotations is utilized to obtain the height resolution. Therefore, single receiver is enough to reconstructing the 3D image, and hence the hardware complexity for 3D ISAR imaging is reduced. Simulation results validate the effectiveness of the proposed method.
Evaluating Climate Models with Sliced Elastic Distance

The validation of global climate models plays a crucial role in ensuring the accuracy of climatological predictions. However, existing statistical methods for evaluating differences between climate fields often overlook time misalignment and therefore fail to distinguish between sources of variability. To more comprehensively measure differences between climate fields, we introduce a new vector-valued metric, the sliced elastic distance. This new metric simultaneously accounts for spatial and temporal variability while decomposing the total distance into shape differences (amplitude), timing variability (phase), and bias (translation). We compare the sliced elastic distance against a classical metric and a newly developed Wasserstein-based approach through a simulation study. Our results demonstrate that the sliced elastic distance outperforms previous methods by capturing a broader range of features. We then apply our metric to evaluate the historical model outputs of the Coupled Model Intercomparison Project (CMIP) members, focusing on monthly average surface temperatures and monthly total precipitation. By comparing these model outputs with quasi-observational ERA5 Reanalysis data products, we rank the CMIP models and assess their performance. Additionally, we investigate the progression from CMIP phase 5 to phase 6 and find modest improvements in the phase 6 models regarding their ability to produce realistic climate dynamics.
Towards Secrecy-Aware Attacks Against Trust Prediction in Signed Social Networks

Signed social networks are widely used to model the trust relationships among online users in security-sensitive systems such as cryptocurrency trading platforms, where trust prediction plays a critical role. In this paper, we investigate how attackers could mislead trust prediction by secretly manipulating signed networks. To this end, we first design effective poisoning attacks against representative trust prediction models. The attacks are formulated as hard bi-level optimization problems, for which we propose several efficient approximation solutions. However, the resulting basic attacks would severely change the structural semantics (in particular, both local and global balance properties) of a signed network, which makes the attacks prone to be detected by the powerful attack detectors we designed. Given this, we further refine the basic attacks by integrating some conflicting metrics as penalty terms into the objective function. The refined attacks become secrecy-aware, i.e., they can successfully evade attack detectors with high probability while sacrificing little attack performance. We conduct comprehensive experiments to demonstrate that the basic attacks can severely disrupt trust prediction but could be easily detected, and the refined attacks perform almost equally well while evading detection. Overall, our results significantly advance the knowledge in designing more practical attacks, reflecting more realistic threats to current trust prediction models. Moreover, the results also provide valuable insights and guidance for building up robust trust prediction systems.
Lived Experience Matters: Automatic Detection of Stigma on Social Media Toward People Who Use Substances

Stigma toward people who use substances (PWUS) is a leading barrier to seeking treatment.Further, those in treatment are more likely to drop out if they experience higher levels of stigmatization. While related concepts of hate speech and toxicity, including those targeted toward vulnerable populations, have been the focus of automatic content moderation research, stigma and, in particular, people who use substances have not. This paper explores stigma toward PWUS using a data set of roughly 5,000 public Reddit posts. We performed a crowd-sourced annotation task where workers are asked to annotate each post for the presence of stigma toward PWUS and answer a series of questions related to their experiences with substance use. Results show that workers who use substances or know someone with a substance use disorder are more likely to rate a post as stigmatizing. Building on this, we use a supervised machine learning framework that centers workers with lived substance use experience to label each Reddit post as stigmatizing. Modeling person-level demographics in addition to comment-level language results in a classification accuracy (as measured by AUC) of 0.69 -- a 17% increase over modeling language alone. Finally, we explore the linguist cues which distinguish stigmatizing content: PWUS substances and those who don't agree that language around othering ("people","they") and terms like"addict"are stigmatizing, while PWUS (as opposed to those who do not) find discussions around specific substances more stigmatizing. Our findings offer insights into the nature of perceived stigma in substance use. Additionally, these results further establish the subjective nature of such machine learning tasks, highlighting the need for understanding their social contexts.
Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces

Let $\Omega = [0,1]^d$ be the unit cube in $\mathbb{R}^d$. We study the problem of how efficiently, in terms of the number of parameters, deep neural networks with the ReLU activation function can approximate functions in the Sobolev spaces $W^s(L_q(\Omega))$ and Besov spaces $B^s_r(L_q(\Omega))$, with error measured in the $L_p(\Omega)$ norm. This problem is important when studying the application of neural networks in a variety of fields, including scientific computing and signal processing, and has previously been solved only when $p=q=\infty$. Our contribution is to provide a complete solution for all $1\leq p,q\leq \infty$ and $s>0$ for which the corresponding Sobolev or Besov space compactly embeds into $L_p$. The key technical tool is a novel bit-extraction technique which gives an optimal encoding of sparse vectors. This enables us to obtain sharp upper bounds in the non-linear regime where $p>q$. We also provide a novel method for deriving $L_p$-approximation lower bounds based upon VC-dimension when $p<\infty$. Our results show that very deep ReLU networks significantly outperform classical methods of approximation in terms of the number of parameters, but that this comes at the cost of parameters which are not encodable.
Response of G-NUMEN LaBr3(Ce) Detectors to High Counting Rates

The G-NUMEN array is the future gamma spectrometer of the NUMEN experiment (nuclear matrix element for neutrinoless double beta decay), to be installed around the object point of the MAGNEX magnetic spectrometer at the INFN-LNS laboratory. This project aims to explore double-charge exchange (DCE) reactions in order to obtain crucial information about neutrinoless double beta decay (0νββ). The primary objective of the G-NUMEN array is to detect the gamma rays emitted from the de-excitation of the excited states that are populated via DCE reactions with a good energy resolution and detection efficiency, amidst a background composed of the transitions from competing reaction channels with far higher cross sections. To achieve this, G-NUMEN signals will be processed in coincidence with those generated by the detection of reaction ejectiles by the MAGNEX focal plane detector (FPD). Under the expected experimental conditions, G-NUMEN detectors will operate at high counting rates, of the order of hundreds of kHz per detector, while maintaining excellent energy and timing resolutions. The complete array will consist of over 100 LaBr3(Ce) scintillators. Initial tests were conducted on the first detectors of the array, allowing for the determination of their performance at high rates.
Medical Masks Versus N95 Respirators for Preventing COVID-19 Among Health Care Workers

TO THE EDITOR: I request that Annals retracts Loeb and colleagues' study (1). It was unethical to perform this type of study during the COVID-19 pandemic when the data clearly support transmission of SARS-CoV-2 by aerosol inhalation and not droplets. Surgical masks are not designed to protect the wearer from inhalation of small infectious particles and will not prevent personto-person transmission from patients or coworkers. Respirators must be worn during all exposures—with all patients and coworkers—to ensure user protection. It is clear from the data provided in the Supplement that this was not the case. Without a control (that is, no mask), it is impossible to conclude that surgical masks or respirators were effective. They were probably equally ineffective for different reasons. Many studies in laboratories, clinical settings, and other environments clearly show the efficacy of fit-tested respirators for protecting workers from hazardous aerosol exposures; however, this is true only if they are worn for every exposure. Loeb and colleagues' article does a substantial disservice to health care workers, who have given their jobs and lives caring for patients throughout this pandemic.
1-Methoxyerythrabyssin II Induces Autophagy in Leukemia Cells via PI3K/Akt/mTOR Pathways

Abstract Leukemia, despite currently being one of the most lethal cancers worldwide, still lacks a focused treatment. The purpose of the present investigation was to evaluate the pharmacological effect of 1-methoxyerythrabyssin II, a pterocarpan identified in the roots of Lespedeza bicolor , on leukemic cells and to explore its underlying mechanism using a network pharmacology strategy. 1-Methoxyerythrabyssin II showed an antiproliferative effect in a concentration-dependent manner and exhibited a higher potency in human acute leukemia T cells (Jurkat). The G1 phase arrest induced by 1-methoxyerythrabyssin II was confirmed using a cell cycle assay, and the downregulation of CDK2 and cyclin D1 was observed using an immunoblot assay. Moreover, 1-methoxyerythrabyssin II-treated cells exhibited higher expression levels of LC3B, Atg-7, and Beclin 1 in addition to an enhanced fluorescence intensity in monodansylcadaverine staining, indicating autophagy induction by 1-methoxyerythrabyssin II. Furthermore, network pharmacology and molecular docking analyses revealed that the PI3K/Akt/mTOR pathway is a potential target of 1-methoxyerythrabyssin II in leukemic cells. In vitro assays further demonstrated that 1-methoxyerythrabyssin II promoted autophagy and suppressed cell proliferation by inhibiting the PI3K/Akt/mTOR pathway in leukemic cells. This discovery will contribute to the development of novel therapeutics and prophylactics against leukemia.
Are Confirmatory Assays Reliable for HIV-1/HIV-2 Infection Differentiation? A Multicenter Study

Immunoblots remain the gold standard for HIV-1/HIV-2 infection confirmation. However, their ability to differentiate HIV-1 from HIV-2 infection on an antigenically diversified HIV-1 and HIV-2 panel remain uncommon. ABSTRACT Immunoblots remain the gold standard for HIV-1/HIV-2 infection confirmation. However, their ability to differentiate HIV-1 from HIV-2 infection on an antigenically diversified HIV-1 and HIV-2 panel remain uncommon. We performed a multicenter study on 116 serum samples accounting for most of the diversity of HIV-1 (9 different subtypes in group M, 17 circulating recombinant forms (CRFs), and 3 group O) and HIV-2 (groups A and B), evaluating seven confirmatory assays (six commercially available assays and one in-house assay) with genotyping as the reference. The assays were INNO-LIA HIV I/II score, HIV-2 blot 1.2, HIV blot 2.2, New Lav blot I and II, Geenius, and an in-house serotyping enzyme-linked immunosorbent assay (ELISA). Among the HIV-1 samples, INNO-LIA, HIV blot 2.2, New Lav blot I, Geenius, and serotyping had comparable high sensitivities, from 98% to 100%, whereas HIV-2 blot 1.2 and New Lav blot II had high rates of “undetermined” results (85% and 95%, respectively). HIV-2 blot 1.2 and New Lav blot II misclassified 7% and 5% of HIV-1 samples as HIV-2, respectively, and HIV-2 blot 1.2 had an 8% false-negative rate. Among the HIV-2 samples, INNO-LIA, New Lav blot II, HIV-2 blot 1.2, and serotyping had high sensitivities, from 96% to 100%. HIV blot 2.2 misclassified 17% of HIV-2 samples as HIV-1/HIV-2 dual infections. New Lav blot I misclassified 19% of HIV-2 samples as HIV-1 with a high (81%) undetermined rate, and Geenius misclassified 2% as HIV-1 and 7% as untypeable HIV positive. For HIV-1/HIV-2 dual infection, the results were less sensitive, with at most 87.5% for INNO-LIA and Geenius and 75% for HIV blot 2.2 and serotyping. Overall, confirmatory assays remain useful for most cases, with the exception of HIV-1/HIV-2 dual-infection suspicion.