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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by complex physiological processes. Previous research has predominantly focused on static cerebral interactions, often neglecting the brain's dynamic nature and the challenges posed by network noise. To address these gaps, we introduce the Masked Connection-based Dynamic Graph Learning Network (MCDGLN). Our approach first segments BOLD signals using sliding temporal windows to capture dynamic brain characteristics. We then employ a specialized weighted edge aggregation (WEA) module, which uses the cross convolution with channel-wise element-wise convolutional kernel, to integrate dynamic functional connectivity and to isolating task-relevant connections. This is followed by topological feature extraction via a hierarchical graph convolutional network (HGCN), with key attributes highlighted by a self-attention module. Crucially, we refine static functional connections using a customized task-specific mask, reducing noise and pruning irrelevant links. The attention-based connection encoder (ACE) then enhances critical connections and compresses static features. The combined features are subsequently used for classification. Applied to the Autism Brain Imaging Data Exchange I (ABIDE I) dataset, our framework achieves a 73.3\% classification accuracy between ASD and Typical Control (TC) groups among 1,035 subjects. The pivotal roles of WEA and ACE in refining connectivity and enhancing classification accuracy underscore their importance in capturing ASD-specific features, offering new insights into the disorder., Comment: 8 pages, 7 figures

Out-of-distribution (OOD) object detection is a challenging task due to the absence of open-set OOD data. Inspired by recent advancements in text-to-image generative models, such as Stable Diffusion, we study the potential of generative models trained on large-scale open-set data to synthesize OOD samples, thereby enhancing OOD object detection. We introduce SyncOOD, a simple data curation method that capitalizes on the capabilities of large foundation models to automatically extract meaningful OOD data from text-to-image generative models. This offers the model access to open-world knowledge encapsulated within off-the-shelf foundation models. The synthetic OOD samples are then employed to augment the training of a lightweight, plug-and-play OOD detector, thus effectively optimizing the in-distribution (ID)/OOD decision boundaries. Extensive experiments across multiple benchmarks demonstrate that SyncOOD significantly outperforms existing methods, establishing new state-of-the-art performance with minimal synthetic data usage., Comment: 19 pages, 4 figures

For an untilted Frenkel-Kontorova chain and any rational $p/q$, Aubry and Mather proved there are minimising equilibrium states that are left- and right-asymptotic to neighbouring pairs of spatially periodic minimisers of type $(p,q)$. They are known as {\em discommensurations} (or kinks or fronts), {\em advancing }if the right-asymptotic equilibrium is to the right of the left-asymptotic one, {\em retreating} otherwise. Following work of Middleton, Floria \& Mazo and Baesens \& MacKay, there is a threshold tilt $F_d(p/q)\ge 0$ up to which there continue to be periodic equilibria of type $(p,q)$ and above which there is a globally attracting periodically sliding solution in the space of sequences of type $(p,q)$. In this paper, we prove that there are values $F_d(p/q\pm)$ of tilt with $0\le F_d(p/q\pm) \le F_d(p/q)$, generically positive and less than $F_d(p/q)$, up to which there continue to be equilibrium advancing or retreating discommensurations, respectively, and such that for $F_d(p/q\pm) < F < F_d(p/q)$ there are periodically sliding discommensurations, apart perhaps from exceptional cases with both a degenerate type $(p,q)$ equilibrium and a degenerate advancing equilibrium discommensuration. We give examples, however, to show that equilibrium and periodically sliding discommensurations may co-exist, both above and below $F_d(p/q\pm)$, so the case of discommensurations is not as clean as that of periodic configurations. On the way, we prove that $F_d(\omega) \to F_d(p/q\pm)$ as $\omega \searrow p/q$ or $\nearrow p/q$ respectively. Finally, we prove that $F_d(p/q\pm)=0$ is equivalent to the existence of a rotational invariant circle consisting of periodic orbits of type $(p,q)$ and right-going (respectively left-going) separatrices, for the corresponding twist map on the cylinder., Comment: 50 pages

We propose a novel method to probe light-quark dipole moments by examining the azimuthal asymmetries between a collinear pair of hadrons in semi-inclusive deep inelastic lepton scattering off an unpolarized proton target at the Electron-Ion Collider. These asymmetries provide a means to observe transversely polarized quarks, which arise exclusively from the interference between the dipole and the Standard Model interactions, thereby depending linearly on the dipole couplings. We demonstrate that this novel approach can enhance current constraints on light-quark dipole operators by an order of magnitude, free from contamination of other new physics effects. Furthermore, it allows for a simultaneous determination of both the real and imaginary parts of the dipole couplings, offering a new avenue for investigating potential $CP$-violating effects at high energies., Comment: 6 pages, 2 figures

The BESIII Collaboration recently reported the observation of the $X(2600)$ state in the $\eta^\prime \pi^+\pi^-$ invariant mass spectrum of $J/\psi \to \gamma \eta^\prime \pi^+\pi^-$, with a significance exceeding $20\sigma$. Its $J^{PC}$ quantum numbers could be either $0^{-+}$ or $2^{-+}$. We explore the possibility of the $X(2600)$ being a higher state of the $\eta_2$ meson family. Through ($n,M^2$) trajectory analysis and the Quark Pair Creation model, we propose that the $X(2600)$ could be the third radial excitation of the $\eta_2(1870)$. However, the theoretical decay width of the $\eta_2(4D)$ is smaller than the experimental width of the $X(2600)$, and branching ratio calculations suggest inconsistencies, leading us to exclude the $X(2600)$ as the $\eta_2(4D)$. Our findings contribute to the understanding of the $X(2600)$ and provide insights for future experimental searches for excited the $\eta_2$ states., Comment: 8 pages, 6 figures, 4 tables

Starting from holography for IIB string theory on AdS$_3\times \mathcal N$ with NS-NS flux, the TsT/$T\bar T$ correspondence is a conjecture that a TsT transformation on the string theory side is holographically dual to the single-trace version of the $T\bar T$ deformation on the field theory side. More precisely, the long string sector of string theory on the TsT-transformed background corresponds to the symmetric product theory whose seed theory is the $T\bar T$-deformed CFT$_2$. In this paper, we study the asymptotic symmetry of the string theory in the bulk. We find a state-dependent, non-local field redefinition under which the worldsheet equations of motion, stress tensor, as well as the symplectic form of string theory after the TsT transformation are mapped to those before the TsT transformation. The asymptotic symmetry in the auxiliary AdS basis is generated by two commuting Virasoro generators, while in the TsT transformed basis is non-linear and non-local. Our result from string theory analysis is compatible with that of the $T\bar T$ deformed CFT$_2$., Comment: 33 pages

Subgroup analysis has garnered increasing attention for its ability to identify meaningful subgroups within heterogeneous populations, thereby enhancing predictive power. However, in many fields such as social science and biology, covariates are often highly correlated due to common factors. This correlation poses significant challenges for subgroup identification, an issue that is often overlooked in existing literature. In this paper, we aim to address this gap in the ``diverging dimension" regime by proposing a center-augmented subgroup identification method within the Factor Augmented (sparse) Linear Model framework. This method bridges dimension reduction and sparse regression. Our proposed approach is adaptable to the high cross-sectional dependence among covariates and offers computational advantages with a complexity of $O(nK)$, compared to the $O(n^2)$ complexity of the conventional pairwise fusion penalty method in the literature, where $n$ is the sample size and $K$ is the number of subgroups. We also investigate the asymptotic properties of the oracle estimators under conditions on the minimal distance between group centroids. To implement the proposed approach, we introduce a Difference of Convex functions-based Alternating Direction Method of Multipliers (DC-ADMM) algorithm and demonstrate its convergence to a local minimizer in a finite number of steps. We illustrate the superiority of the proposed method through extensive numerical experiments and a real macroeconomic data example. An \texttt{R} package, \texttt{SILFS}, implementing the method is also available on CRAN.

In this work, we propose a novel approach for subgraph matching, the problem of finding a given query graph in a large source graph, based on the fused Gromov-Wasserstein distance. We formulate the subgraph matching problem as a partial fused Gromov-Wasserstein problem, which allows us to build on existing theory and computational methods in order to solve this challenging problem. We extend our method by employing a subgraph sliding approach, which makes it efficient even for large graphs. In numerical experiments, we showcase that our new algorithms have the ability to outperform state-of-the-art methods for subgraph matching on synthetic as well as realworld datasets. In particular, our methods exhibit robustness with respect to noise in the datasets and achieve very fast query times.

The insertion of objects into a scene and relighting are commonly utilized applications in augmented reality (AR). Previous methods focused on inserting virtual objects using CAD models or real objects from single-view images, resulting in highly limited AR application scenarios. We propose a novel NeRF-based pipeline for inserting object NeRFs into scene NeRFs, enabling novel view synthesis and realistic relighting, supporting physical interactions like casting shadows onto each other, from two sets of images depicting the object and scene. The lighting environment is in a hybrid representation of Spherical Harmonics and Spherical Gaussians, representing both high- and low-frequency lighting components very well, and supporting non-Lambertian surfaces. Specifically, we leverage the benefits of volume rendering and introduce an innovative approach for efficient shadow rendering by comparing the depth maps between the camera view and the light source view and generating vivid soft shadows. The proposed method achieves realistic relighting effects in extensive experimental evaluations., Comment: 14 pages

Severe data imbalance naturally exists among web-scale vision-language datasets. Despite this, we find CLIP pre-trained thereupon exhibits notable robustness to the data imbalance compared to supervised learning, and demonstrates significant effectiveness in learning generalizable representations. With an aim to investigate the reasons behind this finding, we conduct controlled experiments to study various underlying factors, and reveal that CLIP's pretext task forms a dynamic classification problem wherein only a subset of classes is present in training. This isolates the bias from dominant classes and implicitly balances the learning signal. Furthermore, the robustness and discriminability of CLIP improve with more descriptive language supervision, larger data scale, and broader open-world concepts, which are inaccessible to supervised learning. Our study not only uncovers the mechanisms behind CLIP's generalizability beyond data imbalance but also provides transferable insights for the research community. The findings are validated in both supervised and self-supervised learning, enabling models trained on imbalanced data to achieve CLIP-level performance on diverse recognition tasks. Code and data are available at: https://github.com/CVMI-Lab/clip-beyond-tail.

A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about the potential of new technologies to offer a personal tutor for every learner and a teaching assistant for every teacher. The full extent of this dream, however, has not yet materialised. We argue that this is primarily due to the difficulties with verbalising pedagogical intuitions into gen AI prompts and the lack of good evaluation practices, reinforced by the challenges in defining excellent pedagogy. Here we present our work collaborating with learners and educators to translate high level principles from learning science into a pragmatic set of seven diverse educational benchmarks, spanning quantitative, qualitative, automatic and human evaluations; and to develop a new set of fine-tuning datasets to improve the pedagogical capabilities of Gemini, introducing LearnLM-Tutor. Our evaluations show that LearnLM-Tutor is consistently preferred over a prompt tuned Gemini by educators and learners on a number of pedagogical dimensions. We hope that this work can serve as a first step towards developing a comprehensive educational evaluation framework, and that this can enable rapid progress within the AI and EdTech communities towards maximising the positive impact of gen AI in education.

The data-driven newsvendor problem with features has recently emerged as a significant area of research, driven by the proliferation of data across various sectors such as retail, supply chains, e-commerce, and healthcare. Given the sensitive nature of customer or organizational data often used in feature-based analysis, it is crucial to ensure individual privacy to uphold trust and confidence. Despite its importance, privacy preservation in the context of inventory planning remains unexplored. A key challenge is the nonsmoothness of the newsvendor loss function, which sets it apart from existing work on privacy-preserving algorithms in other settings. This paper introduces a novel approach to estimate a privacy-preserving optimal inventory policy within the f-differential privacy framework, an extension of the classical $(\epsilon, \delta)$-differential privacy with several appealing properties. We develop a clipped noisy gradient descent algorithm based on convolution smoothing for optimal inventory estimation to simultaneously address three main challenges: (1) unknown demand distribution and nonsmooth loss function; (2) provable privacy guarantees for individual-level data; and (3) desirable statistical precision. We derive finite-sample high-probability bounds for optimal policy parameter estimation and regret analysis. By leveraging the structure of the newsvendor problem, we attain a faster excess population risk bound compared to that obtained from an indiscriminate application of existing results for general nonsmooth convex loss. Our bound aligns with that for strongly convex and smooth loss function. Our numerical experiments demonstrate that the proposed new method can achieve desirable privacy protection with a marginal increase in cost.

Deep Learning (DL)-based methods have proven to be effective for software vulnerability detection, with a potential for substantial productivity enhancements for detecting vulnerabilities. Current methods mainly focus on detecting single functions (i.e., intra-procedural vulnerabilities), ignoring the more complex inter-procedural vulnerability detection scenarios in practice. For example, developers routinely engage with program analysis to detect vulnerabilities that span multiple functions within repositories. In addition, the widely-used benchmark datasets generally contain only intra-procedural vulnerabilities, leaving the assessment of inter-procedural vulnerability detection capabilities unexplored. To mitigate the issues, we propose a repository-level evaluation system, named \textbf{VulEval}, aiming at evaluating the detection performance of inter- and intra-procedural vulnerabilities simultaneously. Specifically, VulEval consists of three interconnected evaluation tasks: \textbf{(1) Function-Level Vulnerability Detection}, aiming at detecting intra-procedural vulnerability given a code snippet; \textbf{(2) Vulnerability-Related Dependency Prediction}, aiming at retrieving the most relevant dependencies from call graphs for providing developers with explanations about the vulnerabilities; and \textbf{(3) Repository-Level Vulnerability Detection}, aiming at detecting inter-procedural vulnerabilities by combining with the dependencies identified in the second task. VulEval also consists of a large-scale dataset, with a total of 4,196 CVE entries, 232,239 functions, and corresponding 4,699 repository-level source code in C/C++ programming languages. Our analysis highlights the current progress and future directions for software vulnerability detection., Comment: 12 pages

Recently, there has been a growing interest in automatic software vulnerability detection. Pre-trained model-based approaches have demonstrated superior performance than other Deep Learning (DL)-based approaches in detecting vulnerabilities. However, the existing pre-trained model-based approaches generally employ code sequences as input during prediction, and may ignore vulnerability-related structural information, as reflected in the following two aspects. First, they tend to fail to infer the semantics of the code statements with complex logic such as those containing multiple operators and pointers. Second, they are hard to comprehend various code execution sequences, which is essential for precise vulnerability detection. To mitigate the challenges, we propose a Structured Natural Language Comment tree-based vulnerAbiLity dEtection framework based on the pre-trained models, named SCALE. The proposed Structured Natural Language Comment Tree (SCT) integrates the semantics of code statements with code execution sequences based on the Abstract Syntax Trees (ASTs). Specifically, SCALE comprises three main modules: (1) Comment Tree Construction, which aims at enhancing the model's ability to infer the semantics of code statements by first incorporating Large Language Models (LLMs) for comment generation and then adding the comment node to ASTs. (2) Structured Natural Language Comment Tree Construction}, which aims at explicitly involving code execution sequence by combining the code syntax templates with the comment tree. (3) SCT-Enhanced Representation, which finally incorporates the constructed SCTs for well capturing vulnerability patterns., Comment: Accepted by ISSTA 2024

Quantum key distribution is a cornerstone of quantum technology, offering information-theoretical secure keys for remote parties. With many quantum communication networks established globally, the mode-pairing protocol stands out for its efficacy over inter-city distances using simple setups, emerging as a promising solution. In this study, we employ the mode-pairing scheme into existing inter-city fiber links, conducting field tests across distances ranging from tens to about a hundred kilometers. Our system achieves a key rate of $1.217$ kbit/s in a $195.85$ km symmetric link and $3.089$ kbit/s in a $127.92$ km asymmetric link without global phase locking. The results demonstrate that the mode-pairing protocol can achieve key rates comparable to those of a single quantum link between two trusted nodes on the Beijing-Shanghai backbone line, effectively reducing the need for half of the trusted nodes. These field tests confirm the mode-pairing scheme's adaptability, efficiency, and practicality, positioning it as a highly suitable protocol for quantum networks., Comment: 15 pages, 5 figures, 6 tables

We investigate the suppression of star formation in galaxy pairs based on the isolated galaxy pair sample derived from the SDSS survey. By comparing the star formation rate between late-type galaxies in galaxy pairs and those in the isolated environment, we detect the signal of star formation suppression in galaxy pairs at $d_p < 100$kpc and $200$kpc$ < d_p < 350$kpc. The occurrence of star formation suppression in these late-type galaxies requires their companion galaxies to have an early-type morphology ($n_s > 2.5$). Star formation suppression in wide galaxy pairs with $200$kpc$ < d_p < 350$kpc mainly occurs in massive late-type galaxies, while in close galaxy pairs with $d_p < 100$kpc, it only appears in late-type galaxies with a massive companion ( $\log M_\star > 11.0$), nearly independent of their own stellar mass. Based on these findings, we infer that star formation suppression in wide galaxy pairs is actually a result of galaxy conformity, while in close galaxy pairs, it stems from the influence of hot circum-galactic medium surrounding companion galaxies., Comment: 10 pages, 4 figures, accepted for publication in ApJ

In this paper, we present an empirical study on image recognition fairness, i.e., extreme class accuracy disparity on balanced data like ImageNet. We experimentally demonstrate that classes are not equal and the fairness issue is prevalent for image classification models across various datasets, network architectures, and model capacities. Moreover, several intriguing properties of fairness are identified. First, the unfairness lies in problematic representation rather than classifier bias. Second, with the proposed concept of Model Prediction Bias, we investigate the origins of problematic representation during optimization. Our findings reveal that models tend to exhibit greater prediction biases for classes that are more challenging to recognize. It means that more other classes will be confused with harder classes. Then the False Positives (FPs) will dominate the learning in optimization, thus leading to their poor accuracy. Further, we conclude that data augmentation and representation learning algorithms improve overall performance by promoting fairness to some degree in image classification. The Code is available at https://github.com/dvlab-research/Parametric-Contrastive-Learning., Comment: CVPR 2024

This paper addresses the challenge of integrating sequentially arriving data within the quantile regression framework, where the number of features is allowed to grow with the number of observations, the horizon is unknown, and memory is limited. We employ stochastic sub-gradient descent to minimize the empirical check loss and study its statistical properties and regret performance. In our analysis, we unveil the delicate interplay between updating iterates based on individual observations versus batches of observations, revealing distinct regularity properties in each scenario. Our method ensures long-term optimal estimation irrespective of the chosen update strategy. Importantly, our contributions go beyond prior works by achieving exponential-type concentration inequalities and attaining optimal regret and error rates that exhibit only \textsf{ short-term} sensitivity to initial errors. A key insight from our study is the delicate statistical analyses and the revelation that appropriate stepsize schemes significantly mitigate the impact of initial errors on subsequent errors and regrets. This underscores the robustness of stochastic sub-gradient descent in handling initial uncertainties, emphasizing its efficacy in scenarios where the sequential arrival of data introduces uncertainties regarding both the horizon and the total number of observations. Additionally, when the initial error rate is well-controlled, there is a trade-off between short-term error rate and long-term optimality. Due to the lack of delicate statistical analysis for squared loss, we also briefly discuss its properties and proper schemes. Extensive simulations support our theoretical findings.

Open-Source Software (OSS) vulnerabilities bring great challenges to the software security and pose potential risks to our society. Enormous efforts have been devoted into automated vulnerability detection, among which deep learning (DL)-based approaches have proven to be the most effective. However, the current labeled data present the following limitations: (1) Tangled Patches: Developers may submit code changes unrelated to vulnerability fixes within patches, leading to tangled patches. (2) Lacking Inter-procedural Vulnerabilities: The existing vulnerability datasets typically contain function-level and file-level vulnerabilities, ignoring the relations between functions, thus rendering the approaches unable to detect the inter-procedural vulnerabilities. (3) Outdated Patches: The existing datasets usually contain outdated patches, which may bias the model during training. To address the above limitations, in this paper, we propose an automated data collection framework and construct the first repository-level high-quality vulnerability dataset named ReposVul. The proposed framework mainly contains three modules: (1) A vulnerability untangling module, aiming at distinguishing vulnerability-fixing related code changes from tangled patches, in which the Large Language Models (LLMs) and static analysis tools are jointly employed. (2) A multi-granularity dependency extraction module, aiming at capturing the inter-procedural call relationships of vulnerabilities, in which we construct multiple-granularity information for each vulnerability patch, including repository-level, file-level, function-level, and line-level. (3) A trace-based filtering module, aiming at filtering the outdated patches, which leverages the file path trace-based filter and commit time trace-based filter to construct an up-to-date dataset., Comment: Accepted by ICSE 2024 Industry Challenge Track

The chirality-flipping operators of light fermions are currently poorly constrained by experimental analyses due to the lack of interference with Standard Model (SM) amplitudes in traditional observables. In this work, we propose to investigate the semi-leptonic scalar/tensor four-fermion operators of electron and quarks through the transverse double spin asymmetry (DSA) at Electron-Ion Collider, where both the electron and proton beams could be highly transversely polarized. Due to the chirality-flipping nature of these operators, we demonstrate that their interference with the SM results in an unsuppressed contribution to the DSA, and could lead to non-trivial azimuthal $\cos2\phi$ and $\sin2\phi$ distributions that are linearly dependent on their Wilson coefficients. This new method has the potential to significantly improve the current constraints on these scalar/tensor four-fermion operators without relying on theoretical assumptions about other types of new physics effects, particularly for the tensor type operator of the $u$-quark. Additionally, our findings indicate that both the real and imaginary parts of these operators can be simultaneously constrained and offer a new opportunity for probing potential $CP$-violation effects. However, it is important to note that these results would be sensitive to the quark transversity distributions, which are currently poorly constrained by the experimental data, but could be significantly improved at the upcoming Electron-Ion Collider. Therefore, our work opens up a new avenue to utilize this new spin asymmetry for exploring the new physics effects from the scalar/tensor four-fermion operators., Comment: 8 pages, 4 figures, accepted version to appear in PRD

The quasiparticle model is improved by the free magnetic contribution to investigated the QCD matter in a strong magnetic field. The temperature-dependent bag function is determined by the thermodynamic consistency to represent the difference in energy density between physical vacuum and lowest state of QCD. It is found that the positive bag function vanishes at high temperature indicating the deconfinement. The rapid decrease of the bag function in stronger magnetic fields reveals the so-called inverse magnetic catalysis. The interaction measure at high temperature remains so large that the usual Stefan-Boltzmann limit can not be reached. We suggest a limit $|q_iB_m|T^2/4$ for each landau level pressure. Finally, it is demonstrated that the positive magnetization modified by the bag function and free magnetic contribution indicates the paramagnetic characteristic of QCD matter., Comment: 8 pages, 6 figures, accepted for publication in PRC