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This report provides the most recent national indicators on school crime and safety. The information presented in this report serves as a reference for policymakers and practitioners so that they can develop effective programs and policies aimed at violence and school crime prevention. Accurate information about the nature, extent, and scope of the problem being addressed is essential for developing effective programs and policies. The report is organized into five sections: elementary and secondary student and teacher victimization; school environment; fights and weapons; safety, security, and mental health practices; and postsecondary campus safety and security. Each section begins with a set of key findings. In this report, where available, data on victimization that occurred away from school are offered as a point of comparison for data on victimization that occurred at school. Indicators of crime and safety are compared across different population subgroups and over time. All data reflect the most current data available at the time the report was produced. Data throughout this report represent the 50 states and the District of Columbia. Findings described with comparative language (e.g., higher, lower, increase, and decrease) are statistically significant at the 0.05 level.

In van der Waals heterostructures (vdWHs), the manipulation of interlayer stacking/coupling allows for the construction of customizable quantum systems exhibiting exotic physics. An illustrative example is the diverse range of states of matter achieved through varying the proximity coupling between two-dimensional (2D) quantum spin liquid (QSL) and superconductors within the TaS2 family. This study presents a demonstration of the intertwined physics of spontaneous rotational symmetry breaking, hidden magnetism, and Ising superconductivity in the three-fold rotationally symmetric, non-magnetic natural vdWHs 6R-TaS2. A distinctive phase emerges in 6R-TaS2 below a characteristic temperature (T*) of approximately 30 K, which is characterized by a remarkable set of features, including a giant extrinsic anomalous Hall effect (AHE), Kondo screening, magnetic field-tunable thermal hysteresis, and nematic magneto-resistance. At lower temperatures, a coexistence of nematicity and Kondo screening with Ising superconductivity is observed, providing compelling evidence of hidden magnetism within a superconductor. This research not only sheds light on unexpected emergent physics resulting from the coupling of itinerant electrons and localized/correlated electrons in natural vdWHs but also emphasizes the potential for tailoring exotic quantum states through the manipulation of interlayer interactions., Comment: 16 pages, 4 figures

We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of MindSpore, an advanced open-source deep learning training/inference framework, MindSpore Quantum exhibits exceptional efficiency in the design and training of variational quantum algorithms on both CPU and GPU platforms, delivering remarkable performance. Furthermore, this framework places a strong emphasis on enhancing the operational efficiency of quantum algorithms when executed on real quantum hardware. This encompasses the development of algorithms for quantum circuit compilation and qubit mapping, crucial components for achieving optimal performance on quantum processors. In addition to the core framework, we introduce QuPack, a meticulously crafted quantum computing acceleration engine. QuPack significantly accelerates the simulation speed of MindSpore Quantum, particularly in variational quantum eigensolver (VQE), quantum approximate optimization algorithm (QAOA), and tensor network simulations, providing astonishing speed. This combination of cutting-edge technologies empowers researchers and practitioners to explore the frontiers of quantum computing with unprecedented efficiency and performance.

The quantum Griffiths singularity (QGS) is a phenomenon driven by quenched disorders that break conventional scaling invariance and result in a divergent dynamical critical exponent during quantum phase transitions (QPT). While this phenomenon has been well-documented in low-dimensional conventional superconductors and in three-dimensional (3D) magnetic metal systems, its presence in 3D superconducting systems and in unconventional high-temperature superconductors (high-Tc SCs) remains unclear. In this study, we report the observation of robust QGS in the superconductor-metal transition (SMT) of both quasi-2D and 3D anisotropic unconventional high-Tc superconductor CaFe1-xNixAsF (x < 5%) bulk single crystals, where the QGS states persist to up to 5.3 K. A comprehensive quantum phase diagram is established that delineates the 3D anisotropic QGS of SMT induced by perpendicular and parallel magnetic field. Our findings reveal the universality of QGS in 3D superconducting systems and unconventional high-Tc SCs, thereby substantially expanding the range of applicability of QGS., Comment: 17 pages, 4 figures

In this work we try to search for signals generated by ultra-heavy dark matter at the Large High Altitude Air Shower Observatory (LHAASO) data. We look for possible gamma-ray by dark matter annihilation or decay from 16 dwarf spheroidal galaxies in the field of view of LHAASO. Dwarf spheroidal galaxies are among the most promising targets for indirect detection of dark matter which have low fluxes of astrophysical $\gamma$-ray background while large amount of dark matter. By analyzing more than 700 days observational data at LHAASO, no significant dark matter signal from 1 TeV to 1 EeV is detected. Accordingly we derive the most stringent constraints on the ultra-heavy dark matter annihilation cross-section up to EeV. The constraints on the lifetime of dark matter in decay mode are also derived., Comment: 17 pages, 12 figures, accepted by PRL

In this paper, we prove that for any post-critically finite rational map $f$ on the Riemann sphere $\overline{\mathbb{C}}$ and for each sufficiently large integer $n$, there exists a finite and connected graph $G$ in the Julia set of $f$, such that $f^n(G)\subset G$, $G$ contains all post-critical points in the Julia set and every component of $\overline{\mathbb{C}}\setminus G$ contains at most one post-critical point in the Fatou set. The proof is based on the cluster-\Sie\ decomposition of post-critically finite rational maps., Comment: Feel free to provide any comments

Any-to-any singing voice conversion (SVC) aims to transfer a target singer's timbre to other songs using a short voice sample. However many diffusion model based any-to-any SVC methods, which have achieved impressive results, usually suffered from low efficiency caused by a mass of inference steps. In this paper, we propose LCM-SVC, a latent consistency distillation (LCD) based latent diffusion model (LDM) to accelerate inference speed. We achieved one-step or few-step inference while maintaining the high performance by distilling a pre-trained LDM based SVC model, which had the advantages of timbre decoupling and sound quality. Experimental results show that our proposed method can significantly reduce the inference time and largely preserve the sound quality and timbre similarity comparing with other state-of-the-art SVC models. Audio samples are available at https://sounddemos.github.io/lcm-svc., Comment: Accepted to ISCSLP 2024. arXiv admin note: text overlap with arXiv:2406.05325

We investigate compact stars in the noninteger power (NIP) model of $f(R)$ gravity theory, which includes the higher-curvature correction to the Einstein-Hilbert action. The mass-radius relation of the compact stars in the NIP model predicts large deviations from those in the general relativity in the low-mass region, potentially allowing us to test the NIP model by future astrophysical observations. We also study the nonvanishing scalar hair surrounding the compact star and demonstrate that the chameleon mechanism works efficiently. They result in distinct scalar profiles inside and outside the star, which implies screening the fifth force mediated by the scalar field., Comment: 35 pages, 27 figures

Behaviours of hydrogen, such as fluidity and metallicity, are crucial for our understanding of planetary interiors and the emerging field of high-temperature superconducting hydrides. These behaviours were discovered in complex phase diagrams of hydrogen and hydrides, however, the transition mechanism of behaviours driven by temperature, pressure and chemical compression remain unclear, particularly in the processes of metallization. Until now, a comprehensive theoretical framework to quantify atomization and metallization of hydrogen in phase diagram of hydrides has been lacking. In this study, we address this gap by combining molecular dynamics and electronic structure analysis to propose a theoretical framework, which clarify the content and properties of atomic hydrogen under various temperature and pressure conditions and chemical compression exerted by non-hydrogen elements in hydrides. Applying this framework to the superconducting hydride LaBeH8, we identify three general hydrogen orderings within its phase diagram: molecular, sublattice and warm hydrogens. During the phase transition from molecule to sublattice, hydrogen exhibits different properties from three general hydrogen orderings, such as fast superionicity, metallicity and unusual atomic content response to temperature. These abnormal behaviours were defined as the critical metallization of hydrogen, which not only suggests a potential synthesis route for the metastable phase but also provides valuable insights into the complex synthetic products of superconducting hydrides.

The vision-language modeling capability of multi-modal large language models has attracted wide attention from the community. However, in medical domain, radiology report generation using vision-language models still faces significant challenges due to the imbalanced data distribution caused by numerous negated descriptions in radiology reports and issues such as rough alignment between radiology reports and radiography. In this paper, we propose a truthful radiology report generation framework, namely TRRG, based on stage-wise training for cross-modal disease clue injection into large language models. In pre-training stage, During the pre-training phase, contrastive learning is employed to enhance the ability of visual encoder to perceive fine-grained disease details. In fine-tuning stage, the clue injection module we proposed significantly enhances the disease-oriented perception capability of the large language model by effectively incorporating the robust zero-shot disease perception. Finally, through the cross-modal clue interaction module, our model effectively achieves the multi-granular interaction of visual embeddings and an arbitrary number of disease clue embeddings. This significantly enhances the report generation capability and clinical effectiveness of multi-modal large language models in the field of radiology reportgeneration. Experimental results demonstrate that our proposed pre-training and fine-tuning framework achieves state-of-the-art performance in radiology report generation on datasets such as IU-Xray and MIMIC-CXR. Further analysis indicates that our proposed method can effectively enhance the model to perceive diseases and improve its clinical effectiveness.

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.

Regional differences in carbon emission efficiency arise from disparities in resource distribution, industrial structure, and development level, which are often influenced by government policy preferences. However, currently, most studies fail to consider the impact of government policy preferences and data uncertainty on carbon emission efficiency. To address the above limitations, this study proposes a hybrid model based on $\delta$-slack-based model ($\delta$-SBM) and ordinal priority approach (OPA) for measuring carbon emission efficiency driven by government policy preferences under data uncertainty. The proposed $\delta$-SBM-OPA model incorporates constraints on the importance of input and output variables under different policy preference scenarios. It then develops the efficiency optimization model with Farrell frontiers and efficiency tapes to deal with the data uncertainty in input and output variables. This study demonstrates the proposed model by analyzing industrial carbon emission efficiency of Chinese provinces in 2021. It examines the carbon emission efficiency and corresponding clustering results of provinces under three types of policies: economic priority, environmental priority, and technological priority, with varying priority preferences. The results indicate that the carbon emission efficiency of the 30 provinces can mainly be categorized into technology-driven, development-balanced, and transition-potential types, with most provinces achieving optimal efficiency under the technology-dominant preferences across all policy scenarios. Ultimately, this study suggests a tailored roadmap and crucial initiatives for different provinces to progressively and systematically work towards achieving the low carbon goal., Comment: N.A

Tilting planar samples for multi-zone-axes observation is a routine procedure in electron microscopy. However, this process invariably introduces optical path differences in the electron beam across different sample positions, significantly compromising image quality, particularly over large fields of view. To address this challenge, we developed path difference ptychography (PDP), a method capable of decoupling path differences from the four-dimensional data during reconstruction. This enables the acquisition of high-quality, large-scale images, facilitating a more comprehensive understanding and analysis of materials microstructure. Moreover, PDP has the potential to promote the widespread application of ptychographic tomography in the analysis of planar samples.

Tracking the time evolution of a quantum state allows one to verify the thermalization rate or the propagation speed of correlations in generic quantum systems. Inspired by the energy-time uncertainty principle, bounds have been demonstrated on the maximal speed at which a quantum state can change, resulting in immediate and practical tasks. Based on a programmable superconducting quantum processor, we test the dynamics of various emulated quantum mechanical systems encompassing single- and many-body states. We show that one can test the known quantum speed limits and that modifying a single Hamiltonian parameter allows the observation of the crossover of the different bounds on the dynamics. We also unveil the observation of minimal quantum speed limits in addition to more common maximal ones, i.e., the lowest rate of change of a unitarily evolved quantum state. Our results establish a comprehensive experimental characterization of quantum speed limits and pave the way for their subsequent study in engineered non-unitary conditions., Comment: 9 pages,4 figures + supplementary information

Traditionally, reward models used for reinforcement learning from human feedback (RLHF) are trained to directly predict preference scores without leveraging the generation capabilities of the underlying large language model (LLM). This limits the capabilities of reward models as they must reason implicitly about the quality of a response, i.e., preference modeling must be performed in a single forward pass through the model. To enable reward models to reason explicitly about the quality of a response, we introduce Critique-out-Loud (CLoud) reward models. CLoud reward models operate by first generating a natural language critique of the assistant's response that is then used to predict a scalar reward for the quality of the response. We demonstrate the success of CLoud reward models for both Llama-3-8B and 70B base models: compared to classic reward models CLoud reward models improve pairwise preference classification accuracy on RewardBench by 4.65 and 5.84 percentage points for the 8B and 70B base models respectively. Furthermore, CLoud reward models lead to a Pareto improvement for win rate on ArenaHard when used as the scoring model for Best-of-N. Finally, we explore how to exploit the dynamic inference compute capabilities of CLoud reward models by performing self-consistency decoding for reward prediction.

Neural-based multi-task learning (MTL) has been successfully applied to many recommendation applications. However, these MTL models (e.g., MMoE, PLE) did not consider feature interaction during the optimization, which is crucial for capturing complex high-order features and has been widely used in ranking models for real-world recommender systems. Moreover, through feature importance analysis across various tasks in MTL, we have observed an interesting divergence phenomenon that the same feature can have significantly different importance across different tasks in MTL. To address these issues, we propose Deep Multiple Task-specific Feature Interactions Network (DTN) with a novel model structure design. DTN introduces multiple diversified task-specific feature interaction methods and task-sensitive network in MTL networks, enabling the model to learn task-specific diversified feature interaction representations, which improves the efficiency of joint representation learning in a general setup. We applied DTN to our company's real-world E-commerce recommendation dataset, which consisted of over 6.3 billion samples, the results demonstrated that DTN significantly outperformed state-of-the-art MTL models. Moreover, during online evaluation of DTN in a large-scale E-commerce recommender system, we observed a 3.28% in clicks, a 3.10% increase in orders and a 2.70% increase in GMV (Gross Merchandise Value) compared to the state-of-the-art MTL models. Finally, extensive offline experiments conducted on public benchmark datasets demonstrate that DTN can be applied to various scenarios beyond recommendations, enhancing the performance of ranking models.

Large Language Models (LLMs) have become instrumental across various applications, with the customization of these models to specific scenarios becoming increasingly critical. System message, a fundamental component of LLMs, is consist of carefully crafted instructions that guide the behavior of model to meet intended goals. Despite the recognized potential of system messages to optimize AI-driven solutions, there is a notable absence of a comprehensive benchmark for evaluating how well different LLMs follow these system messages. To fill this gap, we introduce SysBench, a benchmark that systematically analyzes system message following ability in terms of three challenging aspects: constraint complexity, instruction misalignment and multi-turn stability. In order to enable effective evaluation, SysBench constructs multi-turn user conversations covering various interaction relationships, based on six common types of constraints from system messages in real-world scenarios. Our dataset contains 500 system messages from various domains, each paired with 5 turns of user conversations, which have been manually formulated and checked to guarantee high quality. SysBench provides extensive evaluation across various LLMs, measuring their ability to follow specified constraints given in system messages. The results highlight both the strengths and weaknesses of existing models, offering key insights and directions for future research. The open source library SysBench is available at https://github.com/PKU-Baichuan-MLSystemLab/SysBench.

In current web environment, fake news spreads rapidly across online social networks, posing serious threats to society. Existing multimodal fake news detection (MFND) methods can be classified into knowledge-based and semantic-based approaches. However, these methods are overly dependent on human expertise and feedback, lacking flexibility. To address this challenge, we propose a Dynamic Analysis and Adaptive Discriminator (DAAD) approach for fake news detection. For knowledge-based methods, we introduce the Monte Carlo Tree Search (MCTS) algorithm to leverage the self-reflective capabilities of large language models (LLMs) for prompt optimization, providing richer, domain-specific details and guidance to the LLMs, while enabling more flexible integration of LLM comment on news content. For semantic-based methods, we define four typical deceit patterns: emotional exaggeration, logical inconsistency, image manipulation, and semantic inconsistency, to reveal the mechanisms behind fake news creation. To detect these patterns, we carefully design four discriminators and expand them in depth and breadth, using the soft-routing mechanism to explore optimal detection models. Experimental results on three real-world datasets demonstrate the superiority of our approach. The code will be available at: https://github.com/SuXinqi/DAAD.

Air-ground robots (AGRs) are widely used in surveillance and disaster response due to their exceptional mobility and versatility (i.e., flying and driving). Current AGR navigation systems perform well in static occlusion-prone environments (e.g., indoors) by using 3D semantic occupancy networks to predict occlusions for complete local mapping and then computing Euclidean Signed Distance Field (ESDF) for path planning. However, these systems face challenges in dynamic, severe occlusion scenes (e.g., crowds) due to limitations in perception networks' low prediction accuracy and path planners' high computation overhead. In this paper, we propose OMEGA, which contains OccMamba with an Efficient AGR-Planner to address the above-mentioned problems. OccMamba adopts a novel architecture that separates semantic and occupancy prediction into independent branches, incorporating two mamba blocks within these branches. These blocks efficiently extract semantic and geometric features in 3D environments with linear complexity, ensuring that the network can learn long-distance dependencies to improve prediction accuracy. Semantic and geometric features are combined within the Bird's Eye View (BEV) space to minimise computational overhead during feature fusion. The resulting semantic occupancy map is then seamlessly integrated into the local map, providing occlusion awareness of the dynamic environment. Our AGR-Planner utilizes this local map and employs kinodynamic A* search and gradient-based trajectory optimization to guarantee planning is ESDF-free and energy-efficient. Extensive experiments demonstrate that OccMamba outperforms the state-of-the-art 3D semantic occupancy network with 25.0% mIoU. End-to-end navigation experiments in dynamic scenes verify OMEGA's efficiency, achieving a 96% average planning success rate. Code and video are available at https://jmwang0117.github.io/OMEGA/., Comment: OccMamba is Coming!

Endorectal ultrasound (ERUS) is an important imaging modality that provides high reliability for diagnosing the depth and boundary of invasion in colorectal cancer. However, the lack of a large-scale ERUS dataset with high-quality annotations hinders the development of automatic ultrasound diagnostics. In this paper, we collected and annotated the first benchmark dataset that covers diverse ERUS scenarios, i.e. colorectal cancer segmentation, detection, and infiltration depth staging. Our ERUS-10K dataset comprises 77 videos and 10,000 high-resolution annotated frames. Based on this dataset, we further introduce a benchmark model for colorectal cancer segmentation, named the Adaptive Sparse-context TRansformer (ASTR). ASTR is designed based on three considerations: scanning mode discrepancy, temporal information, and low computational complexity. For generalizing to different scanning modes, the adaptive scanning-mode augmentation is proposed to convert between raw sector images and linear scan ones. For mining temporal information, the sparse-context transformer is incorporated to integrate inter-frame local and global features. For reducing computational complexity, the sparse-context block is introduced to extract contextual features from auxiliary frames. Finally, on the benchmark dataset, the proposed ASTR model achieves a 77.6% Dice score in rectal cancer segmentation, largely outperforming previous state-of-the-art methods.

What a large language model (LLM) would respond in ethically relevant context? In this paper, we curate a large benchmark CMoralEval for morality evaluation of Chinese LLMs. The data sources of CMoralEval are two-fold: 1) a Chinese TV program discussing Chinese moral norms with stories from the society and 2) a collection of Chinese moral anomies from various newspapers and academic papers on morality. With these sources, we aim to create a moral evaluation dataset characterized by diversity and authenticity. We develop a morality taxonomy and a set of fundamental moral principles that are not only rooted in traditional Chinese culture but also consistent with contemporary societal norms. To facilitate efficient construction and annotation of instances in CMoralEval, we establish a platform with AI-assisted instance generation to streamline the annotation process. These help us curate CMoralEval that encompasses both explicit moral scenarios (14,964 instances) and moral dilemma scenarios (15,424 instances), each with instances from different data sources. We conduct extensive experiments with CMoralEval to examine a variety of Chinese LLMs. Experiment results demonstrate that CMoralEval is a challenging benchmark for Chinese LLMs. The dataset is publicly available at \url{https://github.com/tjunlp-lab/CMoralEval}., Comment: Accepted by ACL 2024 (Findings)

Pre-training exploits public datasets to pre-train an advanced machine learning model, so that the model can be easily tuned to adapt to various downstream tasks. Pre-training has been extensively explored to mitigate computation and communication resource consumption. Inspired by these advantages, we are the first to explore how model pre-training can mitigate noise detriment in differentially private federated learning (DPFL). DPFL is upgraded from federated learning (FL), the de-facto standard for privacy preservation when training the model across multiple clients owning private data. DPFL introduces differentially private (DP) noises to obfuscate model gradients exposed in FL, which however can considerably impair model accuracy. In our work, we compare head fine-tuning (HT) and full fine-tuning (FT), which are based on pre-training, with scratch training (ST) in DPFL through a comprehensive empirical study. Our experiments tune pre-trained models (obtained by pre-training on ImageNet-1K) with CIFAR-10, CHMNIST and Fashion-MNIST (FMNIST) datasets, respectively. The results demonstrate that HT and FT can significantly mitigate noise influence by diminishing gradient exposure times. In particular, HT outperforms FT when the privacy budget is tight or the model size is large. Visualization and explanation study further substantiates our findings. Our pioneering study introduces a new perspective on enhancing DPFL and expanding its practical applications.

There is a fast-growing literature on estimating optimal treatment rules directly by maximizing the expected outcome. In biomedical studies and operations applications, censored survival outcome is frequently observed, in which case the restricted mean survival time and survival probability are of great interest. In this paper, we propose two robust criteria for learning optimal treatment rules with censored survival outcomes; the former one targets at an optimal treatment rule maximizing the restricted mean survival time, where the restriction is specified by a given quantile such as median; the latter one targets at an optimal treatment rule maximizing buffered survival probabilities, where the predetermined threshold is adjusted to account the restricted mean survival time. We provide theoretical justifications for the proposed optimal treatment rules and develop a sampling-based difference-of-convex algorithm for learning them. In simulation studies, our estimators show improved performance compared to existing methods. We also demonstrate the proposed method using AIDS clinical trial data.

Using $(10087\pm44)\times10^6J/\psi$ events collected with the BESIII detector, we search for the rare decay $J/\psi \to \gamma D^0+c.c.$ for the first time. No obvious signal is observed and the upper limit on the branching fraction is determined to be ${\cal B}(J/\psi \to \gamma D^{0}+c.c.)< 9.1 \times 10^{-8}$ at 90\% confidence level.

To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To enhance the protection of model gradients in FL, differentially private federated learning (DPFL) is proposed which incorporates differentially private (DP) noises to obfuscate gradients before they are exposed. Yet, an essential but largely overlooked problem in DPFL is the heterogeneity of clients' privacy requirement, which can vary significantly between clients and extremely complicates the client selection problem in DPFL. In other words, both the data quality and the influence of DP noises should be taken into account when selecting clients. To address this problem, we conduct convergence analysis of DPFL under heterogeneous privacy, a generic client selection strategy, popular DP mechanisms and convex loss. Based on convergence analysis, we formulate the client selection problem to minimize the value of loss function in DPFL with heterogeneous privacy, which is a convex optimization problem and can be solved efficiently. Accordingly, we propose the DPFL-BCS (biased client selection) algorithm. The extensive experiment results with real datasets under both convex and non-convex loss functions indicate that DPFL-BCS can remarkably improve model utility compared with the SOTA baselines.

Recent research on social contagion processes has revealed the limitations of traditional networks, which capture only pairwise relationships, to characterize complex multiparty relationships and group influences properly. Social contagion processes on higher-order networks (simplicial complexes and general hypergraphs) have therefore emerged as a novel frontier. In this work, we investigate discrete-time Susceptible-Infected-Susceptible (SIS) social contagion processes occurring on weighted and directed hypergraphs and their extensions to bivirus cases and general higher-order SIS processes with the aid of tensor algebra. Our focus lies in comprehensively characterizing the healthy state and endemic equilibria within this framework. The emergence of bistability or multistability behavior phenomena, where multiple equilibria coexist and are simultaneously locally asymptotically stable, is demonstrated in view of the presence of the higher-order interaction. The novel sufficient conditions of the appearance for system behaviors, which are determined by both (higher-order) network topology and transition rates, are provided to assess the likelihood of the SIS social contagion processes causing an outbreak. More importantly, given the equilibrium is locally stable, an explicit domain of attraction associated with the system parameters is constructed. Moreover, a learning method to estimate the transition rates is presented. In the end, the attained theoretical results are supplemented via numerical examples. Specifically, we evaluate the effectiveness of the networked SIS social contagion process by comparing it with the $2^n$-state Markov chain model. These numerical examples are given to highlight the performance of parameter learning algorithms and the system behaviors of the discrete-time SIS social contagion process.

Recently, deep learning-based hybrid iterative methods (DL-HIM) have emerged as a promising approach for designing fast neural solvers to tackle large-scale sparse linear systems. DL-HIM combine the smoothing effect of simple iterative methods with the spectral bias of neural networks, which allows them to effectively eliminate both high-frequency and low-frequency error components. However, their efficiency may decrease if simple iterative methods can not provide effective smoothing, making it difficult for the neural network to learn mid-frequency and high-frequency components. This paper first establishes a convergence analysis framework for general DL-HIM from a spectral viewpoint, concluding that under reasonable assumptions, DL-HIM exhibit a convergence rate independent of grid size $h$ and physical parameters $\boldsymbol{\mu}$. To meet these assumptions, we design a neural network from an eigen perspective, focusing on learning the eigenvalues and eigenvectors corresponding to error components that simple iterative methods struggle to eliminate. Specifically, the eigenvalues are learned by a meta subnet, while the eigenvectors are approximated using Fourier modes with a transition matrix provided by another meta subnet. The resulting DL-HIM, termed the Fourier Neural Solver (FNS), is expected to achieve a $h, \boldsymbol{\mu}$-independent convergence rate with the guarantee of universal approximation and the discretization invariant property of the meta subnets. We verify the performance of FNS on four types of linear parametric PDEs.

Viscosity is the resistance of a liquid to flow, governed by atomic-scale friction between its constituent atoms. While viscosity can be directly computed using the Green-Kubo formalism, this method does not fully elucidate the physical mechanisms underlying such momentum transport coefficient. In fact, the microscopic origin of liquid viscosity remains poorly understood. In this work, we calculate the viscosity of a $\mathrm{Cu_{50}Zr_{50}}$ metallic liquid and a Kob-Andersen model in a large range of temperatures and compare the results with a theoretical formula based on nonaffine linear response and instantaneous normal mode theory. This analysis reveals that only unstable localized instantaneous normal modes (ULINMs) contribute to viscosity, providing a microscopic definition of viscosity as diffusive momentum transport facilitated by local structural excitations mediated by ULINMs as precursors. Leveraging on the concept of configurational entropy, a quantitative model to connect viscosity with ULINMs is also proposed and validated in both the Arrhenius and non-Arrhenius regimes. In summary, our work provides a microscopic definition of liquid viscosity and highlights the fundamental role of ULINMs as its building blocks, ultimately opening the path to an atomic-scale prediction of viscosity in liquids and glasses., Comment: v1: comments welcome!

To achieve dexterity comparable to that of humans, robots must intelligently process tactile sensor data. Taxel-based tactile signals often have low spatial-resolution, with non-standardized representations. In this paper, we propose a novel framework, HyperTaxel, for learning a geometrically-informed representation of taxel-based tactile signals to address challenges associated with their spatial resolution. We use this representation and a contrastive learning objective to encode and map sparse low-resolution taxel signals to high-resolution contact surfaces. To address the uncertainty inherent in these signals, we leverage joint probability distributions across multiple simultaneous contacts to improve taxel hyper-resolution. We evaluate our representation by comparing it with two baselines and present results that suggest our representation outperforms the baselines. Furthermore, we present qualitative results that demonstrate the learned representation captures the geometric features of the contact surface, such as flatness, curvature, and edges, and generalizes across different objects and sensor configurations. Moreover, we present results that suggest our representation improves the performance of various downstream tasks, such as surface classification, 6D in-hand pose estimation, and sim-to-real transfer., Comment: Accepted by IROS 2024

New particles beyond the Standard Model of particle physics, such as axions, can be effectively searched through their interactions with electrons. We use the large liquid xenon detector PandaX-4T to search for novel electronic recoil signals induced by solar axions, neutrinos with anomalous magnetic moment, axion-like particles, dark photons, and light fermionic dark matter. A detailed background model is established with the latest datasets with 1.54 $\rm tonne \cdot year$ exposure. No significant excess above the background has been observed, and we have obtained competitive constraints for axion couplings, neutrino magnetic moment, and fermionic dark matter interactions.

With the development of Multimodal Large Language Models (MLLMs), the evaluation of multimodal models in the context of mathematical problems has become a valuable research field. Multimodal visual-textual mathematical reasoning serves as a critical indicator for evaluating the comprehension and complex multi-step quantitative reasoning abilities of MLLMs. However, previous multimodal math benchmarks have not sufficiently integrated visual and textual information. To address this gap, we proposed MathScape, a new benchmark that emphasizes the understanding and application of combined visual and textual information. MathScape is designed to evaluate photo-based math problem scenarios, assessing the theoretical understanding and application ability of MLLMs through a categorical hierarchical approach. We conduct a multi-dimensional evaluation on 11 advanced MLLMs, revealing that our benchmark is challenging even for the most sophisticated models. By analyzing the evaluation results, we identify the limitations of MLLMs, offering valuable insights for enhancing model performance.

Costal cartilage segmentation is crucial to various medical applications, necessitating precise and reliable techniques due to its complex anatomy and the importance of accurate diagnosis and surgical planning. We propose a novel deep learning-based approach called topology-guided deformable Mamba (TGDM) for costal cartilage segmentation. The TGDM is tailored to capture the intricate long-range costal cartilage relationships. Our method leverages a deformable model that integrates topological priors to enhance the adaptability and accuracy of the segmentation process. Furthermore, we developed a comprehensive benchmark that contains 165 cases for costal cartilage segmentation. This benchmark sets a new standard for evaluating costal cartilage segmentation techniques and provides a valuable resource for future research. Extensive experiments conducted on both in-domain benchmarks and out-of domain test sets demonstrate the superiority of our approach over existing methods, showing significant improvements in segmentation precision and robustness.

By using an parametric value function to replace the Monte-Carlo rollouts for value estimation, the actor-critic (AC) algorithms can reduce the variance of stochastic policy gradient so that to improve the convergence rate. While existing works mainly focus on analyzing convergence rate of AC algorithms under Markovian noise, the impacts of momentum on AC algorithms remain largely unexplored. In this work, we first propose a heavy-ball momentum based advantage actor-critic (\mbox{HB-A2C}) algorithm by integrating the heavy-ball momentum into the critic recursion that is parameterized by a linear function. When the sample trajectory follows a Markov decision process, we quantitatively certify the acceleration capability of the proposed HB-A2C algorithm. Our theoretical results demonstrate that the proposed HB-A2C finds an $\epsilon$-approximate stationary point with $\oo{\epsilon^{-2}}$ iterations for reinforcement learning tasks with Markovian noise. Moreover, we also reveal the dependence of learning rates on the length of the sample trajectory. By carefully selecting the momentum factor of the critic recursion, the proposed HB-A2C can balance the errors introduced by the initialization and the stoschastic approximation.

This paper addresses the problem of inverse rendering from photometric images. Existing approaches for this problem suffer from the effects of self-shadows, inter-reflections, and lack of constraints on the surface reflectance, leading to inaccurate decomposition of reflectance and illumination due to the ill-posed nature of inverse rendering. In this work, we propose a new method for neural inverse rendering. Our method jointly optimizes the light source position to account for the self-shadows in images, and computes indirect illumination using a differentiable rendering layer and an importance sampling strategy. To enhance surface reflectance decomposition, we introduce a new regularization by distilling DINO features to foster accurate and consistent material decomposition. Extensive experiments on synthetic and real datasets demonstrate that our method outperforms the state-of-the-art methods in reflectance decomposition., Comment: Project page: https://jzbao03.site/projects/PIR/

Using $(2712\pm 14)$ $\times$ 10$^{6}$ $\psi(2S)$ events collected with the BESIII detector at the BEPCII collider, we search for the decays $\eta_{c}(2S)\to\omega\omega$ and $\eta_{c}(2S)\to\omega\phi$ via the process $\psi(2S)\to\gamma\eta_{c}(2S)$. Evidence of $\eta_{c}(2S)\to\omega\omega$ is found with a statistical significance of $3.2\sigma$. The branching fraction is measured to be $\mathcal{B}(\eta_{c}(2S)\to\omega\omega)=(5.65\pm3.77(\rm stat.)\pm5.32(\rm syst.))\times10^{-4}$. No statistically significant signal is observed for the decay $\eta_{c}(2S)\to\omega\phi$. The upper limit of the branching fraction at the 90\% confidence level is determined to be $\mathcal{B}(\psi(2S)\to\gamma\eta_{c}(2S),\eta_{c}(2S)\to\omega\phi)<2.24\times 10^{-7}$. We also update the branching fractions of $\chi_{cJ}\to \omega\omega$ and $\chi_{cJ}\to\omega\phi$ decays via the $\psi(2S)\to\gamma\chi_{cJ}$ transition. The branching fractions are determined to be $\mathcal{B}(\chi_{c0}\to\omega\omega)=(10.63\pm0.11\pm0.46)\times 10^{-4}$, $\mathcal{B}(\chi_{c1}\to\omega\omega)=(6.39\pm0.07\pm0.29)\times 10^{-4}$, $\mathcal{B}(\chi_{c2}\to\omega\omega)=(8.50\pm0.08\pm0.38)\times 10^{-4}$, $\mathcal{B}(\chi_{c0}\to\omega\phi)=(1.18\pm0.03\pm0.05)\times 10^{-4}$, $\mathcal{B}(\chi_{c1}\to\omega\phi)=(2.03\pm0.15\pm0.12)\times 10^{-5}$, and $\mathcal{B}(\chi_{c2}\to\omega\phi)=(9.37\pm1.07\pm0.59)\times 10^{-6}$, where the first uncertainties are statistical and the second are systematic.

Embedding Based Retrieval (EBR) is a crucial component of the retrieval stage in (Ads) Recommendation System that utilizes Two Tower or Siamese Networks to learn embeddings for both users and items (ads). It then employs an Approximate Nearest Neighbor Search (ANN) to efficiently retrieve the most relevant ads for a specific user. Despite the recent rise to popularity in the industry, they have a couple of limitations. Firstly, Two Tower model architecture uses a single dot product interaction which despite their efficiency fail to capture the data distribution in practice. Secondly, the centroid representation and cluster assignment, which are components of ANN, occur after the training process has been completed. As a result, they do not take into account the optimization criteria used for retrieval model. In this paper, we present Hierarchical Structured Neural Network (HSNN), a deployed jointly optimized hierarchical clustering and neural network model that can take advantage of sophisticated interactions and model architectures that are more common in the ranking stages while maintaining a sub-linear inference cost. We achieve 6.5% improvement in offline evaluation and also demonstrate 1.22% online gains through A/B experiments. HSNN has been successfully deployed into the Ads Recommendation system and is currently handling major portion of the traffic. The paper shares our experience in developing this system, dealing with challenges like freshness, volatility, cold start recommendations, cluster collapse and lessons deploying the model in a large scale retrieval production system., Comment: 9 pages

Disorder, which is ubiquitous in nature, has been extensively explored in photonics for understanding the fundamental principles of light diffusion and localization, as well as for applications in functional resonators and random lasers. Recently, the investigation of disorder in topological photonics has led to the realization of topological Anderson insulators characterized by an unexpected disorder-induced phase transition. However, the observed photonic topological Anderson insulators so far are limited to the time-reversal symmetry breaking systems. Here, we propose and realize a photonic quantum spin Hall topological Anderson insulator without breaking time-reversal symmetry. The disorder-induced topological phase transition is comprehensively confirmed through the theoretical effective Dirac Hamiltonian, numerical analysis of bulk transmission, and experimental examination of bulk and edge transmissions. We present the convincing evidence for the unidirectional propagation and robust transport of helical edge modes, which are the key features of nontrivial time-reversal invariant topological Anderson insulators. Furthermore, we demonstrate disorder-induced beam steering, highlighting the potential of disorder as a new degree of freedom to manipulate light propagation in magnetic-free systems. Our work not only paves the way for observing unique topological photonic phases but also suggests potential device applications through the utilization of disorder.

We present a catalog of dust clouds at high Galactic latitude based on the Planck 857 GHz dust emission data. Using a clustering hierarchical algorithm, 315 dust cloud at high Galactic latitudes are identified. Additionally, using the optical and ultraviolet extinction of 4 million and 1 million stars, respectively, provided by Sun et al., we derive the distances and physical properties for 190 high Galactic latitude dust clouds and the ultraviolet excess ratios for 165 of them. Through the study of color excess ratios, this work confirms that molecular clouds with large Galactic distances and low extinction likely have a higher proportion of small-sized dust grains. In addition, clouds with well-defined distances in the catalog are used to trace the local bubble, showing good consistency with the boundary of the local bubble from the literature., Comment: Comments: 29 pages, 15 figures, Accepted by The Astronomical Journal on 12/8/2024

Cyber Threat Intelligence (CTI) summarization task requires the system to generate concise and accurate highlights from raw intelligence data, which plays an important role in providing decision-makers with crucial information to quickly detect and respond to cyber threats in the cybersecurity domain. However, efficient techniques for summarizing CTI reports, including facts, analytical insights, attack processes, etc., have largely been unexplored, primarily due to the lack of available dataset. To this end, we present CTISum, a new benchmark for CTI summarization task. Considering the importance of attack process, a novel fine-grained subtask of attack process summarization is proposed to enable defenders to assess risk, identify security gaps, vulnerabilities, and so on. Specifically, we first design a multi-stage annotation pipeline to gather and annotate the CTI data, and then benchmark the CTISum with a collection of extractive and abstractive summarization methods. Experimental results show that current state-of-the-art models exhibit limitations when applied to CTISum, underscoring the fact that automatically producing concise summaries of CTI reports remains an open research challenge.

Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource languages. To mitigate this challenge, we present FuxiTranyu, an open-source multilingual LLM, which is designed to satisfy the need of the research community for balanced and high-performing multilingual capabilities. FuxiTranyu-8B, the base model with 8 billion parameters, is trained from scratch on a meticulously balanced multilingual data repository that contains 600 billion tokens covering 43 natural languages and 16 programming languages. In addition to the base model, we also develop two instruction-tuned models: FuxiTranyu-8B-SFT that is fine-tuned on a diverse multilingual instruction dataset, and FuxiTranyu-8B-DPO that is further refined with DPO on a preference dataset for enhanced alignment ability. Extensive experiments on a wide range of multilingual benchmarks demonstrate the competitive performance of FuxiTranyu against existing multilingual LLMs, e.g., BLOOM-7B, PolyLM-13B, Llama-2-Chat-7B and Mistral-7B-Instruct. Interpretability analyses at both the neuron and representation level suggest that FuxiTranyu is able to learn consistent multilingual representations across different languages. To promote further research into multilingual LLMs and their working mechanisms, we release both the base and instruction-tuned FuxiTranyu models together with 58 pretraining checkpoints at HuggingFace and Github.

Photonic double-zero-index media, distinguished by concurrently zero-valued permittivity and permeability, exhibit extraordinary properties not found in nature. Remarkably, the notion of zero-index can be substantially expanded by generalizing the constitutive parameters from null scalars to nonreciprocal tensors with nonzero matrix elements but zero determinants. Here, we experimentally realize such a new class of gyromagnetic double-zero-index metamaterials possessing both double-zero-index features and nonreciprocal hallmarks. As an intrinsic property, this metamaterial always emerges at a spin-1/2 Dirac point of a topological phase transition. We discover and rigorously prove that a spatiotemporal reflection vortex singularity is always anchored to the metamaterial's Dirac point, with the vortex charge being determined by the topological invariant leap across the phase transition. This establishes a unique bulk-spatiotemporal vortex correspondence that extends the protected boundary effects into the time domain and exclusively characterizes topological phase transition points, setting it apart from any pre-existing bulk-boundary correspondence. Based on this correspondence, we propose and experimentally demonstrate a mechanism to deterministically generate optical spatiotemporal vortex pulses with firmly fixed central frequency and momentum, hence showing unparalleled robustness. Our findings uncover deep connections between zero-refractive-index photonics, topological photonics, and singular optics, opening the avenue for the manipulation of space-time topological light fields via the inherent topology of extreme-parameter metamaterials., Comment: 9 pages, 5 figures

Combining the KSVZ axion model and the type-I seesaw mechanism, we construct a minimal axion model that may affect the phenomenon of neutrino oscillation. This minimal model requires flavor mixed axion-neutrinos interactions and two additional standard model (SM) singlet scalars which generate specific patterns of lepton mass matrices. These patterns yield a particular texture of Majorana neutrino mass matrix. We find that this texture, combined with existing neutrino experimental data, predicts an Inverted Hierarchy of neutrino masses with $17\lesssim m_1/m_3\lesssim 44$, and a hierarchy between the two VEVs of SM-singlet scalars. Finally, we examine the potential effect of this model on neutrino oscillation phenomena., Comment: 8 pages, 3 figures

Implementing cross-modal hashing between 2D images and 3D point-cloud data is a growing concern in real-world retrieval systems. Simply applying existing cross-modal approaches to this new task fails to adequately capture latent multi-modal semantics and effectively bridge the modality gap between 2D and 3D. To address these issues without relying on hand-crafted labels, we propose contrastive masked autoencoders based self-supervised hashing (CMAH) for retrieval between images and point-cloud data. We start by contrasting 2D-3D pairs and explicitly constraining them into a joint Hamming space. This contrastive learning process ensures robust discriminability for the generated hash codes and effectively reduces the modality gap. Moreover, we utilize multi-modal auto-encoders to enhance the model's understanding of multi-modal semantics. By completing the masked image/point-cloud data modeling task, the model is encouraged to capture more localized clues. In addition, the proposed multi-modal fusion block facilitates fine-grained interactions among different modalities. Extensive experiments on three public datasets demonstrate that the proposed CMAH significantly outperforms all baseline methods., Comment: Accepted by ICME 2024

Cell nuclei instance segmentation is a crucial task in digital kidney pathology. Traditional automatic segmentation methods often lack generalizability when applied to unseen datasets. Recently, the success of foundation models (FMs) has provided a more generalizable solution, potentially enabling the segmentation of any cell type. In this study, we perform a large-scale evaluation of three widely used state-of-the-art (SOTA) cell nuclei foundation models (Cellpose, StarDist, and CellViT). Specifically, we created a highly diverse evaluation dataset consisting of 2,542 kidney whole slide images (WSIs) collected from both human and rodent sources, encompassing various tissue types, sizes, and staining methods. To our knowledge, this is the largest-scale evaluation of its kind to date. Our quantitative analysis of the prediction distribution reveals a persistent performance gap in kidney pathology. Among the evaluated models, CellViT demonstrated superior performance in segmenting nuclei in kidney pathology. However, none of the foundation models are perfect; a performance gap remains in general nuclei segmentation for kidney pathology.

Over the past ten years, several breakthroughs have been made in multi-messenger astronomy. Thanks to the IceCube Neutrino Observatory, the detection of astrophysical neutrinos was proved to be practical. However, no source has been significantly identified due to the lack of statistics and uncovered field of view. The next generation of high-energy neutrino telescope is in high demand. We propose the NEutrino Observatory in the Nanhai (NEON), located in the South China Sea to be complementary for the global neutrino detectors. This proposal describes the design and layout of the array and reports on comprehensive simulations conducted to assess its performance. The NEON project, with a volume of 10 km$^3$, achieves an angular resolution of 0.1$^\circ$ at 100 TeV. With 10 years of operation, the project's 5$\sigma$ sensitivity is estimated as $E^2\Phi \sim 2 \times 10^{-10}$ GeV cm$^{-2}$ s$^{-1}$ for a source spectrum index of -2. We found that the variation in depth from 1700 to 3500 meters does not significantly influence the sensitivity to steady sources., Comment: 10 pages, 5 figures

As the open community of large language models (LLMs) matures, multimodal LLMs (MLLMs) have promised an elegant bridge between vision and language. However, current research is inherently constrained by challenges such as the need for high-quality instruction pairs and the loss of visual information in image-to-text training objectives. To this end, we propose a Visual Token Complement framework (VTC) that helps MLLMs regain the missing visual features and thus improve response accuracy. Specifically, our VTC integrates text-to-image generation as a guide to identifying the text-irrelevant features, and a visual selector is then developed to generate complementary visual tokens to enrich the original visual input. Moreover, an iterative strategy is further designed to extract more visual information by iteratively using the visual selector without any additional training. Notably, the training pipeline requires no additional image-text pairs, resulting in a desired instruction tuning-free property. Both qualitative and quantitative experiments demonstrate the superiority and efficiency of our VTC., Comment: Accepted by ECCV2024 (20pages)