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Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal alignment without relying on expensive channel estimation and feedback. This paper proposes NCAirFL, a CSI-free AirFL scheme based on unbiased non-coherent detection at the edge server. By exploiting binary dithering and a long-term memory based error-compensation mechanism, NCAirFL achieves a convergence rate of order $\mathcal{O}(1/\sqrt{T})$ in terms of the average square norm of the gradient for general non-convex and smooth objectives, where $T$ is the number of communication rounds. Experiments demonstrate the competitive performance of NCAirFL compared to vanilla FL with ideal communications and to coherent transmission-based benchmarks., Comment: 6 pages, 2 figures, submitted for possible publication

A graph $G$ is $(c,t)$-sparse if for every pair of vertex subsets $A,B\subset V(G)$ with $|A|,|B|\geq t$, $e(A,B)\leq (1-c)|A||B|$. In this paper we prove that for every $c>0$ and integer $\ell$, there exists $C>1$ such that if an $n$-vertex graph $G$ is $(c,t)$-sparse for some $t$, and has at least $C t^{1-1/\ell}n^{1+1/\ell}$ edges, then $G$ contains an induced copy of $C_{2\ell}$. This resolves a conjecture of Fox, Nenadov and Pham., Comment: 13 pages

The irregular and challenging characteristics of lung adenocarcinoma nodules in computed tomography (CT) images complicate staging diagnosis, making accurate segmentation critical for clinicians to extract detailed lesion information. In this study, we propose a segmentation model, S3TU-Net, which integrates multi-dimensional spatial connectors and a superpixel-based visual transformer. S3TU-Net is built on a multi-view CNN-Transformer hybrid architecture, incorporating superpixel algorithms, structured weighting, and spatial shifting techniques to achieve superior segmentation performance. The model leverages structured convolution blocks (DWF-Conv/D2BR-Conv) to extract multi-scale local features while mitigating overfitting. To enhance multi-scale feature fusion, we introduce the S2-MLP Link, integrating spatial shifting and attention mechanisms at the skip connections. Additionally, the residual-based superpixel visual transformer (RM-SViT) effectively merges global and local features by employing sparse correlation learning and multi-branch attention to capture long-range dependencies, with residual connections enhancing stability and computational efficiency. Experimental results on the LIDC-IDRI dataset demonstrate that S3TU-Net achieves a DSC, precision, and IoU of 89.04%, 90.73%, and 90.70%, respectively. Compared to recent methods, S3TU-Net improves DSC by 4.52% and sensitivity by 3.16%, with other metrics showing an approximate 2% increase. In addition to comparison and ablation studies, we validated the generalization ability of our model on the EPDB private dataset, achieving a DSC of 86.40%.

We have recently witnessed that ``Intelligence" and `` Compression" are the two sides of the same coin, where the language large model (LLM) with unprecedented intelligence is a general-purpose lossless compressor for various data modalities. This attribute particularly appeals to the lossless image compression community, given the increasing need to compress high-resolution images in the current streaming media era. Consequently, a spontaneous envision emerges: Can the compression performance of the LLM elevate lossless image compression to new heights? However, our findings indicate that the naive application of LLM-based lossless image compressors suffers from a considerable performance gap compared with existing state-of-the-art (SOTA) codecs on common benchmark datasets. In light of this, we are dedicated to fulfilling the unprecedented intelligence (compression) capacity of the LLM for lossless image compression tasks, thereby bridging the gap between theoretical and practical compression performance. Specifically, we propose P$^{2}$-LLM, a next-pixel prediction-based LLM, which integrates various elaborated insights and methodologies, \textit{e.g.,} pixel-level priors, the in-context ability of LLM, and a pixel-level semantic preservation strategy, to enhance the understanding capacity of pixel sequences for better next-pixel predictions. Extensive experiments on benchmark datasets demonstrate that P$^{2}$-LLM can beat SOTA classical and learned codecs.

Integrated photonic circuits play a crucial role in implementing quantum information processing in the noisy intermediate-scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical optimization techniques to enhance quantum advantages on NISQ devices. However, most variational algorithms are circuit-model-based and encounter challenges when implemented on integrated photonic circuits, because they involve explicit decomposition of large quantum circuits into sequences of basic entangled gates, leading to an exponential decay of success probability due to the non-deterministic nature of photonic entangling gates. Here, we present a variational learning approach for designing quantum photonic circuits, which directly incorporates post-selection and elementary photonic elements into the training process. The complicated circuit is treated as a single nonlinear logical operator, and a unified design is discovered for it through variational learning. Engineering an integrated photonic chip with automated control, we adjust and optimize the internal parameters of the chip in real time for task-specific cost functions. We utilize a simple case of designing photonic circuits for a single ancilla CNOT gate with improved success rate to illustrate how our proposed approach works, and then apply the approach in the first demonstration of quantum stochastic simulation using integrated photonics.

We study the polarization of an electron scattered by different static potentials. The initial state of the electron is chosen as a wavepacket to construct the definite orbital angular momentum, and the final polarization of the electron, scattered by different static potentials such as vector, pseudovector, scalar and pseudoscalar potentials, is calculated. Numerical results show that, the sign of the polarization of the electron scattered by the vector potential is opposite to the other three cases, and the magnitude order of the polarization value is consistent with recent experimental result in the collision parameter range $0

Optically levitated dielectric nanoparticles have become valuable tools for precision sensing and quantum optomechanical experiments. To predict the dynamic properties of a particle trapped in an optical tweezer with high fidelity, a tool is needed to compute the particle's response to the given optical field accurately. Here, we utilise a numerical solution of the three-dimensional trapping light to accurately simulate optical tweezers and predict key optomechanical parameters. By controlling the numerical aperture and measuring the the particle's oscillation frequencies in the trap, we validate the accuracy of our method. We foresee broad applications of this method in the field of levitodynamics, where precise characterisation of optical tweezers is essential for estimating parameters ranging from motional frequencies to scattering responses of the particle with various dielectric properties.

In this paper, we address the challenging task of multimodal mathematical reasoning by incorporating the ability of ``slow thinking" into multimodal large language models (MLLMs). Contrary to existing methods that rely on direct or fast thinking, our key idea is to construct long chains of thought (CoT) consisting of atomic actions in a step-by-step manner, guiding MLLMs to perform complex reasoning. To this end, we design a novel AtomThink framework composed of three key modules: (i) a CoT annotation engine that automatically generates high-quality CoT annotations to address the lack of high-quality visual mathematical data; (ii) an atomic step fine-tuning strategy that jointly optimizes an MLLM and a policy reward model (PRM) for step-wise reasoning; and (iii) four different search strategies that can be applied with the PRM to complete reasoning. Additionally, we propose AtomMATH, a large-scale multimodal dataset of long CoTs, and an atomic capability evaluation metric for mathematical tasks. Extensive experimental results show that the proposed AtomThink significantly improves the performance of baseline MLLMs, achieving approximately 50\% relative accuracy gains on MathVista and 120\% on MathVerse. To support the advancement of multimodal slow-thinking models, we will make our code and dataset publicly available on https://github.com/Quinn777/AtomThink.

Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Although extensive efforts have been dedicated from both academia and industry to improve the vanilla FL, little work focuses on the data pricing mechanism. In contrast to the straightforward in/post-training pricing techniques, we study a more difficult problem of pre-training pricing without direct information from the learning process. We propose FLMarket that integrates a two-stage, auction-based pricing mechanism with a security protocol to address the utility-privacy conflict. Through comprehensive experiments, we show that the client selection according to FLMarket can achieve more than 10% higher accuracy in subsequent FL training compared to state-of-the-art methods. In addition, it outperforms the in-training baseline with more than 2% accuracy increase and 3x run-time speedup.

Using $e^+e^-$ collision data corresponding to an integrated luminosity of 19 fb$^{-1}$ collected by the BESIII detector at center-of-mass energies ranging from 4.13 to 4.70 GeV, we report the first evidence for a new excited $\Omega^{-}$ hyperon, the $\Omega^*(2109)^{-}$, through the process $e^+ e^- \to \Omega^*(2109)^{-} \bar{\Omega}^{+} +c.c.$ with a significance of 3.7 $\sigma$. The mass and width of $\Omega^*(2109)^{-}$ are measured to be $2108.8 \pm 5.5_{\rm stat} \pm 1.5_{\rm syst} {\rm MeV}/c^{2}$ and $21.6 \pm 17.7_{\rm stat} \pm 9.4_{\rm syst} {\rm MeV}$, respectively. We also present evidence for production of the $\Omega^*(2012)^{-}$ in the process $e^+ e^- \to \Omega^*(2012)^{-} \bar{\Omega}^{+} +c.c.$ with a significance of 3.7 $\sigma$., Comment: 8 pages, 2 figures

Nonlinear electrodynamics in materials manifests as an electronic response that depends on second- or higher-order powers of the applied electromagnetic field. This response is highly dependent on the underlying crystal symmetries in the material and is typically smaller than the linear responses. Nonlinear responses are therefore usually employed to expose the symmetry breaking, geometric properties of the electronic band structure in materials. Naturally, a material system with a strong nonlinear response is also the key component in nonlinear devices. Here we report the strong room-temperature second-harmonic transport response in a quantum magnet,TbMn6Sn6, which is governed by the quantum metric and can be tuned with applied magnetic fields and temperature. We show that around room temperature, which is close to the spontaneous spin-reorientation transition, the magnetic configurations, and therefore the related symmetry breaking phases, are easily controlled. Our results pave the way from quantum materials to high performance tuneable nonlinear device applications at room temperature., Comment: 12 pages, 3 figures

Research on two-dimensional van der Waals materials has demonstrated that the layer degree of freedom can significantly alter the physical properties of materials due to the substantial modification of bulk bands. Inspired by this concept, layered photonic systems have been proposed and realized, revealing novel phenomena absent in their monolayer counterparts. In this work, we experimentally investigate the band structures of bilayer valley photonic crystals. Two typical structures with different stacking configurations are experimentally imaged via the near-field scanning technology, exhibiting distinct bulk band structures. Furthermore, different topological edge modes induced by distinct topology are observed, revealing that the layer degree of freedom can be regarded as a pseudospin and offer further capabilities for controlling the flow of light. Our work not only elucidates the evolution of band structures from monolayer to bilayer topological systems but also provides an experimental platform for the further exploration of bilayer topological insulators., Comment: 20 pages, 5 figures

The geometry of the Comptonization corona in neutron star low-mass X-ray binaries is still unclear. We conducted time-resolved polarimetric analysis of the archival observations of XTE J1701--462 obtained with the \textit{Imaging X-ray Polarimeter Explorer} during its 2022 outburst, and found that the polarization angle (PA) varied significantly with time when the source was in the normal branch (NB), with $67 \pm 8^{\circ}$ in the first epoch, $-34 \pm 8^{\circ}$ in the second, and $-58 \pm 8^{\circ}$ in the third, last epoch. Meanwhile, the polarization degree remained constant at around 2\%, above the minimum detectable polarization at the 99\% confidence level (MDP$_{99}$). The rapid PA variation causes depolarization in the time-averaged data, resulting in a nondetection as reported in the literature. The rapid (intra-day) PA variation may suggest that there is a fast transformation of the corona geometry, likely switching from a slab geometry to a more vertically extended spreading layer geometry, along with enhanced disk emission and reflection., Comment: Submitted to the Astrophysical Journal

Sequential recommendation (SR) aims to predict the next purchasing item according to users' dynamic preference learned from their historical user-item interactions. To improve the performance of recommendation, learning dynamic heterogeneous cross-type behavior dependencies is indispensable for recommender system. However, there still exists some challenges in Multi-Behavior Sequential Recommendation (MBSR). On the one hand, existing methods only model heterogeneous multi-behavior dependencies at behavior-level or item-level, and modelling interaction-level dependencies is still a challenge. On the other hand, the dynamic multi-grained behavior-aware preference is hard to capture in interaction sequences, which reflects interaction-aware sequential pattern. To tackle these challenges, we propose a Multi-Grained Preference enhanced Transformer framework (M-GPT). First, M-GPT constructs a interaction-level graph of historical cross-typed interactions in a sequence. Then graph convolution is performed to derive interaction-level multi-behavior dependency representation repeatedly, in which the complex correlation between historical cross-typed interactions at specific orders can be well learned. Secondly, a novel multi-scale transformer architecture equipped with multi-grained user preference extraction is proposed to encode the interaction-aware sequential pattern enhanced by capturing temporal behavior-aware multi-grained preference . Experiments on the real-world datasets indicate that our method M-GPT consistently outperforms various state-of-the-art recommendation methods., Comment: 12 pages

We performed inelastic neutron scattering experiments on single-crystal samples of the honeycomb lattice magnet, ilmenite NiTiO3. Below the Neel temperature of 22 K, spin wave excitations with a band energy of 3.7 meV were observed. The neutron energy spectra were well-reproduced by modeling the system as a ferromagnetic honeycomb lattice with antiferromagnetic interlayer coupling, using linear spin wave theory. Similar to another ilmenite CoTiO3, a crossing structure was observed at the K point, suggesting the resence of Dirac magnons in NiTiO3. Further calculations suggested the formation of Dirac nodal line.

Unsupervised sentence representation learning remains a critical challenge in modern natural language processing (NLP) research. Recently, contrastive learning techniques have achieved significant success in addressing this issue by effectively capturing textual semantics. Many such approaches prioritize the optimization using negative samples. In fields such as computer vision, hard negative samples (samples that are close to the decision boundary and thus more difficult to distinguish) have been shown to enhance representation learning. However, adapting hard negatives to contrastive sentence learning is complex due to the intricate syntactic and semantic details of text. To address this problem, we propose HNCSE, a novel contrastive learning framework that extends the leading SimCSE approach. The hallmark of HNCSE is its innovative use of hard negative samples to enhance the learning of both positive and negative samples, thereby achieving a deeper semantic understanding. Empirical tests on semantic textual similarity and transfer task datasets validate the superiority of HNCSE.

We study the problem of robot navigation in dense and interactive crowds with environmental constraints such as corridors and furniture. Previous methods fail to consider all types of interactions among agents and obstacles, leading to unsafe and inefficient robot paths. In this article, we leverage a graph-based representation of crowded and constrained scenarios and propose a structured framework to learn robot navigation policies with deep reinforcement learning. We first split the representations of different components in the environment and propose a heterogeneous spatio-temporal (st) graph to model distinct interactions among humans, robots, and obstacles. Based on the heterogeneous st-graph, we propose HEIGHT, a novel navigation policy network architecture with different components to capture heterogeneous interactions among entities through space and time. HEIGHT utilizes attention mechanisms to prioritize important interactions and a recurrent network to track changes in the dynamic scene over time, encouraging the robot to avoid collisions adaptively. Through extensive simulation and real-world experiments, we demonstrate that HEIGHT outperforms state-of-the-art baselines in terms of success and efficiency in challenging navigation scenarios. Furthermore, we demonstrate that our pipeline achieves better zero-shot generalization capability than previous works when the densities of humans and obstacles change. More videos are available at https://sites.google.com/view/crowdnav-height/home.

Adversarial examples represent a serious issue for the application of machine learning models in many sensitive domains. For generating adversarial examples, decision based black-box attacks are one of the most practical techniques as they only require query access to the model. One of the most recently proposed state-of-the-art decision based black-box attacks is Triangle Attack (TA). In this paper, we offer a high-level description of TA and explain potential theoretical limitations. We then propose a new decision based black-box attack, Triangle Attack with Reinforcement Learning (TARL). Our new attack addresses the limits of TA by leveraging reinforcement learning. This creates an attack that can achieve similar, if not better, attack accuracy than TA with half as many queries on state-of-the-art classifiers and defenses across ImageNet and CIFAR-10.

By applying some techniques of set-valued and variational analysis, we study solution stability of nonhomogeneous split equality problems and nonhomogeneous split feasibility problems, where the constraint sets need not be convex. Necessary and sufficient conditions for the Lipschitz-likeness of the solution maps of the problems are given and illustrated by concrete examples. The obtained results complement those given in [Huong VT, Xu HK, Yen ND. Stability analysis of split equality and split feasibility problems. arXiv:2410.16856.], where classical split equality problems and split feasibility problems have been considered.

Textual description of a physical location, commonly known as an address, plays an important role in location-based services(LBS) such as on-demand delivery and navigation. However, the prevalence of abnormal addresses, those containing inaccuracies that fail to pinpoint a location, have led to significant costs. Address rewriting has emerged as a solution to rectify these abnormal addresses. Despite the critical need, existing address rewriting methods are limited, typically tailored to correct specific error types, or frequently require retraining to process new address data effectively. In this study, we introduce AddrLLM, an innovative framework for address rewriting that is built upon a retrieval augmented large language model. AddrLLM overcomes aforementioned limitations through a meticulously designed Supervised Fine-Tuning module, an Address-centric Retrieval Augmented Generation module and a Bias-free Objective Alignment module. To the best of our knowledge, this study pioneers the application of LLM-based address rewriting approach to solve the issue of abnormal addresses. Through comprehensive offline testing with real-world data on a national scale and subsequent online deployment, AddrLLM has demonstrated superior performance in integration with existing logistics system. It has significantly decreased the rate of parcel re-routing by approximately 43\%, underscoring its exceptional efficacy in real-world applications., Comment: Accepted by KDD'25 ADS Track

Pre-trained vision-language models provide a robust foundation for efficient transfer learning across various downstream tasks. In the field of video action recognition, mainstream approaches often introduce additional parameter modules to capture temporal information. While the increased model capacity brought by these additional parameters helps better fit the video-specific inductive biases, existing methods require learning a large number of parameters and are prone to catastrophic forgetting of the original generalizable knowledge. In this paper, we propose a simple yet effective Multi-modal Spatio-Temporal Adapter (MSTA) to improve the alignment between representations in the text and vision branches, achieving a balance between general knowledge and task-specific knowledge. Furthermore, to mitigate over-fitting and enhance generalizability, we introduce a spatio-temporal description-guided consistency constraint. This constraint involves feeding template inputs (i.e., ``a video of $\{\textbf{cls}\}$'') into the trainable language branch, while LLM-generated spatio-temporal descriptions are input into the pre-trained language branch, enforcing consistency between the outputs of the two branches. This mechanism prevents over-fitting to downstream tasks and improves the distinguishability of the trainable branch within the spatio-temporal semantic space. We evaluate the effectiveness of our approach across four tasks: zero-shot transfer, few-shot learning, base-to-novel generalization, and fully-supervised learning. Compared to many state-of-the-art methods, our MSTA achieves outstanding performance across all evaluations, while using only 2-7\% of the trainable parameters in the original model. Code will be avaliable at https://github.com/chenhaoxing/ETL4Video.

Since machine learning model is often trained on a limited data set, the model is trained multiple times on the same data sample, which causes the model to memorize most of the training set data. Membership Inference Attacks (MIAs) exploit this feature to determine whether a data sample is used for training a machine learning model. However, in realistic scenarios, it is difficult for the adversary to obtain enough qualified samples that mark accurate identity information, especially since most samples are non-members in real world applications. To address this limitation, in this paper, we propose a new attack method called CLMIA, which uses unsupervised contrastive learning to train an attack model without using extra membership status information. Meanwhile, in CLMIA, we require only a small amount of data with known membership status to fine-tune the attack model. Experimental results demonstrate that CLMIA performs better than existing attack methods for different datasets and model structures, especially with data with less marked identity information. In addition, we experimentally find that the attack performs differently for different proportions of labeled identity information for member and non-member data. More analysis proves that our attack method performs better with less labeled identity information, which applies to more realistic scenarios.

We report the detection of an extreme stellar prominence eruption on the M dwarf LAMOST J044431.62+235627.9, observed through time-domain H$\alpha$ spectroscopy with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). This prominence eruption was accompanied by a superflare lasting over 160.4 minutes. The H$\alpha$ line profile exhibits significant blue-wing enhancement during the impulsive phase and near the flare peak, with a projected bulk blueshift velocity of $-228\pm11$~km~s$^{-1}$ and a maximum blueshift velocity reaching $-605\pm15$~km~s$^{-1}$. Velocity analysis of the eruptive prominence at various heights above the stellar surface indicates that some of the projected ejection velocities along the line of sight exceed the corresponding escape velocities, suggesting a potential coronal mass ejection (CME). The equivalent width (EW) of the H$\alpha$ blue-wing enhancement in this eruption appears to be the largest observed to date and is comparable to the EW of the H$\alpha$ line profile during the quiescent phase of the host star. We performed a two-cloud modeling for the prominence and the associated flare, which suggests that the eruptive prominence has a mass ranging from $1.6 \times 10^{19}~\text{g}$ to $7.2 \times 10^{19}~\text{g}$. More importantly, the mass ratio of the erupting prominence to its host star is the largest among all reported stellar prominence eruptions/CMEs., Comment: Accepted by ApJL

As density increases, the shape of nuclei transitions to non-spherical ``nuclear pasta" structures. The physical properties of the nuclear pasta, such as thermal conductivity and elasticity, have implications for detecting continuous gravitational waves from a rapidly rotating neutron star. In this work, we investigate the effect of the nuclear pasta on the quadruple moment, and find out that, compared with previous work, the quadrupole moment contributing to continuous gravitational-wave radiation can be up to two orders of magnitude larger. We also discuss the relationship between the quadruple moment and the maximum shear strain. Considering the properties of nuclear pasta, we study the detectability of known accreting neutron stars and compare predicted results to the detectable amplitude limits. These sources are well above the sensitivity curves for Cosmic Explorer and Einstein Telescope detectors. Our work advances the understanding of the properties of nuclear pasta and a possible mechanism for continuous gravitational waves., Comment: 15 pages, 14 figures

Vortex information of scalar pair production in circularly polarized field is investigated in the multiphoton regime. We find that vortex orientation is related to the intrinsic orbital angular momentum of created particles associating with the helicity of absorbed photons, while the magnitude of the orbital angular momentum, i.e., the topology charge is determined by the number of absorbed photons. Moreover, the properties of particle creation and vortices formation can be understood by analyzing the pair production process in quasiparticle representation. This study provides new insights into the angular momentum transfer from field to particle in the scalar pair production process. It is expected that there are similar findings about vortex features for different spin alignment in electron-positron pair production in strong fields via the topology charge as a new freedom., Comment: 7 pages, 4 figures

In semiconductor manufacturing, wafer defect maps (WDMs) play a crucial role in diagnosing issues and enhancing process yields by revealing critical defect patterns. However, accurately categorizing WDM defects presents significant challenges due to noisy data, unbalanced defect classes, and the complexity of failure modes. To address these challenges, this study proposes a novel method combining a self-encoder-based data augmentation technique with a convolutional neural network (CNN). By introducing noise into the latent space, the self-encoder enhances data diversity and mitigates class imbalance, thereby improving the model's generalization capabilities. The augmented dataset is subsequently used to train the CNN, enabling it to deliver precise classification of both common and rare defect patterns. Experimental results on the WM-811K dataset demonstrate that the proposed method achieves a classification accuracy of 98.56%, surpassing Random Forest, SVM, and Logistic Regression by 19%, 21%, and 27%, respectively. These findings highlight the robustness and effectiveness of the proposed approach, offering a reliable solution for wafer defect detection and classification., Comment: 26 pages, 11 figures, including dataset preprocessing, proposed methods, and experimental results

Learners of a second language (L2) often unconsciously substitute unfamiliar L2 phonemes with similar phonemes from their native language (L1), even though native speakers of the L2 perceive these sounds as distinct and non-interchangeable. This phonemic substitution leads to deviations from the standard phonological patterns of the L2, creating challenges for learners in acquiring accurate L2 pronunciation. To address this, we propose Inter-linguistic Phonetic Composition (IPC), a novel computational method designed to minimize incorrect phonological transfer by reconstructing L2 phonemes as composite sounds derived from multiple L1 phonemes. Tests with two automatic speech recognition models demonstrated that when L2 speakers produced IPC-generated composite sounds, the recognition rate of target L2 phonemes improved by 20% compared to when their pronunciation was influenced by original phonological transfer patterns. The improvement was observed within a relatively shorter time frame, demonstrating rapid acquisition of the composite sound.

We present a unified controllable video generation approach AnimateAnything that facilitates precise and consistent video manipulation across various conditions, including camera trajectories, text prompts, and user motion annotations. Specifically, we carefully design a multi-scale control feature fusion network to construct a common motion representation for different conditions. It explicitly converts all control information into frame-by-frame optical flows. Then we incorporate the optical flows as motion priors to guide final video generation. In addition, to reduce the flickering issues caused by large-scale motion, we propose a frequency-based stabilization module. It can enhance temporal coherence by ensuring the video's frequency domain consistency. Experiments demonstrate that our method outperforms the state-of-the-art approaches. For more details and videos, please refer to the webpage: https://yu-shaonian.github.io/Animate_Anything/.

The vision tokens in multimodal large language models usually exhibit significant spatial and temporal redundancy and take up most of the input tokens, which harms their inference efficiency. To solve this problem, some recent works were introduced to drop the unimportant tokens during inference where the importance of each token is decided only by the information in either the vision encoding stage or the prefilling stage. In this paper, we propose Multi-stage Token Dropping (MustDrop) to measure the importance of each token from the whole lifecycle, including the vision encoding stage, prefilling stage, and decoding stage. Concretely, in the visual encoding stage, MustDrop merges spatially adjacent tokens with high similarity, and establishes a key token set to retain the most vision-critical tokens, preventing them from being discarded in later stages. In the prefilling stage, MustDrop further compresses vision tokens by the guidance of text semantics, with a dual-attention filtering strategy. In the decoding stage, an output-aware cache policy is proposed to further reduce the size of the KV cache. By leveraging tailored strategies in the multi-stage process, MustDrop can more precisely recognize the important and redundant tokens, thus achieving an optimal balance between performance and efficiency. For instance, MustDrop reduces about 88.5\% FLOPs on LLaVA with a compression ratio of 92.2\% while maintaining comparable accuracy. Our codes are available at \url{https://github.com/liuting20/MustDrop}., Comment: 8 pages, 4figures

A minority carrier lifetime of 25.46 $\mu$s in a P-type 4H-SiC epilayer has been attained through sequential thermal oxidation and hydrogen annealing. Thermal oxidation can enhance the minority carrier lifetime in the 4H-SiC epilayer by reducing carbon vacancies. However, this process also generates carbon clusters with limited diffusivity and contributes to the enlargement of surface pits on the 4H-SiC. High-temperature hydrogen annealing effectively reduces stacking fault and dislocation density. Moreover, electron spin resonance analysis indicates a significant reduction in carbon vacancy defects after hydrogen annealing. The mechanisms of the elimination of carbon vacancies by hydrogen annealing include the decomposition of carbon clusters formed during thermal oxidation and the low-pressure selective etching by hydrogen, which increases the carbon content on the 4H-SiC surface and facilitates carbon diffusion. Consequently, the combination of thermal oxidation and hydrogen annealing eliminates carbon vacancies more effectively, substantially enhancing the minority carrier lifetime in P-type 4H-SiC. This improvement is advantageous for the application of high-voltage SiC bipolar devices.

We developed a shoe-mounted gait monitoring system capable of tracking up to 17 gait parameters, including gait length, step time, stride velocity, and others. The system employs a stereo camera mounted on one shoe to track a marker placed on the opposite shoe, enabling the estimation of spatial gait parameters. Additionally, a Force Sensitive Resistor (FSR) affixed to the heel of the shoe, combined with a custom-designed algorithm, is utilized to measure temporal gait parameters. Through testing on multiple participants and comparison with the gait mat, the proposed gait monitoring system exhibited notable performance, with the accuracy of all measured gait parameters exceeding 93.61%. The system also demonstrated a low drift of 4.89% during long-distance walking. A gait identification task conducted on participants using a trained Transformer model achieved 95.7% accuracy on the dataset collected by the proposed system, demonstrating that our hardware has the potential to collect long-sequence gait data suitable for integration with current Large Language Models (LLMs). The system is cost-effective, user-friendly, and well-suited for real-life measurements., Comment: 13 pages, 14 figures. This paper was submitted for publication to the IEEE Transactions on Instrumentation and Measurement

Large Language Models (LLMs) have shown the potential to be valuable teaching tools, with the potential of giving every student a personalized tutor. However, one challenge with using LLMs to learn new concepts is that when learning a topic in an unfamiliar domain, it can be difficult to know what questions to ask. Further, language models do not always encourage "active learning" where students can test and assess their understanding. In this paper, we propose ways to combine large language models with "traditional" learning materials (like e-books) to give readers the benefits of working with LLMs (the ability to ask personally interesting questions and receive personalized answers) with the benefits of a traditional e-book (having a structure and content that is pedagogically sound). This work shows one way that LLMs have the potential to improve learning materials and make personalized programming education more accessible to a broader audience., Comment: 21 pages

Large Language Models (LLMs) have revolutionized natural language processing by unifying tasks into text generation, yet their large parameter sizes and autoregressive nature limit inference speed. SAM-Decoding addresses this by introducing a novel retrieval-based speculative decoding method that uses a suffix automaton for efficient and accurate draft generation. Unlike n-gram matching used by the existing method, SAM-Decoding finds the longest suffix match in generating text and text corpuss, achieving an average time complexity of $O(1)$ per generation step. SAM-Decoding constructs static and dynamic suffix automatons for the text corpus and input prompts, respectively, enabling fast and precise draft generation. Meanwhile, it is designed as an approach that can be combined with existing methods, allowing SAM-Decoding to adaptively select a draft generation strategy based on the matching length, thus increasing the inference speed of the LLM. When combined with Token Recycling, evaluations show SAM-Decoding outperforms existing model-free methods, achieving a speedup of $2.27\times$ over autoregressive decoding on Spec-Bench. When combined with EAGLE2, it reaches a speedup of $2.49\times$, surpassing all current approaches. Our code is available at https://github.com/hyx1999/SAM-Decoding., Comment: 17 pages, 5 figures

The method of adiabatic elimination has been widely adopted in quantum optics in the past several decades. In the study of cavity-based light-matter interactions, the bad-cavity limit is often encountered, where the damping rate of the cavity is much larger than the interaction strength. The fast-damped cavity will quickly relax to a quasi-stationary state, and one can eliminate the cavity from the equation of motion by setting its time derivative to zero. Elimination of the cavity in the bad-cavity limit can reduce the dimensionality of the equations of motion of the system. However, we find that the adiabatic elimination procedure performed in the quantum Langevin equation leads to an incorrect commutation relation, which was rarely discussed in the former studies, as far as we know. Here, we show the incorrect commutation relation arises from the fact that the high frequency of the vacuum noise should be cut off to perform adiabatic elimination, but the noise with high frequency cutoff is still treated as white noise with infinite bandwidth and delta commutation relation. We also study the correlation function and show that the high frequency part of noise contributes very little when averaged over the bath. Therefore, the adiabatic elimination method can reduce the complexity of the calculations while maintaining physical reliability.

Generative Adversarial Networks (GANs) are a class of neural networks that have been widely used in the field of image-to-image translation. In this paper, we propose a streamlined image-to-image translation network with a simpler architecture compared to existing models. We investigate the relationship between GANs and autoencoders and provide an explanation for the efficacy of employing only the GAN component for tasks involving image translation. We show that adversarial for GAN models yields results comparable to those of existing methods without additional complex loss penalties. Subsequently, we elucidate the rationale behind this phenomenon. We also incorporate experimental results to demonstrate the validity of our findings.

Large language models (LLMs) based on the Transformer architecture are widely employed across various domains and tasks. However, their increasing size imposes significant hardware demands, limiting practical deployment. To mitigate this, model pruning techniques have been developed to create more efficient models while maintaining high performance. Despite this, post-training after pruning is crucial for performance recovery and can be resource-intensive. This paper investigates the post-training requirements of pruned LLMs and introduces a scaling law to determine the optimal amount of post-training data. Post-training experiments with the Llama-3 and Qwen-2.5 series models, pruned using depth pruning, width pruning, and 2:4 semi-structured pruning, show that higher pruning ratios necessitate more post-training data for performance recovery, whereas larger LLMs require less. The proposed scaling law predicts a model's loss based on its parameter counts before and after pruning, as well as the post-training token counts. Furthermore, we find that the scaling law established from smaller LLMs can be reliably extrapolated to larger LLMs. This work provides valuable insights into the post-training of pruned LLMs and offers a practical scaling law for optimizing post-training data usage.

We explore the fractional advection-diffusion equation and rare events associated with the ACTRW model. When waiting times have a finite mean but infinite variance, and the displacements follow a narrow distribution, the fractional operator is defined in terms of space rather than time. The far tail of the positional distribution is governed by rare events, which exhibit a different scaling compared to typical fluctuations. Additionally, we establish a strong relationship between the number of renewals and the positional distribution in the context of large deviations. Throughout the manuscript, the theoretical results are validated through simulations., Comment: 12pages

The small Kerr-anti-de Sitter black hole demonstrates instability due to the superradiance of either a massive or massless scalar field. Previous leading-order approximations of the spectrum are inefficient. In particular, the leading-order real part of the eigenfrequency is insensitive to the spin of the black hole. In this work, we improve the analysis by including the next-to-leading-order contribution. Compared to the numerical calculation, the new spin-dependent real part presents significantly better agreement, and the error in the imaginary part is also reduced to less than 60% for most black hole spins., Comment: 10 pages, 6 figures

In this talk, we present a new interpretation for the recently observed $\Xi(1820)$ resonance. We recall that the chiral unitary approach for the interaction of pseudoscalar mesons with the baryons of the decuplet predicts two states for the $\Xi(1820)$ resonance, one with a narrow width and the other one with a large width. We contrast this fact with the recent BESIII measurement of the $K^- \Lambda$ mass distribution in the $\psi(3686)$ decay to $K^- \Lambda \bar\Xi^+ $, which demands a width much larger than the average of the PDG, and show how the consideration of the two $\Xi(1820)$ states provides a natural explanation to this apparent contradiction., Comment: Proceeding of the QNP 2024 Conference. 6 pages, 3 figures, 1 table. arXiv admin note: substantial text overlap with arXiv:2309.03618

The distributed recursion (DR) algorithm is an effective method for solving the pooling problem that arises in many applications. It is based on the well-known P-formulation of the pooling problem, which involves the flow and quality variables; and it can be seen as a variant of the successive linear programming (SLP) algorithm, where the linear programming (LP) approximation problem can be transformed from the LP approximation problem derived by using the first-order Taylor series expansion technique. In this paper, we first propose a new nonlinear programming (NLP) formulation for the pooling problem involving only the flow variables, and show that the DR algorithm can be seen as a direct application of the SLP algorithm to the newly proposed formulation. With this new useful theoretical insight, we then develop a new variant of DR algorithm, called penalty DR (PDR) algorithm, based on the proposed formulation. The proposed PDR algorithm is a penalty algorithm where violations of the (linearized) nonlinear constraints are penalized in the objective function of the LP approximation problem with the penalty terms increasing when the constraint violations tend to be large. Compared with the LP approximation problem in the classic DR algorithm, the LP approximation problem in the proposed PDR algorithm can return a solution with a better objective value, which makes it more suitable for finding high-quality solutions for the pooling problem. Numerical experiments on benchmark and randomly constructed instances show that the proposed PDR algorithm is more effective than the classic SLP and DR algorithms in terms of finding a better solution for the pooling problem., Comment: 22 pages, 2 figures, submitted for possible publication

The theory of direct reciprocity explores how individuals cooperate when they interact repeatedly. In repeated interactions, individuals can condition their behaviour on what happened earlier. One prominent example of a conditional strategy is Tit-for-Tat, which prescribes to cooperate if and only if the co-player did so in the previous round. The evolutionary dynamics among such memory-1 strategies have been explored in quite some detail. However, obtaining analytical results on the dynamics of higher memory strategies becomes increasingly difficult, due to the rapidly growing size of the strategy space. Here, we derive such results for the adaptive dynamics in the donation game. In particular, we prove that for every orbit forward in time, there is an associated orbit backward in time that also solves the differential equation. Moreover, we analyse the dynamics by separating payoffs into a symmetric and an anti-symmetric part and demonstrate some properties of the anti-symmetric part. These results highlight some interesting symmetries that arise when interchanging player one with player two, and cooperation with defection.

Vaccination is widely recognised as one of the most effective forms of public health interventions. Individuals decisions regarding vaccination creates a complex social dilemma between individual and collective interests, where each person's decision affects the overall public health outcome. In this paper, we study the adaptive dynamics for the evolutionary dynamics of strategies in a fundamental game-theoretic model of vaticination. We show the existence of an (Nash) equilibrium and analyse the stability and bifurcations when varying the relevant parameters. We also demonstrate our analytical results by several concrete examples.

By treating intervals as inseparable sets, this paper proposes sparse machine learning regressions for high-dimensional interval-valued time series. With LASSO or adaptive LASSO techniques, we develop a penalized minimum distance estimation, which covers point-based estimators are special cases. We establish the consistency and oracle properties of the proposed penalized estimator, regardless of whether the number of predictors is diverging with the sample size. Monte Carlo simulations demonstrate the favorable finite sample properties of the proposed estimation. Empirical applications to interval-valued crude oil price forecasting and sparse index-tracking portfolio construction illustrate the robustness and effectiveness of our method against competing approaches, including random forest and multilayer perceptron for interval-valued data. Our findings highlight the potential of machine learning techniques in interval-valued time series analysis, offering new insights for financial forecasting and portfolio management., Comment: 37 pages, 3 figures

DarkSHINE is a newly proposed fixed-target experiment initiative to search for the invisible decay of Dark Photon via missing energy/momentum signatures, based on the high repetition rate electron beam to be deployed/delivered by the Shanghai High repetition rate XFEL and Extreme light facility (SHINE). This report elaborates the baseline design of DarkSHINE experiment by introducing the physics goals, experimental setups, details of each sub-detector system technical designs, signal and backgground modelings, expected search sensitivities and future prospects, which mark an important step towards the further prototyping and technical demonstrations.

Boron-rich lithium borocarbides are promising candidates for phonon-mediated high-temperature superconductors due to their metallic $\sigma$-bonding electrons. Here, we use the cluster expansion method to identify energetically stable configurations (colorings) of Li$_2$B$_3$C and Li$_3$B$_4$C$_2$, which are characterized by a distinctive pattern of alternating B-B and B-C zigzag chains. Surprisingly, the optimal configuration of Li$_2$B$_3$C exhibits an extremely low superconducting transition temperature of $T_c < 0.03$ K, which is attributed to the suppression of deformation potentials near the Fermi level caused by the specific electron filling of B-B zigzag chains. However, the $\sigma$-bonding electrons at the Fermi level are highly sensitive to external strain or pressure. Specifically, applying a -5\% compressive uniaxial strain can significantly enhance the electron-phonon coupling and the Eliashberg spectral function, boosting up $T_c$ to 37 K. This work not only presents a novel strategy for achieving phonon-mediated high-temperature superconductivity in Li$_n$B$_{n+1}$C$_{n-1}$ compounds but also provides valuable insights into the complex interplay between electronic structure and superconducting interaction., Comment: 10 pages, 5 figures

We present the first HI mass function (HIMF) measurement for the recent FAST All Sky HI (FASHI) survey and the most complete measurements of HIMF in the local universe so far by combining the HI catalogues from HI Parkes All Sky Survey (HIPASS), Arecibo Legacy Fast ALFA (ALFALFA) and FASHI surveys at redshift 0 < z < 0.05, covering 76% of the entire sky. We adopt the same methods to estimate distances, calculate sample completeness, and determine the HIMF for all three surveys. The best-fitting Schechter function for the total HIMF has a low-mass slope parameter alpha = -1.30 and a knee mass log(Ms) = 9.86 and a normalization phi_s = 0.00658. This gives the cosmic HI abundance omega_HI= 0.000454. We find that a double Schechter function with the same slope alpha better describes our HIMF, and the two different knee masses are log(Ms1) = 9.96 and log(Ms2) = 9.65. We verify that the measured HIMF is marginally affected by the choice of distance estimates. The effect of cosmic variance is significantly suppressed by combining the three surveys and it provides a unique opportunity to obtain an unbiased estimate of the HIMF in the local universe., Comment: 10 pages, 7 figures, submitted to A&A

This study employs numerical simulations to explore the relationship between the dynamical instability of planetary systems and the uniformity of planetary masses within the system, quantified by the Gini index. Our findings reveal a significant correlation between system stability and mass uniformity. Specifically, planetary systems with higher mass uniformity demonstrate increased stability, particularly when they are distant from first-order mean motion resonances (MMRs). In general, for non-resonant planetary systems with a constant total mass, non-equal mass systems are less stable than equal mass systems for a given spacing in units of mutual Hill radius. This instability may arise from the equipartition of the total random energy, which can lead to higher eccentricities in smaller planets, ultimately destabilizing the system. This work suggests that the observed mass uniformity within multi-planet systems detected by \textit{Kepler} may result from a combination of survival bias and ongoing dynamical evolution processes., Comment: 9 pages, 5 figures, accepted to The Astronomical Journal

The integration of Distributed Energy Resources (DERs) into power distribution systems has made microgrids foundational to grid modernization. These DERs, connected through power electronic inverters, create power electronics dominated grid architecture, introducing unique challenges for fault detection. While external line faults are widely studied, inverter faults remain a critical yet underexplored issue. This paper proposes various data mining techniques for the effective detection and localization of inverter faults-essential for preventing catastrophic grid failures. Furthermore, the difficulty of differentiating between system anomalies and internal inverter faults within Power Electronics-Driven Grids (PEDGs) is addressed. To enhance grid resilience, this work applies advanced artificial intelligence methods to distinguish anomalies from true internal faults, identifying the specific malfunctioning switch. The proposed FaultNet-ML methodology is validated on a 9-bus system dominated by inverters, illustrating its robustness in a PEDG environment., Comment: 5 pages, 2 figures, submitted to 2025 IEEE Power and Energy Society General Meeting (PESGM 2025), Austin, TX

The distinct characteristics of multiomics data, including complex interactions within and across biological layers and disease heterogeneity (e.g., heterogeneity in etiology and clinical symptoms), drive us to develop novel designs to address unique challenges in multiomics prediction. In this paper, we propose the multi-view knowledge transfer learning (MVKTrans) framework, which transfers intra- and inter-omics knowledge in an adaptive manner by reviewing data heterogeneity and suppressing bias transfer, thereby enhancing classification performance. Specifically, we design a graph contrastive module that is trained on unlabeled data to effectively learn and transfer the underlying intra-omics patterns to the supervised task. This unsupervised pretraining promotes learning general and unbiased representations for each modality, regardless of the downstream tasks. In light of the varying discriminative capacities of modalities across different diseases and/or samples, we introduce an adaptive and bi-directional cross-omics distillation module. This module automatically identifies richer modalities and facilitates dynamic knowledge transfer from more informative to less informative omics, thereby enabling a more robust and generalized integration. Extensive experiments on four real biomedical datasets demonstrate the superior performance and robustness of MVKTrans compared to the state-of-the-art. Code and data are available at https://github.com/Yaolab-fantastic/MVKTrans.

Axions and axion-like particles can be probed through gravitational waves indirectly, often referred to as "audible axions". The usual concept of audible axion relies on the coupling between the axions and the gauge fields. Here we consider an axion-like mechanism with coupling to the Nieh-Yan term. This interaction leads to the direct and efficient production of gravitational waves during the radiation-dominated era, originating from the tachyonic instability of the gravitational perturbations with the Nieh-Yan term. We calculate the energy spectral density of the chiral gravitational wave background and the comoving energy density of axion-like fields. Based on the numerical results, we explore the parameter space of axion masses and decay constants for detectable gravitational wave signals, either in pulsar timing arrays or space-based gravitational wave detections., Comment: 11 pages, 9 figures, 1 table