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Nonreciprocal superconductivity, also called as superconducting diode effect that spontaneously breaks time-reversal symmetry, is characterized by asymmetric critical currents under opposite applied current directions. This distinct state unveils a rich ore of intriguing physical properties, particularly in the realm of nanoscience application of superconductors. Towards the experimental realization of superconducting diode effect, the construction of two-dimensional heterostructures of magnets and $s$-wave superconductors is considered to be a promising pathway. In this study, we present our findings of superconducting diode effect manifested in the magnetic Mott insulator $\alpha$-RuCl$_3$. This phenomenon is induced by the proximity effect within a van der Waals heterostructure, consisting of thin $\alpha$-RuCl$_3$/NbSe$_2$ flakes. Through transport property measurements, we have confirmed a weak superconducting gap of 0.2 meV, which is significantly lower than the intrinsic gap of NbSe$_2$(1.2 meV). Upon the application of a weak magnetic field below 70 mT, we observed an asymmetry in the critical currents under positive and negative applied currents. This observation demonstrates a typical superconducting diode effect in the superconducting $\alpha$-RuCl$_3$. The superconducting diode effect and nonreciprocal resistance are observed exclusively when the magnetic field is aligned out-of-plane. This suggests that an Ising-type spin-orbit coupling in the superconducting $\alpha$-RuCl$_3$ may be responsible for the mechanism. Our findings furnish a platform for the exploration of superconducting diode effect via the artificial construction of heterostructures.

We study cluster structures on spinor helicity and momentum twistor varieties which describe the kinematic spaces of massless scattering (with or without dual conformal symmetry). Both are instances of partial flag varieties. We exhibit embeddings of the corresponding cluster algebras into cluster algebras of sufficiently large Grassmannians and show how the former is obtained from the latter by freezing certain cluster variables and specializing some cluster variables to one., Comment: 26 pages, 9 figures, comments are welcome

Federated Learning (FL) aims to train a shared model using data and computation power on distributed agents coordinated by a central server. Decentralized FL (DFL) utilizes local model exchange and aggregation between agents to reduce the communication and computation overheads on the central server. However, when agents are mobile, the communication opportunity between agents can be sporadic, largely hindering the convergence and accuracy of DFL. In this paper, we study delay-tolerant model spreading and aggregation enabled by model caching on mobile agents. Each agent stores not only its own model, but also models of agents encountered in the recent past. When two agents meet, they exchange their own models as well as the cached models. Local model aggregation works on all models in the cache. We theoretically analyze the convergence of DFL with cached models, explicitly taking into account the model staleness introduced by caching. We design and compare different model caching algorithms for different DFL and mobility scenarios. We conduct detailed case studies in a vehicular network to systematically investigate the interplay between agent mobility, cache staleness, and model convergence. In our experiments, cached DFL converges quickly, and significantly outperforms DFL without caching., Comment: 27 pages

The low elastic modulus of soft materials, combined with geometric nonlinearity and rate dependence, presents significant challenges in the characterization of their mechanical response. We introduce a novel method for measuring the mechanical properties of soft materials under large deformations via cylindrical cavity expansion. In this method, a cylindrical cavity is fabricated in the material and expanded by volume-controlled injection of an incompressible fluid with simultaneous measurement of the applied pressure at the cavity wall. The relationship between applied pressure and deformation at the cavity wall is then employed to characterize the nonlinear mechanical properties. We demonstrate the feasibility of the proposed method and validate it by measuring the mechanical properties of synthetic polydimethylsiloxane (PDMS) and comparing with reported values in the literature. Results indicate that the cylindrical cavitation method effectively captures the response of PDMS over a wide range of stiffness (shear modulus ranging from 5 kPa to 300 kPa) and exhibit high repeatability. The proposed method overcomes limitations in characterization of ultra-soft materials using traditional testing methods, such as challenges with fabrication and clamping in unaxial tension testing and friction and adhesion effects in compression and indentation testing, thus enabling accurate and precise characterization. It also offers improved accuracy and repeatability over other needle induced cavity expansion methods due to precise control over the initial cavity dimension and shape at the cost of increased invasiveness of testing.

Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both academia and industry due to their remarkable performance in various applications such as visual question answering, visual perception, understanding, and reasoning. Over the past few years, significant efforts have been made to examine MLLMs from multiple perspectives. This paper presents a comprehensive review of 200 benchmarks and evaluations for MLLMs, focusing on (1)perception and understanding, (2)cognition and reasoning, (3)specific domains, (4)key capabilities, and (5)other modalities. Finally, we discuss the limitations of the current evaluation methods for MLLMs and explore promising future directions. Our key argument is that evaluation should be regarded as a crucial discipline to support the development of MLLMs better. For more details, please visit our GitHub repository: https://github.com/swordlidev/Evaluation-Multimodal-LLMs-Survey.

We propose a feasible method for producing an ultrarelativistic kiloampere spin-polarized electron beam through a multi-GeV unpolarized dense electron beam grazing a solid target composed of two foil layers. In this scheme, a strong asymmetric plasma field is generated by plasma electron backflows between the two foils, leading to a significant radiation via the nonlinear Compton scatter. We show that by positioning the target at an appropriate tilt angle, the electrons emitting high-energy photons can gain high polarization and be reflected above the target. Moreover, the double-layer target setup not only strengthens the asymmetric plasma field but also induces a plasma bubble that further focuses the reflected electrons and reshape their polarization distribution. Our particle-in-cell simulations demonstrate the generation of a dense polarized electron beam with an average polarization exceeding 30% and a conversion rate greater than 10%. Through further selection of energy and angle, a picocoulomb electron beam with a current of approximately 0.4 kA and a polarization degree of 65% can be obtained at a energy peak of 1.5 GeV within 35$\times$35 mrad$^2$ angular spread and 10% energy spread. Such polarized electron beams have potential applications in high-energy physics and laboratory astrophysics.

In this paper, we present MooER, a LLM-based large-scale automatic speech recognition (ASR) / automatic speech translation (AST) model of Moore Threads. A 5000h pseudo labeled dataset containing open source and self collected speech data is used for training. We achieve performance comparable to other open source models trained with up to hundreds of thousands of hours of labeled speech data. Meanwhile, experiments conducted on Covost2 Zh2en testset suggest that our model outperforms other open source Speech LLMs. A BLEU score of 25.2 can be obtained. The main contributions of this paper are summarized as follows. First, this paper presents a training strategy for encoders and LLMs on speech related tasks (including ASR and AST) using a small size of pseudo labeled data without any extra manual annotation and selection. Second, we release our ASR and AST models and plan to open-source our training code and strategy in the near future. Moreover, a model trained on 8wh scale training data is planned to be released later on.

Visual Spatial Description (VSD) aims to generate texts that describe the spatial relationships between objects within images. Traditional visual spatial relationship classification (VSRC) methods typically output the spatial relationship between two objects in an image, often neglecting world knowledge and lacking general language capabilities. In this paper, we propose a Large Language-and-Vision Assistant for Visual Spatial Description, named LLaVA-VSD, which is designed for the classification, description, and open-ended description of visual spatial relationships. Specifically, the model first constructs a VSD instruction-following dataset using given figure-caption pairs for the three tasks. It then employs LoRA to fine-tune a Large Language and Vision Assistant for VSD, which has 13 billion parameters and supports high-resolution images. Finally, a large language model (Qwen-2) is used to refine the generated sentences, enhancing their diversity and accuracy. LLaVA-VSD demonstrates excellent multimodal conversational capabilities and can follow open-ended instructions to assist with inquiries about object relationships in images., Comment: We have discovered a significant error in the paper that affects the main conclusions. To ensure the accuracy of our research, we have decided to withdraw this paper and will resubmit it after making the necessary corrections

Investigating vortex electron interactions with electromagnetic fields is essential for advancing particle acceleration techniques, scattering theory in background fields, and obtaining novel electron beams for material diagnostics. A systematic investigation into the dynamics of vortex electrons in external laser fields and the exploration of laser-induced vortex modes remains lacking. In this work, we study the propagation of vortex electrons in linearly polarized (LP) and circularly polarized (CP) laser pulses, both separately and in their combined form in two-mode laser pulses. The theoretical formalism is developed by utilizing Volkov-Bessel wave functions, and the four-current density is obtained as a crucial observable quantity. Numerical results illustrate the dynamics of vortex electrons in external lasers, showing that the beam center of the vortex electron follows the classical motion of a point charge electron, while maintaining the probability distribution structure for both vortex eigenstates and superposition modes. The combined effect of LP and CP laser pulses in the two-mode laser field allows for the versatile control of vortex electrons, which is absent with LP or CP lasers alone, at femtosecond and sub-nanometer scales. Our findings demonstrate the versatile control over vortex electrons via laser pulses, with our formalism providing a reference for vortex scattering in laser backgrounds and inspiring the laser-controlled achievement of novel vortex modes as targeted diagnostic probes for specialized materials., Comment: 4 figures

Robotic datasets are important for scientific benchmarking and developing algorithms, for example for Simultaneous Localization and Mapping (SLAM). Modern robotic datasets feature video data of high resolution and high framerates. Storing and sharing those datasets becomes thus very costly, especially if more than one camera is used for the datasets. It is thus essential to store this video data in a compressed format. This paper investigates the use of modern video encoders for robotic datasets. We provide a software that can replay mp4 videos within ROS 1 and ROS 2 frameworks, supporting the synchronized playback in simulated time. Furthermore, the paper evaluates different encoders and their settings to find optimal configurations in terms of resulting size, quality and encoding time. Through this work we show that it is possible to store and share even highest quality video datasets within reasonable storage constraints.

In the e-commerce realm, compelling advertising images are pivotal for attracting customer attention. While generative models automate image generation, they often produce substandard images that may mislead customers and require significant labor costs to inspect. This paper delves into increasing the rate of available generated images. We first introduce a multi-modal Reliable Feedback Network (RFNet) to automatically inspect the generated images. Combining the RFNet into a recurrent process, Recurrent Generation, results in a higher number of available advertising images. To further enhance production efficiency, we fine-tune diffusion models with an innovative Consistent Condition regularization utilizing the feedback from RFNet (RFFT). This results in a remarkable increase in the available rate of generated images, reducing the number of attempts in Recurrent Generation, and providing a highly efficient production process without sacrificing visual appeal. We also construct a Reliable Feedback 1 Million (RF1M) dataset which comprises over one million generated advertising images annotated by human, which helps to train RFNet to accurately assess the availability of generated images and faithfully reflect the human feedback. Generally speaking, our approach offers a reliable solution for advertising image generation., Comment: ECCV2024

Motivated by very recent experimental observations of the 1/9 magnetization plateaus in YCu$_3$(OH)$_{6+x}$Br$_{3-x}$ and YCu$_3$(OD)$_{6+x}$Br$_{3-x}$, our study delves into the magnetic field-induced phase transitions in the nearest-neighbor antiferromagnetic Heisenberg model on the kagome lattice using the variational Monte Carlo technique. We uncover a phase transition from a zero-field Dirac spin liquid to a field-induced magnetically disordered phase that exhibits the 1/9 magnetization plateau. Through a comprehensive analysis encompassing the magnetization distribution, spin correlations, chiral order parameter, topological entanglement entropy, ground-state degeneracy, Chern number and excitation spectrum, we pinpoint the phase associated with this magnetization plateau as a chiral $\mathbb{Z}_3$ topological quantum spin liquid and elucidate its diverse physical properties., Comment: Accepted by Phys. Rev. Lett

We investigate the spin-$\frac{1}{2}$ antiferromagnetic Heisenberg model with a Dzyaloshinskii-Moriya interaction on kagome lattice, making use of the variational Monte Carlo technique. An exotic quantum spin state is found to arise from a melting of the $\boldsymbol{Q} = 0$ long-range magnetic order by a topological transition, when a small anisotropic third nearest-neighbor antiferromagnetic Heisenberg interaction is turned on. This novel state is a gapped quantum spin liquid, characterized by a topological order with ground-state degeneracy $n_g = 4$ and topological entanglement entropy $\gamma = \ln 2$, suggesting it is an Abelian topological phase. Furthermore, the Chern numbers of the spin-up (-down) spinon occupied bands of this state are $C_{\uparrow \downarrow} = \pm 1$, respectively. From this perspective, this state is also a time-reversal symmetric (total Chern number $C_{total} = 0$) topological insulator with spinons as the chiral edge states, which carry opposite spin and move in the opposite direction. It is analogous to the quantum spin Hall state but the spin current is carried by deconfined spinons in a quantum spin liquid, so is dubbed as the spinon quantum spin Hall state.

IGR J17091-3624, in addition to GRS 1915+105, is the only black hole X-ray binary that displays ``heartbeat"-like variability characterized by structured flares with high amplitudes. In this study, we conduct a detailed phase-resolved analysis of the recently identified ``heartbeat"-like Class X variability in IGR J17091-3624 during its 2022 outburst, utilizing data from NICER and NuSTAR observations. A shortage in the high-energy (>20 keV) X-ray flux is detected at peak phases of the soft X-ray flare at a ~15 sigma confidence level from the phase-folded light curves. Furthermore, our phase-resolved spectral analysis reveals variations in the spectral shape, particularly showing significant synchronous variations in the disk temperature and flux with the count rate. These findings imply that the flare is primarily driven by instabilities within the accretion disk, consistent with previous studies on the well-known Class rho variability in GRS 1915+105. However, we also observe a positive correlation between the disk temperature and flux over the flare cycle, which differs from a loop relation between the two parameters found in the Class rho variability. This could suggest differences in underlying physical processes between the two variability classes. Variations in the Componization component during flares are also observed: the electron temperature and covering fraction show anti-correlations with the disk flux, revealing potential interactions between the accretion disk and the corona during these flares., Comment: Accepted for publication in The Astrophysical Journal

Low-frequency quasi-periodic oscillations (LFQPOs) are commonly observed in X-ray light curves of black hole X-ray binaries (BHXRBs); however, their origin remains a topic of debate. In order to thoroughly investigate variations in spectral properties on the QPO timescale, we utilized the Hilbert-Huang transform technique to conduct phase-resolved spectroscopy across a broad energy band for LFQPOs in the newly discovered BHXRB Swift J1727.8-1613. This is achieved through quasi-simultaneous observations from Neutron star Interior Composition ExploreR (NICER), Nuclear Spectroscopic Telescope ARray (NuSTAR), and Hard X-ray Modulation Telescope (Insight-HXMT). Our analysis reveals that both the non-thermal and disk-blackbody components exhibit variations on the QPO timescale, with the former dominating the QPO variability. For the spectral parameters, we observe modulation of the disk temperature, spectral indices, and reflection fraction with the QPO phase with high statistical significance (>5\sigma). Notably, the variation in the disk temperature is found to precede the variations in the non-thermal and disk fluxes by ~0.4-0.5 QPO cycles. We suggest that these findings offer further evidence that the type-C QPO variability is a result of geometric effects of the accretion flow., Comment: Accepted for pulication in The Astrophysical Journal

We investigate the nonlinear magneto-optical response in non-centrosymmetric magnetic Weyl semimetals featuring a quadratic tilt, focusing particularly on the influence of the van Hove singularity (VHS). In the absence of a magnetic field, the second-order nonlinear Drude conductivity components exhibit inflection or dip behavior across the VHS. In contrast, the second-order nonlinear anomalous Hall conductivity, primarily governed by the Berry curvature dipole, manifests a subtle plateau-like structure. As the tilt strength increases, the VHS energy escalates, thereby amplifying the VHS-induced characteristics within these second-order conductivity components. However, in the presence of a magnetic field, we show that the resultant magnetic moment suppresses nonlinear electron transport while enhancing nonlinear hole transport. %both suppresses and notably enhances nonlinear magnetic-optical transport in the electron and hole regions, respectively. This effect serves to mitigate the impact of the VHS, resulting specifically in an asymmetric peak or a kinked-like structure in the magnetic field-induced contribution to the second-order nonlinear conductivity near the Weyl nodes. These findings provide new insights into the intricate interplay among the VHS, Berry curvature, and magnetic moment in nonlinear magneto-optical transport through non-centrosymmetric magnetic Weyl semimetals., Comment: 10 pages, 5 figures. Accepted for publication in Phys. Rev. B

In this paper, a third-order time adaptive algorithm with less computation, low complexity is provided for shale reservoir model based on coupled fluid flow with porous media flow. The algorithm combines the three-step linear time filters method for simple post-processing and the second-order backward differential formula (BDF2), is third-order accurate and provides, at no extra computational complexity. At the same time, the time filter method can also be used to damp non-physical oscillations inherent in the BDF2 method, ensuring stability. We proves the variable time stepsize second-order backward differential formula plus time filter (BDF2-TF) algorithm's stability and the convergence properties of the fluid velocity u and hydraulic head $\phi$ in the $L^2$ norm with an order of $O(k_{n+1}^3 + h^3)$. In the experiments, the adaptive algorithm automatically adjusts the time step in response to the varying characteristics of different models, ensuring that errors are maintained within acceptable limits. This algorithm addresses the issue that high-order algorithms may select inappropriate time steps, resulting in instability or reduced precision of the numerical solution, thereby enhancing calculation accuracy and efficiency. We perform three-dimensional numerical experiments to verify the BDF2-TF algorithm's effectiveness, stability, and third-order convergence. Simultaneously, a simplified model is employed to simulate the process of shale oil extraction from reservoirs, further demonstrating the algorithm's practical applicability.

Semantic communication (SemCom) has been a transformative paradigm, emphasizing the precise exchange of meaningful information over traditional bit-level transmissions. However, existing SemCom research, primarily centered on simplified scenarios like single-pair transmissions with direct wireless links, faces significant challenges when applied to real-world radio access networks (RANs). This article introduces a Semantic-aware Radio Access Network (S-RAN), offering a holistic systematic view of SemCom beyond single-pair transmissions. We begin by outlining the S-RAN architecture, introducing new physical components and logical functions along with key design challenges. We then present transceiver design for end-to-end transmission to overcome conventional SemCom transceiver limitations, including static channel conditions, oversimplified background knowledge models, and hardware constraints. Later, we delve into the discussion on radio resource management for multiple users, covering semantic channel modeling, performance metrics, resource management algorithms, and a case study, to elaborate distinctions from resource management for legacy RANs. Finally, we highlight open research challenges and potential solutions. The objective of this article is to serve as a basis for advancing SemCom research into practical wireless systems.

Large Language Models (LLMs) have demonstrated exceptional proficiency in mathematical reasoning tasks due to their extensive parameter counts and training on vast datasets. Despite these capabilities, deploying LLMs is hindered by their computational demands. Distilling LLM mathematical reasoning into Smaller Language Models (SLMs) has emerged as a solution to this challenge, although these smaller models often suffer from errors in calculation and semantic understanding. Prior work has proposed Program-of-Thought Distillation (PoTD) to avoid calculation error. To further address semantic understanding errors, we propose Key-Point-Driven Mathematical Reasoning Distillation (KPDD). KPDD enhances the reasoning performance of SLMs by breaking down the problem-solving process into three stages: Core Question Extraction, Problem-Solving Information Extraction, and Step-by-Step Solution. This method is further divided into KPDD-CoT, which generates Chain-of-Thought rationales, and KPDD-PoT, which creates Program-of-Thought rationales. The experiment results show that KPDD-CoT significantly improves reasoning abilities, while KPDD-PoT achieves state-of-the-art performance in mathematical reasoning tasks. Our approach effectively mitigates misunderstanding errors, advancing the deployment of efficient and capable SLMs., Comment: Modify the description error in the experiment settings, i.e., the teacher LLM changes deepseek-v2 from GPT-4

We obtain a trichotomy for the orbits of a hypercyclic operator $T$ on a separable Banach space $X$: (1) every vector is mean asymptotic to zero; (2) generic vectors are absolutely mean irregular; (3) every hypercyclic vector is mean divergent to infinity. Examples of weighted backward shifts on $\ell^p$ show that all three cases can happen., Comment: 12 papers,to appear in Proc. Amer. Math. Soc

Fine-tuning large-scale pretrained models is prohibitively expensive in terms of computational and memory costs. LoRA, as one of the most popular Parameter-Efficient Fine-Tuning (PEFT) methods, offers a cost-effective alternative by fine-tuning an auxiliary low-rank model that has significantly fewer parameters. Although LoRA reduces the computational and memory requirements significantly at each iteration, extensive empirical evidence indicates that it converges at a considerably slower rate compared to full fine-tuning, ultimately leading to increased overall compute and often worse test performance. In our paper, we perform an in-depth investigation of the initialization method of LoRA and show that careful initialization (without any change of the architecture and the training algorithm) can significantly enhance both efficiency and performance. In particular, we introduce a novel initialization method, LoRA-GA (Low Rank Adaptation with Gradient Approximation), which aligns the gradients of low-rank matrix product with those of full fine-tuning at the first step. Our extensive experiments demonstrate that LoRA-GA achieves a convergence rate comparable to that of full fine-tuning (hence being significantly faster than vanilla LoRA as well as various recent improvements) while simultaneously attaining comparable or even better performance. For example, on the subset of the GLUE dataset with T5-Base, LoRA-GA outperforms LoRA by 5.69% on average. On larger models such as Llama 2-7B, LoRA-GA shows performance improvements of 0.34, 11.52%, and 5.05% on MT-bench, GSM8K, and Human-eval, respectively. Additionally, we observe up to 2-4 times convergence speed improvement compared to vanilla LoRA, validating its effectiveness in accelerating convergence and enhancing model performance. Code is available at https://github.com/Outsider565/LoRA-GA.

Polymer-related materials exhibit rich glassy behaviors at different length scales due to their various molecular structures and topological constraints. Recent studies have identified transient interpenetration of the long-chain rings contributing to dynamic arrest on the center-of-mass level. Interpenetration of rings is proposed as an approach to facilitate glass formation in polymer melts. In this work, inspired by recent advances in the synthesis of mechanically interlocked polymers, we investigate glass transition on the nanometer-scale segments influenced by permanent interpenetration of rings using molecular dynamics simulations. We find that decreasing chain length in the mechanically interlocked system is equivalent to inducing an effective chain stiffness on the sub-rings. The induced stiffness provides a unified explanation for these unique structural features and transient dynamic arrest in the system of interlocked rings with rather short chains. Further, a crossover is observed in the scaling relation between localization and glassy depth upon cooling. Our work reveals a dynamic transition from weak to strong caging at the crossover temperature. According to the localization model, we demonstrate that the chain stiffness increases the critical temperature and oscillation distance, therefore leads to more fragile dynamics and deeper glassy state. These findings are consistent with the predictions of molecular simulations and theories for polymers with real local stiffness. Our work deepens the understanding of the role of induced stiffness on glass transition, and opens up a new direction to design rich glass materials by manipulating stiffness through mechanical bonds., Comment: 35 pages, 8 figures

6-DoF grasp detection of small-scale grasps is crucial for robots to perform specific tasks. This paper focuses on enhancing the recognition capability of small-scale grasping, aiming to improve the overall accuracy of grasping prediction results and the generalization ability of the network. We propose an enhanced receptive field method that includes a multi-radii cylinder grouping module and a passive attention module. This method enhances the receptive field area within the graspable space and strengthens the learning of graspable features. Additionally, we design a graspable balance sampling module based on a segmentation network, which enables the network to focus on features of small objects, thereby improving the recognition capability of small-scale grasping. Our network achieves state-of-the-art performance on the GraspNet-1Billion dataset, with an overall improvement of approximately 10% in average precision@k (AP). Furthermore, we deployed our grasp detection model in pybullet grasping platform, which validates the effectiveness of our method.

This study evaluates whether large language models (LLMs) exhibit biases towards medical professionals. Fictitious candidate resumes were created to control for identity factors while maintaining consistent qualifications. Three LLMs (GPT-4, Claude-3-haiku, and Mistral-Large) were tested using a standardized prompt to evaluate resumes for specific residency programs. Explicit bias was tested by changing gender and race information, while implicit bias was tested by changing names while hiding race and gender. Physician data from the Association of American Medical Colleges was used to compare with real-world demographics. 900,000 resumes were evaluated. All LLMs exhibited significant gender and racial biases across medical specialties. Gender preferences varied, favoring male candidates in surgery and orthopedics, while preferring females in dermatology, family medicine, obstetrics and gynecology, pediatrics, and psychiatry. Claude-3 and Mistral-Large generally favored Asian candidates, while GPT-4 preferred Black and Hispanic candidates in several specialties. Tests revealed strong preferences towards Hispanic females and Asian males in various specialties. Compared to real-world data, LLMs consistently chose higher proportions of female and underrepresented racial candidates than their actual representation in the medical workforce. GPT-4, Claude-3, and Mistral-Large showed significant gender and racial biases when evaluating medical professionals for residency selection. These findings highlight the potential for LLMs to perpetuate biases and compromise healthcare workforce diversity if used without proper bias mitigation strategies., Comment: 36 pages, 6 figures

Let $(\mathbf{U}, \mathbf{U}^\imath)$ be a split affine quantum symmetric pair of type $\mathsf{B}_n^{(1)}, \mathsf{C}_n^{(1)}$ or $\mathsf{D}_n^{(1)}$. We prove factorization and coproduct formulae for the Drinfeld-Cartan operators $\Theta_i(z)$ in the Lu-Wang Drinfeld-type presentation, generalizing the type $\mathsf{A}_n^{(1)}$ result from [Prz23]. As an application, we show that a boundary analogue of the $q$-character map, defined via the spectra of these operators, is compatible with the usual $q$-character map. As an auxiliary result, we also produce explicit reduced expressions for the fundamental weights in the extended affine Weyl groups of classical types., Comment: 38 pages

Let $G$ be a countable infinite discrete group. We show that a subset $F$ of $G$ contains a return time set of some piecewise syndetic recurrent point $x$ in a compact Hausdorff space $X$ with a $G$-action if and only if $F$ is a quasi-central set. As an application, we show that if a nonempty closed subsemigroup $S$ of the Stone-\v{C}ech compactification $\beta G$ contains the smallest ideal $K(\beta G)$ of $\beta G$ then $S$-product recurrent is equivalent to distality, which partially answers a question of Auslander and Furstenberg (Trans. Amer. Math. Soc. 343, 1994, 221--232)., Comment: 22 pages

This report presents our team's 'PCIE_EgoHandPose' solution for the EgoExo4D Hand Pose Challenge at CVPR2024. The main goal of the challenge is to accurately estimate hand poses, which involve 21 3D joints, using an RGB egocentric video image provided for the task. This task is particularly challenging due to the subtle movements and occlusions. To handle the complexity of the task, we propose the Hand Pose Vision Transformer (HP-ViT). The HP-ViT comprises a ViT backbone and transformer head to estimate joint positions in 3D, utilizing MPJPE and RLE loss function. Our approach achieved the 1st position in the Hand Pose challenge with 25.51 MPJPE and 8.49 PA-MPJPE. Code is available at https://github.com/KanokphanL/PCIE_EgoHandPose

Twisted transition metal dichalcogenides are a new platform for realizing strongly correlated physics with high tunability. Recent transport experiments have reported the realization of a Mott insulator, its bandwidth-driven evolution to a metal, and the strange metal behavior in proximity to the transition via the tuning of a displacement field in twisted $\mathrm{WSe_2}$($\mathrm{tWSe_2}$) fixed at half filling. However, the nature of the correlated states and the related Mott physics involved in the whole process remain to be determined. Here, we unveil theoretically the evolution of the ground state of the half-filled $\boldsymbol{\mathrm{moir\acute{e}}}$ Hubbard model as applied to $\mathrm{tWSe_2}$, transiting from a pseudogap state with Fermi arcs to a 120$^\circ$ Ne$\boldsymbol{\acute{\mathrm{e}}}$l ordered Mott insulator, then to another pseudogap state with Fermi pockets, and eventually to a Fermi liquid via a Lifshitz transition. The pseudogap phases are definitively identified by the vanishing of quasiparticle weights over an extended region of the free Fermi surface, with the remaining parts forming disconnected Fermi arcs or pockets that contain well-defined quasiparticles. The loss of quasiparticle weights in the pseudogap phases indicates the breakdown of Landau's Fermi liquid theory. We demonstrate that the emergence of the Fermi arcs/pockets results from the electronic structure reconstruction driven by strong electron correlations, marked by the existence of Luttinger surface enclosed by the zeros of single-particle Green's function. This work reveals the fundamental aspects of the Mottness in $\boldsymbol{\mathrm{moir\acute{e}}}$ systems and will stimulate the advance of the direct probes of the Fermi surface and quasiparticles beyond transports, such as angle-resolved photoemission spectroscopy (ARPES) and scanning tunnelling microscopy (STM) experiments., Comment: 8+11 pages, 4+8 figures

Multimodal Large Language Models (MLLMs) have exhibited impressive capabilities in visual understanding and reasoning, providing sightly reasonable answers, such as image descriptions. This has spurred extensive research on the evaluation of MLLMs. Most evaluation benchmarks assume that incorrect answers indicate a lack of understanding of the visual content. However, our findings reveal that, in many cases, MLLMs answer questions incorrectly despite correctly understanding the visual content. This suggests that incorrect answers do not necessarily imply a lack of comprehension but may instead result from lacking robustness to leading questions. To comprehensively measure MLLMs' understanding capability and robustness to leading questions, we introduce a MultiModal Robustness benchmark (MMR). MMR contains paired positive and negative questions across 12 categories, meticulously annotated by humans. We evaluate 18 leading MLLMs on the MMB benchmark, revealing that MLLMs suffer from fragility to leading questions despite understanding the visual content. To enhance MLLMs' understanding capability and robustness, we further present a training set with paired positive and negative visual question-answer samples. Experiments verify that MLLMs' robustness can be significantly enhanced by tuning on this new training set. The benchmark, training set, and code can be found at https://github.com/BAAI-DCAI/Multimodal-Robustness-Benchmark.

As a robust and large-scale multilingual speech recognition model, Whisper has demonstrated impressive results in many low-resource and out-of-distribution scenarios. However, its encoder-decoder structure hinders its application to streaming speech recognition. In this paper, we introduce Simul-Whisper, which uses the time alignment embedded in Whisper's cross-attention to guide auto-regressive decoding and achieve chunk-based streaming ASR without any fine-tuning of the pre-trained model. Furthermore, we observe the negative effect of the truncated words at the chunk boundaries on the decoding results and propose an integrate-and-fire-based truncation detection model to address this issue. Experiments on multiple languages and Whisper architectures show that Simul-Whisper achieves an average absolute word error rate degradation of only 1.46% at a chunk size of 1 second, which significantly outperforms the current state-of-the-art baseline., Comment: Accepted by INTERSPEECH 2024

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

Millimeter-wave (mmWave) radars are indispensable for perception tasks of autonomous vehicles, thanks to their resilience in challenging weather conditions. Yet, their deployment is often limited by insufficient spatial resolution for precise semantic scene interpretation. Classical super-resolution techniques adapted from optical imaging inadequately address the distinct characteristics of radar signal data. In response, our study redefines radar imaging super-resolution as a one-dimensional (1D) signal super-resolution spectra estimation problem by harnessing the radar signal processing domain knowledge, introducing innovative data normalization and a domain-informed signal-to-noise ratio (SNR)-guided loss function. Our tailored deep learning network for automotive radar imaging exhibits remarkable scalability, parameter efficiency and fast inference speed, alongside enhanced performance in terms of radar imaging quality and resolution. Extensive testing confirms that our SR-SPECNet sets a new benchmark in producing high-resolution radar range-azimuth images, outperforming existing methods across varied antenna configurations and dataset sizes. Source code and new radar dataset will be made publicly available online.

Giant resonances (GRs) provide crucial insights into nuclear physics and astrophysics. Exciting GRs using particles like electrons is effective, yet the angular momentum (AM) transfer of electrons, including both intrinsic spin and orbital degrees of freedom in inelastic scattering, has never been studied. Here, we investigate AM transfer in GRs excited by plane-wave and vortex electrons, developing a comprehensive AM-resolved inelastic electron scattering theory. We find that even plane-wave electrons can model-independently extract transition strengths of higher multipolarity by selecting specific AM states of scattered electrons. Additionally, relativistic vortex electrons with orbital angular momentum (OAM) $\pm1$ can be efficiently generated. Vortex electrons can also be used to extract GR transition strength as in the plane-wave case, regardless of the position of nucleus relative to the beam axis. Furthermore, relativistic vortex electrons with larger OAM can be generated for on-axis nuclei due to AM conservation. Our method offers new perspectives for nuclear structure research and paves the way for generating vortex particles.

We theoretically investigate the RKKY interaction in helical higher-order topological insulators (HOTIs), revealing distinct behaviors mediated by hinge and Dirac-type bulk carriers. Our findings show that hinge-mediated interactions consist of Heisenberg, Ising, and Dzyaloshinskii-Moriya (DM) terms, exhibiting a decay with impurity spacing z and oscillations with Fermi energy {\epsilon}F . These interactions demonstrate ferromagnetic behaviors for the Heisenberg and Ising terms and alternating behavior for the DM term. In contrast, bulk-mediated interactions include Heisenberg, twisted Ising, and DM terms, with a conventional cubic oscillating decay. This study highlights the nuanced interplay between hinge and bulk RKKY interactions in HOTIs, offering insights into the design of next-generation quantum devices based on the HOTIs., Comment: 8 pages, 5 figures

The prediction of formation resistivity plays a crucial role in the evaluation of oil and gas reservoirs, identification and assessment of geothermal energy resources, groundwater detection and monitoring, and carbon capture and storage. However, traditional well logging techniques fail to measure accurate resistivity in cased boreholes, and the transient electromagnetic method for cased borehole resistivity logging encounters challenges of high-frequency disaster (the problem of inadequate learning by neural networks in high-frequency features) and noise interference, badly affecting accuracy. To address these challenges, frequency-aware framework and temporal anti-noise block are proposed to build frequency aware LSTM (FAL). The frequency-aware framework implements a dual-stream structure through wavelet transformation, allowing the neural network to simultaneously handle high-frequency and low-frequency flows of time-series data, thus avoiding high-frequency disaster. The temporal anti-noise block integrates multiple attention mechanisms and soft-threshold attention mechanisms, enabling the model to better distinguish noise from redundant features. Ablation experiments demonstrate that the frequency-aware framework and temporal anti-noise block contribute significantly to performance improvement. FAL achieves a 24.3% improvement in R2 over LSTM, reaching the highest value of 0.91 among all models. In robustness experiments, the impact of noise on FAL is approximately 1/8 of the baseline, confirming the noise resistance of FAL. The proposed FAL effectively reduces noise interference in predicting formation resistivity from cased transient electromagnetic well logging curves, better learns high-frequency features, and thereby enhances the prediction accuracy and noise resistance of the neural network model.

We investigate kinetic energy properties of an irrotational deep-water Stokes wave. By introducing a conformal hodograph transform, we perform complex analysis in the new variables, and show that the existence of the streamline time-period for deep-water Stokes waves. Relying on the conformal mappings, we prove some qualitative results about the kinetic energy such as the convexity property of integral means of kinetic energy, the streamline time-period being non-increasing and independent of initial data and the monotonicity and logarithmic convexity of the total kinetic energy over a streamline time-period. Moreover, taking advantage of the non-increasing nature of the streamline time-period, we show that the streamline time-period strictly larger than the wave period, which suggests the fact that there are no closed paths for all particles in a deep-water Stokes flow and the drift of any streamline is positive. These results allow us to quantify the observation that the kinetic energy and the streamline time-period are larger near the free surface and decrease with increasing the depth., Comment: arXiv admin note: text overlap with arXiv:2405.02554. arXiv admin note: text overlap with arXiv:2405.02554

The aim of this work is to build a theory of homomorphisms of Hecke and Artin monoids with possible applications to representation theory. Our original motivation was the striking observation that parabolic projections of Hecke monoids respect all parabolic elements. We found other classes of homomorphisms of Hecke monoids with the same property and discovered that many of them lift to homomorphisms of covering Artin monoids with a similar property. It turned out that they belong to a much larger class (in fact, a category) of homomorphisms of Artin monoids, most of which appear to be new., Comment: AMSLaTeX, 112 pages

In slowly driven classical systems, work is a stochastic quantity and its probability distribution is known to satisfy the work fluctuation-dissipation relation, which states that the mean and variance of the dissipated work are linearly related. Recently, it was shown that generation of quantum coherence in the instantaneous energy eigenbasis leads to a correction to this linear relation in the slow-driving regime. Here, we go even further by investigating nonclassical features of work fluctuations in setups with more than one system. To do this, we first generalize slow control protocols to encompass multipartite systems, allowing for the generation of quantum correlations during the driving process. Then, focussing on two-qubit systems, we show that entanglement generation leads to a positive contribution to the dissipated work, which is distinct from the quantum correction due to local coherence generation known from previous work. Our results show that entanglement generated during slow control protocols, e.g. as an unavoidable consequence of qubit crosstalk, comes at the cost of increased dissipation.

Mesoscopic superconducting islands hosting Majorana zero modes (MZMs), or Majorana islands in short, offer a prototype of topological qubits. In this work we investigate theoretically the model of a generic Majorana island tunneling-coupled to a single-piece metallic substrate, hence an \textit{embedded Majorana island}. We show the crucial consequences of an interplay between the topological ground states nonlocally addressed by the MZMs and the metallic bath with coherent electron propagation: on the one hand, the topological degeneracy on the Majorana island can be preserved, by virtue of the particle-hole symmetry, despite the apparent bath-induced coupling between MZMs; on the other hand, the electronic interference in the metallic bath may lead to profound alterations to the renormalization group behavior of the hybrid system towards low energy/temperature compared with conventional Kondo physics. This work serves to establish the model of embedded Majorana islands as an experimentally relevant and theoretically intriguing problem particularly in the direction of topological quantum computation., Comment: 11 pages, 3 figures

The KM2A is the largest sub-array of the Large High Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic ray and gamma-ray showers. This information is used for physical analysis in gamma-ray astronomy and cosmic ray physics. To ensure the reliability of the LHAASO-KM2A data, a three-level quality control system has been established. It is used to monitor the status of detector units, stability of reconstructed parameters and the performance of the array based on observations of the Crab Nebula and Moon shadow. This paper will introduce the control system and its application on the LHAASO-KM2A data collected from August 2021 to July 2023. During this period, the pointing and angular resolution of the array were stable. From the observations of the Moon shadow and Crab Nebula, the results achieved using the two methods are consistent with each other. According to the observation of the Crab Nebula at energies from 25 TeV to 100 TeV, the time averaged pointing errors are estimated to be $-0.003^{\circ} \pm 0.005^{\circ}$ and $0.001^{\circ} \pm 0.006^{\circ}$ in the R.A. and Dec directions, respectively., Comment: 15 pages, 9 figures

Compact spin-polarized positron accelerators play a major role in promoting significant positron application research, which typically require high acceleration gradients and polarization degree, both of which, however, are still great challenging. Here, we put forward a novel spin-polarized positron acceleration method which employs an ultrarelativistic high-density electron beam passing through any hole of multi-layer microhole array films to excite strong electrostatic and transition radiation fields. Positrons in the polarized electron-positron pair plasma, filled in the front of the multi-layer films, can be captured, accelerated, and focused by the electrostatic and transition radiation fields, while maintaining high polarization of above 90% and high acceleration gradient of about TeV/m. Multi-layer design allows for capturing more positrons and achieving cascade acceleration. Our method offers a promising solution for accelerator miniaturization, positron injection, and polarization maintaining, and also can be used to accelerate other charged particles.

In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning. However, the extensive model size and high training and inference costs have hindered the widespread application of MLLMs in academia and industry. Thus, studying efficient and lightweight MLLMs has enormous potential, especially in edge computing scenarios. In this survey, we provide a comprehensive and systematic review of the current state of efficient MLLMs. Specifically, we summarize the timeline of representative efficient MLLMs, research state of efficient structures and strategies, and the applications. Finally, we discuss the limitations of current efficient MLLM research and promising future directions. Please refer to our GitHub repository for more details: https://github.com/lijiannuist/Efficient-Multimodal-LLMs-Survey.

Relativistic spin-polarized (SP) lepton beams are important for investigating spin-dependent interaction processes. In particular, spatially structured spin-polarized (SSP) lepton beams may find new applications in material, atomic, nuclear, high-energy physics and new physics beyond the Standard Model. However, realizing ultrafast generation and spin-manipulation of relativistic SSP lepton beams pose significant challenges. Here, we put forward a novel method of ultrafast (picosecond-timescale) generation of a relativistic SSP lepton beam via employing a moderate terahertz (THz) wave in a dielectric-lined waveguide (DWL). We first find that lepton beams with customizable spin-polarization structures can be generated by utilizing different electromagnetic modes, and optimizing the lepton velocity and THz phase velocity can improve efficiency of spin-manipulation and visibility of the SP structure. These SSP beams play a profound role in studying magnetic effects in material physics, chiral-selective chemistry, generation of structured $\gamma$-rays, etc., and open a new avenue for research on relativistic SP particles.

The first source catalog of Large High Altitude Air Shower Observatory reported the detection of a very-high-energy gamma ray source, 1LHAASO J1219+2915. In this paper a further detailed study of the spectral and temporal behavior of this point-like source have been carried. The best-fit position of the TeV source ($\rm{RA}=185.05^{\circ}\pm0.04^{\circ}$, $\rm{Dec}=29.25^{\circ}\pm0.03^{\circ}$) is compatible with NGC 4278 within $\sim0.03$ degree. Variation analysis shows an indication of the variability at a few months level in the TeV band, which is consistent with low frequency observations. Based on these observations, we report the detection of TeV $\gamma$-ray emissions from this low-luminosity AGN NGC 4278. The observations by LHAASO-WCDA during active period has a significance level of 8.8\,$\sigma$ with best-fit photon spectral index $\varGamma=2.56\pm0.14$ and a flux $f_{1-10\,\rm{TeV}}=(7.0\pm1.1_{\rm{sta}}\pm0.35_{\rm{syst}})\times10^{-13}\,\rm{photons\,cm^{-2}\,s^{-1}}$, or approximately $5\%$ of the Crab Nebula. The discovery of VHE from NGC 4278 indicates that the compact, weak radio jet can efficiently accelerate particles and emit TeV photons., Comment: 11 pages, 5 figures

Polarized attosecond $\gamma-$rays may offer excitation and hyperfine tracking of reactions relevant to nuclear physics, astrophysics, high-energy physics, etc. However, unfortunately, generation of a feasible and easy-to-deploy source is still a great challenge. Here, we put forward a novel method for producing ultra-collimated high-brilliance polarized attosecond $\gamma-$rays via the interaction of an unpolarized electron beam with a solid-density plasma. As a relativistic electron beam enters a solid-density plasma, it can be modulated into high-density clusters via the self-modulation instability of itself and further into attosecond slices due to its own hosing instability. This is accompanied by the generation of similar pulse-width $\gamma-$slices via nonlinear Compton scattering. The severe hosing instability breaks the symmetry of the excited electromagnetic fields, resulting in net linear polarization of $\gamma-$slices, which challenges the conventional perception that the interaction of an axially symmetric unpolarized electron beam with a uniform plasma cannot generate polarized radiation. In addition, we also obtain high-quality electron microbunches which may serve as an alternative source for prebunched free-electron lasers.

Recent advancements in Deep Learning (DL) for Direction of Arrival (DOA) estimation have highlighted its superiority over traditional methods, offering faster inference, enhanced super-resolution, and robust performance in low Signal-to-Noise Ratio (SNR) environments. Despite these advancements, existing research predominantly focuses on multi-snapshot scenarios, a limitation in the context of automotive radar systems which demand high angular resolution and often rely on limited snapshots, sometimes as scarce as a single snapshot. Furthermore, the increasing interest in sparse arrays for automotive radar, owing to their cost-effectiveness and reduced antenna element coupling, presents additional challenges including susceptibility to random sensor failures. This paper introduces a pioneering DL framework featuring a sparse signal augmentation layer, meticulously crafted to bolster single snapshot DOA estimation across diverse sparse array setups and amidst antenna failures. To our best knowledge, this is the first work to tackle this issue. Our approach improves the adaptability of deep learning techniques to overcome the unique difficulties posed by sparse arrays with single snapshot. We conduct thorough evaluations of our network's performance using simulated and real-world data, showcasing the efficacy and real-world viability of our proposed solution. The code and real-world dataset employed in this study are available at https://github.com/ruxinzh/Deep_RSA_DOA., Comment: Invited paper for IEEE Asilomar conference 2024

In this paper, we investigate kinetic energy and streamline properties for an irrotational periodic geophysical traveling surface water waves propagating in equatorial oceanic regions. Relying on the methods from complex analysis, we prove the logarithmic convexity and monotonicity of specific flow variables. By means of conformal mappings, we derive some qualitative results for kinetic energy and streamline, such as streamline time-period being independent of any moment and any point on the streamline in steady flow, the concavity and monotonicity of total kinetic energy within the region between two streamlines and the convexity and monotonicity of total kinetic energy over a streamline time-period. Moreover, we present several results about irrotational equatorial wind waves, such as an upper bound of the minimum of streamline time-period, an upper bound of the maximum of area within the region between two streamlines. Taking advantage of the Bernoulli's law and the Schwarz reflection principle, we show that the extremum of the kinetic energy is attained on the free surface for irrotational equatorial wind waves.

Restless multi-armed bandits (RMAB) play a central role in modeling sequential decision making problems under an instantaneous activation constraint that at most B arms can be activated at any decision epoch. Each restless arm is endowed with a state that evolves independently according to a Markov decision process regardless of being activated or not. In this paper, we consider the task of learning in episodic RMAB with unknown transition functions and adversarial rewards, which can change arbitrarily across episodes. Further, we consider a challenging but natural bandit feedback setting that only adversarial rewards of activated arms are revealed to the decision maker (DM). The goal of the DM is to maximize its total adversarial rewards during the learning process while the instantaneous activation constraint must be satisfied in each decision epoch. We develop a novel reinforcement learning algorithm with two key contributors: a novel biased adversarial reward estimator to deal with bandit feedback and unknown transitions, and a low-complexity index policy to satisfy the instantaneous activation constraint. We show $\tilde{\mathcal{O}}(H\sqrt{T})$ regret bound for our algorithm, where $T$ is the number of episodes and $H$ is the episode length. To our best knowledge, this is the first algorithm to ensure $\tilde{\mathcal{O}}(\sqrt{T})$ regret for adversarial RMAB in our considered challenging settings., Comment: Accepted by ICML 2024

We study the data packet transmission problem (mmDPT) in dense cell-free millimeter wave (mmWave) networks, i.e., users sending data packet requests to access points (APs) via uplinks and APs transmitting requested data packets to users via downlinks. Our objective is to minimize the average delay in the system due to APs' limited service capacity and unreliable wireless channels between APs and users. This problem can be formulated as a restless multi-armed bandits problem with fairness constraint (RMAB-F). Since finding the optimal policy for RMAB-F is intractable, existing learning algorithms are computationally expensive and not suitable for practical dynamic dense mmWave networks. In this paper, we propose a structured reinforcement learning (RL) solution for mmDPT by exploiting the inherent structure encoded in RMAB-F. To achieve this, we first design a low-complexity and provably asymptotically optimal index policy for RMAB-F. Then, we leverage this structure information to develop a structured RL algorithm called mmDPT-TS, which provably achieves an \tilde{O}(\sqrt{T}) Bayesian regret. More importantly, mmDPT-TS is computation-efficient and thus amenable to practical implementation, as it fully exploits the structure of index policy for making decisions. Extensive emulation based on data collected in realistic mmWave networks demonstrate significant gains of mmDPT-TS over existing approaches., Comment: IEEE Transactions on Wireless Communications