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The integration of backscatter communication (BackCom) technology with integrated sensing and communication (ISAC) technology not only enhances the system sensing performance, but also enables low-power information transmission. This is expected to provide a new paradigm for communication and sensing in internet of everything (IoE) applications. Existing works only consider sensing rate and detection performance, while none consider the estimation performance. The design of the system in different task modes also needs to be further studied. In this paper, we propose a novel system called backscatter-ISAC (B-ISAC) and design a joint beamforming framework for different stages (task modes). We derive communication performance metrics of the system in terms of the signal-to-interference-plus-noise ratio (SINR) and communication rate, and derive sensing performance metrics of the system in terms of probability of detection, estimation error of linear least squares (LS) estimation, and the estimation error of linear minimum mean square error (LMMSE) estimation. The proposed joint beamforming framework consists of three stages: tag detection, tag estimation, and communication enhancement. We develop corresponding joint beamforming schemes aimed at enhancing the performance objectives of their respective stages by solving complex non-convex optimization problems. Extensive simulation results demonstrate the effectiveness of the proposed joint beamforming schemes. The proposed B-ISAC system has broad application prospect in sixth generation (6G) IoE scenarios., Comment: 15 pages, 11 figures, submitted to IEEE Internet of Things Journal (IoTJ) on April 1st 2024

We investigate the conversion of kinetic mixing dark photon dark matter into gravitational waves within the magnetic field of the Sun. Our study reveals that the WKB approximation is invalid in this scenario. We derive an analytic solution for the conversion probability with unitary evolution feature. This solution aligns in form with previous studies on photon-gravitational wave conversion. Interestingly, it is applicable in situations where the WKB approximation fails. We extend the unitary evolution solution to other conversion processes, such as axion-photon and dark photon-photon conversions. When the WKB approximation conditions are met, this solution reduces to the WKB result. We compute the characteristic strain of gravitational waves resulting from dark photon conversion in the solar magnetic field, spanning frequencies from $10^{-5}$ Hz to $10^3$ Hz. Our findings indicate that the characteristic strain derived from the unitary evolution solution differs significantly from that of the WKB solution. The resulting strain signal is far below the sensitivity of current gravitational wave interferometers. Nevertheless, we have proposed an exotic gravitational wave source, especially at high frequencies, from the dark photon dark matter local conversion inside the Sun., Comment: 28 pages, 5 figures

We study the ultraviolet completion of semileptonic four-fermion operator $O_{ledq}^{\alpha \beta 11}$ that incorporates Majorana dark matter (DM) in both lepton-flavor-conserving (LFC) and lepton-flavor-violating (LFV) scenarios at the one-loop level via box diagram, which effectively alleviates the lower bounds on the new physics scale. The interplay between the model-independent constraints on the Wilson coefficients and DM direct detection, relic density, and collider searches in the context of fermion portal DM model with two mediators is investigated. We find that both the projected future constraint on the LFC Wilson coefficient $C_{ledq}^{2211}/\Lambda^2< (12.3~\text{TeV})^{-2}$ from the measurements of neutrino non-standard interaction in the next-generation neutrino oscillation experiments, and LFV constraint $C_{ledq}^{1211}/\Lambda^2< \left(2.2\times 10^3 ~\text{TeV} \right)^{-2}$ from ongoing charged-lepton-flavor-violation searches, provide a complementary exploration of the parameter space encompassing the DM mass and scalar mass. With the colored mediator mass typically around $2~\text{TeV}$, the sensitivity of the indirect constraints on the four-fermion operator could surpass those of collider searches and DM direct detection, in scenarios where the masses of the DM and scalar are close. By ensuring the correct DM relic density, however, we obtain that the collider searches and DM direct detection are more sensitive to the electroweak scale DM and scalar compared to the indirect constraints., Comment: 27 pages

Despite extensive exploration of acoustic vortices carrying orbital angular momentum (OAM), the generation of acoustic vortices with OAM orientations beyond the conventional longitudinal direction remains largely unexplored. Spatiotemporal (ST) vortices, featuring spiral phase twisting in the ST domain and carrying transverse OAM, have recently attracted considerable interest in optics and acoustics. Here, we report the generation of three-dimensional (3D) ST acoustic vortices with arbitrarily oriented OAM, thereby opening up a new dimension in acoustic OAM control. By utilizing a two-dimensional (2D) acoustic phased array, we introduce two approaches to manipulate the orientation of OAM: through the direct rotation of vortices in 3D space and the intersection of vortices carrying distinct types of OAM. These methods enable unprecedented control over the orientation of acoustic OAM, providing a new degree of freedom in the manipulation of acoustic waves. The arbitrarily oriented OAM holds promise for enhancing acoustic communication by broadening capacity and enabling more complex particle manipulation techniques. Our work establishes a foundation for future explorations into the complex dynamics of novel structured acoustic fields in the ST domain.

Tactile sensors, which provide information about the physical properties of objects, are an essential component of robotic systems. The visuotactile sensing technology with the merits of high resolution and low cost has facilitated the development of robotics from environment exploration to dexterous operation. Over the years, several reviews on visuotactile sensors for robots have been presented, but few of them discussed the significance of signal processing methods to visuotactile sensors. Apart from ingenious hardware design, the full potential of the sensory system toward designated tasks can only be released with the appropriate signal processing methods. Therefore, this paper provides a comprehensive review of visuotactile sensors from the perspective of signal processing methods and outlooks possible future research directions for visuotactile sensors., Comment: Accepted by IEEE Journal of Selected Topics in Signal Processing

Explaining the decisions made by audio spoofing detection models is crucial for fostering trust in detection outcomes. However, current research on the interpretability of detection models is limited to applying XAI tools to post-trained models. In this paper, we utilize the wav2vec 2.0 model and attentive utterance-level features to integrate interpretability directly into the model's architecture, thereby enhancing transparency of the decision-making process. Specifically, we propose a class activation representation to localize the discriminative frames contributing to detection. Furthermore, we demonstrate that multi-label training based on spoofing types, rather than binary labels as bonafide and spoofed, enables the model to learn distinct characteristics of different attacks, significantly improving detection performance. Our model achieves state-of-the-art results, with an EER of 0.51% and a min t-DCF of 0.0165 on the ASVspoof2019-LA set., Comment: 5 pages, 2 figures, Accepted to Interspeech2024

As the penetration of distributed energy resources (DER) and renewable energy sources (RES) increases, carbon footprint tracking requires more granular analysis results. Existing carbon footprint tracking methods focus on deterministic steady-state analysis where the high uncertainties of RES cannot be considered. Considering the deficiency of the existing deterministic method, this paper proposes two stochastic carbon footprint tracking methods to cope with the impact of RES uncertainty on load-side carbon footprint tracing. The first method introduces probabilistic analysis in the framework of carbon emissions flow (CEF) to provide a global reference for the spatial characteristic of the power system component carbon intensity distribution. Considering that the CEF network expands with the increasing penetration of DERs, the second method can effectively improve the computational efficiency over the first method while ensuring the computational accuracy on the large power systems. These proposed models are tested and compared in a synthetic 1004-bus test system in the case study to demonstrate the performance of the two proposed methods

A general reverse Pinsker's inequality is derived to give an upper bound on f-divergences in terms of total variational distance when two distributions are close measured under our proposed generalized local information geometry framework. In addition, relationships between two f-divergences equipped with functions that are third order differentiable are established in terms of the lower and upper bounds of their ratio, when the underlying distributions are within a generalized quasi-$\varepsilon$-neighborhood.

The newly proposed Generalized Referring Expression Segmentation (GRES) amplifies the formulation of classic RES by involving multiple/non-target scenarios. Recent approaches focus on optimizing the last modality-fused feature which is directly utilized for segmentation and object-existence identification. However, the attempt to integrate all-grained information into a single joint representation is impractical in GRES due to the increased complexity of the spatial relationships among instances and deceptive text descriptions. Furthermore, the subsequent binary target justification across all referent scenarios fails to specify their inherent differences, leading to ambiguity in object understanding. To address the weakness, we propose a $\textbf{H}$ierarchical Semantic $\textbf{D}$ecoding with $\textbf{C}$ounting Assistance framework (HDC). It hierarchically transfers complementary modality information across granularities, and then aggregates each well-aligned semantic correspondence for multi-level decoding. Moreover, with complete semantic context modeling, we endow HDC with explicit counting capability to facilitate comprehensive object perception in multiple/single/non-target settings. Experimental results on gRefCOCO, Ref-ZOM, R-RefCOCO, and RefCOCO benchmarks demonstrate the effectiveness and rationality of HDC which outperforms the state-of-the-art GRES methods by a remarkable margin. Code will be available $\href{https://github.com/RobertLuo1/HDC}{here}$.

In recent years, large-scale auto-regressive models have made significant progress in various tasks, such as text or video generation. However, the environmental impact of these models has been largely overlooked, with a lack of assessment and analysis of their carbon footprint. To address this gap, we introduce OpenCarbonEval, a unified framework for integrating large-scale models across diverse modalities to predict carbon emissions, which could provide AI service providers and users with a means to estimate emissions beforehand and help mitigate the environmental pressure associated with these models. In OpenCarbonEval, we propose a dynamic throughput modeling approach that could capture workload and hardware fluctuations in the training process for more precise emissions estimates. Our evaluation results demonstrate that OpenCarbonEval can more accurately predict training emissions than previous methods, and can be seamlessly applied to different modal tasks. Specifically, we show that OpenCarbonEval achieves superior performance in predicting carbon emissions for both visual models and language models. By promoting sustainable AI development and deployment, OpenCarbonEval can help reduce the environmental impact of large-scale models and contribute to a more environmentally responsible future for the AI community.

Haze severely degrades the visual quality of remote sensing images and hampers the performance of automotive navigation, intelligent monitoring, and urban management. The emerging denoising diffusion probabilistic model (DDPM) exhibits the significant potential for dense haze removal with its strong generation ability. Since remote sensing images contain extensive small-scale texture structures, it is important to effectively restore image details from hazy images. However, current wisdom of DDPM fails to preserve image details and color fidelity well, limiting its dehazing capacity for remote sensing images. In this paper, we propose a novel unified Fourier-aware diffusion model for remote sensing image dehazing, termed RSHazeDiff. From a new perspective, RSHazeDiff explores the conditional DDPM to improve image quality in dense hazy scenarios, and it makes three key contributions. First, RSHazeDiff refines the training phase of diffusion process by performing noise estimation and reconstruction constraints in a coarse-to-fine fashion. Thus, it remedies the unpleasing results caused by the simple noise estimation constraint in DDPM. Second, by taking the frequency information as important prior knowledge during iterative sampling steps, RSHazeDiff can preserve more texture details and color fidelity in dehazed images. Third, we design a global compensated learning module to utilize the Fourier transform to capture the global dependency features of input images, which can effectively mitigate the effects of boundary artifacts when processing fixed-size patches. Experiments on both synthetic and real-world benchmarks validate the favorable performance of RSHazeDiff over multiple state-of-the-art methods. Source code will be released at https://github.com/jm-xiong/RSHazeDiff.

This paper focuses on optimal constants and optimizers of the second order Caffarelli-Kohn-Nirenberg inequalities. Firstly, we aim to study optimal constants and optimizers for the following second order Caffarelli-Kohn-Nirenberg inequality in radial space: let $N\ge1$, $t\ge p>1$, \begin{equation}\label{0.1} \left(\int_{\mathbb{R}^N} \frac{|\Delta u|^p}{|x|^{p\alpha}} \mathrm{d}x\right)^{\frac{1}{p}} \left[\int_{\mathbb{R}^N} \frac{\left|\nabla u\right|^{\frac{p(t-1)}{p-1}}} {|x|^{\frac{p(t-1)}{p-1}\beta}} \mathrm{d}x\right]^{\frac{p-1}{p}} \ge C(N,p,t,\alpha,\beta) \int_{\mathbb{R}^N} \frac{\left|\nabla u\right|^t}{|x|^{t\gamma}} \mathrm{d}x. \end{equation} Secondly, we establish second order $L^p$-Caffarelli-Kohn-Nirenberg identities, and obtain optimal constants and optimizers of the second order $L^p$-Caffarelli-Kohn-Nirenberg inequalities (i.e., $p=t$ in \eqref{0.1}) in general space. Lastly, under some more general assumptions, we consider the optimal weighted second order Heisenberg Uncertainty Principles, which complements the recent work [``The sharp second order Caffareli-Kohn-Nirenberg inequality and stability estimates for the sharp second order uncertainty principle'', 2022, arXiv:2102.01425]. This paper's main novelty lies in the fact that we research the optimal versions of the second order Caffarelli-Kohn-Nirenberg inequalities \eqref{0.1} in radial space or in general space, and also establish the second order $L^p$-Caffarelli-Kohn-Nirenberg identities.

Recent studies have proposed using a geocentric muon spin force to account for the $(g-2)_\mu$ anomaly, with the long-range force mediator being a light axion-like particle. The mediator exhibits a CP-violating scalar coupling to nucleons and a normal derivative coupling to muons. Due to the weak symmetry, this axion inevitably couples to neutrinos, providing potential impact on neutrino oscillations. By utilizing neutrino data from BOREXINO, IceCube DeepCore, Super-Kamiokande, and SNO, we have identified that both atmospheric and solar neutrino data can impose stringent constraints on the long-range muon spin force model and the $(g-2)_\mu$ parameter space. With optimized data analysis techniques and the potential from future experiments, such as JUNO, Hyper-Kamiokande, SNO+, and IceCube PINGU, there exists a promising opportunity to achieve even greater sensitivities. Indeed, neutrino oscillations offer a robust and distinctive cross-check for the model, offering stringent constraints on the $(g-2)_\mu$ parameter space., Comment: 8 pages, 2 figures, v2: matched to journal version

Although large-scale pre-trained models hold great potential for adapting to downstream tasks through fine-tuning, the performance of such fine-tuned models is often limited by the difficulty of collecting sufficient high-quality, task-specific data. Federated Learning (FL) offers a promising solution by enabling fine-tuning across large-scale clients with a variety of task data, but it is bottlenecked by significant communication overhead due to the pre-trained models' extensive size. This paper addresses the high communication cost for fine-tuning large pre-trained models within FL frameworks through low-rank fine-tuning. Specifically, we train a low-rank adapter for each individual task on the client side, followed by server-side clustering for similar group of adapters to achieve task-specific aggregation. Extensive experiments on various language and vision tasks, such as GLUE and CIFAR-10/100, reveal the evolution of task-specific adapters throughout the FL training process and verify the effectiveness of the proposed low-rank task-specific adapter clustering (TAC) method.

Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference. This paper presents a comprehensive survey of the existing literature on efficient LLM inference. We start by analyzing the primary causes of the inefficient LLM inference, i.e., the large model size, the quadratic-complexity attention operation, and the auto-regressive decoding approach. Then, we introduce a comprehensive taxonomy that organizes the current literature into data-level, model-level, and system-level optimization. Moreover, the paper includes comparative experiments on representative methods within critical sub-fields to provide quantitative insights. Last but not least, we provide some knowledge summary and discuss future research directions.

The availability of smart devices leads to an exponential increase in multimedia content. However, the rapid advancements in deep learning have given rise to sophisticated algorithms capable of manipulating or creating multimedia fake content, known as Deepfake. Audio Deepfakes pose a significant threat by producing highly realistic voices, thus facilitating the spread of misinformation. To address this issue, numerous audio anti-spoofing detection challenges have been organized to foster the development of anti-spoofing countermeasures. This survey paper presents a comprehensive review of every component within the detection pipeline, including algorithm architectures, optimization techniques, application generalizability, evaluation metrics, performance comparisons, available datasets, and open-source availability. For each aspect, we conduct a systematic evaluation of the recent advancements, along with discussions on existing challenges. Additionally, we also explore emerging research topics on audio anti-spoofing, including partial spoofing detection, cross-dataset evaluation, and adversarial attack defence, while proposing some promising research directions for future work. This survey paper not only identifies the current state-of-the-art to establish strong baselines for future experiments but also guides future researchers on a clear path for understanding and enhancing the audio anti-spoofing detection mechanisms., Comment: submitted to ACM Computing Surveys

In the realm of media technology, digital humans have gained prominence due to rapid advancements in computer technology. However, the manual modeling and control required for the majority of digital humans pose significant obstacles to efficient development. The speech-driven methods offer a novel avenue for manipulating the mouth shape and expressions of digital humans. Despite the proliferation of driving methods, the quality of many generated talking head (TH) videos remains a concern, impacting user visual experiences. To tackle this issue, this paper introduces the Talking Head Quality Assessment (THQA) database, featuring 800 TH videos generated through 8 diverse speech-driven methods. Extensive experiments affirm the THQA database's richness in character and speech features. Subsequent subjective quality assessment experiments analyze correlations between scoring results and speech-driven methods, ages, and genders. In addition, experimental results show that mainstream image and video quality assessment methods have limitations for the THQA database, underscoring the imperative for further research to enhance TH video quality assessment. The THQA database is publicly accessible at https://github.com/zyj-2000/THQA.

We obtained new hairy black hole solutions in Einstein-scalar theory, including asymptotic flat, de Sitter and anti-de Sitter black holes. The theory is inspired by Ref. [1], where traversable wormhole solutions from an Einstein-phantom scalar theory are constructed. In this work, we found new black hole solutions in an Einstein-normal scalar theory. Comparing with Schwarzschild metric, the hairy black holes have two interesting properties: i) the areas of the black holes are always smaller than the same mass Schwarzschild black holes; ii) A naked singularity with positive mass arises when the black hole mass decreases. The energy conditions for the black holes and naked singularities are checked. We found that, as hairy black holes, the null energy condition(NEC) and the strong energy condition(SEC) are hold, while the weak energy condition(WEC) is violated in the vicinity of black hole horizon. The naked singularity respects to all three energy conditions. We also investigate the quasinormal modes(QNMs) of the hairy black holes by a test scalar field. The results indicate that one can distinguish hairy black holes with the same mass Schwarzschilid black hole by their QNM spectra., Comment: 26 pages, 7 figures and 1 table. Comments are welcome

We study a millicharged co-interacting dark matter scenario, where the primary dark matter constituent is the dark photon $A'$ and the secondary component is the fermion $\chi$. In this model, $\chi$ interacts with $A'$ via a $U(1)'$ interaction while being millicharged with respect to normal photons. Our investigation focuses on the oscillation of $A'$ dark matter into photons within the background of $\chi$ particles, revealing that the $A'-\chi$ scattering rate benefits from a Bose enhancement of the $A'$ final state. As the oscillation production rate is directly linked to the scattering rate, the conversion of $A'$ dark matter into monochromatic photons experiences significant amplification owing to this Bose enhancement, especially when the scattering rate $\Gamma_{\rm sca}$ approaches the dark photon mass $m_{A'}$. These converted monochromatic photons are detectable through radio telescopes and can induce distortions in the Cosmic Microwave Background (CMB) spectrum. We find that the sensitivity of radio telescopes and the constraints imposed by CMB distortion on the kinetic mixing parameter are notably heightened compared to scenarios without the subdominant millicharged dark matter., Comment: 9 pages, 1 figure

Layered transition metal chalcogenides have stimulated a wide research interest due to their many exotic physical properties. In this paper, we studied the magnetotransport properties of the exfoliated TaNiTe5, a recently discovered Dirac nodal-line semimetal. A giant positive magnetoresistance (MR) is observed when the current is parallel to the crystallographic c axis, while it is strongly diminished when the current flows along the a axis. The observed giant MR is gradually suppressed either on reducing the thickness of nanoflake or on increasing temperature. By performing MR measurement in tilted magnetic fields, the interlayer coupling is found to be weakened both by reducing the thickness and by increasing temperature. We propose a mechanism of electron-electron interaction-assisted interlayer transport as a origin of the giant MR. The mechanism is likely to provide a explanation for the giant MR in other layered materials., Comment: 21 pages, 7 figures, 1 table

A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAVs. We find an opportunity to accomplish the task by transforming AMAVs into mobile localization infrastructures for BMAVs. However, turning this insight into a practical system is non-trivial due to challenges in location estimation with BMAVs' unknown and diverse localization errors and resource allocation of AMAVs given coupled influential factors. This study proposes TransformLoc, a new framework that transforms AMAVs into mobile localization infrastructures, specifically designed for low-cost and resource-constrained BMAVs. We first design an error-aware joint location estimation model to perform intermittent joint location estimation for BMAVs and then design a proximity-driven adaptive grouping-scheduling strategy to allocate resources of AMAVs dynamically. TransformLoc achieves a collaborative, adaptive, and cost-effective localization system suitable for large-scale heterogeneous MAV swarms. We implement TransformLoc on industrial drones and validate its performance. Results show that TransformLoc outperforms baselines including SOTA up to 68\% in localization performance, motivating up to 60\% navigation success rate improvement., Comment: 10 pages, accepted by IEEE INFOCOM 2024

To foster an immersive and natural human-robot interaction, the implementation of tactile perception and feedback becomes imperative, effectively bridging the conventional sensory gap. In this paper, we propose a dual-modal electronic skin (e-skin) that integrates magnetic tactile sensing and vibration feedback for enhanced human-robot interaction. The dual-modal tactile e-skin offers multi-functional tactile sensing and programmable haptic feedback, underpinned by a layered structure comprised of flexible magnetic films, soft silicone, a Hall sensor and actuator array, and a microcontroller unit. The e-skin captures the magnetic field changes caused by subtle deformations through Hall sensors, employing deep learning for accurate tactile perception. Simultaneously, the actuator array generates mechanical vibrations to facilitate haptic feedback, delivering diverse mechanical stimuli. Notably, the dual-modal e-skin is capable of transmitting tactile information bidirectionally, enabling object recognition and fine-weighing operations. This bidirectional tactile interaction framework will enhance the immersion and efficiency of interactions between humans and robots., Comment: 7 pages, 8 figures. Submitted to 2024 IEEE International Conference on Robotics and Automation (ICRA), Japan, Yokohama

Crowd simulation holds crucial applications in various domains, such as urban planning, architectural design, and traffic arrangement. In recent years, physics-informed machine learning methods have achieved state-of-the-art performance in crowd simulation but fail to model the heterogeneity and multi-modality of human movement comprehensively. In this paper, we propose a social physics-informed diffusion model named SPDiff to mitigate the above gap. SPDiff takes both the interactive and historical information of crowds in the current timeframe to reverse the diffusion process, thereby generating the distribution of pedestrian movement in the subsequent timeframe. Inspired by the well-known social physics model, i.e., Social Force, regarding crowd dynamics, we design a crowd interaction module to guide the denoising process and further enhance this module with the equivariant properties of crowd interactions. To mitigate error accumulation in long-term simulations, we propose a multi-frame rollout training algorithm for diffusion modeling. Experiments conducted on two real-world datasets demonstrate the superior performance of SPDiff in terms of macroscopic and microscopic evaluation metrics. Code and appendix are available at https://github.com/tsinghua-fib-lab/SPDiff.

To tackle the issues of catastrophic forgetting and overfitting in few-shot class-incremental learning (FSCIL), previous work has primarily concentrated on preserving the memory of old knowledge during the incremental phase. The role of pre-trained model in shaping the effectiveness of incremental learning is frequently underestimated in these studies. Therefore, to enhance the generalization ability of the pre-trained model, we propose Learning with Prior Knowledge (LwPK) by introducing nearly free prior knowledge from a few unlabeled data of subsequent incremental classes. We cluster unlabeled incremental class samples to produce pseudo-labels, then jointly train these with labeled base class samples, effectively allocating embedding space for both old and new class data. Experimental results indicate that LwPK effectively enhances the model resilience against catastrophic forgetting, with theoretical analysis based on empirical risk minimization and class distance measurement corroborating its operational principles. The source code of LwPK is publicly available at: \url{https://github.com/StevenJ308/LwPK}.

3D Dirac semimetals with square-net non-symmorphic symmetry, such as ternary ZrXY (X=Si, Ge; Y=S, Se, Te) compounds, have attracted significant attention owing to the presence of topological nodal lines, loops, or networks in their bulk. Orbital symmetry plays a profound role such materials as the different branches of the nodal dispersion can be distinguished by their distinct orbital symmetry eigenvalues. The presence of different eigenvalues suggests that scattering between states of different orbital symmetry may be strongly suppressed. Indeed, in ZrSiS, there has been no clear experimental evidence of quasiparticle scattering between states of different symmetry eigenvalue has been reported at small wave vector $q$. Here we show, using quasiparticle interference (QPI), that atomic step-edges in the ZrSiS surface facilitate quasiparticle scattering between states of different symmetry eigenvalues. This symmetry eigenvalue mixing quasiparticle scattering is the first to be reported for ZrSiS and contrasts quasiparticle scattering with no mixing of symmetry eigenvalues, where the latter occurs with scatterers preserving the glide mirror symmetry of the crystal lattice, e.g., native point defects in ZrSiS. Finally, we show that the electronic structure of the ZrSiS surface, including its unique floating band surface state (FBSS), can be tuned by a vertical electric field locally applied by the tip of a scanning tunneling microscope (STM), enabling control of a spin-orbit induced avoided crossing near the Fermi level by as much as 300%.

Most vision-based tactile sensors use elastomer deformation to infer tactile information, which can not sense some modalities, like temperature. As an important part of human tactile perception, temperature sensing can help robots better interact with the environment. In this work, we propose a novel multimodal vision-based tactile sensor, SATac, which can simultaneously perceive information of temperature, pressure, and shear. SATac utilizes thermoluminescence of strontium aluminate (SA) to sense a wide range of temperatures with exceptional resolution. Additionally, the pressure and shear can also be perceived by analyzing Voronoi diagram. A series of experiments are conducted to verify the performance of our proposed sensor. We also discuss the possible application scenarios and demonstrate how SATac could benefit robot perception capabilities.

The $L_1/L_2$ norm ratio arose as a sparseness measure and attracted a considerable amount of attention due to three merits: (i) sharper approximations of $L_0$ compared to the $L_1$; (ii) parameter-free and scale-invariant; (iii) more attractive than $L_1$ under highly-coherent matrices. In this paper, we first establish the partly smooth property of $L_1$ over $L_2$ minimization relative to an active manifold ${\cal M}$ and also demonstrate its prox-regularity property. Second, we reveal that ADMM$_p$ (or ADMM$^+_p$) can identify the active manifold within a finite iterations. This discovery contributes to a deeper understanding of the optimization landscape associated with $L_1$ over $L_2$ minimization. Third, we propose a novel heuristic algorithm framework that combines ADMM$_p$ (or ADMM$^+_p$) with a globalized semismooth Newton method tailored for the active manifold ${\cal M}$. This hybrid approach leverages the strengths of both methods to enhance convergence. Finally, through extensive numerical simulations, we showcase the superiority of our heuristic algorithm over existing state-of-the-art methods for sparse recovery.

Intelligent robot is the ultimate goal in the robotics field. Existing works leverage learning-based or optimization-based methods to accomplish human-defined tasks. However, the challenge of enabling robots to explore various environments autonomously remains unresolved. In this work, we propose a framework named GExp, which enables robots to explore and learn autonomously without human intervention. To achieve this goal, we devise modules including self-exploration, knowledge-base-building, and close-loop feedback based on foundation models. Inspired by the way that infants interact with the world, GExp encourages robots to understand and explore the environment with a series of self-generated tasks. During the process of exploration, the robot will acquire skills from beneficial experiences that are useful in the future. GExp provides robots with the ability to solve complex tasks through self-exploration. GExp work is independent of prior interactive knowledge and human intervention, allowing it to adapt directly to different scenarios, unlike previous studies that provided in-context examples as few-shot learning. In addition, we propose a workflow of deploying the real-world robot system with self-learned skills as an embodied assistant., Comment: 19 pages

According to the Mermin Wagner theorem, two-dimensional material is difficult to have the Curie temperature above room temperature. By using the method of band engineering, we design a promising two-dimensional ferromagnetic semiconductor Cr2XP (X=P, As, Sb) with large magnetization, high Curie temperature and sizable band gap. The formation of gap is discussed in terms of the hybridizations, occupation and distribution of electronic states and charge transfer. Large magnetic moments about 6.16~6.37uB origin from the occupation of Cr-d electrons in crystal field.Competition and cooperation between d-d (Cr-d~Cr-d) and d-p-d (Cr-d~X-p~Cr-d) exchange interactions lead to the emergence of ferromagnetic ordering phase. Furthermore, Curie temperatures, approaching to 269 K, 332 K and 400 K for Cr2P2, Cr2AsP and Cr2SbP, are estimated by employing Monte Carlo simulation based on the Heisenberg model. Magnetic anisotropy energy of Cr2XP is determined by calculating the total energy dependence on the angle along different directions, and the origin is also discussed by the second-order perturbation theory. In addition, the Cr2XP possesses excellent thermodynamical, dynamical and mechanical stabilities, and can overcome their own gravity to keep their planar structure without the support of substrate. These above-mentioned advantages will offer some valuable hints for two-dimensional ferromagnetic semiconductor Cr2XP in spintronic devices.

For semi-supervised learning with imbalance classes, the long-tailed distribution of data will increase the model prediction bias toward dominant classes, undermining performance on less frequent classes. Existing methods also face challenges in ensuring the selection of sufficiently reliable pseudo-labels for model training and there is a lack of mechanisms to adjust the selection of more reliable pseudo-labels based on different training stages. To mitigate this issue, we introduce uncertainty into the modeling process for pseudo-label sampling, taking into account that the model performance on the tailed classes varies over different training stages. For example, at the early stage of model training, the limited predictive accuracy of model results in a higher rate of uncertain pseudo-labels. To counter this, we propose an Uncertainty-Aware Dynamic Threshold Selection (UDTS) approach. This approach allows the model to perceive the uncertainty of pseudo-labels at different training stages, thereby adaptively adjusting the selection thresholds for different classes. Compared to other methods such as the baseline method FixMatch, UDTS achieves an increase in accuracy of at least approximately 5.26%, 1.75%, 9.96%, and 1.28% on the natural scene image datasets CIFAR10-LT, CIFAR100-LT, STL-10-LT, and the medical image dataset TissueMNIST, respectively. The source code of UDTS is publicly available at: https://github.com/yangk/UDTS., Comment: Submitted to TPAMI

In this paper, we propose an iterative convolution-thresholding method (ICTM) based on prediction-correction for solving the topology optimization problem in steady-state heat transfer equations. The problem is formulated as a constrained minimization problem of the complementary energy, incorporating a perimeter/surface-area regularization term, while satisfying a steady-state heat transfer equation. The decision variables of the optimization problem represent the domains of different materials and are represented by indicator functions. The perimeter/surface-area term of the domain is approximated using Gaussian kernel convolution with indicator functions. In each iteration, the indicator function is updated using a prediction-correction approach. The prediction step is based on the variation of the objective functional by imposing the constraints, while the correction step ensures the monotonically decreasing behavior of the objective functional. Numerical results demonstrate the efficiency and robustness of our proposed method, particularly when compared to classical approaches based on the ICTM., Comment: 29 pages, 25 figures

Recently, the vector-like leptons (VLLs) as a simple extension to the standard model (SM) have attracted widespread attention both in theory and experiments. The present collider searches mainly focus on the studies of their prompt decays, which prefer a relatively large coupling. In this paper, we concentrate on searches for long-lived signatures of the singlet VLLs $F$ or their accompanying scalar particles $\phi$ both in the hadronic and electronic colliders. The long-lived signatures are naturally induced from small chiral mass mixing between VLLs and SM leptons. Two specific models distinguished by whether the VLLs couple to scalar particles are introduced to realize the aforementioned features. For long-lived VLLs case, we find that with the kink track method the sensitivities at future HL-LHC with $\sqrt{s}=14~\text{TeV}$ can reach the regions for VLL mass $m_F \in [200,1100]~\text{GeV}$ and the mass mixing parameter $\theta_L \in [10^{-10},3\times 10^{-8}]$. For the long-lived accompanying scalar particle case, by fixing VLLs or scalar mass, or the mass ratio between VLL and the accompanying scalar, we explore the projected sensitivities through the time delay and displaced vertex strategies, which can probe the regions for $m_F \in [200,1200]~\text{GeV}$ and coupling $y\theta_L\in [10^{-11},10^{-6}]$. Furthermore, we also explore the long-lived accompanying scalars at the future CEPC provided that the VLLs can couple to the SM first-generation leptons. We find that CEPC has good performances for $m_\phi < 120~\text{GeV}$ and $m_F<1200~\text{GeV}$. These long-lived searches are complementary to previous studies, which opens the door towards the smaller coupling regions., Comment: 33 pages, 9 figures; v2: matched to journal version

Based on first-principles calculations, we predict that nitrogen atoms can assemble into a single-layer double kagome lattice (DKL), which possesses the characteristics of an intrinsic direct band gap semiconductor, boasting a substantial band gap of 3.460 eV. The DKL structure results in a flat valence band with high effective mass and a conduction band with small effective mass comes from Dirac electrons. These distinctive band edges lead to a significant disparity in carrier mobilities, with electron mobility being four orders of magnitude higher than that of holes. The presence of flat band in DKL-nitrogene can be further discerned through the enhanced optical absorption and correlated effects as exemplified by hole-induced ferromagnetism. Interestingly, DKL-nitrogene exhibits inherent second-order topological states, confirmed by a non-trivial second Stiefel-Whitney number and the presence of 1D floating edge states and 0D corner states within the bulk band gap. Additionally, the robust N-N bonds and the lattice's bending structure ensure thermodynamic stability and mechanical stiffness. These attributes make it exceptionally stable for potential applications in nano-devices., Comment: 9 pages, 4 figures

Black widows (BWs) are millisecond pulsars ablating their companion stars. The out-flowing material from the companion can block the radio emission of the pulsar, resulting in eclipses. In this paper, we construct a model for the radio eclipse by calculating the geometry of the bow shock between the winds of the pulsar and companion, where the shock shapes the eclipsing medium but had not been described in detail in previous works. The model is further used to explain the variations of the flux density and dispersion measure (DM) of three BW pulsars (i.e., PSR B1957$+$20, J2055$+$3829, and J2051$-$0827) detected by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Consequently, we constrained the parameters of the three BW systems such as the inclination angles and true anomalies of the observer as well as the mass-loss rates and wind velocity of the companion stars. With the help of these constraints, it is expected that magnetic fields of companion stars and even masses of pulsars could further be determined as some extra observation can be achieved in the future., Comment: 12 pages, 6 figures, accepted for publication in RAA

Experiments aimed at detecting ultralight dark matter typically rely on resonant effects, which are sensitive to the dark matter mass that matches the resonance frequency. In this study, we investigate the nucleon couplings of ultralight axion dark matter using a magnetometer operating in a nuclear magnetic resonance (NMR) mode. Our approach involves the use of a $^{21}$Ne spin-based sensor, which features the lowest nuclear magnetic moment among noble-gas spins. This configuration allows us to achieve an ultrahigh sensitivity of 0.73 fT/Hz$^{1/2}$ at around 5 Hz, corresponding to energy resolution of approximately 1.5$\times 10^{-23}\,\rm{eV/Hz^{1/2}}$. Our analysis reveals that under certain conditions it is beneficial to scan the frequency with steps significantly larger than the resonance width. The analytical results are in agreement with experimental data and the scan strategy is potentially applicable to other resonant searches. Further, our study establishes stringent constraints on axion-like particles (ALP) in the 4.5--15.5 Hz Compton-frequency range coupling to neutrons and protons, improving on prior work by several-fold. Within a band around 4.6--6.6 Hz and around 7.5 Hz, our laboratory findings surpass astrophysical limits derived from neutron-star cooling. Hence, we demonstrate an accelerated resonance search for ultralight dark matter, achieving an approximately 30-fold increase in scanning step while maintaining competitive sensitivity., Comment: 13 pages, 11 figures, accepted by Communications Physics

Structural investigations conducted using transmission electron microscopy (TEM) on LuH2 synthesized under atmospheric pressure (AP-LuH2) and nitrogen-doped LuH2 synthesized under high pressure (HP-LuH2) have revealed numerous microstructural phenomena. Both materials show a clear superstructure modulation with wave vector, q^* = 1/4 (2-20), and this modulation can be well interpreted by the displacements of Lu atoms. Further investigations on the nitrogen-doped HP-LuH2 materials reveal the appearance of high-density antiphase boundaries, in particular, domain walls of a few atomic layer thickness without structural modulation can be observed, suggesting possible interface properties could be detected in this system. In-situ TEM observations of AP-LuH2 suggest that no evident structural phase transition occurs between 94 K and 673 K., Comment: 8 pages, 7 figures

Xenes, two-dimensional (2D) monolayers composed of a single element, with graphene as a typical representative, have attracted widespread attention. Most of the previous Xenes, X from group-IIIA to group-VIA elements have bonding characteristics of covalent bonds. In this work, we for the first time unveil the pivotal role of a halogen bond, which is a distinctive type of bonding with interaction strength between that of a covalent bond and a van der Waals interaction, in 2D group-VIIA monolayers. Combing the ingenious non-edge-to-edge tiling theory and state-of-art ab initio method with refined local density functional M06-L, we provide a precise and effective bottom-up construction of 2D iodine monolayer sheets, iodinenes, primarily governed by halogen bonds, and successfully design a category of stable iodinenes, encompassing herringbone, Pythagorean, gyrated truncated hexagonal, i.e. diatomic-kagome, and gyrated hexagonal tiling pattern. These iodinene structures exhibit a wealth of properties, such as flat bands, nontrivial topology, and fascinating optical characteristics, offering valuable insights and guidance for future experimental investigations. Our work not only unveils the unexplored halogen bonding mechanism in 2D materials but also opens a new avenue for designing other non-covalent bonding 2D materials., Comment: 6 pages, 4 figures

Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few considering the non-IID challenges in FL. Our work presents the first non-vacuous federated PAC-Bayesian bound tailored for non-IID local data. This bound assumes unique prior knowledge for each client and variable aggregation weights. We also introduce an objective function and an innovative Gibbs-based algorithm for the optimization of the derived bound. The results are validated on real-world datasets.

Employing a combination of first-principles calculations and low-energy effective models, we present a comprehensive investigation on the electronic structure of Pb$_{10}$(PO$_{4}$)$_{6}$O$_{4}$, which exhibits remarkable quasi-one-dimensional flat-band around the Fermi level that contains novel multi-dimensional fermions. These flat bands predominantly originate from $p_x/p_y$ orbital of the oxygen molecules chain at $4e$ Wyckoff positions, and thus can be well-captured by a four-band tight-binding model. Furthermore, the abundant crystal symmetry inherent to Pb$_{10}$(PO$_{4}$)$_{6}$O$_{4}$ provides an ideal platform for the emergence of various multi-dimensional fermions, including a 0D four-fold degenerated Dirac fermion with quadratic dispersion, a 1D quadratic/linear nodal-line (QNL/LNL) fermion along symmetric $k$-paths, 1D hourglass nodal-line (HNL) fermion linked to the Dirac fermion, and a 2D symmetry-enforced nodal surface (NS) found on the $k_z$=$\pi$ plane. Moreover, when considering the weak ferromagnetic order, Pb$_{10}$(PO$_{4}$)$_{6}$O$_{4}$ transforms into a rare semi-half-metal, which is characterized by the presence of Dirac fermion and HNL fermion at the Fermi level for a single spin channel exhibiting 100$\%$ spin polarization. Our findings reveal the coexistence of flat bands, diverse topological semimetal states and ferromagnetism within in Pb$_{10}$(PO$_{4}$)$_{6}$O$_{4}$, which may provide valuable insights for further exploring intriguing interplay between superconductivity and exotic electronic states., Comment: 8 pages, 4 figures