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Although the constraints on general relativity (GR) from each individual gravitational-wave (GW) event can be combined to form a cumulative estimate of the deviations from GR, the ever-increasing number of GW events used also leads to the ever-increasing computational cost during the parameter estimation. Therefore, in this paper, we will introduce the deviations from GR into GWs from all events in advance and then create a modified stochastic gravitational-wave background (SGWB) to perform tests of GR. More precisely, we use the $\mathtt{pSEOBNRv4HM\_PA}$ model to include the model-independent hairs and calculate the corresponding SGWB with a given merger rate. Then we turn to the Fisher information matrix to forecast the constraints on the no-hair theorem from SGWB at frequency $10[{\rm Hz}]\lesssim f\lesssim10^3[{\rm Hz}]$ detected by the third-generation ground-based GW detectors, such as the Cosmic Explorer. We find that the forecasting constraints on hairs at $68\%$ confidence range are $\delta\omega_{220}=0\pm0.1296$ and $\delta\tau_{220}=0\pm0.0678$ when the flat priors about the merger rate are added but $\delta\omega_{220}=0\pm0.0903$ and $\delta\tau_{220}=0\pm0.0608$ when the non-flat priors about the merger rate are added., Comment: 12 pages, 8 figures

We fabricate a twisted trilayer graphene device with consecutive twist angles of 1.33 and 1.64 degrees, in which we electrostatically tune the electronic states from each of the two co-existing moir\'e superlattices and the interactions between them. When both moir\'e superlattices contribute equally to electrical transport, we report a new type of inter-moir\'e Hofstadter butterfly. Its Brown-Zak oscillation corresponds to one of the intermediate quasicrystal length scales of the reconstructed moir\'e of moir\'e (MoM) superlattice, shedding new light on emergent physics from competing atomic orders.

This paper presents an overview of the QUARKS survey, which stands for `Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures'. The QUARKS survey is observing 139 massive clumps covered by 156 pointings at ALMA Band 6 ($\lambda\sim$ 1.3 mm). In conjunction with data obtained from the ALMA-ATOMS survey at Band 3 ($\lambda\sim$ 3 mm), QUARKS aims to carry out an unbiased statistical investigation of massive star formation process within protoclusters down to a scale of 1000 au. This overview paper describes the observations and data reduction of the QUARKS survey, and gives a first look at an exemplar source, the mini-starburst Sgr B2(M). The wide-bandwidth (7.5 GHz) and high-angular-resolution (~0.3 arcsec) observations of the QUARKS survey allow to resolve much more compact cores than could be done by the ATOMS survey, and to detect previously unrevealed fainter filamentary structures. The spectral windows cover transitions of species including CO, SO, N$_2$D$^+$, SiO, H$_{30}\alpha$, H$_2$CO, CH$_3$CN and many other complex organic molecules, tracing gas components with different temperatures and spatial extents. QUARKS aims to deepen our understanding of several scientific topics of massive star formation, such as the mass transport within protoclusters by (hub-)filamentary structures, the existence of massive starless cores, the physical and chemical properties of dense cores within protoclusters, and the feedback from already formed high-mass young protostars., Comment: 9 figures, 4 tables, accepted by RAA

Application-level caches are widely adopted by web applications to minimize the response time of user requests as well as to reduce the burden on the system backend, such as the database servers. In the state of practice, developers have to take care of the data freshness of application-level caches manually. Given the growing complexities of today's web applications, it becomes increasingly challenging for developers to understand, reason about, and implement cache invalidation methods. Furthermore, according to our survey of open-source web application projects and engineers, it is indeed challenging to map database updates with cache entries at the application level. Therefore, we propose a design to handle data validity in a transparent and precise manner, without requiring any intervention from developers. Its main idea is to modify the DBMS to provide necessary information for cache management and enhance the cache with an invalidation index to identify and invalidate outdated data automatically and efficiently. Based on the design, we further provide two specific solutions. Our preliminary experiments indicate that our solutions could effectively achieve transparent cache invalidation while maintaining cost-effectiveness.

Context. Traditionally, supersonic turbulence is considered to be one of the most likely mechanisms to slow down the gravitational collapse in dense clumps, thereby enabling the formation of massive stars. However, several recent studies have raised differing points of view based on observations carried out with sufficiently high spatial and spectral resolution. These studies call for a re-evaluation of the role turbulence plays in massive star-forming regions. Aims. Our aim is to study the gas properties, especially the turbulence, in a sample of massive star-forming regions with sufficient spatial and spectral resolution, which can both resolve the core fragmentation and the thermal line width. Methods. We observed NH3 metastable lines with the Very Large Array (VLA) to assess the intrinsic turbulence. Results. Analysis of the turbulence distribution histogram for 32 identified NH3 cores reveals the presence of three distinct components. Furthermore, our results suggest that (1) sub- and transonic turbulence is a prevalent (21 of 32) feature of massive star-forming regions and those cold regions are at early evolutionary stage. This investigation indicates that turbulence alone is insufficient to provide the necessary internal pressure required for massive star formation, necessitating further exploration of alternative candidates; and (2) studies of seven multi-core systems indicate that the cores within each system mainly share similar gas properties and masses. However, two of the systems are characterized by the presence of exceptionally cold and dense cores that are situated at the spatial center of each system. Our findings support the hub-filament model as an explanation for this observed distribution, Comment: 34 pages, 15 figures, 4 tables. Accepted for publication on A&A

Over the last decade, the possibility of realizing topological superconductivity (TSC) has generated much excitement, mainly due to the potential use of its excitations (Majorana zero modes) in a fault-tolerant topological quantum computer 1,2. TSC can be created in electronic systems where the topological and superconducting orders coexist3, motivating the continued exploration of candidate material platforms to this end. Here, we use molecular beam epitaxy (MBE) to synthesize heterostructures that host emergent interfacial superconductivity when a non-superconducting antiferromagnet (FeTe) is interfaced with a topological insulator (TI) (Bi, Sb)2Te3 wherein the chemical potential can be tuned through varying the Bi/Sb ratio. By performing in-vacuo angle-resolved photoemission spectroscopy (ARPES) and ex-situ electrical transport measurements, we find that the superconducting transition temperature and the upper critical magnetic field are suppressed when the chemical potential approaches the Dirac point. This observation implies a direct correlation between the interfacial superconductivity and Dirac electrons of the TI layer. We provide evidence to show that the observed interfacial superconductivity and its chemical potential dependence is the result of the competition between the Ruderman-Kittel-Kasuya-Yosida-type ferromagnetic coupling mediated by Dirac surface states and antiferromagnetic exchange couplings that generate the bicollinear antiferromagnetic order in the FeTe layer. The Dirac-fermion-assisted interfacial superconductivity in (Bi,Sb)2Te3/FeTe heterostructures provides a new approach to probe TSC and Majorana physics in hybrid devices and potentially constitutes an alternative platform for topological quantum computation., Comment: 32 pages and 4 figures. Accepted by Nature Communications

We studied the unique kinematic properties in massive filament G352.63-1.07 at $10^3$-AU spatial scale with the dense molecular tracers observed with the Atacama Large Millimeter/submillimeter Array (ALMA). We find the central massive core M1 (12 $M_\odot$) being separated from the surrounding filament with a velocity difference of $v- {v}_{sys}=-2$ km/s and a transverse separation within 3 arcsec. Meanwhile, as shown in multiple dense-gas tracers, M1 has a spatial extension closely aligned with the main filament and is connected to the filament towards its both ends. M1 thus represents a very beginning state for a massive young star-forming core escaping from the parental filament, within a time scale of $\sim 4000$ years. Based on its kinetic energy ($3.5\times10^{44}$ erg), the core escape is unlikely solely due to the original filament motion or magnetic field, but requires more energetic events such as a rapid intense anisotropic collapse. The released energy also seems to noticeably increase the environmental turbulence. This may help the filament to become stabilized again., Comment: 15 pages, 6 figures, accepted for publication in the Astrophysical Journal

Label noise is a pervasive problem in deep learning that often compromises the generalization performance of trained models. Recently, leveraging privileged information (PI) -- information available only during training but not at test time -- has emerged as an effective approach to mitigate this issue. Yet, existing PI-based methods have failed to consistently outperform their no-PI counterparts in terms of preventing overfitting to label noise. To address this deficiency, we introduce Pi-DUAL, an architecture designed to harness PI to distinguish clean from wrong labels. Pi-DUAL decomposes the output logits into a prediction term, based on conventional input features, and a noise-fitting term influenced solely by PI. A gating mechanism steered by PI adaptively shifts focus between these terms, allowing the model to implicitly separate the learning paths of clean and wrong labels. Empirically, Pi-DUAL achieves significant performance improvements on key PI benchmarks (e.g., +6.8% on ImageNet-PI), establishing a new state-of-the-art test set accuracy. Additionally, Pi-DUAL is a potent method for identifying noisy samples post-training, outperforming other strong methods at this task. Overall, Pi-DUAL is a simple, scalable and practical approach for mitigating the effects of label noise in a variety of real-world scenarios with PI., Comment: Accepted ICML 2024

Interactive machine translation (IMT) has emerged as a progression of the computer-aided translation paradigm, where the machine translation system and the human translator collaborate to produce high-quality translations. This paper introduces Synslator, a user-friendly computer-aided translation (CAT) tool that not only supports IMT, but is adept at online learning with real-time translation memories. To accommodate various deployment environments for CAT services, Synslator integrates two different neural translation models to handle translation memories for online learning. Additionally, the system employs a language model to enhance the fluency of translations in an interactive mode. In evaluation, we have confirmed the effectiveness of online learning through the translation models, and have observed a 13% increase in post-editing efficiency with the interactive functionalities of Synslator. A tutorial video is available at:https://youtu.be/K0vRsb2lTt8.

Employing flux-grown single crystal WSe$_2$, we report charge carrier scattering behaviors measured in $h$-BN encapsulated monolayer field effect transistors. We perform quantum transport measurements across various hole densities and temperatures and observe a non-monotonic change of transport mobility $\mu$ as a function of hole density in the degenerately doped sample. This unusual behavior can be explained by energy dependent scattering amplitude of strong defects calculated using the T-matrix approximation. Utilizing long mean-free path ($>$500 nm), we demonstrate the high quality of our electronic devices by showing quantized conductance steps from an electrostatically-defined quantum point contact. Our results show the potential for creating ultra-high quality quantum optoelectronic devices based on atomically thin semiconductors., Comment: 6 pages, 4 figures

The recently released GPT-4 Code Interpreter has demonstrated remarkable proficiency in solving challenging math problems, primarily attributed to its ability to seamlessly reason with natural language, generate code, execute code, and continue reasoning based on the execution output. In this paper, we present a method to fine-tune open-source language models, enabling them to use code for modeling and deriving math equations and, consequently, enhancing their mathematical reasoning abilities. We propose a method of generating novel and high-quality datasets with math problems and their code-based solutions, referred to as MathCodeInstruct. Each solution interleaves natural language, code, and execution results. We also introduce a customized supervised fine-tuning and inference approach. This approach yields the MathCoder models, a family of models capable of generating code-based solutions for solving challenging math problems. Impressively, the MathCoder models achieve state-of-the-art scores among open-source LLMs on the MATH (45.2%) and GSM8K (83.9%) datasets, substantially outperforming other open-source alternatives. Notably, the MathCoder model not only surpasses ChatGPT-3.5 and PaLM-2 on GSM8K and MATH but also outperforms GPT-4 on the competition-level MATH dataset. The dataset and models will be released at https://github.com/mathllm/MathCoder., Comment: The state-of-the-art open-source language models for mathematical reasoning

Toward robust malware detection, we explore the attack surface of existing malware detection systems. We conduct root-cause analyses of the practical binary-level black-box adversarial malware examples. Additionally, we uncover the sensitivity of volatile features within the detection engines and exhibit their exploitability. Highlighting volatile information channels within the software, we introduce three software pre-processing steps to eliminate the attack surface, namely, padding removal, software stripping, and inter-section information resetting. Further, to counter the emerging section injection attacks, we propose a graph-based section-dependent information extraction scheme for software representation. The proposed scheme leverages aggregated information within various sections in the software to enable robust malware detection and mitigate adversarial settings. Our experimental results show that traditional malware detection models are ineffective against adversarial threats. However, the attack surface can be largely reduced by eliminating the volatile information. Therefore, we propose simple-yet-effective methods to mitigate the impacts of binary manipulation attacks. Overall, our graph-based malware detection scheme can accurately detect malware with an area under the curve score of 88.32\% and a score of 88.19% under a combination of binary manipulation attacks, exhibiting the efficiency of our proposed scheme., Comment: 12 pages

The recent observations of a stable molecular condensate emerging from a condensate of bosonic atoms and related "super-chemical" dynamics have raised an intriguing set of questions. Here we provide a microscopic understanding of this unexpected stability and dynamics in atom-molecule superfluids; we show one essential element behind these phenomena is an extremely narrow Feshbach resonance in $^{133}$Cs at 19.849G. Comparing theory and experiment we demonstrate how this narrow resonance enables the dynamical creation of a large closed-channel molecular fraction superfluid, appearing in the vicinity of unitarity. Theoretically the observed superchemistry (\textit{i.e.}, Bose enhanced reactions of atoms and molecules), is found to be assisted by the formation of Cooper-like pairs of bosonic atoms that have opposite momenta. Importantly, this narrow resonance opens the possibility to explore the quantum critical point of a molecular Bose superfluid and related phenomena which would not be possible near a more typically broad Feshbach resonance., Comment: 14 pages, 8 figures; close to the published version

To explore the influence of the cosmological constant on black hole images, we have developed a comprehensive analytical method for simulating images of Kerr-de Sitter black holes illuminated by equatorial thin accretion disks. Through the application of explicit equations, we simulate images of Kerr-de Sitter black holes illuminated by both prograde and retrograde accretion disks, examining the impact of the cosmological constant on their characteristic curves, relative sizes, and observed intensities. Our findings reveal that, in comparison to Kerr black holes, the cosmological constant not only diminishes the relative size of a black hole but also amplifies its luminosity. Moreover, an observer's relative position in the universe ($r_0/r_C$) can influence both the relative size and luminosity of a black hole, where $r_0$ is the distance from the observer to the black hole, $r_C$ is the cosmological horizon determined by the value of the cosmological constant $\Lambda$., Comment: 17 pages, 10 figures, refs added, typos corrected

Gas infall motions play a crucial role in high-mass star formation and are characterized by observable signatures in the form of blue-shifted asymmetric spectral line profiles ("blue profiles"). However, the connection between blue profiles and infall motions is unclear due to complex gas motions at parsec scales. In this study, we present the results of an HCN (4-3) mapping survey conducted with the JCMT, towards 38 massive clumps exhibiting blue profiles in HCO+ (3-2). We extract 34 HCN cores from the 38 observed fields. The core-averaged spectra show various line profiles, indicating that blue-profile HCO+ (3-2) does not guarantee the same in HCN (4-3). Through non-LTE radiation transfer calculations, we attribute the low detection rate of high-$J$ blue profiles to a combination of insufficient HCN (4-3) opacity and intricate gas motion across different density layers. The comparison between the MALT90 and BGPS line surveys highlights the importance of appropriate tracers, high spectral resolution, and column density thresholds when searching for blue profiles. We select 11 reliable infall candidates and adopt the Hill5 model to fit the infall velocity of 0.2-1.9 km/s, corresponding to 5% to 74% of free-fall velocity. Assuming a spherically collapsing model, we estimate the median and mean mass infall rates to be 4.5E-3 and 7.6E-3 Msun/year, respectively. The consistency of the mass infall rates among different transitions suggests a steady accretion process from the clump gas envelope to the inner region., Comment: 36 pages, 9 figures, 5 tables; accepted for publication in ApJS

The ALMA Survey of Star Formation and Evolution in Massive Protoclusters with Blue Profiles (ASSEMBLE) aims to investigate the process of mass assembly and its connection to high-mass star formation theories in protoclusters in a dynamic view. We observed 11 massive (Mclump>1000 Msun), luminous (Lbol>10,000 Lsun), and blue-profile (infall signature) clumps by ALMA with resolution of 2200-5500 au at 350 GHz (870 um) in continuum and line emission. 248 dense cores were identified, including 106 cores showing protostellar signatures and 142 prestellar core candidates. Compared to early-stage infrared dark clouds (IRDCs) by ASHES, the core mass and surface density within the ASSEMBLE clumps exhibited significant increment, suggesting concurrent core accretion during the evolution of the clumps. The maximum mass of prestellar cores was found to be 2 times larger than that in IRDCs, indicating evolved protoclusters have the potential to harbor massive prestellar cores. The mass relation between clumps and their most massive core (MMCs) is observed in ASSEMBLE but not in IRDCs, which is suggested to be regulated by multiscale mass accretion. The mass correlation between the core clusters and their MMCs has a steeper slope compared to that observed in stellar clusters, which can be due to fragmentation of the MMC and stellar multiplicity. We observe a decrease in core separation and an increase in central concentration as protoclusters evolve. We confirm primordial mass segregation in the ASSEMBLE protoclusters, possibly resulting from gravitational concentration and/or gas accretion., Comment: 37 pages, 13 figures, 5 tables; accepted for publication in ApJS

Accurate segmentation of clustered microcalcifications in mammography is crucial for the diagnosis and treatment of breast cancer. Despite exhibiting expert-level accuracy, recent deep learning advancements in medical image segmentation provide insufficient contribution to practical applications, due to the domain shift resulting from differences in patient postures, individual gland density, and imaging modalities of mammography etc. In this paper, a novel framework named MLN-net, which can accurately segment multi-source images using only single source images, is proposed for clustered microcalcification segmentation. We first propose a source domain image augmentation method to generate multi-source images, leading to improved generalization. And a structure of multiple layer normalization (LN) layers is used to construct the segmentation network, which can be found efficient for clustered microcalcification segmentation in different domains. Additionally, a branch selection strategy is designed for measuring the similarity of the source domain data and the target domain data. To validate the proposed MLN-net, extensive analyses including ablation experiments are performed, comparison of 12 baseline methods. Extensive experiments validate the effectiveness of MLN-net in segmenting clustered microcalcifications from different domains and the its segmentation accuracy surpasses state-of-the-art methods. Code will be available at https://github.com/yezanting/MLN-NET-VERSON1., Comment: 17 pages, 9 figures, 3 tables

SHAP (SHapley Additive exPlanations) has become a popular method to attribute the prediction of a machine learning model on an input to its features. One main challenge of SHAP is the computation time. An exact computation of Shapley values requires exponential time complexity. Therefore, many approximation methods are proposed in the literature. In this paper, we propose methods that can compute SHAP exactly in polynomial time or even faster for SHAP definitions that satisfy our additivity and dummy assumptions (eg, kernal SHAP and baseline SHAP). We develop different strategies for models with different levels of model structure information: known functional decomposition, known order of model (defined as highest order of interaction in the model), or unknown order. For the first case, we demonstrate an additive property and a way to compute SHAP from the lower-order functional components. For the second case, we derive formulas that can compute SHAP in polynomial time. Both methods yield exact SHAP results. Finally, if even the order of model is unknown, we propose an iterative way to approximate Shapley values. The three methods we propose are computationally efficient when the order of model is not high which is typically the case in practice. We compare with sampling approach proposed in Castor & Gomez (2008) using simulation studies to demonstrate the efficacy of our proposed methods., Comment: 15 pages

In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task. We propose OFVL-MS, a unified framework that dispenses with the traditional practice of training a model for each individual scene and relieves gradient conflict induced by optimizing multiple scenes collectively, enabling efficient storage yet precise visual localization for all scenes. Technically, in the forward pass of OFVL-MS, we design a layer-adaptive sharing policy with a learnable score for each layer to automatically determine whether the layer is shared or not. Such sharing policy empowers us to acquire task-shared parameters for a reduction of storage cost and task-specific parameters for learning scene-related features to alleviate gradient conflict. In the backward pass of OFVL-MS, we introduce a gradient normalization algorithm that homogenizes the gradient magnitude of the task-shared parameters so that all tasks converge at the same pace. Furthermore, a sparse penalty loss is applied on the learnable scores to facilitate parameter sharing for all tasks without performance degradation. We conduct comprehensive experiments on multiple benchmarks and our new released indoor dataset LIVL, showing that OFVL-MS families significantly outperform the state-of-the-arts with fewer parameters. We also verify that OFVL-MS can generalize to a new scene with much few parameters while gaining superior localization performance.

In recent years, vision transformers have been introduced into face recognition and analysis and have achieved performance breakthroughs. However, most previous methods generally train a single model or an ensemble of models to perform the desired task, which ignores the synergy among different tasks and fails to achieve improved prediction accuracy, increased data efficiency, and reduced training time. This paper presents a multi-purpose algorithm for simultaneous face recognition, facial expression recognition, age estimation, and face attribute estimation (40 attributes including gender) based on a single Swin Transformer. Our design, the SwinFace, consists of a single shared backbone together with a subnet for each set of related tasks. To address the conflicts among multiple tasks and meet the different demands of tasks, a Multi-Level Channel Attention (MLCA) module is integrated into each task-specific analysis subnet, which can adaptively select the features from optimal levels and channels to perform the desired tasks. Extensive experiments show that the proposed model has a better understanding of the face and achieves excellent performance for all tasks. Especially, it achieves 90.97% accuracy on RAF-DB and 0.22 $\epsilon$-error on CLAP2015, which are state-of-the-art results on facial expression recognition and age estimation respectively. The code and models will be made publicly available at https://github.com/lxq1000/SwinFace.

Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest version of GPT-4, known as GPT-4 Code Interpreter, shows remarkable performance on challenging math datasets. In this paper, we explore the effect of code on enhancing LLMs' reasoning capability by introducing different constraints on the \textit{Code Usage Frequency} of GPT-4 Code Interpreter. We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs. Based on this insight, we propose a novel and effective prompting method, explicit \uline{c}ode-based \uline{s}elf-\uline{v}erification~(CSV), to further boost the mathematical reasoning potential of GPT-4 Code Interpreter. This method employs a zero-shot prompt on GPT-4 Code Interpreter to encourage it to use code to self-verify its answers. In instances where the verification state registers as ``False'', the model shall automatically amend its solution, analogous to our approach of rectifying errors during a mathematics examination. Furthermore, we recognize that the states of the verification result indicate the confidence of a solution, which can improve the effectiveness of majority voting. With GPT-4 Code Interpreter and CSV, we achieve an impressive zero-shot accuracy on MATH dataset \textbf{(53.9\% $\to$ 84.3\%)}., Comment: Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification

3D Multi-object tracking (MOT) empowers mobile robots to accomplish well-informed motion planning and navigation tasks by providing motion trajectories of surrounding objects. However, existing 3D MOT methods typically employ a single similarity metric and physical model to perform data association and state estimation for all objects. With large-scale modern datasets and real scenes, there are a variety of object categories that commonly exhibit distinctive geometric properties and motion patterns. In this way, such distinctions would enable various object categories to behave differently under the same standard, resulting in erroneous matches between trajectories and detections, and jeopardizing the reliability of downstream tasks (navigation, etc.). Towards this end, we propose Poly-MOT, an efficient 3D MOT method based on the Tracking-By-Detection framework that enables the tracker to choose the most appropriate tracking criteria for each object category. Specifically, Poly-MOT leverages different motion models for various object categories to characterize distinct types of motion accurately. We also introduce the constraint of the rigid structure of objects into a specific motion model to accurately describe the highly nonlinear motion of the object. Additionally, we introduce a two-stage data association strategy to ensure that objects can find the optimal similarity metric from three custom metrics for their categories and reduce missing matches. On the NuScenes dataset, our proposed method achieves state-of-the-art performance with 75.4\% AMOTA. The code is available at https://github.com/lixiaoyu2000/Poly-MOT, Comment: Accepted to IROS 2023, 1st on the NuScenes Tracking benchmark with 75.4 AMOTA

In this paper, we consider the asymptotic behavior of weak solutions for non-autonomous diffusion equations with delay in time-dependent spaces when the nonlinear function $f$ is critical growth, the delay term $g(t, u_t)$ contains some hereditary characteristics and the external force $h \in L_{l o c}^{2}\left(\mathbb{R} ; L^{2}(\Omega)\right)$. Firstly, we prove the well-posedness of solutions by using the Faedo-Galerkin approximation method. Then after a series of elaborate energy estimates and calculations, we establish the existence and regularity of pullback attractors in time-dependent spaces $C_{\mathcal{H}_{t}(\Omega)}$ and $C_{\mathcal{H}^{1}_{t}(\Omega)}$ respectively., Comment: 30 pages

We present a device architecture of hybrid-edge and dual-gated quantum point contact. We demonstrate improved electrostatic control over the separation, position, and coupling of each broken-symmetry compressible strip in graphene. Via low-temperature magneto-transport measurement, we demonstrate selective manipulation over the evolution, hybridization, and transmission of arbitrarily chosen quantum Hall states in the channel. With gate-tunable tunneling spectroscopy, we characterize the energy gap of each symmetry-broken quantum Hall state with high resolution on the order of ~0.1 meV.

Whether SiC$_2$ is a parent species, that is formed in the photosphere or as a by-product of high-temperature dust formation, or a daughter species, formed in a chemistry driven by the photodestruction of parent species in the outer envelope, has been debated for a long time. Here, we analyze the ALMA observations of four SiC$_2$ transitions in the CSEs of three C-rich AGB stars (AI Vol, II Lup, and RAFGL 4211), and found that SiC$_2$ exhibits an annular, shell-like distribution in these targets, suggesting that SiC$_2$ can be a daughter species in the CSEs of carbon-rich AGB stars. The results can provide important references for future chemical models., Comment: Accepted in Frontiers in Astronomy and Space Sciences

Skin image datasets often suffer from imbalanced data distribution, exacerbating the difficulty of computer-aided skin disease diagnosis. Some recent works exploit supervised contrastive learning (SCL) for this long-tailed challenge. Despite achieving significant performance, these SCL-based methods focus more on head classes, yet ignoring the utilization of information in tail classes. In this paper, we propose class-Enhancement Contrastive Learning (ECL), which enriches the information of minority classes and treats different classes equally. For information enhancement, we design a hybrid-proxy model to generate class-dependent proxies and propose a cycle update strategy for parameters optimization. A balanced-hybrid-proxy loss is designed to exploit relations between samples and proxies with different classes treated equally. Taking both "imbalanced data" and "imbalanced diagnosis difficulty" into account, we further present a balanced-weighted cross-entropy loss following curriculum learning schedule. Experimental results on the classification of imbalanced skin lesion data have demonstrated the superiority and effectiveness of our method.

Models for open quantum systems, which play important roles in electron transport problems and quantum computing, must take into account the interaction of the quantum system with the surrounding environment. Although such models can be derived in some special cases, in most practical situations, the exact models are unknown and have to be calibrated. This paper presents a learning method to infer parameters in Markovian open quantum systems from measurement data. One important ingredient in the method is a direct simulation technique of the quantum master equation, which is designed to preserve the completely-positive property with guaranteed accuracy. The method is particularly helpful in the situation where the time intervals between measurements are large. The approach is validated with error estimates and numerical experiments.

In this work, we study the role of triangle singularity in the $J/\psi \to \gamma \bar{p} \Delta$ decay. We find that through a triangle mechanism, involving a triangle loop composed by $\omega$, $\pi$ and $p$, this decay may develop a triangle singularity and produce a visible peak in the invariant mass $M_{\gamma\Delta}$ around 1.73 GeV with a width of 0.02 GeV. Such a triangle mechanism may also cause significant spin effects on the final $\Delta$, which can be detected by measuring its spin density matrix elements. Our calculations show that the branching ratios due to the triangle mechanism is Br($J/\psi\to \gamma \bar p\Delta,\Delta\to \pi p$)=$1.058\times 10^{-6}$. Hopefully, this reaction can be investigated at BESIII and future experiments, e.g. Super Tau-Charm Facility, and the narrow width of the induced structure, the moving TS position and the distinct features of the spin density matrix elements of the $\Delta$ may serve as signals for the triangle singularity mechanism., Comment: 7 pages, 5 figures

An axion insulator is a three-dimensional (3D) topological insulator (TI), in which the bulk maintains the time-reversal symmetry or inversion symmetry but the surface states are gapped by surface magnetization. The axion insulator state has been observed in molecular beam epitaxy (MBE)-grown magnetically doped TI sandwiches and exfoliated intrinsic magnetic TI MnBi2Te4 flakes with an even number layer. All these samples have a thickness of ~10 nm, near the 2D-to-3D boundary. The coupling between the top and bottom surface states in thin samples may hinder the observation of quantized topological magnetoelectric response. Here, we employ MBE to synthesize magnetic TI sandwich heterostructures and find that the axion insulator state persists in a 3D sample with a thickness of ~106 nm. Our transport results show that the axion insulator state starts to emerge when the thickness of the middle undoped TI layer is greater than ~3 nm. The 3D hundred-nanometer-thick axion insulator provides a promising platform for the exploration of the topological magnetoelectric effect and other emergent magnetic topological states, such as the high-order TI phase., Comment: 25 pages, 5 figures. Comments are very much welcome

The oscillating pressure of the ultralight scalar dark matter (DM) can induce the oscillation of the local gravitational potential. Similar to the time-dependent frequency shift for the pulse signals of pulsars, the oscillation of the local gravitational potential can induce a time-dependent frequency shift (or frequency modulation) for quasi-monochromatic gravitational wave (GW) signals from galactic white dwarf (WD) binaries. To make this effects detectable, we suppose that some galactic WD binaries are located in the DM clumps/subhalos where the energy density of DM is about eight orders of magnitude higher than that at the position of the Earth. Turn to the fisher information matrix, we find that the amplified GW frequency modulation induced by the ultralight scalar DM with mass $m=1.67\times10^{-23}-4.31\times10^{-23}[{\rm eV}/c^2]$ can be detected by LISA., Comment: 6 pages, 2 figures

There is growing evidence that high-mass star formation and hub-filament systems (HFS) are intricately linked. The gas kinematics along the filaments and the forming high-mass star(s) in the central hub are in excellent agreement with the new generation of global hierarchical high-mass star formation models. In this paper, we present an observational investigation of a typical HFS cloud, G310.142+0.758 (G310 hereafter) which reveals unambiguous evidence of mass inflow from the cloud scale via the filaments onto the forming protostar(s) at the hub conforming with the model predictions. Continuum and molecular line data from the ATOMS and MALT90 surveys are used that cover different spatial scales. Three filaments (with total mass $5.7\pm1.1\times 10^3~M_{\odot}$) are identified converging toward the central hub region where several signposts of high-mass star formation have been observed. The hub region contains a massive clump ($1280\pm260~M_{\odot}$) harbouring a central massive core. Additionally, five outflow lobes are associated with the central massive core implying a forming cluster. The observed large-scale, smooth and coherent velocity gradients from the cloud down to the core scale, and the signatures of infall motion seen in the central massive clump and core, clearly unveil a nearly-continuous, multi-scale mass accretion/transfer process at a similar mass infall rate of $\sim 10^{-3}~M_{\odot}~yr^{-1}$ over all scales, feeding the central forming high-mass protostar(s) in the G310 HFS cloud., Comment: Accepted to publish in ApJ. 10 pages with 6 figures and 2 tables

Tool-augmented large language models (LLMs) have achieved remarkable progress in tackling a broad range of tasks. However, existing methods are mainly restricted to specifically designed tools and fail to fulfill complex instructions, having great limitations when confronted with real-world scenarios. In this paper, we explore a more realistic scenario by connecting LLMs with RESTful APIs, which adhere to the widely adopted REST software architectural style for web service development. To address the practical challenges of tackling complex instructions, we propose RestGPT, which exploits the power of LLMs and conducts a coarse-to-fine online planning mechanism to enhance the abilities of task decomposition and API selection. RestGPT also contains an API executor tailored for calling RESTful APIs, which can meticulously formulate parameters and parse API responses. To fully evaluate the performance of RestGPT, we propose RestBench, a high-quality benchmark which consists of two real-world scenarios and human-annotated instructions with gold solution paths. Experiments show that RestGPT is able to achieve impressive results in complex tasks and has strong robustness, which paves a new way towards AGI. RestGPT and RestBench is publicly available at https://restgpt.github.io/., Comment: Add RestBench to evaluate RestGPT

Planning multi-contact motions in a receding horizon fashion requires a value function to guide the planning with respect to the future, e.g., building momentum to traverse large obstacles. Traditionally, the value function is approximated by computing trajectories in a prediction horizon (never executed) that foresees the future beyond the execution horizon. However, given the non-convex dynamics of multi-contact motions, this approach is computationally expensive. To enable online Receding Horizon Planning (RHP) of multi-contact motions, we find efficient approximations of the value function. Specifically, we propose a trajectory-based and a learning-based approach. In the former, namely RHP with Multiple Levels of Model Fidelity, we approximate the value function by computing the prediction horizon with a convex relaxed model. In the latter, namely Locally-Guided RHP, we learn an oracle to predict local objectives for locomotion tasks, and we use these local objectives to construct local value functions for guiding a short-horizon RHP. We evaluate both approaches in simulation by planning centroidal trajectories of a humanoid robot walking on moderate slopes, and on large slopes where the robot cannot maintain static balance. Our results show that locally-guided RHP achieves the best computation efficiency (95\%-98.6\% cycles converge online). This computation advantage enables us to demonstrate online receding horizon planning of our real-world humanoid robot Talos walking in dynamic environments that change on-the-fly.

The altermagnetism can achieve spin-split bands in collinear symmetry-compensated antiferromagnets. Here, we predict altermagnetic order in Janus monolayer $\mathrm{Cr_2SO}$ with eliminated inversion symmetry, which can realize the combination of piezoelectricity and altermagnetism in a two-dimensional material, namely 2D piezoelectric altermagnetism. It is found that $\mathrm{Cr_2SO}$ is an altermagnetic semiconductor, and the spin-split bands of both valence and conduction bands are near the Fermi level. The $\mathrm{Cr_2SO}$ has large out-of-plane piezoelectricity ($|d_{31}|$$=$0.97 pm/V), which is highly desirable for ultrathin piezoelectric device application. Due to spin-valley locking, both spin and valley can be polarized by simply breaking the corresponding crystal symmetry with uniaxial strain. Our findings provide a platform to integrate spin, piezoelectricity and valley in a single material, which is useful for multi-functional device applications., Comment: 6 pages, 5 figures