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Skyrmion qubits are a new highly promising logic element for quantum information processing. However, their scalability to multiple interacting qubits remains challenging. We propose a hybrid quantum setup with skyrmion qubits strongly coupled to nanomechanical cantilevers via magnetic coupling, which harnesses phonons as quantum interfaces for the manipulation of distant skyrmion qubits. A linear drive is utilized to achieve the modulation of the stiffness coefficient of the cantilever, resulting in an exponential enhancement of the coupling strength between the skyrmion qubit and the mechanical mode. We also consider the case of a topological resonator array, which allows us to study interactions between skyrmion qubits and topological phonon band structure, as well as chiral skyrmion-skyrmion interactions. The scheme suggested here offers a fascinating platform for investigating quantum information processing and quantum simulation with magnetic microstructures., Comment: To appear in PR Research, 16 pages, 9 figures

Coherent and dissipative interactions between different quantum systems are essential for the construction of hybrid quantum systems and the investigation of novel quantum phenomena. Here, we propose and analyze a magnon-skyrmion hybrid quantum system, consisting of a micromagnet and nearby magnetic skyrmions. We predict a strong coupling mechanism between the magnonic mode of the micromagnet and the quantized helicity degree of freedom of the skyrmion. We show that with this hybrid setup it is possible to induce magnon-mediated nonreciprocal interactions and responses between distant skyrmion qubits or between skyrmion qubits and other quantum systems like superconducting qubits. This work provides a quantum platform for the investigation of diverse quantum effects and quantum information processing with magnetic microstructures., Comment: To appear in PRL, 9 pages, 4 figures

In-plane anisotropic layered materials (ALMs), such as black phosphorus (BP), exhibit unique angle-resolved polarized Raman (ARPR) spectroscopy characteristics, as attributed to birefringence, linear dichroism and complex Raman tensor. Moreover, the ARPR intensity profiles of BP flakes deposited on multilayer dielectrics are notably sensitive to their thickness, owing to interference effects. The intricate anisotropic effects present challenges in accurately predicting the ARPR intensity of BP flakes. In this study, we propose a comprehensive strategy for predicting the ARPR intensity of BP flakes by explicitly considering optical anisotropy, encompassing birefringence, linear dichroism, and anisotropic cavity interference effects within multilayered structures. Through this approach, we have identified the intrinsic complex Raman tensors for phonon modes, independent of the BP flake thickness. By leveraging this methodology, we have elucidated the flake thickness-dependent effective complex Raman tensor elements, allowing for precise prediction of the observed ARPR intensity profile for the BP flake. This work provides a profound understanding of ARPR behaviors for ALM flakes., Comment: 6 pages, 4 figures

The generalization of monocular metric depth estimation (MMDE) has been a longstanding challenge. Recent methods made progress by combining relative and metric depth or aligning input image focal length. However, they are still beset by challenges in camera, scene, and data levels: (1) Sensitivity to different cameras; (2) Inconsistent accuracy across scenes; (3) Reliance on massive training data. This paper proposes SM4Depth, a seamless MMDE method, to address all the issues above within a single network. First, we reveal that a consistent field of view (FOV) is the key to resolve ``metric ambiguity'' across cameras, which guides us to propose a more straightforward preprocessing unit. Second, to achieve consistently high accuracy across scenes, we explicitly model the metric scale determination as discretizing the depth interval into bins and propose variation-based unnormalized depth bins. This method bridges the depth gap of diverse scenes by reducing the ambiguity of the conventional metric bin. Third, to reduce the reliance on massive training data, we propose a ``divide and conquer" solution. Instead of estimating directly from the vast solution space, the correct metric bins are estimated from multiple solution sub-spaces for complexity reduction. Finally, with just 150K RGB-D pairs and a consumer-grade GPU for training, SM4Depth achieves state-of-the-art performance on most previously unseen datasets, especially surpassing ZoeDepth and Metric3D on mRI$_\theta$. The code can be found at https://github.com/1hao-Liu/SM4Depth., Comment: Project Page: xuefeng-cvr.github.io/SM4Depth

In the quantum Monte Carlo (QMC) method, the Pseudo-Random Number Generator (PRNG) plays a crucial role in determining the computation time. However, the hidden structure of the PRNG may lead to serious issues such as the breakdown of the Markov process. Here, we systematically analyze the performance of the different PRNGs on the widely used QMC method -- stochastic series expansion (SSE) algorithm. To quantitatively compare them, we introduce a quantity called QMC efficiency that can effectively reflect the efficiency of the algorithms. After testing several representative observables of the Heisenberg model in one and two dimensions, we recommend using LCG as the best choice of PRNGs. Our work can not only help improve the performance of the SSE method but also shed light on the other Markov-chain-based numerical algorithms., Comment: 5 pages, 1 figure, almost published version, comments are welcome and more information at http://cqutp.org/users/xfzhang/

Recently, neural networks (NN) have made great strides in combinatorial optimization. However, they face challenges when solving the capacitated arc routing problem (CARP) which is to find the minimum-cost tour covering all required edges on a graph, while within capacity constraints. In tackling CARP, NN-based approaches tend to lag behind advanced metaheuristics, since they lack directed arc modeling and efficient learning methods tailored for complex CARP. In this paper, we introduce an NN-based solver to significantly narrow the gap with advanced metaheuristics while exhibiting superior efficiency. First, we propose the direction-aware attention model (DaAM) to incorporate directionality into the embedding process, facilitating more effective one-stage decision-making. Second, we design a supervised reinforcement learning scheme that involves supervised pre-training to establish a robust initial policy for subsequent reinforcement fine-tuning. It proves particularly valuable for solving CARP that has a higher complexity than the node routing problems (NRPs). Finally, a path optimization method is proposed to adjust the depot return positions within the path generated by DaAM. Experiments illustrate that our approach surpasses heuristics and achieves decision quality comparable to state-of-the-art metaheuristics for the first time while maintaining superior efficiency.

Magnetic skyrmions, topologically nontrivial whirling spin textures at nanometer scales, have emerged as potential information carriers for spintronic devices. The ability to efficiently create and erase magnetic skyrmions is vital yet challenging for such applications. Based on first-principles studies, we find that switching between intrinsic magnetic skyrmion and high-temperature ferromagnetic states can be achieved in two-dimensional van der Waals (vdW) multiferroic heterostructure CrSeI/In2Te3 by reversing the ferroelectric polarization of In2Te3. The core mechanism of this switching is traced to the controllable magnetic anisotropy of CrSeI influenced by the ferroelectric polarization of In2Te3. We propose a useful descriptor linking the presence of magnetic skyrmions to magnetic parameters, and validate this connection through studies of a variety of similar vdW multiferroic heterostructures. Our work demonstrates that manipulating magnetic skyrmions via tunable magnetic anisotropies in vdW multiferroic heterostructures represents a highly promising and energy-efficient strategy for future development of spintronics., Comment: 20 Pages, 3 figures, 1 table; accepted by Nano Letters

Topological and valleytronic materials are promising for spintronic and quantum applications due to their unique properties. Using first principles calculations, we demonstrate that germanene (Ge)-based ferromagnetic heterostructures can exhibit multiple quantum states such as quantum anomalous Hall effect (QAHE) with Chern numbers of C=-1 or C=-2, quantum valley Hall effect (QVHE) with a valley Chern number of C$v$=2, valley-polarized quantum anomalous Hall effect (VP-QAHE) with two Chern numbers of C=-1 and C$v$=-1 as well as time-reversal symmetry broken quantum spin Hall effect (T-broken QSHE) with a spin Chern number of C$s$~1. Furthermore, we find that the transitions between different quantum states can occur by changing the magnetic orientation of ferromagnetic layers through applying a magnetic field. Our discovery provides new routes and novel material platforms with a unique combination of diverse properties that make it well suitable for applications in electronics, spintronics and valley electronics., Comment: 15 pages, 3 figures

Correlated errors can significantly impact the quantum error correction, which challenges the assumption that errors occur in different qubits independently in both space and time. Superconducting qubits have been found to suffer correlated errors across multiple qubits, which could be attributable to ionizing radiations and cosmic rays. Nevertheless, the direct evidence and a quantitative understanding of this relationship are currently lacking. In this work, we propose to continuously monitor multi-qubit simultaneous charge-parity jumps to detect correlated errors and find that occur more frequently than multi-qubit simultaneous bit flips. Then, we propose to position two cosmic-ray muon detectors directly beneath the sample box in a dilution refrigerator and successfully observe the correlated errors in a superconducting qubit array triggered by muons. By introducing a lead shielding layer on the refrigerator, we also reveal that the majority of other correlated errors are primarily induced by gamma rays. Furthermore, we find the superconducting film with a higher recombination rate of quasiparticles used in the qubits is helpful in reducing the duration of correlated errors. Our results provide experimental evidence of the impact of gamma rays and muons on superconducting quantum computation and offer practical insights into mitigation strategies for quantum error correction. In addition, we observe the average occurrence rate of muon-induced correlated errors in our processor is approximately 0.40 min$^{-1}$cm$^{-2}$, which is comparable to the muon event rate detected by the muon detector with 0.506 min$^{-1}$cm$^{-2}$. This demonstrates the potential applications of superconducting qubit arrays as low-energy threshold sensors in the field of high-energy physics., Comment: 7 pages and 5 figures for the main text, 20 pages and 20 figures for the supplementary materials

Achieving real-time and accuracy on embedded platforms has always been the pursuit of road segmentation methods. To this end, they have proposed many lightweight networks. However, they ignore the fact that roads are "stuff" (background or environmental elements) rather than "things" (specific identifiable objects), which inspires us to explore the feasibility of representing roads with low-level instead of high-level features. Surprisingly, we find that the primary stage of mainstream network models is sufficient to represent most pixels of the road for segmentation. Motivated by this, we propose a Low-level Feature Dominated Road Segmentation network (LFD-RoadSeg). Specifically, LFD-RoadSeg employs a bilateral structure. The spatial detail branch is firstly designed to extract low-level feature representation for the road by the first stage of ResNet-18. To suppress texture-less regions mistaken as the road in the low-level feature, the context semantic branch is then designed to extract the context feature in a fast manner. To this end, in the second branch, we asymmetrically downsample the input image and design an aggregation module to achieve comparable receptive fields to the third stage of ResNet-18 but with less time consumption. Finally, to segment the road from the low-level feature, a selective fusion module is proposed to calculate pixel-wise attention between the low-level representation and context feature, and suppress the non-road low-level response by this attention. On KITTI-Road, LFD-RoadSeg achieves a maximum F1-measure (MaxF) of 95.21% and an average precision of 93.71%, while reaching 238 FPS on a single TITAN Xp and 54 FPS on a Jetson TX2, all with a compact model size of just 936k parameters. The source code is available at https://github.com/zhouhuan-hust/LFD-RoadSeg., Comment: 11 pages, 7 figures, IEEE TITS

The pulse widths of fast radio bursts are always broadened due to the scattering of the plasma medium through which the electromagnetic wave passes. The recorded pulse width will be further affected by the radio telescopes since the sampling time and the bandwidth cannot be infinitely small. In this study, we focus on the pulse widths of the 3287 bursts detected from FRB 20121102A as of October 2023. Various effects such as the scattering broadening, the redshift broadening and the instrumental broadening are examined. It is found that the instrumental broadening only contributes a fraction of $10^{-3}$--$10^{-1}$ to the observed pulse width. The scattering broadening is even smaller, which constitutes a tiny fraction of $10^{-5}$--$10^{-2}$ in the observed pulse width. After correcting for these broadenings, the intrinsic pulse width is derived for each burst. The maximum and minimum pulse widths at different frequencies are highlighted. Interestingly, both the mean value and the dispersion range of intrinsic pulse width are found to be inversely proportional to the square of the central frequency. The intrinsic widths of most bursts are in a narrow range of 1--10 ms, which leads to a quasi-linear correlation between the fluence and the peak flux.

Axions or axion-like particles (ALPs) are one of the most promising candidates of dark matter (DM). A prevalent method to detect axion-like DM is to seek the periodic oscillation feature in the polarization angles of linearly polarized light emitted from astrophysical sources. In this work, we use the time-resolved polarization measurements of the hyperactive repeating fast radio burst, FRB 20220912A, detected by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) to search for extragalactic axion-like DM for the first time. Given a DM density profile of FRB 20220912A's host, we obtain upper limits on the ALP-photon coupling constant of $g_{a \gamma}<(2.9 \times 10^{-11}-1.1\times 10^{-9})\,\mathrm{GeV}^{-1}$ for the ALP masses $m_a \sim (1.4\times10^{-21}-5.2\times10^{-20})$ eV. Persistent polarimetric observations with FAST would further improve the constraints. We prove that FRBs offer an alternative way to detect axion-like DM on extragalactic distance scales, complementary to other galactic DM probes., Comment: 7 pages, 2 figures

This paper studies zero-shot anomaly classification (AC) and segmentation (AS) in industrial vision. We reveal that the abundant normal and abnormal cues implicit in unlabeled test images can be exploited for anomaly determination, which is ignored by prior methods. Our key observation is that for the industrial product images, the normal image patches could find a relatively large number of similar patches in other unlabeled images, while the abnormal ones only have a few similar patches. We leverage such a discriminative characteristic to design a novel zero-shot AC/AS method by Mutual Scoring (MuSc) of the unlabeled images, which does not need any training or prompts. Specifically, we perform Local Neighborhood Aggregation with Multiple Degrees (LNAMD) to obtain the patch features that are capable of representing anomalies in varying sizes. Then we propose the Mutual Scoring Mechanism (MSM) to leverage the unlabeled test images to assign the anomaly score to each other. Furthermore, we present an optimization approach named Re-scoring with Constrained Image-level Neighborhood (RsCIN) for image-level anomaly classification to suppress the false positives caused by noises in normal images. The superior performance on the challenging MVTec AD and VisA datasets demonstrates the effectiveness of our approach. Compared with the state-of-the-art zero-shot approaches, MuSc achieves a $\textbf{21.1%}$ PRO absolute gain (from 72.7% to 93.8%) on MVTec AD, a $\textbf{19.4%}$ pixel-AP gain and a $\textbf{14.7%}$ pixel-AUROC gain on VisA. In addition, our zero-shot approach outperforms most of the few-shot approaches and is comparable to some one-class methods. Code is available at https://github.com/xrli-U/MuSc., Comment: Accepted at ICLR2024

Utilizing a tritter with variable parameter $T$ and induced by vacuum-one-photon superpositions $\left\vert 0\right\rangle +\alpha \left\vert 1\right\rangle $ with $\alpha =\left\vert \alpha \right\vert e^{i\phi }$, we generate a class of three-qubit pure states. These states take the form of $\left\vert \psi \right\rangle _{123}=c_{0}\left\vert 000\right\rangle +c_{1}\left\vert 100\right\rangle +c_{2}\left\vert 010\right\rangle +c_{3}\left\vert 001\right\rangle $. The coefficients ($ c_{0}$, $c_{1}$, $c_{2}$, and $c_{3}$) can be manipulated through interaction parameters ($\left\vert \alpha \right\vert $, $\phi $, and $T$). In line with Xie and Eberly's work[Phys. Rev. Lett. 127, 040403 (2021)], we investigate the genuine tripartite entanglement for $\left\vert \psi \right\rangle _{123}$ using the concurrence triangle measure. Drawing on Hao et al.'s research [Phys. Rev. Lett. 128, 120402 (2021)], we examine tripartite steering for $\left\vert \psi \right\rangle _{123}$ under certain measurements based on the uncertainty relations criterion. We identify nine potential configurations exhibiting varying steerability across different parameter spaces. It is important to highlight that while the state $% \left\vert \psi \right\rangle _{123}$ exhibits entanglement, steering remains unattainable in a substantial portion of the parameter space., Comment: 10 pages, 8 figures, comments are welcome

Visual obstacle discovery is a key step towards autonomous navigation of indoor mobile robots. Successful solutions have many applications in multiple scenes. One of the exceptions is the reflective ground. In this case, the reflections on the floor resemble the true world, which confuses the obstacle discovery and leaves navigation unsuccessful. We argue that the key to this problem lies in obtaining discriminative features for reflections and obstacles. Note that obstacle and reflection can be separated by the ground plane in 3D space. With this observation, we firstly introduce a pre-calibration based ground detection scheme that uses robot motion to predict the ground plane. Due to the immunity of robot motion to reflection, this scheme avoids failed ground detection caused by reflection. Given the detected ground, we design a ground-pixel parallax to describe the location of a pixel relative to the ground. Based on this, a unified appearance-geometry feature representation is proposed to describe objects inside rectangular boxes. Eventually, based on segmenting by detection framework, an appearance-geometry fusion regressor is designed to utilize the proposed feature to discover the obstacles. It also prevents our model from concentrating too much on parts of obstacles instead of whole obstacles. For evaluation, we introduce a new dataset for Obstacle on Reflective Ground (ORG), which comprises 15 scenes with various ground reflections, a total of more than 200 image sequences and 3400 RGB images. The pixel-wise annotations of ground and obstacle provide a comparison to our method and other methods. By reducing the misdetection of the reflection, the proposed approach outperforms others. The source code and the dataset will be available at https://github.com/XuefengBUPT/IndoorObstacleDiscovery-RG., Comment: International Journal of Computer Vision (IJCV) 2023. Project Page: https://xuefeng-cvr.github.io/IODRG

The field of fast radio bursts (FRBs) has entered the age of fine characterization as observational results from different radio telescopes become more and more abundant. The large FRB sample is suitable for a statistical study. There is an interesting finding that the waiting-time distributions of very active repeating FRBs show a universal double-peaked feature, with left peaks lower than right ones. Assuming these two peaks are independent and initially comparable, we show that the observed asymmetric shape can be ascribed to the propagational effect in the magnetosphere. An FRB passing through the magnetized plasma will induce the circular motion of charged particles to form a current loop. This further leads to an induced magnetic field with opposite direction respect to the background field. As the effective field strength changes, the scattering absorption probability of the following FRB will be influenced. The absorption can be important under certain physical conditions and bursts with smaller time-lags are easier to be absorbed. Also there will be an imprint on the flux distribution as the scattering optical depth depends on burst luminosity as well., Comment: 10 pages, 8 figures, accepted by ApJ

In this paper, we introduce a noiselessly amplified thermal state (ATS), by operating the noiseless amplification operator ($g^{\hat{n}}$) on the thermal state (TS) with corresponding mean photon number (MPN) $\bar{n}$. Actually, the ATS is an new TS with MPN $\bar{N}=g^{2}\bar{n}/[1-\bar{n}\left(g^{2}-1\right)]$. Furthermore, we introduce photon-added-ATS (PAATS) and photon-subtracted-ATS (PSATS) by operating $m$-photon addition ($\hat{a}^{\dag m}$) and $m$-photon subtraction ($\hat{a}^{m}$) on the ATS, respectively. We study photon number distributions (PNDs), purities, and Wigner functions (WFs) for all these states., Comment: 8 pages, 8 figures

Particle-in-cell simulations have unveiled that shock-accelerated electrons do not follow a pure power-law distribution, but have an additional low-energy "thermal" part, which owns a considerable portion of the total energy of electrons. Investigating the effects of these thermal electrons on gamma-ray burst (GRB) afterglows may provide valuable insights into the particle acceleration mechanisms. We solve the continuity equation of electrons in the energy space, from which multi-wavelength afterglows are derived by incorporating processes including synchrotron radiation, synchrotron self-absorption, synchrotron self-Compton scattering, and gamma-gamma annihilation. First, there is an underlying positive correlation between temporal and spectral indices due to the cooling of electrons. Moreover, thermal electrons would result in the simultaneous non-monotonic variation in both spectral and temporal indices at multi-wavelength, which could be individually recorded by the 2.5-meter Wide Field Survey Telescope and Vera Rubin Observatory Legacy Survey of Space and Time (LSST). The thermal electrons could also be diagnosed from afterglow spectra by synergy observation in the optical (with LSST) and X-ray bands (with the Microchannel X-ray Telescope on board the Space Variable Objects Monitor). Finally, we use Monte Carlo simulations to obtain the distribution of peak flux ratio ($R_{\rm X}$) between soft and hard X-rays, and of the time delay ($\Delta t$) between peak times of soft X-ray and optical light curves. The thermal electrons significantly raise the upper limits of both $R_{\rm X}$ and $\Delta t$. Thus the distribution of GRB afterglows with thermal electrons is more dispersive in the $R_{\rm X} - \Delta t$ plane., Comment: 17 pages, 15 figures

The application of eigenvalue theory to dual quaternion Hermitian matrices holds significance in the realm of multi-agent formation control. In this paper, we study the Rayleigh quotient iteration (RQI) for solving the right eigenpairs of dual quaternion Hermitian matrices. Combined with dual representation, the RQI algorithm can effectively compute the extreme eigenvalue along with the associated eigenvector of the large dual quaternion Hermitian matrices. Furthermore, a convergence analysis of the Rayleigh quotient iteration is derived, demonstrating a local convergence rate of at least cubic, which is faster than the linear convergence rate of the power method. Numerical examples are provided to illustrate the high accuracy and low CPU time cost of the proposed Rayleigh quotient iteration compared with the power method for solving the dual quaternion Hermitian eigenvalue problem., Comment: arXiv admin note: text overlap with arXiv:2111.12211 by other authors

Achieving both high-performance and wide field-of-view (FOV) super-resolution imaging has been attracting increasing attention in recent years. However, such goal suffers from long reconstruction time and huge storage space. Parallel compressive imaging (PCI) provides an efficient solution, but the super-resolution quality and imaging speed are strongly dependent on precise optical transfer function (OTF), modulation masks and reconstruction algorithm. In this work, we propose a wide FOV parallel compressive super-resolution imaging approach based on physics enhanced network. By training the network with the prior OTF of an arbitrary 128x128-pixel region and fine-tuning the network with other OTFs within rest regions of FOV, we realize both mask optimization and super-resolution imaging with up to 1020x1500 wide FOV. Numerical simulations and practical experiments demonstrate the effectiveness and superiority of the proposed approach. We achieve high-quality reconstruction with 4x4 times super-resolution enhancement using only three designed masks to reach real-time imaging speed. The proposed approach promotes the technology of rapid imaging for super-resolution and wide FOV, ranging from infrared to Terahertz.

Anomalous Hall effect (AHE), one of the most important electronic transport phenomena, generally appears in ferromagnetic materials but is rare in materials without magnetic elements. Here, we present a study of La$_3$MgBi$_5$, whose band structure carries multitype Dirac fermions. Although magnetic elements are absent in La$_3$MgBi$_5$, clear signals of AHE can be observed. In particular, the anomalous Hall conductivity is extremely large, reaching 42,356 $\Omega^{-1}$ cm$^{-1}$ with an anomalous Hall angle of 8.8 %, the largest one that has been observed in the current AHE systems. The AHE is suggested to originate from the combination of skew scattering and Berry curvature. Another unique property discovered in La$_3$MgBi$_5$ is the axial diamagnetism. The diamagnetism is significantly enhanced and dominates the magnetization in the axial directions, which is the result of restricted motion of the Dirac fermion at Fermi level. Our findings not only establish La$_3$MgBi$_5$ as a suitable platform to study AHE and quantum transport, but also indicate the great potential of 315-type Bi-based materials for exploring novel physical properties.

Phase modulation has scarcely been mentioned in diffusive systems since the diffusion process does not carry momentum like waves. Recently, the non-Hermitian physics provides a new perspective for understanding diffusion and shows prospects in the phase regulation of heat flow, for example, the discovery of anti-parity-time (APT) symmetry in diffusive systems. The precise control of thermal phase however remains elusive hitherto and can hardly be realized in APT-symmetric thermal systems due to the existence of phase oscillation. Here we construct the counterpart of APT-symmetric diffusive systems, i.e., PT-symmetric diffusive systems, which can achieve complete suppression of thermal phase oscillation. We find the real coupling of diffusive fields can be readily established through a strong convective background, where the decay-rate detuning is enabled by thermal metamaterial design. Moreover, we observe phase transition of PT symmetry breaking in diffusive systems with the symmetry-determined amplitude distribution and phase regulation of coupled temperature fields. Our work uncovers the existence of PT-symmetry in dissipative energy exchanges and provides a unique approach for harnessing the mass transfer of particles, the wave propagation in strongly scattering systems as well as thermal conduction.

The paradigm shift of the Hermitian systems into the non-Hermitian regime profoundly modifies the inherent topological property, leading to various unprecedented effects such as the non-Hermitian skin effect (NHSE). In the past decade, the NHSE effect has been demonstrated in quantum, optical and acoustic systems. Besides in those non-Hermitian wave systems, the NHSE in diffusive systems has not yet been explicitly demonstrated, despite recent abundant advances in the study of topological thermal diffusion. Here we first design a thermal diffusion lattice based on a modified Su-Schrieffer-Heeger model which enables the observation of diffusive NHSE. In the proposed model, the periodic heat exchange rate among adjacent unit cells and the asymmetric temperature field coupling inside unit cells can be judiciously realized by appropriate configurations of structural parameters of unit cells. The transient concentration feature of temperature field on the boundary regardless of initial excitation conditions can be clearly observed, indicating the occurrence of transient thermal skin effect. Nonetheless, we experimentally demonstrated the NHSE and verified the remarkable robustness against various defects. Our work provides a platform for exploration of non-Hermitian physics in the diffusive systems, which has important applications in efficient heat collection, highly sensitive thermal sensing and others., Comment: 23 pages, 5 figures

Unmanned Aerial Vehicles (UAVs) have been widely used in many areas, including transportation, surveillance, and military. However, their potential for safety and privacy violations is an increasing issue and highly limits their broader applications, underscoring the critical importance of UAV perception and defense (anti-UAV). Still, previous works have simplified such an anti-UAV task as a tracking problem, where the prior information of UAVs is always provided; such a scheme fails in real-world anti-UAV tasks (i.e. complex scenes, indeterminate-appear and -reappear UAVs, and real-time UAV surveillance). In this paper, we first formulate a new and practical anti-UAV problem featuring the UAVs perception in complex scenes without prior UAVs information. To benchmark such a challenging task, we propose the largest UAV dataset dubbed AntiUAV600 and a new evaluation metric. The AntiUAV600 comprises 600 video sequences of challenging scenes with random, fast, and small-scale UAVs, with over 723K thermal infrared frames densely annotated with bounding boxes. Finally, we develop a novel anti-UAV approach via an evidential collaboration of global UAVs detection and local UAVs tracking, which effectively tackles the proposed problem and can serve as a strong baseline for future research. Extensive experiments show our method outperforms SOTA approaches and validate the ability of AntiUAV600 to enhance UAV perception performance due to its large scale and complexity. Our dataset, pretrained models, and source codes will be released publically.

Machine Learning as a Service (MLaaS) platforms have gained popularity due to their accessibility, cost-efficiency, scalability, and rapid development capabilities. However, recent research has highlighted the vulnerability of cloud-based models in MLaaS to model extraction attacks. In this paper, we introduce FDINET, a novel defense mechanism that leverages the feature distribution of deep neural network (DNN) models. Concretely, by analyzing the feature distribution from the adversary's queries, we reveal that the feature distribution of these queries deviates from that of the model's training set. Based on this key observation, we propose Feature Distortion Index (FDI), a metric designed to quantitatively measure the feature distribution deviation of received queries. The proposed FDINET utilizes FDI to train a binary detector and exploits FDI similarity to identify colluding adversaries from distributed extraction attacks. We conduct extensive experiments to evaluate FDINET against six state-of-the-art extraction attacks on four benchmark datasets and four popular model architectures. Empirical results demonstrate the following findings FDINET proves to be highly effective in detecting model extraction, achieving a 100% detection accuracy on DFME and DaST. FDINET is highly efficient, using just 50 queries to raise an extraction alarm with an average confidence of 96.08% for GTSRB. FDINET exhibits the capability to identify colluding adversaries with an accuracy exceeding 91%. Additionally, it demonstrates the ability to detect two types of adaptive attacks., Comment: 13 pages, 7 figures

We present an X-ray spectrum of the diffuse X-ray background (DXRB) between 1.5 and 120 keV, as measured with the Low-Energy Detector (LE) and the High-Energy Detector (HE) aboard the Insight-HXMT satellite, based on 'blank-sky' observations. LE covers a nominal energy range of 1-15 keV and HE 20-250 keV, but calibration issues and data quality narrowed the energy range for this work. The LE background was directly measured with `blind' detector modules, while the HE background was derived from Earth-occultation data. With the LE data alone, the measured DXRB spectrum can be well described by a power law; fitting the LE and HE data jointly, however, a spectral cut-off must be introduced in the model to account for the measurements above 30 keV. Modelling the combined spectrum with a cut-off power law, the best-fit photon index is 1.40, normalisation $9.57$~$\rm ph~cm^{-2}~s^{-1}~keV^{-1}~sr^{-1} $ (at 1 keV), and cut-off energy 55 keV, after correcting for the effects of the Earth albedo and atmospheric emission (which are significant in the HE band). Based on the best-fit cut-off power law, we derived the spectral energy distribution (SED) of the DXRB. The shape of the SED is in general agreement with the published measurements, but the overall normalization is lower by varying amounts, except for the HEAO-1 result, with which our result is in good agreement., Comment: 9 pages, 14 figures, published in MNRAS