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Non-Markovianity, the intricate dependence of an open quantum system on its temporal evolution history, holds tremendous implications across various scientific disciplines. However, accurately characterizing the complex non-Markovian effects has posed a formidable challenge for numerical simulations. While quantum computing technologies show promise, a universal theory enabling practical quantum algorithm implementation has been elusive. We address this gap by introducing the dissipaton-embedded quantum master equation in second quantization (DQME-SQ). This exact and compact theory offers two key advantages: representability by quantum circuits and universal applicability to any Gaussian environment. We demonstrate these capabilities through digital quantum simulations of non-Markovian dissipative dynamics in both bosonic and fermionic environments. The DQME-SQ framework opens a new horizon for the efficient exploration of complex open quantum systems by leveraging the rapidly advancing quantum computing technologies., Comment: 14 pages, 5 figures

Spintronic logic devices require efficient spin-charge interconversion: converting charge current to spin current and spin current to charge current. In spin-orbit materials that are regarded as the most promising candidate for spintronic logic devices, one mechanism that is responsible for spin-charge interconversion is Edelstein and inverse Edelstein effects based on spin-momentum locking in materials with Rashba-type spin-orbit coupling. Over last decade, there has been rapid progresses for increasing interconversion efficiencies due to the Edelstein effect in a few Rashba-Dresselhaus materials and topological insulators, making Rashba spin-momentum locking a promising technological solution for spin-orbit logic devices. However, despite the rapid progress that leads to high spin-charge interconversion efficiency at cryogenic temperatures, the room-temperature efficiency needed for technological applications is still low. This paper presents our understanding on the challenges and opportunities in searching for Rashba-Dresselhaus materials for efficient spin-charge interconversion at room temperature by focusing on materials properties such as Rashba coefficients, momentum relaxation times, spin-momentum locking relations and electrical conductivities., Comment: 5 figures

We introduce Coverage Axis++, a novel and efficient approach to 3D shape skeletonization. The current state-of-the-art approaches for this task often rely on the watertightness of the input or suffer from substantial computational costs, thereby limiting their practicality. To address this challenge, Coverage Axis++ proposes a heuristic algorithm to select skeletal points, offering a high-accuracy approximation of the Medial Axis Transform (MAT) while significantly mitigating computational intensity for various shape representations. We introduce a simple yet effective strategy that considers shape coverage, uniformity, and centrality to derive skeletal points. The selection procedure enforces consistency with the shape structure while favoring the dominant medial balls, which thus introduces a compact underlying shape representation in terms of MAT. As a result, Coverage Axis++ allows for skeletonization for various shape representations (e.g., water-tight meshes, triangle soups, point clouds), specification of the number of skeletal points, few hyperparameters, and highly efficient computation with improved reconstruction accuracy. Extensive experiments across a wide range of 3D shapes validate the efficiency and effectiveness of Coverage Axis++. Our codes are available at https://github.com/Frank-ZY-Dou/Coverage_Axis., Comment: SGP2024. Project Page: https://frank-zy-dou.github.io/projects/CoverageAxis++/index.html

Unsupervised anomaly detection has gained significant attention in the field of medical imaging due to its capability of relieving the costly pixel-level annotation. To achieve this, modern approaches usually utilize generative models to produce healthy references of the diseased images and then identify the abnormalities by comparing the healthy references and the original diseased images. Recently, diffusion models have exhibited promising potential for unsupervised anomaly detection in medical images for their good mode coverage and high sample quality. However, the intrinsic characteristics of the medical images, e.g. the low contrast, and the intricate anatomical structure of the human body make the reconstruction challenging. Besides, the global information of medical images often remain underutilized. To address these two issues, we propose a novel Masked Autoencoder-enhanced Diffusion Model (MAEDiff) for unsupervised anomaly detection in brain images. The MAEDiff involves a hierarchical patch partition. It generates healthy images by overlapping upper-level patches and implements a mechanism based on the masked autoencoders operating on the sub-level patches to enhance the condition on the unnoised regions. Extensive experiments on data of tumors and multiple sclerosis lesions demonstrate the effectiveness of our method.

Spherical black-hole (BH) solutions have been found in the bumblebee gravity where a vector field nonminimally couples to the Ricci tensor. We study dynamic (in)stability associated with the gravitational and vector perturbations of odd parity against these bumblebee BHs. Under the plane-wave approximation, we find that bumblebee BHs do not suffer ghost instability, but gradient instability and tachyonic instability exist when the bumblebee charge exceeds certain values. The existence of the instabilities also depends on the nonminimal coupling constant $\xi$ that, there is a minimal value $\xi \sim 4\pi G$ with $G$ the gravitational constant for the instabilities to happen. The theoretical consideration for bumblebee BH stability turns out to place stronger constraints on the parameter space than those from the recent observations of supermassive BH shadows by the Event Horizon Telescope Collaboration. It is also reminiscent of Penrose's cosmic censorship conjecture since the charge of bumblebee BHs cannot be too large due to the dynamic instabilities. Specifically, for $\xi(\xi-16\pi G) > 0$, we find that the charge of a bumblebee BH cannot be larger than its mass., Comment: Accepted by PRD, 13 pages, 4 figures, references added

Omnidirectional stereo matching (OSM) is an essential and reliable means for $360^{\circ}$ depth sensing. However, following earlier works on conventional stereo matching, prior state-of-the-art (SOTA) methods rely on a 3D encoder-decoder block to regularize the cost volume, causing the whole system complicated and sub-optimal results. Recently, the Recurrent All-pairs Field Transforms (RAFT) based approach employs the recurrent update in 2D and has efficiently improved image-matching tasks, ie, optical flow, and stereo matching. To bridge the gap between OSM and RAFT, we mainly propose an opposite adaptive weighting scheme to seamlessly transform the outputs of spherical sweeping of OSM into the required inputs for the recurrent update, thus creating a recurrent omnidirectional stereo matching (RomniStereo) algorithm. Furthermore, we introduce two techniques, ie, grid embedding and adaptive context feature generation, which also contribute to RomniStereo's performance. Our best model improves the average MAE metric by 40.7\% over the previous SOTA baseline across five datasets. When visualizing the results, our models demonstrate clear advantages on both synthetic and realistic examples. The code is available at \url{https://github.com/HalleyJiang/RomniStereo}., Comment: accepted by IEEE RA-L, https://github.com/HalleyJiang/RomniStereo

Many-body open quantum systems (OQS) have a profound impact on various subdisciplines of physics, chemistry, and biology. Thus, the development of a computer program capable of accurately, efficiently, and versatilely simulating many-body OQS is highly desirable. In recent years, we have focused on the advancement of numerical algorithms based on the fermionic hierarchical equations of motion (HEOM) theory. Being in-principle exact, this approach allows for the precise characterization of many-body correlations, non-Markovian memory, and non-equilibrium thermodynamic conditions. These efforts now lead to the establishment of a new computer program, HEOM for QUantum Impurity with a Correlated Kernel, version 2 (HEOM-QUICK2), which, to the best of our knowledge, is currently the only general-purpose simulator for fermionic many-body OQS. Compared with version 1, the HEOM-QUICK2 program features more efficient solvers for stationary states, more accurate treatment of non-Markovian memory, and improved numerical stability for long-time dissipative dynamics. Integrated with quantum chemistry software, HEOM-QUICK2 has become a valuable theoretical tool for the precise simulation of realistic many-body OQS, particularly the single atomic or molecular junctions. Furthermore, the unprecedented precision achieved by HEOM-QUICK2 enables accurate simulation of low-energy spin excitations and coherent spin relaxation. The unique usefulness of HEOM-QUICK2 is demonstrated through several examples of strongly correlated quantum impurity systems under non-equilibrium conditions. Thus, the new HEOM-QUICK2 program offers a powerful and comprehensive tool for studying many-body OQS with exotic quantum phenomena and exploring applications in various disciplines., Comment: 22 pages; 9 figures

The complex correlated charge density wave (CDW) phases of 1T-TaS2 have attracted great attention due to their emergent quantum states, such as intricate CDW phase, Mott-Hubbard state, superconductivity and quantum spin liquid. The delicate interplay among the complex intra-/inter-layer electron-electron and electron-lattice interactions is the fundamental prerequisite of these exotic quantum states. Here, we report a real-space titration-like investigation of correlated CDW state in 1T-TaS2 upon hole-doping via low-temperature scanning tunneling microscopy (LT-STM). The gradual increased hole-doping results in the sequential emergence of electron voids, phase domains, stacking disordering and mixed phase/chiral domains attributed to the reduced electron correlations. The achiral intermediate ring-like clusters and nematic CDW states emerge at the intralayer chiral domain wall and interlayer heterochiral stacking regions via the chiral-overlapping configurations. The local reversible CDW manipulation is further realized by the non-equilibrium transient charge-injections of STM field-emission spectra. Our results provide an in-depth insight of this intricate correlated CDW state, and pave a way to realize exotic quantum states via the accurate tuning of interior interactions in correlated materials.

A system-bath entanglement theorem (SBET) with Gaussian environments was established previously in J. Chem. Phys. 152, 034102 (2020) in terms of linear response functions. This theorem connects the system-bath entanglement responses to the local system and bare bath ones. In this work, we generalize it to correlation functions. Key steps in derivation are the generalized Langevin dynamics for the hybridizing bath modes as in the previous work, together with the Bogoliubov transformation mapping the original finite-temperature canonical reservoir to an effective zero-temperature vacuum via an auxiliary bath. With the theorem, the system-bath entangled correlations and the bath modes correlations in the full composite space can be evaluated as long as the bare-bath statistical properties are known and the reduced system correlations are obtained. Numerical demonstrations are carried out for the evaluation of the solvation free energy of an electron transfer system with a certain intramolecular vibrational modes., Comment: 9 pages, 3 figures

Timing a pulsar orbiting around Sagittarius A* (Sgr A*) can provide us with a unique opportunity of testing gravity theories. We investigate the detectability of a vector charge carried by the Sgr A* black hole (BH) in the bumblebee gravity model with simulated future pulsar timing observations. The spacetime of a bumblebee BH introduces characteristic changes to the orbital dynamics of the pulsar and the light propagation of radio signals. Assuming a timing precision of 1 ms, our simulation shows that a 5-yr observation of a pulsar with an orbital period $P_b\sim 0.5\,{\rm yr}$ and an orbital eccentricity $e\sim 0.8$ can probe a vector charge-to-mass ratio as small as $Q/M\sim 10^{-3}$, which is much more stringent than the current constraint from the Event Horizon Telescope (EHT) observations, and comparable to the prospective constraint from extreme mass-ratio inspirals with the Laser Interferometer Space Antenna (LISA)., Comment: 18 pages, 6 figures

It is commonly believed that black holes are the smallest self-gravitating objects of the same mass in the Universe. Here, we demonstrate, in a subclass of higher-order pure gravities known as quasi-topological gravity, that by modifying general relativity (GR) to reduce the strength of gravity in strong-field regimes while keeping GR unchanged in weak-field regimes, it is possible for stars to collapse to radii less than $2M$ while still maintaining equilibrium between gravity and pressure gradients, leading to physically-reasonable neutron stars smaller in size than a black hole of the same mass. We present concrete solutions for such objects and discuss some of their observational consequences. These objects may furnish new avenues for understanding the nature of gravity in strong-field regimes and leave imprints on gravitational wave echoes from compact binary mergers. An observation of these imprints may constitute evidence for new physics beyond GR when effects of gravity in strong-field regimes are concerned., Comment: 15 pages, 3 figures, submitted in August

Visual commonsense reasoning (VCR) is a challenging multi-modal task, which requires high-level cognition and commonsense reasoning ability about the real world. In recent years, large-scale pre-training approaches have been developed and promoted the state-of-the-art performance of VCR. However, the existing approaches almost employ the BERT-like objectives to learn multi-modal representations. These objectives motivated from the text-domain are insufficient for the excavation on the complex scenario of visual modality. Most importantly, the spatial distribution of the visual objects is basically neglected. To address the above issue, we propose to construct the spatial relation graph based on the given visual scenario. Further, we design two pre-training tasks named object position regression (OPR) and spatial relation classification (SRC) to learn to reconstruct the spatial relation graph respectively. Quantitative analysis suggests that the proposed method can guide the representations to maintain more spatial context and facilitate the attention on the essential visual regions for reasoning. We achieve the state-of-the-art results on VCR and two other vision-and-language reasoning tasks VQA, and NLVR.

Deep Neural Networks (DNNs) are expected to provide explanation for users to understand their black-box predictions. Saliency map is a common form of explanation illustrating the heatmap of feature attributions, but it suffers from noise in distinguishing important features. In this paper, we propose a model-agnostic learning method called Saliency Constrained Adaptive Adversarial Training (SCAAT) to improve the quality of such DNN interpretability. By constructing adversarial samples under the guidance of saliency map, SCAAT effectively eliminates most noise and makes saliency maps sparser and more faithful without any modification to the model architecture. We apply SCAAT to multiple DNNs and evaluate the quality of the generated saliency maps on various natural and pathological image datasets. Evaluations on different domains and metrics show that SCAAT significantly improves the interpretability of DNNs by providing more faithful saliency maps without sacrificing their predictive power.

Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis. However, multiple steps from tissue preparation to slide imaging introduce various image corruptions, making it difficult for deep neural network (DNN) models to achieve stable diagnostic results for clinical use. In order to assess and further enhance the robustness of the models, we analyze the physical causes of the full-stack corruptions throughout the pathological life-cycle and propose an Omni-Corruption Emulation (OmniCE) method to reproduce 21 types of corruptions quantified with 5-level severity. We then construct three OmniCE-corrupted benchmark datasets at both patch level and slide level and assess the robustness of popular DNNs in classification and segmentation tasks. Further, we explore to use the OmniCE-corrupted datasets as augmentation data for training and experiments to verify that the generalization ability of the models has been significantly enhanced.

Pathological captioning of Whole Slide Images (WSIs), though is essential in computer-aided pathological diagnosis, has rarely been studied due to the limitations in datasets and model training efficacy. In this paper, we propose a new paradigm Subtype-guided Masked Transformer (SGMT) for pathological captioning based on Transformers, which treats a WSI as a sequence of sparse patches and generates an overall caption sentence from the sequence. An accompanying subtype prediction is introduced into SGMT to guide the training process and enhance the captioning accuracy. We also present an Asymmetric Masked Mechansim approach to tackle the large size constraint of pathological image captioning, where the numbers of sequencing patches in SGMT are sampled differently in the training and inferring phases, respectively. Experiments on the PatchGastricADC22 dataset demonstrate that our approach effectively adapts to the task with a transformer-based model and achieves superior performance than traditional RNN-based methods. Our codes are to be made available for further research and development.

Role-playing agents (RPAs), powered by large language models, have emerged as a flourishing field of applications. However, a key challenge lies in assessing whether RPAs accurately reproduce the personas of target characters, namely their character fidelity. Existing methods mainly focus on the knowledge and linguistic patterns of characters. This paper, instead, introduces a novel perspective to evaluate the personality fidelity of RPAs with psychological scales. Overcoming drawbacks of previous self-report assessments on RPAs, we propose InCharacter, namely Interviewing Character agents for personality tests. Experiments include various types of RPAs and LLMs, covering 32 distinct characters on 14 widely used psychological scales. The results validate the effectiveness of InCharacter in measuring RPA personalities. Then, with InCharacter, we show that state-of-the-art RPAs exhibit personalities highly aligned with the human-perceived personalities of the characters, achieving an accuracy up to 80.7%., Comment: ACL 2024

Virtual character animation and movement synthesis have advanced rapidly during recent years, especially through a combination of extensive motion capture datasets and machine learning. A remaining challenge is interactively simulating characters that fatigue when performing extended motions, which is indispensable for the realism of generated animations. However, capturing such movements is problematic, as performing movements like backflips with fatigued variations up to exhaustion raises capture cost and risk of injury. Surprisingly, little research has been done on faithful fatigue modeling. To address this, we propose a deep reinforcement learning-based approach, which -- for the first time in literature -- generates control policies for full-body physically simulated agents aware of cumulative fatigue. For this, we first leverage Generative Adversarial Imitation Learning (GAIL) to learn an expert policy for the skill; Second, we learn a fatigue policy by limiting the generated constant torque bounds based on endurance time to non-linear, state- and time-dependent limits in the joint-actuation space using a Three-Compartment Controller (3CC) model. Our results demonstrate that agents can adapt to different fatigue and rest rates interactively, and discover realistic recovery strategies without the need for any captured data of fatigued movement., Comment: 16 pages, 22 figures. To be published in ACM SIGGRAPH Asia Conference Papers 2023. ACM ISBN 979-8-4007-0315-7/23/12

With the advancement of deep learning technologies, general-purpose large models such as GPT-4 have demonstrated exceptional capabilities across various domains. Nevertheless, there remains a demand for high-quality, domain-specific outputs in areas like healthcare, law, and finance. This paper first evaluates the existing large models for specialized domains and discusses their limitations. To cater to the specific needs of certain domains, we introduce the ``MiChao-HuaFen 1.0'' pre-trained corpus dataset, tailored for the news and governmental sectors. The dataset, sourced from publicly available internet data from 2022, underwent multiple rounds of cleansing and processing to ensure high quality and reliable origins, with provisions for consistent and stable updates. This dataset not only supports the pre-training of large models for Chinese vertical domains but also aids in propelling deep learning research and applications in related fields., Comment: 4 pages,2 figures

We develop a consistent approach to calculate the moment of inertia (MOI) for axisymmetric neutron stars (NSs) in the Lorentz-violating Standard-Model Extension (SME) framework. To our knowledge, this is the first relativistic MOI calculation for axisymmetric NSs in a Lorentz-violating gravity theory other than deformed, rotating NSs in the General Relativity. Under Lorentz violation, there is a specific direction in the spacetime and NSs get stretched or compressed along that direction. When a NS is spinning stationarily along this direction, a conserved angular momentum and the concept of MOI are well defined. In the SME framework, we calculate the partial differential equation governing the rotation and solve it numerically with the finite element method to get the MOI for axisymmetric NSs caused by Lorentz violation. Besides, we study an approximate case where the correction to the MOI is regarded solely from the deformation of the NS and compare it with its counterpart in the Newtonian gravity. Our formalism and the numerical method can be extended to other theories of gravity for static axisymmetric NSs., Comment: 9 pages, 3 figures; accepted by PRD

Neural implicit representation is a promising approach for reconstructing surfaces from point clouds. Existing methods combine various regularization terms, such as the Eikonal and Laplacian energy terms, to enforce the learned neural function to possess the properties of a Signed Distance Function (SDF). However, inferring the actual topology and geometry of the underlying surface from poor-quality unoriented point clouds remains challenging. In accordance with Differential Geometry, the Hessian of the SDF is singular for points within the differential thin-shell space surrounding the surface. Our approach enforces the Hessian of the neural implicit function to have a zero determinant for points near the surface. This technique aligns the gradients for a near-surface point and its on-surface projection point, producing a rough but faithful shape within just a few iterations. By annealing the weight of the singular-Hessian term, our approach ultimately produces a high-fidelity reconstruction result. Extensive experimental results demonstrate that our approach effectively suppresses ghost geometry and recovers details from unoriented point clouds with better expressiveness than existing fitting-based methods.

Weakly supervised object localization (WSOL) is one of the most popular and challenging tasks in computer vision. This task is to localize the objects in the images given only the image-level supervision. Recently, dividing WSOL into two parts (class-agnostic object localization and object classification) has become the state-of-the-art pipeline for this task. However, existing solutions under this pipeline usually suffer from the following drawbacks: 1) they are not flexible since they can only localize one object for each image due to the adopted single-class regression (SCR) for localization; 2) the generated pseudo bounding boxes may be noisy, but the negative impact of such noise is not well addressed. To remedy these drawbacks, we first propose to replace SCR with a binary-class detector (BCD) for localizing multiple objects, where the detector is trained by discriminating the foreground and background. Then we design a weighted entropy (WE) loss using the unlabeled data to reduce the negative impact of noisy bounding boxes. Extensive experiments on the popular CUB-200-2011 and ImageNet-1K datasets demonstrate the effectiveness of our method., Comment: Accepted by ACM International Conference on Multimedia 2023

Relay-enabled backscatter communication (BC) is an intriguing paradigm to alleviate energy shortage and improve throughput of Internet-of-Things (IoT) devices. Most of the existing works focus on the resource allocation that considered the unequal and continuous time allocation for both source-relay and relay-destination links. However, the continuous time allocation may be infeasible since in practice, the time allocation shall be carried out in integral multiple of the subframe duration unit. In this article, we study a discrete time scheme from the perspective of frame structure, where one transmission block is divided into two phases and the linear mapping is employed as a re-encoding method to determine the number of subframes for both phases and the power allocation for each subframe in a relay-enabled BC system. Based on this, we derive an accurate system-throughput expression and formulate a mixed-integral non-convex optimization problem to maximize the system throughput by jointly optimizing the power reflection coefficient (PRC) of the IoT node, the power allocation of the hybrid access point (HAP) and the linear mapping matrix, and solve it via a three-step approach. Accordingly, we propose a low complexity iterative algorithm to obtain the throughput maximization-based resource allocation solution. Numerical results analyze the performance of our proposed algorithm, verify the superiority of our proposed scheme, and evaluate the impacts of network parameters on the system throughput.

An effective field theory framework is used to investigate some Lorentz-violating effects on the generation of electromagnetic and gravitational waves, complementing previous work on propagation. Specifically we find solutions to a modified, anisotropic wave equation, sourced by charge or fluid matter. We derive the radiation fields for scalars, classical electromagnetic radiation, and partial results for gravitational radiation. For gravitational waves, the results show longitudinal and breathing polarizations proportional to coefficients for spacetime-symmetry breaking., Comment: Clarifications and references added. Matches published version