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Yu Wang

Publications and source records attributed to Yu Wang.

At least 919 records · Page 51Linked to original sources
arXiv2023

Colossal nonreciprocal Hall effect and broadband frequency mixing due to a room temperature nonlinear Hall effect

Nonreciprocal (NR) charge transport in quantum materials has attracted enormous interest since it offers an avenue to investigate quantum symmetry related physics and holds many prospective applications such as rectification and photodetection over a wide range of frequencies. The NR transport reported to date occurs along the longitudinal direction with the NR resistance limited to a few percent of the ohmic resistance. Here we report a transverse nonreciprocal transport phenomenon with divergent nonreciprocity - colossal NR Hall effect. This is revealed in direct current (DC) measurements on the microscale Hall devices made of the Pt wires deposited by focused ion beam (FIB) on Si substrates and the Weyl semimetal NbP with FIB-deposited Pt electrodes at 0 magnetic field. When a DC is applied along the x-axis of the devices Ix, it generates a voltage along the y-axis Vy near room temperature, with Vy quadratically scaling with Ix. The transverse resistance, which shows a sign reversal upon switching the current direction, results from a colossal extrinsic nonlinear Hall effect (NLHE) rooted in the disorder scatterings in the Pt wires. While NbP was not found to show NLHE, the NLHE generated in the Pt electrodes can be transmitted to the NbP Hall devices, which yields a surprisingly large nonlinear anomalous Hall effect in NbP with the Hall angle (${Θ_H}$) far exceeding the record value of the anomalous Hall angle of magnetic conductors at room temperature. Furthermore, we find such a strong NLHE can lead to broadband frequency mixing, with the frequency spectrum of the Hall voltage including 2nd-harmonic generation, sum & difference frequency generations, and other multiple wave mixing components. These results not only demonstrate the concept of the NRHE for the first time but also pave the way for exploring NLHE's applications in Thz communication, imaging, and energy harvesting.

cond-mat.mes-hall↗
arXiv2023

An Optimization Framework For Anomaly Detection Scores Refinement With Side Information

This paper considers an anomaly detection problem in which a detection algorithm assigns anomaly scores to multi-dimensional data points, such as cellular networks' Key Performance Indicators (KPIs). We propose an optimization framework to refine these anomaly scores by leveraging side information in the form of a causality graph between the various features of the data points. The refinement block builds on causality theory and a proposed notion of confidence scores. After motivating our framework, smoothness properties are proved for the ensuing mathematical expressions. Next, equipped with these results, a gradient descent algorithm is proposed, and a proof of its convergence to a stationary point is provided. Our results hold (i) for any causal anomaly detection algorithm and (ii) for any side information in the form of a directed acyclic graph. Numerical results are provided to illustrate the advantage of our proposed framework in dealing with False Positives (FPs) and False Negatives (FNs). Additionally, the effect of the graph's structure on the expected performance advantage and the various trade-offs that take place are analyzed.

cs.IT↗
arXiv2023

DDH-QA: A Dynamic Digital Humans Quality Assessment Database

In recent years, large amounts of effort have been put into pushing forward the real-world application of dynamic digital human (DDH). However, most current quality assessment research focuses on evaluating static 3D models and usually ignores motion distortions. Therefore, in this paper, we construct a large-scale dynamic digital human quality assessment (DDH-QA) database with diverse motion content as well as multiple distortions to comprehensively study the perceptual quality of DDHs. Both model-based distortion (noise, compression) and motion-based distortion (binding error, motion unnaturalness) are taken into consideration. Ten types of common motion are employed to drive the DDHs and a total of 800 DDHs are generated in the end. Afterward, we render the video sequences of the distorted DDHs as the evaluation media and carry out a well-controlled subjective experiment. Then a benchmark experiment is conducted with the state-of-the-art video quality assessment (VQA) methods and the experimental results show that existing VQA methods are limited in assessing the perceptual loss of DDHs.

cs.CV↗
arXiv2023

Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition

In recent years, speech-based self-supervised learning (SSL) has made significant progress in various tasks, including automatic speech recognition (ASR). An ASR model with decent performance can be realized by fine-tuning an SSL model with a small fraction of labeled data. Reducing the demand for labeled data is always of great practical value. In this paper, we further extend the use of SSL to cut down labeling costs with active learning. Three types of units on different granularities are derived from speech signals in an unsupervised way, and their effects are compared by applying a contrastive data selection method. The experimental results show that our proposed data selection framework can effectively improve the word error rate (WER) by more than 11% with the same amount of labeled data, or halve the labeling cost while maintaining the same WER, compared to random selection.

eess.AS↗
arXiv2023

Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory

Risk-sensitive reinforcement learning (RL) has garnered significant attention in recent years due to the growing interest in deploying RL agents in real-world scenarios. A critical aspect of risk awareness involves modeling highly rare risk events (rewards) that could potentially lead to catastrophic outcomes. These infrequent occurrences present a formidable challenge for data-driven methods aiming to capture such risky events accurately. While risk-aware RL techniques do exist, their level of risk aversion heavily relies on the precision of the state-action value function estimation when modeling these rare occurrences. Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value function distribution. To achieve this, we formulate the extreme values of the state-action value function distribution as parameterized distributions, drawing inspiration from the principles of extreme value theory (EVT). This approach effectively addresses the issue of infrequent occurrence by leveraging EVT-based parameterization. Importantly, we theoretically demonstrate the advantages of employing these parameterized distributions in contrast to other risk-averse algorithms. Our evaluations show that the proposed method outperforms other risk averse RL algorithms on a diverse range of benchmark tasks, each encompassing distinct risk scenarios.

cs.LG↗
arXiv2023

Pushing the Limits of 3D Shape Generation at Scale

We present a significant breakthrough in 3D shape generation by scaling it to unprecedented dimensions. Through the adaptation of the Auto-Regressive model and the utilization of large language models, we have developed a remarkable model with an astounding 3.6 billion trainable parameters, establishing it as the largest 3D shape generation model to date, named Argus-3D. Our approach addresses the limitations of existing methods by enhancing the quality and diversity of generated 3D shapes. To tackle the challenges of high-resolution 3D shape generation, our model incorporates tri-plane features as latent representations, effectively reducing computational complexity. Additionally, we introduce a discrete codebook for efficient quantization of these representations. Leveraging the power of transformers, we enable multi-modal conditional generation, facilitating the production of diverse and visually impressive 3D shapes. To train our expansive model, we leverage an ensemble of publicly-available 3D datasets, consisting of a comprehensive collection of approximately 900,000 objects from renowned repositories such as ModelNet40, ShapeNet, Pix3D, 3D-Future, and Objaverse. This diverse dataset empowers our model to learn from a wide range of object variations, bolstering its ability to generate high-quality and diverse 3D shapes. Extensive experimentation demonstrate the remarkable efficacy of our approach in significantly improving the visual quality of generated 3D shapes. By pushing the boundaries of 3D generation, introducing novel methods for latent representation learning, and harnessing the power of transformers for multi-modal conditional generation, our contributions pave the way for substantial advancements in the field. Our work unlocks new possibilities for applications in gaming, virtual reality, product design, and other domains that demand high-quality and diverse 3D objects.

cs.CV↗
arXiv2023

The Behavior of Error Bounds via Moreau Envelopes

In this paper, we first establish the equivalence of three types of error bounds: uniformized Kurdyka-Łojasiewicz (u-KL) property, uniformized level-set subdifferential error bound (u-LSEB) and uniformized Hölder error bound (u-HEB) for prox-regular functions. Then we study the behavior of the level-set subdifferential error bound (LSEB) and the local Hölder error bound (LHEB) which is expressed respectively by Moreau envelopes, under suitable assumptions. Finally, in order to illustrate our main results and to compare them with those of recent references, some examples are also given.

math.OC↗
arXiv2023

Theoretical Study of Inelastic Processes in Collisions of Y and Y$^+$ with Hydrogen Atom

Utilizing a simplified quantum model approach, the low-energy inelastic collision processes between yttrium atoms (ions) and hydrogen atoms have been studied. Rate coefficients corresponding to the mutual neutralization, ion-pair formation, excitation, and de-excitation processes for the above collision systems have been provided in the temperature range of 1000-10000K. 3 ionic states and 73 covalent states are considered in calculations for the collisions of yttrium atoms with hydrogen atoms, which include 6 molecular symmetries and 4074 partial inelastic reaction processes. For the collisions of yttrium ions with hydrogen atoms, 1 ionic state and 116 covalent states are included, which related to 3 molecular symmetries and 13572 partial inelastic collision processes. It is found that the rate coefficients for the mutual neutralization process have a maximum at T = 6000K, which is an order of magnitude higher than those of other processes. Notably, the positions of optimal windows for the collisions of yttrium atoms and ions with hydrogen atoms are found near electronic binding energy -2eV (Y) and -4.4eV (Y$^+$), respectively. The scattering channels located in or near these optimal windows have intermediate-to-large rate coefficients (greater than $10^{-12}$ cm$^3$s$^{-1}$). The reported data should be useful in the study of non-local thermodynamic equilibrium modeling.

astro-ph.SR↗
arXiv2023

Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation

Recent leading zero-shot video object segmentation (ZVOS) works devote to integrating appearance and motion information by elaborately designing feature fusion modules and identically applying them in multiple feature stages. Our preliminary experiments show that with the strong long-range dependency modeling capacity of Transformer, simply concatenating the two modality features and feeding them to vanilla Transformers for feature fusion can distinctly benefit the performance but at a cost of heavy computation. Through further empirical analysis, we find that attention dependencies learned in Transformer in different stages exhibit completely different properties: global query-independent dependency in the low-level stages and semantic-specific dependency in the high-level stages. Motivated by the observations, we propose two Transformer variants: i) Context-Sharing Transformer (CST) that learns the global-shared contextual information within image frames with a lightweight computation. ii) Semantic Gathering-Scattering Transformer (SGST) that models the semantic correlation separately for the foreground and background and reduces the computation cost with a soft token merging mechanism. We apply CST and SGST for low-level and high-level feature fusions, respectively, formulating a level-isomerous Transformer framework for ZVOS task. Compared with the baseline that uses vanilla Transformers for multi-stage fusion, ours significantly increase the speed by 13 times and achieves new state-of-the-art ZVOS performance. Code is available at https://github.com/DLUT-yyc/Isomer.

cs.CV↗
arXiv2023

Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology

Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health. The current description and quantification of features on pathology slides are limited, prompting the need for innovative solutions to comprehensively assess diverse phenotypic attributes within Whole Slide Images (WSIs). In particular, understanding the morphological characteristics of podocytes, terminally differentiated glomerular epithelial cells, is crucial for studying glomerular injury. This paper introduces the Spatial Pathomics Toolkit (SPT) and applies it to podocyte pathomics. The SPT consists of three main components: (1) instance object segmentation, enabling precise identification of podocyte nuclei; (2) pathomics feature generation, extracting a comprehensive array of quantitative features from the identified nuclei; and (3) robust statistical analyses, facilitating a comprehensive exploration of spatial relationships between morphological and spatial transcriptomics features.The SPT successfully extracted and analyzed morphological and textural features from podocyte nuclei, revealing a multitude of podocyte morphomic features through statistical analysis. Additionally, we demonstrated the SPT's ability to unravel spatial information inherent to podocyte distribution, shedding light on spatial patterns associated with glomerular injury. By disseminating the SPT, our goal is to provide the research community with a powerful and user-friendly resource that advances cellular spatial pathomics in renal pathology. The implementation and its complete source code of the toolkit are made openly accessible at https://github.com/hrlblab/spatial_pathomics.

q-bio.QM↗
arXiv2023

Ada3D : Exploiting the Spatial Redundancy with Adaptive Inference for Efficient 3D Object Detection

Voxel-based methods have achieved state-of-the-art performance for 3D object detection in autonomous driving. However, their significant computational and memory costs pose a challenge for their application to resource-constrained vehicles. One reason for this high resource consumption is the presence of a large number of redundant background points in Lidar point clouds, resulting in spatial redundancy in both 3D voxel and dense BEV map representations. To address this issue, we propose an adaptive inference framework called Ada3D, which focuses on exploiting the input-level spatial redundancy. Ada3D adaptively filters the redundant input, guided by a lightweight importance predictor and the unique properties of the Lidar point cloud. Additionally, we utilize the BEV features' intrinsic sparsity by introducing the Sparsity Preserving Batch Normalization. With Ada3D, we achieve 40% reduction for 3D voxels and decrease the density of 2D BEV feature maps from 100% to 20% without sacrificing accuracy. Ada3D reduces the model computational and memory cost by 5x, and achieves 1.52x/1.45x end-to-end GPU latency and 1.5x/4.5x GPU peak memory optimization for the 3D and 2D backbone respectively.

cs.CV↗
arXiv2023

Learning Concise and Descriptive Attributes for Visual Recognition

Recent advances in foundation models present new opportunities for interpretable visual recognition -- one can first query Large Language Models (LLMs) to obtain a set of attributes that describe each class, then apply vision-language models to classify images via these attributes. Pioneering work shows that querying thousands of attributes can achieve performance competitive with image features. However, our further investigation on 8 datasets reveals that LLM-generated attributes in a large quantity perform almost the same as random words. This surprising finding suggests that significant noise may be present in these attributes. We hypothesize that there exist subsets of attributes that can maintain the classification performance with much smaller sizes, and propose a novel learning-to-search method to discover those concise sets of attributes. As a result, on the CUB dataset, our method achieves performance close to that of massive LLM-generated attributes (e.g., 10k attributes for CUB), yet using only 32 attributes in total to distinguish 200 bird species. Furthermore, our new paradigm demonstrates several additional benefits: higher interpretability and interactivity for humans, and the ability to summarize knowledge for a recognition task.

cs.CV↗
arXiv2023

Audio-aware Query-enhanced Transformer for Audio-Visual Segmentation

The goal of the audio-visual segmentation (AVS) task is to segment the sounding objects in the video frames using audio cues. However, current fusion-based methods have the performance limitations due to the small receptive field of convolution and inadequate fusion of audio-visual features. To overcome these issues, we propose a novel \textbf{Au}dio-aware query-enhanced \textbf{TR}ansformer (AuTR) to tackle the task. Unlike existing methods, our approach introduces a multimodal transformer architecture that enables deep fusion and aggregation of audio-visual features. Furthermore, we devise an audio-aware query-enhanced transformer decoder that explicitly helps the model focus on the segmentation of the pinpointed sounding objects based on audio signals, while disregarding silent yet salient objects. Experimental results show that our method outperforms previous methods and demonstrates better generalization ability in multi-sound and open-set scenarios.

cs.SD↗
arXiv2023

Manipulation of $γ$ ray polarization in Compton scattering

High-brilliance high-polarization $γ$ rays based on Compton scattering are of great significance in broad areas, such as nuclear, high-energy, astro-physics, etc. However, the transfer mechanism of spin angular momentum in the transition from linear, through weakly into strongly nonlinear processes is still unclear, which severely limits the simultaneous control of brilliance and polarization of high-energy $γ$ rays. In this work, we investigate the manipulation mechanism of high-quality polarized $γ$ rays in Compton scattering of the ultrarelativistic electron beam colliding with an intense laser pulse. We find that the contradiction lies in the simultaneous achievement of high-brilliance and high-polarization of $γ$ rays by increasing laser intensity, since the polarization is predominately contributed by the electron (laser photon) spin via multi-photon (single-photon) absorption channel. Moreover, we confirm that the signature of $γ$-ray polarization can be applied for observing the nonlinear effects (multi-photon absorption) of Compton scattering with moderate-intensity laser facilities.

physics.plasm-ph↗
arXiv2023

A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diagnosis of Skin Diseases in Clinical Images

Skin diseases are among the most prevalent health issues, and accurate computer-aided diagnosis methods are of importance for both dermatologists and patients. However, most of the existing methods overlook the essential domain knowledge required for skin disease diagnosis. A novel multi-task model, namely DermImitFormer, is proposed to fill this gap by imitating dermatologists' diagnostic procedures and strategies. Through multi-task learning, the model simultaneously predicts body parts and lesion attributes in addition to the disease itself, enhancing diagnosis accuracy and improving diagnosis interpretability. The designed lesion selection module mimics dermatologists' zoom-in action, effectively highlighting the local lesion features from noisy backgrounds. Additionally, the presented cross-interaction module explicitly models the complicated diagnostic reasoning between body parts, lesion attributes, and diseases. To provide a more robust evaluation of the proposed method, a large-scale clinical image dataset of skin diseases with significantly more cases than existing datasets has been established. Extensive experiments on three different datasets consistently demonstrate the state-of-the-art recognition performance of the proposed approach.

cs.CV↗
arXiv2023

Chemical footprints of giant planet formation. Role of planet accretion in shaping the C/O ratio of protoplanetary disks

Protoplanetary disks, the birthplaces of planets, commonly feature bright rings and dark gaps in both continuum and line emission maps. Accreting planets are interacting with the disk, not only through gravity, but also by changing the local irradiation and elemental abundances, which are essential ingredients for disk chemistry. We propose that giant planet accretion can leave chemical footprints in the gas local to the planet, which potentially leads to the spatial coincidence of molecular emissions with the planet in ALMA observation. Through 2D multi-fluid hydrodynamical simulations in Athena++ with built-in sublimation, we simulate the process of an accreting planet locally heating up its vicinity, opening a gas gap in the disk, and creating the conditions for C-photochemistry. An accreting planet located outside the methane snowline can render the surrounding gas hot enough to sublimate the C-rich organics off pebbles before they are accreted by the planet. This locally elevates the disk gas-phase C/O ratio, providing a potential explanation for the C$_2$H line-emission rings observed with ALMA. In particular, our findings provide an explanation for the MWC480 disk, where previous work has identified a statistically significant spatial coincidence of line-emission rings inside a continuum gap. Our findings present a novel view of linking the gas accretion of giant planets and their natal disks through the chemistry signals. This model demonstrates that giant planets can actively shape their forming chemical environment, moving beyond the traditional understanding of the direct mapping of primordial disk chemistry onto planets.

astro-ph.EP↗
arXiv2023

LINFA: a Python library for variational inference with normalizing flow and annealing

Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions. We developed LINFA (Library for Inference with Normalizing Flow and Annealing), a Python library for variational inference to accommodate computationally expensive models and difficult-to-sample distributions with dependent parameters. We discuss the theoretical background, capabilities, and performance of LINFA in various benchmarks. LINFA is publicly available on GitHub at https://github.com/desResLab/LINFA.

cs.LG↗
arXiv2023

Frustration-induced magnetic bimerons in transition metal halide CoX2 (X = Cl, Br) monolayers

With the field of two-dimensional (2D) magnetic materials expanding rapidly, noncollinear topological magnetic textures in 2D materials are attracting growing interest recently. As the in-plane counterpart of magnetic skyrmions, magnetic bimerons have the same topological advantages, but are rarely observed in experiments. Employing first-principles calculations and Monte Carlo simulations, we predict that the centrosymmetric transition metal halide CoX2 (X = Cl, Br) monolayers can be promising candidates for observing the frustration-induced bimerons. These bimerons crystallize into stable triangular lattice under an appropriate magnetic field. Compared to the skyrmions driven by the Dzyaloshinskii-Moriya interaction or the long-ranged magnetic dipole-dipole interactions, these frustration-induced bimerons have much smaller size and flexible tunability. Furthermore, the biaxial strain provides an effective method to tune the frustration and thereby to tune the bimeron lattice. In detail, for CoCl2 monolayer, tensile strain can be applied to generate bimeron lattice, further shrink bimeron size and increase the density of bimerons. For CoBr2 monolayer with inherent bimeron lattice state, a unique orientation rotation of bimeron lattice controlled by compressive strain is predicted.

cond-mat.mtrl-sci↗