Searcharxiv⌕ Search

arXiv subjects

Hui Zhang

Publications and source records attributed to Hui Zhang.

At least 217 records · Page 12Linked to original sources
arXiv2025

Product of Brauer--Manin obstruction for 0-cycles over number fields and function fields

It is conjectured that the Brauer--Manin obstruction is expected to control the existence of 0-cycles of degree 1 on smooth proper varieties over number fields. In this paper, we prove that the existence of Brauer--Manin obstruction to Hasse principle for 0-cycles of degree 1 on the product of smooth (non-necessarily proper) varieties is equivalent to the simultaneous existence of such an obstruction on each factor. We also prove an analogous statement for smooth varieties defined over function fields of $\mathbb{C}((t))$-curves.

math.AG↗
arXiv2024

Double-Flow GAN model for the reconstruction of perceived faces from brain activities

Face plays an important role in humans visual perception, and reconstructing perceived faces from brain activities is challenging because of its difficulty in extracting high-level features and maintaining consistency of multiple face attributes, such as expression, identity, gender, etc. In this study, we proposed a novel reconstruction framework, which we called Double-Flow GAN, that can enhance the capability of discriminator and handle imbalances in images from certain domains that are too easy for generators. We also designed a pretraining process that uses features extracted from images as conditions for making it possible to pretrain the conditional reconstruction model from fMRI in a larger pure image dataset. Moreover, we developed a simple pretrained model for fMRI alignment to alleviate the problem of cross-subject reconstruction due to the variations of brain structure among different subjects. We conducted experiments by using our proposed method and traditional reconstruction models. Results showed that the proposed method is significant at accurately reconstructing multiple face attributes, outperforms the previous reconstruction models, and exhibited state-of-the-art reconstruction abilities.

cs.CV↗
arXiv2024

RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers

The high performance of tree ensemble classifiers benefits from a large set of rules, which, in turn, makes the models hard to understand. To improve interpretability, existing methods extract a subset of rules for approximation using model reduction techniques. However, by focusing on the reduced rule set, these methods often lose fidelity and ignore anomalous rules that, despite their infrequency, play crucial roles in real-world applications. This paper introduces a scalable visual analysis method to explain tree ensemble classifiers that contain tens of thousands of rules. The key idea is to address the issue of losing fidelity by adaptively organizing the rules as a hierarchy rather than reducing them. To ensure the inclusion of anomalous rules, we develop an anomaly-biased model reduction method to prioritize these rules at each hierarchical level. Synergized with this hierarchical organization of rules, we develop a matrix-based hierarchical visualization to support exploration at different levels of detail. Our quantitative experiments and case studies demonstrate how our method fosters a deeper understanding of both common and anomalous rules, thereby enhancing interpretability without sacrificing comprehensiveness.

cs.LG↗
arXiv2024

AdaDiff: Adaptive Step Selection for Fast Diffusion Models

Diffusion models, as a type of generative model, have achieved impressive results in generating images and videos conditioned on textual conditions. However, the generation process of diffusion models involves denoising dozens of steps to produce photorealistic images/videos, which is computationally expensive. Unlike previous methods that design ``one-size-fits-all'' approaches for speed up, we argue denoising steps should be sample-specific conditioned on the richness of input texts. To this end, we introduce AdaDiff, a lightweight framework designed to learn instance-specific step usage policies, which are then used by the diffusion model for generation. AdaDiff is optimized using a policy gradient method to maximize a carefully designed reward function, balancing inference time and generation quality. We conduct experiments on three image generation and two video generation benchmarks and demonstrate that our approach achieves similar visual quality compared to the baseline using a fixed 50 denoising steps while reducing inference time by at least 33%, going as high as 40%. Furthermore, our method can be used on top of other acceleration methods to provide further speed benefits. Lastly, qualitative analysis shows that AdaDiff allocates more steps to more informative prompts and fewer steps to simpler prompts.

cs.CV↗
arXiv2024

The nucleon properties in finite temperature and density with vector meson

We introduce the vector meson $ω$ into the Quark Meson model, and study the impact of vector interactions on the properties of static hadrons using the mean-field approximation. The short-range repulsive force associated with vector interactions leads to an expansion of the root mean square radius of nucleons. While the mass of hadrons increases, the gap between this mass and the energy of the three free constituent quarks decreases, resulting in the instability of hadrons. Our study of nucleon mass and radius at finite temperature and density has potential applications for particle yield in heavy ion collisions and the mass-radius relationship in compact stars.

hep-ph↗
arXiv2024

HGSFusion: Radar-Camera Fusion with Hybrid Generation and Synchronization for 3D Object Detection

Millimeter-wave radar plays a vital role in 3D object detection for autonomous driving due to its all-weather and all-lighting-condition capabilities for perception. However, radar point clouds suffer from pronounced sparsity and unavoidable angle estimation errors. To address these limitations, incorporating a camera may partially help mitigate the shortcomings. Nevertheless, the direct fusion of radar and camera data can lead to negative or even opposite effects due to the lack of depth information in images and low-quality image features under adverse lighting conditions. Hence, in this paper, we present the radar-camera fusion network with Hybrid Generation and Synchronization (HGSFusion), designed to better fuse radar potentials and image features for 3D object detection. Specifically, we propose the Radar Hybrid Generation Module (RHGM), which fully considers the Direction-Of-Arrival (DOA) estimation errors in radar signal processing. This module generates denser radar points through different Probability Density Functions (PDFs) with the assistance of semantic information. Meanwhile, we introduce the Dual Sync Module (DSM), comprising spatial sync and modality sync, to enhance image features with radar positional information and facilitate the fusion of distinct characteristics in different modalities. Extensive experiments demonstrate the effectiveness of our approach, outperforming the state-of-the-art methods in the VoD and TJ4DRadSet datasets by $6.53\%$ and $2.03\%$ in RoI AP and BEV AP, respectively. The code is available at https://github.com/garfield-cpp/HGSFusion.

cs.CV↗
arXiv2024

SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud

4D LiDAR semantic segmentation, also referred to as multi-scan semantic segmentation, plays a crucial role in enhancing the environmental understanding capabilities of autonomous vehicles or robots. It classifies the semantic category of each LiDAR measurement point and detects whether it is dynamic, a critical ability for tasks like obstacle avoidance and autonomous navigation. Existing approaches often rely on computationally heavy 4D convolutions or recursive networks, which result in poor real-time performance, making them unsuitable for online robotics and autonomous driving applications. In this paper, we introduce SegNet4D, a novel real-time 4D semantic segmentation network offering both efficiency and strong semantic understanding. SegNet4D addresses 4D segmentation as two tasks: single-scan semantic segmentation and moving object segmentation, each tackled by a separate network head. Both results are combined in a motion-semantic fusion module to achieve comprehensive 4D segmentation. Additionally, instance information is extracted from the current scan and exploited for instance-wise segmentation consistency. Our approach surpasses state-of-the-art in both multi-scan semantic segmentation and moving object segmentation while offering greater efficiency, enabling real-time operation. Besides, its effectiveness and efficiency have also been validated on a real-world unmanned ground platform. Our code will be released at https://github.com/nubot-nudt/SegNet4D.

cs.CV↗
arXiv2024

FunGrasp: Functional Grasping for Diverse Dexterous Hands

Functional grasping is essential for humans to perform specific tasks, such as grasping scissors by the finger holes to cut materials or by the blade to safely hand them over. Enabling dexterous robot hands with functional grasping capabilities is crucial for their deployment to accomplish diverse real-world tasks. Recent research in dexterous grasping, however, often focuses on power grasps while overlooking task- and object-specific functional grasping poses. In this paper, we introduce FunGrasp, a system that enables functional dexterous grasping across various robot hands and performs one-shot transfer to unseen objects. Given a single RGBD image of functional human grasping, our system estimates the hand pose and transfers it to different robotic hands via a human-to-robot (H2R) grasp retargeting module. Guided by the retargeted grasping poses, a policy is trained through reinforcement learning in simulation for dynamic grasping control. To achieve robust sim-to-real transfer, we employ several techniques including privileged learning, system identification, domain randomization, and gravity compensation. In our experiments, we demonstrate that our system enables diverse functional grasping of unseen objects using single RGBD images, and can be successfully deployed across various dexterous robot hands. The significance of the components is validated through comprehensive ablation studies. Project page: https://hly-123.github.io/FunGrasp/ .

cs.RO↗
arXiv2024

The Moon-forming Impact as a Constraint for the Inner Solar System's Formation

The solar system planets are benchmarks for the planet formation theory. Yet two paradigms coexist for the four terrestrial planets: the prolonged collisional growth among planetesimals lasting $>100$ million years (Myr) and the fast formation via planetesimals accreting pebbles within 10 Myr. Despite their dramatic difference, we can hardly tell which theory is more relevant to the true history of the terrestrial planets' formation. Here, we show that the Moon's origin puts stringent constraints on the pebble accretion scenario, rendering it less favourable. In the pebble accretion model, the one-off giant impact between proto-Earth and Theia rarely (probability $<$ 1\textperthousand) occurs at the right timing and configuration for the Moon formation. Even if a potential impact happens by chance, giant impact simulations reveal perfect mixing between proto-Earth and Theia, leaving no room for the observed primordial Earth mantle heterogeneity and the compositional difference, though small, between Earth and the Moon. Thus, the Earth-Moon system along other terrestrial planets should preferably form from chaotic collisional growth in the inner solar system.

astro-ph.EP↗
arXiv2024

Occlusion-Aware Seamless Segmentation

Panoramic images can broaden the Field of View (FoV), occlusion-aware prediction can deepen the understanding of the scene, and domain adaptation can transfer across viewing domains. In this work, we introduce a novel task, Occlusion-Aware Seamless Segmentation (OASS), which simultaneously tackles all these three challenges. For benchmarking OASS, we establish a new human-annotated dataset for Blending Panoramic Amodal Seamless Segmentation, i.e., BlendPASS. Besides, we propose the first solution UnmaskFormer, aiming at unmasking the narrow FoV, occlusions, and domain gaps all at once. Specifically, UnmaskFormer includes the crucial designs of Unmasking Attention (UA) and Amodal-oriented Mix (AoMix). Our method achieves state-of-the-art performance on the BlendPASS dataset, reaching a remarkable mAPQ of 26.58% and mIoU of 43.66%. On public panoramic semantic segmentation datasets, i.e., SynPASS and DensePASS, our method outperforms previous methods and obtains 45.34% and 48.08% in mIoU, respectively. The fresh BlendPASS dataset and our source code are available at https://github.com/yihong-97/OASS.

cs.CV↗
arXiv2024

Variational learning of integrated quantum photonic circuits

Integrated photonic circuits play a crucial role in implementing quantum information processing in the noisy intermediate-scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical optimization techniques to enhance quantum advantages on NISQ devices. However, most variational algorithms are circuit-model-based and encounter challenges when implemented on integrated photonic circuits, because they involve explicit decomposition of large quantum circuits into sequences of basic entangled gates, leading to an exponential decay of success probability due to the non-deterministic nature of photonic entangling gates. Here, we present a variational learning approach for designing quantum photonic circuits, which directly incorporates post-selection and elementary photonic elements into the training process. The complicated circuit is treated as a single nonlinear logical operator, and a unified design is discovered for it through variational learning. Engineering an integrated photonic chip with automated control, we adjust and optimize the internal parameters of the chip in real time for task-specific cost functions. We utilize a simple case of designing photonic circuits for a single ancilla CNOT gate with improved success rate to illustrate how our proposed approach works, and then apply the approach in the first demonstration of quantum stochastic simulation using integrated photonics.

quant-ph↗
arXiv2024

Study of Asymptotic Free Scalar Field Theories from Adaptive Perturbation Method

We focus on the behavior of (2+1)d $λϕ^4$ and (5+1)d $λϕ^3$ or $λ|ϕ|^3$ theories in different regimes and compare the results obtained from the adaptive perturbation method with those obtained from lattice simulation. These theories are simple models that exhibit asymptotic freedom, which is a property that is also observed in more complex theories such as QCD, which describes the strong interaction between quarks and gluons. Asymptotic freedom is an important feature of these theories because it allows for a perturbative treatment of interactions at high energies. However, the standard perturbation scheme breaks down in the presence of strong interactions, and the adaptive perturbation method, which involves resuming the Feynman diagrams, is more suitable for studying these interactions. Our research involves comparing the perturbation result to lattice simulation. In the case of the $ϕ^3$ theory, there is no stable vacuum, so we explore evidence from the $|ϕ|^3$ theory instead. Our results appear to show that resummation improves the strong coupling result for both the $λϕ^4$ and $λ|ϕ|^3$ theories. Additionally, we improve the resummation method for the three-point coupling vertex and study the RG flow to analyze the resummation contribution and theoretical properties.

hep-th↗
arXiv2024

Transient Upstream Mesoscale Structures: Drivers of Solar-Quiet Space Weather

In recent years, it has become increasingly clear that space weather disturbances can be triggered by transient upstream mesoscale structures (TUMS), independently of the occurrence of large-scale solar wind (SW) structures, such as interplanetary coronal mass ejections and stream interaction regions. Different types of magnetospheric pulsations, transient perturbations of the geomagnetic field and auroral structures are often observed during times when SW monitors indicate quiet conditions, and have been found to be associated to TUMS. In this mini-review we describe the space weather phenomena that have been associated with four of the largest-scale and the most energetic TUMS, namely hot flow anomalies, foreshock bubbles, travelling foreshocks and foreshock compressional boundaries. The space weather phenomena associated with TUMS tend to be more localized and less intense compared to geomagnetic storms. However, the quiet time space weather may occur more often since, especially during solar minima, quiet SW periods prevail over the perturbed times.

astro-ph.SR↗
arXiv2024

Transverse Bending Mimicry of Longitudinal Piezoelectricity

The origin of frequently observed ultrahigh electric-induced longitudinal strain, ranging from 1% to 26%, remains an open question. Recent evidence suggests that this phenomenon is linked to the bending deformation of samples, but the mechanisms driving this bending and the strong dependence of nominal strain on sample thickness have yet to be fully understood. Here, we demonstrate that the bending in piezoceramics can be induced by non-zero gradient of d31 acrcoss thickness direction. Our calculations show that in standard perovskite piezoceramics, such as KNbO3, a 0.69% concentration of oxygen vacancies results in a 6.3 pC/N change in d31 by inhibiting polarization rotation, which is sufficient to produce ultrahigh nominal strain in thin samples. The gradients of defect concentration, composition, and stress can all cause sufficient inhomogeneity in the distribution of d31, leading to the bending effect. We propose several approaches to distinguish true electric-induced strain from bending-induced effects. Our work provides clarity on the origin of nominal ultrahigh electricinduced strain and offers valuable insights for advancing piezoelectric materials.

cond-mat.mtrl-sci↗
arXiv2024

Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling

Recent works have proposed to craft adversarial clothes for evading person detectors, while they are either only effective at limited viewing angles or very conspicuous to humans. We aim to craft adversarial texture for clothes based on 3D modeling, an idea that has been used to craft rigid adversarial objects such as a 3D-printed turtle. Unlike rigid objects, humans and clothes are non-rigid, leading to difficulties in physical realization. In order to craft natural-looking adversarial clothes that can evade person detectors at multiple viewing angles, we propose adversarial camouflage textures (AdvCaT) that resemble one kind of the typical textures of daily clothes, camouflage textures. We leverage the Voronoi diagram and Gumbel-softmax trick to parameterize the camouflage textures and optimize the parameters via 3D modeling. Moreover, we propose an efficient augmentation pipeline on 3D meshes combining topologically plausible projection (TopoProj) and Thin Plate Spline (TPS) to narrow the gap between digital and real-world objects. We printed the developed 3D texture pieces on fabric materials and tailored them into T-shirts and trousers. Experiments show high attack success rates of these clothes against multiple detectors.

cs.CV↗
arXiv2024

MAGORINO: Magnitude-only fat fraction and R2* estimation with Rician noise modelling

Purpose: Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and R2* estimation where complex-based methods fail or when phase data is inaccessible or unreliable. However, traditional magnitude-based fitting algorithms do not account for Rician noise, creating a source of bias. To address these issues, we propose an algorithm for Magnitude-Only PDFF and R2* estimation with Rician Noise modelling (MAGORINO). Methods: Simulations of multi-echo gradient echo signal intensities are used to investigate the performance and behavior of MAGORINO over the space of clinically-plausible PDFF, R2* and signal-to-noise ratio (SNR) values. Fitting performance is assessed through detailed simulation, including likelihood function visualization, and in a multi-site, multi-vendor and multi-field-strength phantom dataset and in vivo. Results: Simulations show that Rician noise-based magnitude fitting outperforms existing Gaussian noise-based fitting and reveals two key mechanisms underpinning the observed improvement. Firstly, the likelihood functions exhibit two local optima; Rician noise modelling increases the chance the global optimum corresponds to the ground truth. Secondly, when the global optimum corresponds to ground truth for both noise models, the optimum from Rician noise modelling is closer to ground truth. Multisite phantom experiments show good agreement of MAGORINO PDFF with reference values, and in vivo experiments replicate the performance benefits observed in simulation. Conclusion: MAGORINO reduces Rician noise-related bias in PDFF and R2* estimation, thus addressing a key limitation of existing magnitude-only fitting methods. Our results offer an insight into the importance of the noise model for selecting the correct optimum when multiple plausible optima exist.

q-bio.QM↗
arXiv2024

Bidirectional Optimization onto Thermoelectric Performance via Hydrostatic-Pressure in Chalcopyrite AgXTe2 (X=In, Ga)

Pressure tuning has emerged as a powerful strategy for manipulating the thermoelectric properties of materials by inducing structural and electronic modifications. Herein, we systematically investigate the transport properties and thermoelectric performance concerning lattice distortions induced by hydrostatic pressure in Ag-based chalcopyrite AgXTe2 (X=In, Ga). The findings reveal that the lattice distortion in AgXTe2 exhibits distinct behaviors under lattice compression, diverging from trends observed at ambient pressure. Importantly, the hydrostatic pressure breaks the phenomenally negative correlation between thermal conductivity and lattice distortion. Pressure-induced softening of low-frequency acoustic phonons broadens the low-energy phonon spectrum, enhancing interactions between acoustic and optical phonons. Such broadening substantially increases the number of available three-phonon scattering channels, resulting in a marked reduction in thermal conductivity. Meanwhile, we establish a macroscopic connection between metavalent bonding and anharmonicity, providing an indirect explanation for lattice anharmonicity through pressure-driven transferred charge. Additionally, the applied pressure achieves a notable net increase in the power factor despite the strong coupling of electrical transport parameters, which underscores the potential for bidirectional optimization of transport properties in AgXTe2. As a result, the maximum ZT value of AgInTe2 is nearly doubled, demonstrating that pressure modulation is a powerful strategy for enhancing thermoelectric performance. Our work not only establishes the link between pressure, lattice dynamics, and thermoelectric properties within chalcopyrite AgXTe2, but also inspires the exploration of pressure-related optimization strategies for conventional thermoelectric materials.

cond-mat.mtrl-sci↗