arXiv subjects

Wei Deng

Publications and source records attributed to Wei Deng.

At least 55 records · Page 3Linked to original sources

arXiv2025

Two-Component gamma-ray Emission Spectrum and X-Ray Polarization of the Radio Galaxy Pictor A

Pictor A is a $γ$-ray emitting radio galaxy and has a bright hotspot called WHS, located $\sim$4 arcmin away from the nucleus. In this work, we present an analysis of its 16-year Fermi-LAT data and report the Imaging X-ray Polarimetry Explorer (IXPE) observations for this source. Our analysis of the Fermi-LAT observations reveals evidence of two components in the average $γ$-ray spectrum of Pictor A, exhibiting a statistically significant hardening from $Γ_{\rm γ,1}=3.25\pm0.15$ to $Γ_{\rm γ,2}=1.81\pm0.07$ at a break energy of $2.46\pm0.09$ GeV. Notably, variability of $γ$-rays is evident in Pictor A, predominantly driven by the component below the break energy, while the component above the break energy remains stable. Furthermore, our analysis reveals that a power-law function provides an adequate fit for the high-flux-state spectrum, while a broken power-law function remains necessary to accurately model the low-flux-state spectrum. We suggest that the low-energy component originates from the nucleus, while the high-energy component primarily stems from WHS. The broadband spectral energy distributions of both nucleus and WHS can be well represented by a simple leptonic model, with both $γ$-ray components attributed to the synchrotron-self-Compton (SSC) process. Analysis of IXPE data provides upper limits on the polarization degree of $Π_{\rm X}<$6.6% for the nucleus and $Π_{\rm X}<$56.4% for the WHS within the 2--8 keV band. For the nucleus, this result aligns with X-ray emission originating from the SSC process. However, the upper limit of $Π_{\rm X}<$56.4% for WHS is insufficient to conclusively determine the X-ray emission mechanism in this region.

astro-ph.HE↗

arXiv2025

Variational Schrödinger Diffusion Models

Schrödinger bridge (SB) has emerged as the go-to method for optimizing transportation plans in diffusion models. However, SB requires estimating the intractable forward score functions, inevitably resulting in the costly implicit training loss based on simulated trajectories. To improve the scalability while preserving efficient transportation plans, we leverage variational inference to linearize the forward score functions (variational scores) of SB and restore simulation-free properties in training backward scores. We propose the variational Schrödinger diffusion model (VSDM), where the forward process is a multivariate diffusion and the variational scores are adaptively optimized for efficient transport. Theoretically, we use stochastic approximation to prove the convergence of the variational scores and show the convergence of the adaptively generated samples based on the optimal variational scores. Empirically, we test the algorithm in simulated examples and observe that VSDM is efficient in generations of anisotropic shapes and yields straighter sample trajectories compared to the single-variate diffusion. We also verify the scalability of the algorithm in real-world data and achieve competitive unconditional generation performance in CIFAR10 and conditional generation in time series modeling. Notably, VSDM no longer depends on warm-up initializations and has become tuning-friendly in training large-scale experiments.

cs.LG↗

arXiv2025

X-ray Polarization of the High-Synchrotron-Peaked BL Lac H 1426+428

We report the X-ray polarization properties of the high-synchrotron-peaked BL Lac H 1426+428, based on two-epoch observational data from the Imaging X-ray Polarimetry Explorer (IXPE). For the first observation, only an upper limit of polarization degree ($Π_{\rm X}$), $Π_{\rm X}<19.5\%$, at the $99\%$ confidence level (C.L.) is determined. In contrast, for the second observation, we derive $Π_{\rm X}=20.6\%\pm2.9\%$ with a polarization angle ($ψ_{\rm X}$) of $ψ_{\rm X}=116.1^{\circ}\pm4.1^{\circ}$ at a C.L. of 7.1 $σ$. The time-resolved and energy-resolved polarization analysis reveals no significant variation in $ψ_{\rm X}$ and no detectable polarization within narrower energy bins for the first observation, while the polarization during the second observation is dominated by low-energy photons. Furthermore, the X-rays during the second observation are found to be in a higher flux state with a harder spectrum compared to that observed during the first observation, consistent with a {\it harder-when-brighter} behavior. We propose that the enhanced X-ray emission observed during the second observation is produced by shock-accelerated electrons within an ordered magnetic field region via synchrotron radiation. Nonetheless, no significant detection of polarization during the first IXPE observation may be due to the limited number of detected photons.

astro-ph.HE↗

arXiv2025

Energy-resolved polarisation study of the Crab Nebula with IXPE

This work presents a new detailed study on the energy-dependent variation in the X-ray polarisation of the Crab Pulsar Wind Nebula (PWN), obtained using data from the Imaging X-ray Polarimetry Explorer (IXPE). For the entire PWN, we observed a linear variation in polarisation degree (PD), and detected the rotation of the polarisation angle (PA) with the energy at higher than 99.9999\% of the confidence level. This energy-dependent polarisation variation is in line with the indication found in Vela PWN by IXPE, and it can be interpreted as the emitting region of the polarised photons shrinks with increasing energy, leading to higher PD because they are less influenced by the turbulence of the magnetic field. We compared the IXPE polarisation results with those of other hard X-ray/gamma observatories (PoGO+, Intregral, AstroSat) for the PWN, finding the same trend from soft-X to hard-X with the PD increasing with the energy and the PA approaching the pulsar's spin axis. In fact, in this wide energy band, the fitting results show an energy trend for the PA compatible with the estimated pulsar's spin axis within 3$σ$ of confidence level.

astro-ph.HE↗

arXiv2025

Spatially resolved polarization variation of the Crab Nebula

We examined the spatially resolved polarization variations in the Crab Nebula over 2 yr, using observational data from the Imaging X-ray Polarimetry Explorer, and offer key insights into its magnetic field structures and evolution. The results show significant temporal changes in the polarization degree (PD) across three regions of interest in the 2-8 keV energy band. Regions (a) and (b), located in the northern and the southwestern parts of the study area, exhibit PD variations with significance levels greater than 4 sigma and 3 sigma , respectively. Region (c), located in the southwest,shows a notable decrease in PD with a significance greater than 5 sigma. However, no significant variation in the polarization angle was observed. Meanwhile, notable flux variations were detected, likely influenced by dynamic processes such as magnetized turbulence within the nebula.

astro-ph.HE↗

arXiv2025

Global-Local Tree Search in VLMs for 3D Indoor Scene Generation

Large Vision-Language Models (VLMs), such as GPT-4, have achieved remarkable success across various fields. However, there are few studies on 3D indoor scene generation with VLMs. This paper considers this task as a planning problem subject to spatial and layout common sense constraints. To solve the problem with a VLM, we propose a new global-local tree search algorithm. Globally, the method places each object sequentially and explores multiple placements during each placement process, where the problem space is represented as a tree. To reduce the depth of the tree, we decompose the scene structure hierarchically, i.e. room level, region level, floor object level, and supported object level. The algorithm independently generates the floor objects in different regions and supported objects placed on different floor objects. Locally, we also decompose the sub-task, the placement of each object, into multiple steps. The algorithm searches the tree of problem space. To leverage the VLM model to produce positions of objects, we discretize the top-down view space as a dense grid and fill each cell with diverse emojis to make to cells distinct. We prompt the VLM with the emoji grid and the VLM produces a reasonable location for the object by describing the position with the name of emojis. The quantitative and qualitative experimental results illustrate our approach generates more plausible 3D scenes than state-of-the-art approaches. Our source code is available at https://github.com/dw-dengwei/TreeSearchGen .

cs.CV↗

arXiv2025

Optimal Stochastic Trace Estimation in Generative Modeling

Hutchinson estimators are widely employed in training divergence-based likelihoods for diffusion models to ensure optimal transport (OT) properties. However, this estimator often suffers from high variance and scalability concerns. To address these challenges, we investigate Hutch++, an optimal stochastic trace estimator for generative models, designed to minimize training variance while maintaining transport optimality. Hutch++ is particularly effective for handling ill-conditioned matrices with large condition numbers, which commonly arise when high-dimensional data exhibits a low-dimensional structure. To mitigate the need for frequent and costly QR decompositions, we propose practical schemes that balance frequency and accuracy, backed by theoretical guarantees. Our analysis demonstrates that Hutch++ leads to generations of higher quality. Furthermore, this method exhibits effective variance reduction in various applications, including simulations, conditional time series forecasts, and image generation.

cs.LG↗

arXiv2025

A remark on the zero-filter limit for the Camassa-Holm equation in $B^s_{2,\infty}(\R)$

This paper investigates the zero-filter limit problem associated with the Camassa-Holm equation. In the work cited as \cite{C.L.L.W.L}, it was established that, under the hypothesis of initial data $u_0\in B^s_{2,r}(\R)$ with $s>\frac32$ and $1\leq r<\infty$, the solutions $\mathbf{S}_{t}^{\mathbfα}(u_0)$ of the Camassa-Holm equation exhibit convergence in the $L^\infty_T(B^s_{2,r})$ norm to the unique solution of the Burgers equation as $α\rightarrow 0$. Contrary to this result, the present study demonstrates that for initial data $u_0\in B^s_{2,\infty}(\R)$ the solutions of the Camassa-Holm equation fail to converge strongly in the $L^\infty_T(B^s_{2,\infty})$ norm to the Burgers equation as $α\rightarrow 0$.

math.AP↗

arXiv2025

IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System

Recently, large language model (LLM) based text-to-speech (TTS) systems have gradually become the mainstream in the industry due to their high naturalness and powerful zero-shot voice cloning capabilities.Here, we introduce the IndexTTS system, which is mainly based on the XTTS and Tortoise model. We add some novel improvements. Specifically, in Chinese scenarios, we adopt a hybrid modeling method that combines characters and pinyin, making the pronunciations of polyphonic characters and long-tail characters controllable. We also performed a comparative analysis of the Vector Quantization (VQ) with Finite-Scalar Quantization (FSQ) for codebook utilization of acoustic speech tokens. To further enhance the effect and stability of voice cloning, we introduce a conformer-based speech conditional encoder and replace the speechcode decoder with BigVGAN2. Compared with XTTS, it has achieved significant improvements in naturalness, content consistency, and zero-shot voice cloning. As for the popular TTS systems in the open-source, such as Fish-Speech, CosyVoice2, FireRedTTS and F5-TTS, IndexTTS has a relatively simple training process, more controllable usage, and faster inference speed. Moreover, its performance surpasses that of these systems. Our demos are available at https://index-tts.github.io.

cs.SD↗

arXiv2025

Variational Schrödinger Momentum Diffusion

The momentum Schrödinger Bridge (mSB) has emerged as a leading method for accelerating generative diffusion processes and reducing transport costs. However, the lack of simulation-free properties inevitably results in high training costs and affects scalability. To obtain a trade-off between transport properties and scalability, we introduce variational Schrödinger momentum diffusion (VSMD), which employs linearized forward score functions (variational scores) to eliminate the dependence on simulated forward trajectories. Our approach leverages a multivariate diffusion process with adaptively transport-optimized variational scores. Additionally, we apply a critical-damping transform to stabilize training by removing the need for score estimations for both velocity and samples. Theoretically, we prove the convergence of samples generated with optimal variational scores and momentum diffusion. Empirical results demonstrate that VSMD efficiently generates anisotropic shapes while maintaining transport efficacy, outperforming overdamped alternatives, and avoiding complex denoising processes. Our approach also scales effectively to real-world data, achieving competitive results in time series and image generation.

stat.ML↗

arXiv2024

Denoising Fisher Training For Neural Implicit Samplers

Efficient sampling from un-normalized target distributions is pivotal in scientific computing and machine learning. While neural samplers have demonstrated potential with a special emphasis on sampling efficiency, existing neural implicit samplers still have issues such as poor mode covering behavior, unstable training dynamics, and sub-optimal performances. To tackle these issues, in this paper, we introduce Denoising Fisher Training (DFT), a novel training approach for neural implicit samplers with theoretical guarantees. We frame the training problem as an objective of minimizing the Fisher divergence by deriving a tractable yet equivalent loss function, which marks a unique theoretical contribution to assessing the intractable Fisher divergences. DFT is empirically validated across diverse sampling benchmarks, including two-dimensional synthetic distribution, Bayesian logistic regression, and high-dimensional energy-based models (EBMs). Notably, in experiments with high-dimensional EBMs, our best one-step DFT neural sampler achieves results on par with MCMC methods with up to 200 sampling steps, leading to a substantially greater efficiency over 100 times higher. This result not only demonstrates the superior performance of DFT in handling complex high-dimensional sampling but also sheds light on efficient sampling methodologies across broader applications.

cs.LG↗

arXiv2024

Comparison between the emission torus and the measured toroidal magnetic field for the Crab and Vela nebula

Polarization measurements provide insight into the magnetic field, a critical aspect of the dynamics and emission properties around the compact object. In this paper, we present the polarized magnetic field of the Crab outer torus and the Vela arc utilizing Imaging X-ray Polarimetry Explorer observation data. The polarization angle (PA) measured for the Crab outer torus exhibits two monotonic evolutions along the azimuth angle, which are consistent with the normal line of the elliptical ring. There is a slight increase in PA along the azimuth angle for both the inner arc and the outer arc of the Vela nebula. The polarized magnetic vector along the outer torus of the Crab nebula shows the polarized magnetic field aligns with Crab outer torus structure. The PA variation along the Crab outer torus suggests a bulk flow speed of 0.8c. Meanwhile, the Vela nebula polarized magnetic field does not exactly align with the Vela arc structure. We noted that the Crab nebula possesses a polarized toroidal magnetic field, where as the Vela nebula exhibits an incomplete toroidal magnetic field.

astro-ph.SR↗

arXiv2024

Recurrent Interpolants for Probabilistic Time Series Prediction

Sequential models like recurrent neural networks and transformers have become standard for probabilistic multivariate time series forecasting across various domains. Despite their strengths, they struggle with capturing high-dimensional distributions and cross-feature dependencies. Recent work explores generative approaches using diffusion or flow-based models, extending to time series imputation and forecasting. However, scalability remains a challenge. This work proposes a novel method combining recurrent neural networks' efficiency with diffusion models' probabilistic modeling, based on stochastic interpolants and conditional generation with control features, offering insights for future developments in this dynamic field.

cs.LG↗

arXiv2024

DIffSteISR: Harnessing Diffusion Prior for Superior Real-world Stereo Image Super-Resolution

We introduce DiffSteISR, a pioneering framework for reconstructing real-world stereo images. DiffSteISR utilizes the powerful prior knowledge embedded in pre-trained text-to-image model to efficiently recover the lost texture details in low-resolution stereo images. Specifically, DiffSteISR implements a time-aware stereo cross attention with temperature adapter (TASCATA) to guide the diffusion process, ensuring that the generated left and right views exhibit high texture consistency thereby reducing disparity error between the super-resolved images and the ground truth (GT) images. Additionally, a stereo omni attention control network (SOA ControlNet) is proposed to enhance the consistency of super-resolved images with GT images in the pixel, perceptual, and distribution space. Finally, DiffSteISR incorporates a stereo semantic extractor (SSE) to capture unique viewpoint soft semantic information and shared hard tag semantic information, thereby effectively improving the semantic accuracy and consistency of the generated left and right images. Extensive experimental results demonstrate that DiffSteISR accurately reconstructs natural and precise textures from low-resolution stereo images while maintaining a high consistency of semantic and texture between the left and right views.

cs.CV↗

arXiv2024

Analysis of Crab X-ray Polarization using Deeper IXPE Observations

We present Crab X-ray polarization measurements using IXPE data with a total exposure of 300ks, three times more than the initial 2022 discovery paper. Polarization is detected in three times more pulsar phase bins, revealing an S-shaped $+40^\circ$ polarization angle sweep in the main pulse and ${>}1σ$ departures from the OPTIMA optical polarization in both pulses, suggesting different radiation mechanisms or sites for the polarized emission at the two wavebands. Our polarization map of the inner nebula reveals a toroidal magnetic field, as seen in prior IXPE analyses. Along the southern jet, the magnetic field orientation relative to the jet axis changes from perpendicular to parallel and the polarization degree decreases by ${\sim}6\%$. These observations may be explained by kink instabilities along the jet or a collision with a dense, jet-deflecting medium at the tip. Using spectropolarimetric analysis, we find asymmetric polarization in the four quadrants of the inner nebula, as expected for a toroidal field geometry, and a spatial correlation between polarization degree and photon index.

astro-ph.HE↗

arXiv2024

Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory

This work introduces a novel and efficient Bayesian federated learning algorithm, namely, the Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC), for parameter estimation and uncertainty quantification. We establish rigorous convergence guarantees of FA-HMC on non-iid distributed data sets, under the strong convexity and Hessian smoothness assumptions. Our analysis investigates the effects of parameter space dimension, noise on gradients and momentum, and the frequency of communication (between the central node and local nodes) on the convergence and communication costs of FA-HMC. Beyond that, we establish the tightness of our analysis by showing that the convergence rate cannot be improved even for continuous FA-HMC process. Moreover, extensive empirical studies demonstrate that FA-HMC outperforms the existing Federated Averaging-Langevin Monte Carlo (FA-LD) algorithm.

cs.LG↗

arXiv2024

ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolution

Despite advances in the paradigm of pre-training then fine-tuning in low-level vision tasks, significant challenges persist particularly regarding the increased size of pre-trained models such as memory usage and training time. Another concern often encountered is the unsatisfying results yielded when directly applying pre-trained single-image models to multi-image domain. In this paper, we propose a efficient method for transferring a pre-trained single-image super-resolution (SISR) transformer network to the domain of stereo image super-resolution (SteISR) through a parameter-efficient fine-tuning (PEFT) method. Specifically, we introduce the concept of stereo adapters and spatial adapters which are incorporated into the pre-trained SISR transformer network. Subsequently, the pre-trained SISR model is frozen, enabling us to fine-tune the adapters using stereo datasets along. By adopting this training method, we enhance the ability of the SISR model to accurately infer stereo images by 0.79dB on the Flickr1024 dataset. This method allows us to train only 4.8% of the original model parameters, achieving state-of-the-art performance on four commonly used SteISR benchmarks. Compared to the more complicated full fine-tuning approach, our method reduces training time and memory consumption by 57% and 15%, respectively.

cs.CV↗

arXiv2024

A Wolf in Sheep's Clothing: Practical Black-box Adversarial Attacks for Evading Learning-based Windows Malware Detection in the Wild

Given the remarkable achievements of existing learning-based malware detection in both academia and industry, this paper presents MalGuise, a practical black-box adversarial attack framework that evaluates the security risks of existing learning-based Windows malware detection systems under the black-box setting. MalGuise first employs a novel semantics-preserving transformation of call-based redividing to concurrently manipulate both nodes and edges of malware's control-flow graph, making it less noticeable. By employing a Monte-Carlo-tree-search-based optimization, MalGuise then searches for an optimized sequence of call-based redividing transformations to apply to the input Windows malware for evasions. Finally, it reconstructs the adversarial malware file based on the optimized transformation sequence while adhering to Windows executable format constraints, thereby maintaining the same semantics as the original. MalGuise is systematically evaluated against three state-of-the-art learning-based Windows malware detection systems under the black-box setting. Evaluation results demonstrate that MalGuise achieves a remarkably high attack success rate, mostly exceeding 95%, with over 91% of the generated adversarial malware files maintaining the same semantics. Furthermore, MalGuise achieves up to a 74.97% attack success rate against five anti-virus products, highlighting potential tangible security concerns to real-world users.

cs.CR↗