arXiv subjects

Xin Zhang

Publications and source records attributed to Xin Zhang.

At least 559 records · Page 31Linked to original sources

arXiv2024

Bethe ansatz approach for the steady state of the asymmetric simple exclusion process with open boundaries

We study the asymmetric simple exclusion process with non-diagonal boundary terms under a specific constraint. A symmetric chiral basis is constructed and a special string solution of the Bethe ansatz equations corresponding to the steady state is presented. Using the coordinate Bethe ansatz method, we derive a concise expression for the steady state. The current and density profile in the steady state are also studied.

math-ph↗

arXiv2024

ArgMed-Agents: Explainable Clinical Decision Reasoning with LLM Disscusion via Argumentation Schemes

There are two main barriers to using large language models (LLMs) in clinical reasoning. Firstly, while LLMs exhibit significant promise in Natural Language Processing (NLP) tasks, their performance in complex reasoning and planning falls short of expectations. Secondly, LLMs use uninterpretable methods to make clinical decisions that are fundamentally different from the clinician's cognitive processes. This leads to user distrust. In this paper, we present a multi-agent framework called ArgMed-Agents, which aims to enable LLM-based agents to make explainable clinical decision reasoning through interaction. ArgMed-Agents performs self-argumentation iterations via Argumentation Scheme for Clinical Discussion (a reasoning mechanism for modeling cognitive processes in clinical reasoning), and then constructs the argumentation process as a directed graph representing conflicting relationships. Ultimately, use symbolic solver to identify a series of rational and coherent arguments to support decision. We construct a formal model of ArgMed-Agents and present conjectures for theoretical guarantees. ArgMed-Agents enables LLMs to mimic the process of clinical argumentative reasoning by generating explanations of reasoning in a self-directed manner. The setup experiments show that ArgMed-Agents not only improves accuracy in complex clinical decision reasoning problems compared to other prompt methods, but more importantly, it provides users with decision explanations that increase their confidence.

cs.AI↗

arXiv2024

MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark

Multiple-choice question (MCQ) datasets like Massive Multitask Language Understanding (MMLU) are widely used to evaluate the commonsense, understanding, and problem-solving abilities of large language models (LLMs). However, the open-source nature of these benchmarks and the broad sources of training data for LLMs have inevitably led to benchmark contamination, resulting in unreliable evaluation results. To alleviate this issue, we propose a contamination-free and more challenging MCQ benchmark called MMLU-CF. This benchmark reassesses LLMs' understanding of world knowledge by averting both unintentional and malicious data leakage. To avoid unintentional data leakage, we source data from a broader domain and design three decontamination rules. To prevent malicious data leakage, we divide the benchmark into validation and test sets with similar difficulty and subject distributions. The test set remains closed-source to ensure reliable results, while the validation set is publicly available to promote transparency and facilitate independent verification. Our evaluation of mainstream LLMs reveals that the powerful GPT-4o achieves merely a 5-shot score of 73.4% and a 0-shot score of 71.9% on the test set, which indicates the effectiveness of our approach in creating a more rigorous and contamination-free evaluation standard. The GitHub repository is available at https://github.com/microsoft/MMLU-CF and the dataset refers to https://huggingface.co/datasets/microsoft/MMLU-CF.

cs.CL↗

arXiv2024

Stochastic linear quadratic optimal control problems with regime-switching jumps in infinite horizon

This paper investigates a stochastic linear-quadratic (SLQ, for short) control problem regulated by a time-invariant Markov chain in infinite horizon. Under the $L^2$-stability framework, we study a class of linear backward stochastic differential equations (BSDE, for short) in infinite horizon and discuss the open-loop and closed-loop solvabilities of the SLQ problem. The open-loop solvability is characterized by the solvability of a system of coupled forward-backward stochastic differential equations (FBSDEs, for short) in infinite horizon and the convexity of the cost functional, and the closed-loop solvability is shown to be equivalent to the open-loop solvability, which in turn is equivalent to the existence of a static stabilizing solution to the associated constrained coupled algebra Riccati equations (CAREs, for short). Under the uniform convexity assumption, we obtain the unique solvability of associated CAREs and construct the corresponding closed-loop optimal strategy. Finally, we also solve a class of discounted SLQ problems and give two concrete examples to illustrate the results developed in the earlier sections.

math.OC↗

arXiv2024

Scatter correction for photon-counting detector based CBCT imaging

Objective: The aim of this study is to validate the effectiveness of an energy-modulated scatter correction method in suppressing scatter in photon-counting detector (PCD)-based cone beam CT (CBCT) imaging. Approach: The scatter correction method, named e-Grid, which was initially applied to dual-layer flat-panel detector (DLFPD)-based CBCT imaging, was tested for its performance in PCD-CBCT imaging. Benchtop PCD-CBCT imaging experiments were conducted to verify the effectiveness of the e-Grid method. Additionally, quantitative metrics were measured from these experimental results. Main results: It was found that the use of the e-Grid method could significantly eliminate cupping artifacts caused by Compton scatter in PCD-CBCT imaging. Meanwhile, its effectiveness was observed in both low- and high-energy images, as well as for objects of varying sizes. Quantitative results showed that the e-Grid method could reduce scatter artifacts by at least 71% in low-energy images and 75% in high-energy images. Significance: It was demonstrated that the scatter correction method originally applied to DLFPD-based CBCT could also perform well in PCD-CBCT, showing that the e-Grid method has great potential for application in other spectral CBCT imaging systems.

physics.med-ph↗

arXiv2024

Rapid Bayesian Seismic Tomography using Graph Mixture Density Networks

Seismic tomography is a methodology to image subsurface properties of the Earth. In order to better interpret the resulting images, it is important to assess uncertainty in the results. Mixture density networks (MDNs) provide an efficient way to estimate Bayesian posterior probability density functions (pdfs) that describe the uncertainty of tomographic images. However, the method can only be applied in cases where the number of data is fixed, and consequently a large number of practical applications that have variable data sizes cannot be solved. To resolve this issue, we introduce graph neural networks (GNNs) to solve seismic tomographic problems. Graphs are data structure which provides flexible representation of complex, variable systems. GNNs are neural networks that manipulates graph data, and can be combined with MDNs (called graph MDNs) to provide efficient estimates of posterior pdfs for graph data. In this study we apply graph MDNs to seismic tomography by representing travel time data with a graph. We demonstrate the method using both synthetic and real data, and compare the results with those obtained using Markov chain Monte Carlo (McMC). The results show that graph MDNs can provide comparable posterior pdfs to those obtained using McMC at significantly lower cost. We thus conclude that graph MDNs can be used in a range of practical applications that require many similar seismic tomographic problems with different number of data to be solved.

physics.geo-ph↗

arXiv2024

PointTalk: Audio-Driven Dynamic Lip Point Cloud for 3D Gaussian-based Talking Head Synthesis

Talking head synthesis with arbitrary speech audio is a crucial challenge in the field of digital humans. Recently, methods based on radiance fields have received increasing attention due to their ability to synthesize high-fidelity and identity-consistent talking heads from just a few minutes of training video. However, due to the limited scale of the training data, these methods often exhibit poor performance in audio-lip synchronization and visual quality. In this paper, we propose a novel 3D Gaussian-based method called PointTalk, which constructs a static 3D Gaussian field of the head and deforms it in sync with the audio. It also incorporates an audio-driven dynamic lip point cloud as a critical component of the conditional information, thereby facilitating the effective synthesis of talking heads. Specifically, the initial step involves generating the corresponding lip point cloud from the audio signal and capturing its topological structure. The design of the dynamic difference encoder aims to capture the subtle nuances inherent in dynamic lip movements more effectively. Furthermore, we integrate the audio-point enhancement module, which not only ensures the synchronization of the audio signal with the corresponding lip point cloud within the feature space, but also facilitates a deeper understanding of the interrelations among cross-modal conditional features. Extensive experiments demonstrate that our method achieves superior high-fidelity and audio-lip synchronization in talking head synthesis compared to previous methods.

cs.SD↗

arXiv2024

Sum-product phenomenon in quotients of rings of algebraic integers

We obtain a bounded generation theorem over $\mathcal O/\mathfrak a$, where $\mathcal O$ is the ring of integers of a number field and $\mathfrak a$ a general ideal of $\mathcal O$. This addresses a conjecture of Salehi-Golsefidy. Along the way, we obtain nontrivial bounds for additive character sums over $\mathcal O/\mathcal P^n$ for a prime ideal $\mathcal P$ with the aid of certain sum-product estimates.

math.NT↗

arXiv2024

High-pressure synthesis of K_{4}N_{6} compound entirely composed of aromatic hexazine [N_{6}]^{4-} anion

The synthesis of hexazine N_{6} ring is another milestone in nitrogen chemistry after that of aromatic [N_{5}]^{-} anion. However, due to the diversity of carried charges, realizing compounds entirely composed of aromatic hexazine N_{6} ring potentially with high-stability is a challenge. The first reported hexazine N_{6} ring is [N_{6}]^{2-} anion in K_{2}N_{6} [Nat. Chem. 14, 794 (2022)] that does not adhere to H\''uckel's rule, and subsequently, the aromatic hexazine [N_{6}]^{4-} anion mixed with [N_{5}]^{-} anion and N_{2} dimers is realized in the complex compound K_{9}N_{56} [Nat. Chem. 15, 641 (2023)], where 5.36\% of N atoms form aromatic N_{6} ring. Here, we theoretically predict that all N atoms form aromatic hexazine [N_{6}]^{4-} anion in K_{4}N_{6}, which becomes stable at 60 GPa and can stably exist up to 600 K at 0 GPa. Following this approach, based on the diamond anvil cell, K_{4}N_{6} composed of 100\% aromatic hexazine [N_{6}]^{4-} anion is synthesized at 45 GPa after laser-heating and identified by synchrotron X-ray diffraction and Raman spectroscopy. Our results bring us closer to achieving aromatic N6 rings at ambient condition.

cond-mat.mtrl-sci↗

arXiv2024

Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise

Deep learning-based MRI reconstruction models have achieved superior performance these days. Most recently, diffusion models have shown remarkable performance in image generation, in-painting, super-resolution, image editing and more. As a generalized diffusion model, cold diffusion further broadens the scope and considers models built around arbitrary image transformations such as blurring, down-sampling, etc. In this paper, we propose a k-space cold diffusion model that performs image degradation and restoration in k-space without the need for Gaussian noise. We provide comparisons with multiple deep learning-based MRI reconstruction models and perform tests on a well-known large open-source MRI dataset. Our results show that this novel way of performing degradation can generate high-quality reconstruction images for accelerated MRI.

eess.IV↗

arXiv2024

InfinityDrive: Breaking Time Limits in Driving World Models

Autonomous driving systems struggle with complex scenarios due to limited access to diverse, extensive, and out-of-distribution driving data which are critical for safe navigation. World models offer a promising solution to this challenge; however, current driving world models are constrained by short time windows and limited scenario diversity. To bridge this gap, we introduce InfinityDrive, the first driving world model with exceptional generalization capabilities, delivering state-of-the-art performance in high fidelity, consistency, and diversity with minute-scale video generation. InfinityDrive introduces an efficient spatio-temporal co-modeling module paired with an extended temporal training strategy, enabling high-resolution (576$\times$1024) video generation with consistent spatial and temporal coherence. By incorporating memory injection and retention mechanisms alongside an adaptive memory curve loss to minimize cumulative errors, achieving consistent video generation lasting over 1500 frames (more than 2 minutes). Comprehensive experiments in multiple datasets validate InfinityDrive's ability to generate complex and varied scenarios, highlighting its potential as a next-generation driving world model built for the evolving demands of autonomous driving. Our project homepage: https://metadrivescape.github.io/papers_project/InfinityDrive/page.html

cs.CV↗

arXiv2024

Frictional Adhesive Contact of Multiferroic Coatings Based on the Hybrid Element Method

We study the frictional adhesive contact of a rigid insulating sphere sliding past a multiferroic coating deposed onto a rigid substrate, based on the hybrid element method (HEM). The adhesion behavior is described based on the Maugis-Dugdale (MD) model. The adhesion-driven conjugate gradient method (AD-CGM) is employed to calculate the distribution of unknown pressures, while the discrete convolution-fast Fourier transform (DC-FFT) is utilized to compute the deformations, surface electric and magnetic potentials as well as the subsurface stresses, electric displacements, and magnetic inductions. We found that the coating thickness affect the contact stiffness and the interplay between friction and adhesion. More importantly, friction and gap-dependent MD adhesion affects elastic, electric, and magnetic behavior of the interface, breaking the symmetry between leading and trailing edges behaviors in all the investigated fields. Indeed, increasing the friction coefficient, the contact shape is no longer circular, the pressure distribution shifts towards the leading edge, the electric/magnetic surface potentials distributions sharpen at the leading edge, and the subsurface stress fields concentrates at the trailing edges.

cond-mat.soft↗

arXiv2024

Neural-Network-Enhanced Metalens Camera for High-Definition, Dynamic Imaging in the Long-Wave Infrared Spectrum

To provide a lightweight and cost-effective solution for the long-wave infrared imaging using a singlet, we develop a camera by integrating a High-Frequency-Enhancing Cycle-GAN neural network into a metalens imaging system. The High-Frequency-Enhancing Cycle-GAN improves the quality of the original metalens images by addressing inherent frequency loss introduced by the metalens. In addition to the bidirectional cyclic generative adversarial network, it incorporates a high-frequency adversarial learning module. This module utilizes wavelet transform to extract high-frequency components, and then establishes a high-frequency feedback loop. It enables the generator to enhance the camera outputs by integrating adversarial feedback from the high-frequency discriminator. This ensures that the generator adheres to the constraints imposed by the high-frequency adversarial loss, thereby effectively recovering the camera's frequency loss. This recovery guarantees high-fidelity image output from the camera, facilitating smooth video production. Our camera is capable of achieving dynamic imaging at 125 frames per second with an End Point Error value of 12.58. We also achieve 0.42 for Fréchet Inception Distance, 30.62 for Peak Signal to Noise Ratio, and 0.69 for Structural Similarity in the recorded videos.

eess.IV↗

arXiv2024

Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows

Gravitational wave (GW) detection is of paramount importance in fundamental physics and GW astronomy, yet it presents formidable challenges. One significant challenge is the removal of noise transient artifacts known as glitches, which greatly impact the search and identification of GWs. Recent research has achieved remarkable results in data denoising, often using effective modeling methods to remove glitches. However, for glitches from uncertain or unknown sources, current methods cannot completely eliminate them from the GW signal. In this work, we leverage the inherent robustness of machine learning to obtain reliable posterior parameter distributions directly from GW data contaminated by glitches. Our network model provides reasonable and rapid parameter inference even in the presence of glitches, without needing to remove them. We also investigate various factors affecting the rationality of parameter inference in our normalizing flow network, including glitch and GW parameters. The results demonstrate that the normalizing flow can reasonably infer the source parameters of GWs even with unknown contamination. We find that the nature of the glitch itself is the only factor that can affect the rationality of the inferred results. With improvements to our model, we anticipate accelerating the localization of electromagnetic counterparts and providing priors for more accurate deglitching, thereby speeding up subsequent data processing procedures.

gr-qc↗

arXiv2024

Phased Gradient Ultra Open Metamaterials for Broadband Acoustic Silencing

Noise pollution is a persistent environmental concern with severe implications for human health and resources. Acoustic metamaterials offer the potential for ultrathin silencing devices; however, existing designs often lack practical openness and are thereby limited by their functional bandwidths. This paper introduces a novel approach utilizing a phase gradient ultra-open metamaterial (PGUOM) to address these challenges. The PGUOM, characterized by a phase gradient across three unit cells, efficiently transforms incident waves into spoof surface waves, effectively blocking sound while allowing for a high degree of ventilation. Our design provides adjustable openness, accommodates various boundary conditions, and ensures sustained broadband sound insulation. Theoretical, numerical, and experimental validations demonstrate the efficacy of our concept. This innovative approach represents a significant advancement in ventilated acoustic metamaterials, providing both ventilation and high-performance, broadband sound insulation simultaneously.

physics.app-ph↗

arXiv2024

Prospects for Observing High-redshift Radio-loud Quasars in the SKA Era: Paving the Way for 21-cm Forest Observations

The 21-cm forest is a sensitive probe for the early heating process and small-scale structures during the epoch of reionization (EoR), to be realized with the upcoming Square Kilometre Array (SKA). Its detection relies on the availability of radio-bright background sources, among which the radio-loud quasars are very promising, but their abundance during the EoR is still poorly constrained due to limited observations. Here, we use a physics-driven model to forecast future radio-loud quasar observations. We fit the parameters of the model using observational data of high-redshift quasars. Assuming Eddington accretion, the model yields an average lifetime of $t_{\rm q} \sim 10^{5.3}$yr for quasars at $z\sim6$, consistent with recent results obtained from quasar proximity zone pre-study. We show that if the radio-loud fraction of quasars evolves with redshift, it will significantly reduce the abundance of observable radio-loud quasars in the SKA era, making 21-cm forest studies challenging. With a constant radio-loud fraction, our model suggests that a one-year sky survey conducted with SKA-LOW has the capability to detect approximately 20 radio-loud quasars at $z\sim 9$, with sufficient sensitivity to resolve individual 21-cm forest lines.

astro-ph.CO↗

arXiv2024

Multitask Learning for SAR Ship Detection with Gaussian-Mask Joint Segmentation

Detecting ships in synthetic aperture radar (SAR) images is challenging due to strong speckle noise, complex surroundings, and varying scales. This paper proposes MLDet, a multitask learning framework for SAR ship detection, consisting of object detection, speckle suppression, and target segmentation tasks. An angle classification loss with aspect ratio weighting is introduced to improve detection accuracy by addressing angular periodicity and object proportions. The speckle suppression task uses a dual-feature fusion attention mechanism to reduce noise and fuse shallow and denoising features, enhancing robustness. The target segmentation task, leveraging a rotated Gaussian-mask, aids the network in extracting target regions from cluttered backgrounds and improves detection efficiency with pixel-level predictions. The Gaussian-mask ensures ship centers have the highest probabilities, gradually decreasing outward under a Gaussian distribution. Additionally, a weighted rotated boxes fusion (WRBF) strategy combines multi-direction anchor predictions, filtering anchors beyond boundaries or with high overlap but low confidence. Extensive experiments on SSDD+ and HRSID datasets demonstrate the effectiveness and superiority of MLDet.

cs.CV↗

arXiv2024

Prospects for searching for sterile neutrinos with gravitational wave and $γ$-ray burst joint observations

Sterile neutrinos can influence the evolution of the universe, and thus cosmological observations can be used to detect them. Future gravitational wave (GW) observations can precisely measure absolute cosmological distances, helping to break parameter degeneracies generated by traditional cosmological observations. This advancement can lead to much tighter constraints on sterile neutrino parameters. This work provides a preliminary forecast for detecting sterile neutrinos using third-generation GW detectors in combination with future short $γ$-ray burst observations from a THESEUS-like telescope, an approach not previously explored in the literature. Both massless and massive sterile neutrinos are considered within the $Λ$CDM cosmology. We find that using GW data can greatly enhance the detection capability for massless sterile neutrinos, reaching 3$σ$ level. For massive sterile neutrinos, GW data can also greatly assist in improving the parameter constraints, but it seems that effective detection is still not feasible.

astro-ph.HE↗