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Lu Xu

Publications and source records attributed to Lu Xu.

At least 37 records · Page 2Linked to original sources

Scaling Properties of Current Fluctuations in Periodic TASEP

We study current fluctuations in the Totally Asymmetric Simple Exclusion Process (TASEP) on a ring with $N$ sites and $p$ particles. By introducing a deformation parameter $γ$, we analyze the tilted operator that governs the statistics of the time-integrated current. Employing the coordinate Bethe ansatz, we derive implicit expressions for the scaled cumulant generating function (SCGF), i.e. the largest eigenvalue, and the spectral gap, both in terms of Bethe roots. Their asymptotic behaviour is characterized by using the geometric structure of Cassini oval. In the thermodynamic limit at fixed particle density, we identify a dynamical phase transition separating fluctuation regimes. For $γ>0$, the SCGF exhibits ballistic growth with system size, $λ_1 \sim N$. In contrast, for $γ<0$, the SCGF converges to $-1$ as $N\to\infty$. This transition is reflected in the spectral gap, which controls the system's relaxation timescale. For $γ>0$, the gap closes at polynomial speed, $Δ\sim N^{-1}$, consistent with rapid relaxation with enhanced current. For $γ<0$, the gap vanishes exponentially, $Δ\sim \exp(-cN)$, signaling metastability with diminished current. Our non-perturbative results provide insights into large deviations and the relaxation dynamics in driven particle systems.

cond-mat.stat-mech↗

A GPU-Accelerated Three-Dimensional Crack Element Method for Transient Dynamic Fracture Simulation

This work presents a novel three-dimensional Crack Element Method (CEM) designed to model transient dynamic crack propagation in quasi-brittle materials efficiently. CEM introduces an advanced element-splitting algorithm that enables element-wise crack growth, including crack branching. Based on the evolving topology of split elements, an original formulation for computing the fracture energy release rate in three dimensions is derived. A series of benchmark examples is conducted to demonstrate that the proposed 3D CEM accurately simulates both single crack propagation and complex crack branching scenarios. Furthermore, all three-dimensional simulations are GPU-accelerated, achieving high levels of computational efficiency, consistency, and accuracy.

cs.CE↗

A Practical Finite Element Approach for Simulating Dynamic Crack Growth in Cu/Ultra Low-k Interconnect Structures

This work presents a practical finite element modeling strategy, the Crack Element Method (CEM), for simulating the dynamic crack propagation in two-dimensional structures. The method employs an element-splitting algorithm based on the Edge-based Smoothed Finite Element Method (ES-FEM) to capture the element-wise crack growth while reducing the formation of poorly shaped elements that can compromise numerical accuracy and computational performance. A fracture energy release rate formulation is also developed based on the evolving topology of the split elements. The proposed approach is validated through a series of classical benchmark problems, demonstrating its accuracy and robustness in addressing dynamic fracture scenarios. Finally, the applicability of the CEM is illustrated in a case study involving patterned Cu/Ultra Low-k interconnect structures.

cs.CE↗

Seed LiveInterpret 2.0: End-to-end Simultaneous Speech-to-speech Translation with Your Voice

Simultaneous Interpretation (SI) represents one of the most daunting frontiers in the translation industry, with product-level automatic systems long plagued by intractable challenges: subpar transcription and translation quality, lack of real-time speech generation, multi-speaker confusion, and translated speech inflation, especially in long-form discourses. In this study, we introduce Seed-LiveInterpret 2.0, an end-to-end SI model that delivers high-fidelity, ultra-low-latency speech-to-speech generation with voice cloning capabilities. As a fully operational product-level solution, Seed-LiveInterpret 2.0 tackles these challenges head-on through our novel duplex speech-to-speech understanding-generating framework. Experimental results demonstrate that through large-scale pretraining and reinforcement learning, the model achieves a significantly better balance between translation accuracy and latency, validated by human interpreters to exceed 70% correctness in complex scenarios. Notably, Seed-LiveInterpret 2.0 outperforms commercial SI solutions by significant margins in translation quality, while slashing the average latency of cloned speech from nearly 10 seconds to a near-real-time 3 seconds, which is around a near 70% reduction that drastically enhances practical usability.

cs.CL↗

From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition

Recent advances in Automatic Speech Recognition (ASR) have been largely fueled by massive speech corpora. However, extending coverage to diverse languages with limited resources remains a formidable challenge. This paper introduces Speech Back-Translation, a scalable pipeline that improves multilingual ASR models by converting large-scale text corpora into synthetic speech via off-the-shelf text-to-speech (TTS) models. We demonstrate that just tens of hours of real transcribed speech can effectively train TTS models to generate synthetic speech at hundreds of times the original volume while maintaining high quality. To evaluate synthetic speech quality, we develop an intelligibility-based assessment framework and establish clear thresholds for when synthetic data benefits ASR training. Using Speech Back-Translation, we generate more than 500,000 hours of synthetic speech in ten languages and continue pre-training Whisper-large-v3, achieving average transcription error reductions of over 30\%. These results highlight the scalability and effectiveness of Speech Back-Translation for enhancing multilingual ASR systems.

cs.CL↗

RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification

In this paper, we propose RFUAV as a new benchmark dataset for radio-frequency based (RF-based) unmanned aerial vehicle (UAV) identification and address the following challenges: Firstly, many existing datasets feature a restricted variety of drone types and insufficient volumes of raw data, which fail to meet the demands of practical applications. Secondly, existing datasets often lack raw data covering a broad range of signal-to-noise ratios (SNR), or do not provide tools for transforming raw data to different SNR levels. This limitation undermines the validity of model training and evaluation. Lastly, many existing datasets do not offer open-access evaluation tools, leading to a lack of unified evaluation standards in current research within this field. RFUAV comprises approximately 1.3 TB of raw frequency data collected from 37 distinct UAVs using the Universal Software Radio Peripheral (USRP) device in real-world environments. Through in-depth analysis of the RF data in RFUAV, we define a drone feature sequence called RF drone fingerprint, which aids in distinguishing drone signals. In addition to the dataset, RFUAV provides a baseline preprocessing method and model evaluation tools. Rigorous experiments demonstrate that these preprocessing methods achieve state-of-the-art (SOTA) performance using the provided evaluation tools. The RFUAV dataset and baseline implementation are publicly available at https://github.com/kitoweeknd/RFUAV/.

cs.RO↗

5f Electron Induced Spin Transport by Sandwich-Type Phthalocyanine

In this study, we employed the non-equilibrium Green's function method combined with density functional theory to investigate the spin transport properties of the actinide sandwich phthalocyanine molecule U(Pc)2.This study aims to provide beneficial assistance for the development of actinide phthalocyanine molecular spintronic devices.

physics.comp-ph↗

Coupling hydrodynamics of several Facilitated Exclusion Processes with closed boundaries

In this paper, we prove the hydrodynamic limit for the ergodic dynamics of the Facilitated Exclusion Process with closed boundaries in the symmetric, asymmetric and weakly asymmetric regimes. For this, we couple it with a Simple Exclusion Process by constructing a mapping that transforms the facilitated dynamics into the simple one. As the hydrodynamic behaviour of the simple exclusion process with closed boundaries has been extensively studied, we can deduce the corresponding hydrodynamics for the facilitated exclusion process.

math.PR↗

"Molecular waveplate" for the control of ultrashort pulses carrying orbital angular momentum

Ultrashort laser pulses carrying orbital angular momentum (OAM) have become essential tools in Atomic, Molecular, and Optical (AMO) studies, particularly for investigating strong-field light-matter interactions. However, controlling and generating ultrashort vortex pulses presents significant challenges, since their broad spectral content complicates manipulation with conventional optical elements, while the high peak power inherent in short-duration pulses risks damaging optical components. Here, we introduce a novel method for generating and controlling broadband ultrashort vortex beams by exploiting the non-adiabatic alignment of linear gas-phase molecules induced by vector beams. The interaction between the vector beam and the gas-phase molecules results in spatially varying polarizability, imparting a phase modulation to a probe laser. This process effectively creates a tunable ``molecular waveplate'' that adapts naturally to a broad spectral range. By leveraging this approach, we can generate ultrashort vortex pulses across a wide range of wavelengths. Under optimized gas pressure and interaction length conditions, this method allows for highly efficient conversion of circularly polarized light into the desired OAM pulse, thus enabling the generation of few-cycle, high-intensity vortex beams. This molecular waveplate, which overcomes the limitations imposed by conventional optical elements, opens up new possibilities for exploring strong-field physics, ultrafast science, and other applications that require high-intensity vortex beams.

physics.optics↗

Informative Scene Graph Generation via Debiasing

Scene graph generation aims to detect visual relationship triplets, (subject, predicate, object). Due to biases in data, current models tend to predict common predicates, e.g. "on" and "at", instead of informative ones, e.g. "standing on" and "looking at". This tendency results in the loss of precise information and overall performance. If a model only uses "stone on road" rather than "stone blocking road" to describe an image, it may be a grave misunderstanding. We argue that this phenomenon is caused by two imbalances: semantic space level imbalance and training sample level imbalance. For this problem, we propose DB-SGG, an effective framework based on debiasing but not the conventional distribution fitting. It integrates two components: Semantic Debiasing (SD) and Balanced Predicate Learning (BPL), for these imbalances. SD utilizes a confusion matrix and a bipartite graph to construct predicate relationships. BPL adopts a random undersampling strategy and an ambiguity removing strategy to focus on informative predicates. Benefiting from the model-agnostic process, our method can be easily applied to SGG models and outperforms Transformer by 136.3%, 119.5%, and 122.6% on mR@20 at three SGG sub-tasks on the SGG-VG dataset. Our method is further verified on another complex SGG dataset (SGG-GQA) and two downstream tasks (sentence-to-graph retrieval and image captioning).

cs.CV↗

A novel approach to hydrodynamics for long-range generalized exclusion

We consider a class of generalized long-range exclusion processes evolving either on $\mathbb Z$ or on a finite lattice with an open boundary. The jump rates are given in terms of a general kernel depending on both the departure and destination sites, and it is such that the particle displacement has an infinite expectation, but some tail bounds are satisfied. We study the superballisitic scaling limit of the particle density and prove that its space-time evolution is concentrated on the set of weak solutions to a non-local transport equation. Since the stationary states of the dynamics are unknown, we develop a new approach to such a limit relying only on the algebraic structure of the Markovian generator.

math.PR↗

Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model

The emerging video LMMs (Large Multimodal Models) have achieved significant improvements on generic video understanding in the form of VQA (Visual Question Answering), where the raw videos are captured by cameras. However, a large portion of videos in real-world applications are edited videos, \textit{e.g.}, users usually cut and add effects/modifications to the raw video before publishing it on social media platforms. The edited videos usually have high view counts but they are not covered in existing benchmarks of video LMMs, \textit{i.e.}, ActivityNet-QA, or VideoChatGPT benchmark. In this paper, we leverage the edited videos on a popular short video platform, \textit{i.e.}, TikTok, and build a video VQA benchmark (named EditVid-QA) covering four typical editing categories, i.e., effect, funny, meme, and game. Funny and meme videos benchmark nuanced understanding and high-level reasoning, while effect and game evaluate the understanding capability of artificial design. Most of the open-source video LMMs perform poorly on the EditVid-QA benchmark, indicating a huge domain gap between edited short videos on social media and regular raw videos. To improve the generalization ability of LMMs, we collect a training set for the proposed benchmark based on both Panda-70M/WebVid raw videos and small-scale TikTok/CapCut edited videos, which boosts the performance on the proposed EditVid-QA benchmark, indicating the effectiveness of high-quality training data. We also identified a serious issue in the existing evaluation protocol using the GPT-3.5 judge, namely a "sorry" attack, where a sorry-style naive answer can achieve an extremely high rating from the GPT judge, e.g., over 4.3 for correctness score on VideoChatGPT evaluation protocol. To avoid the "sorry" attacks, we evaluate results with GPT-4 judge and keyword filtering. The dataset is released at https://github.com/XenonLamb/EditVid-QA.

cs.CV↗

Towards Achieving Human Parity on End-to-end Simultaneous Speech Translation via LLM Agent

In this paper, we present Cross Language Agent -- Simultaneous Interpretation, CLASI, a high-quality and human-like Simultaneous Speech Translation (SiST) System. Inspired by professional human interpreters, we utilize a novel data-driven read-write strategy to balance the translation quality and latency. To address the challenge of translating in-domain terminologies, CLASI employs a multi-modal retrieving module to obtain relevant information to augment the translation. Supported by LLMs, our approach can generate error-tolerated translation by considering the input audio, historical context, and retrieved information. Experimental results show that our system outperforms other systems by significant margins. Aligned with professional human interpreters, we evaluate CLASI with a better human evaluation metric, valid information proportion (VIP), which measures the amount of information that can be successfully conveyed to the listeners. In the real-world scenarios, where the speeches are often disfluent, informal, and unclear, CLASI achieves VIP of 81.3% and 78.0% for Chinese-to-English and English-to-Chinese translation directions, respectively. In contrast, state-of-the-art commercial or open-source systems only achieve 35.4% and 41.6%. On the extremely hard dataset, where other systems achieve under 13% VIP, CLASI can still achieve 70% VIP.

cs.CL↗

Hydrodynamic limit for asymmetric simple exclusion with accelerated boundaries

We consider the asymmetric simple exclusion process (ASEP) on the one-dimensional finite lattice $\{1,2,\ldots,N\}$. The particles can be created/annihilated at the boundaries with given rates. These rates are $L^\infty$ functions of time and are independent of the jump rates in the bulk. The boundary dynamics is modified by a factor $N^θ$ with $θ>0$. We study the hydrodynamic limit for the particle density profile under the hyperbolic space-time scale. The macroscopic equation is given by (inviscid) Burgers equation with Dirichlet type boundaries that is characterized by the boundary entropy. A grading scheme is developed to control the formulation of boundary layers on the microscopic level.

math.PR↗

Hydrodynamics for asymmetric simple exclusion on a finite segment with Glauber-type source

We consider an open interacting particle system on a finite lattice. The particles perform asymmetric simple exclusion and are randomly created or destroyed at all sites, with rates that grow rapidly near the boundaries. We study the hydrodynamic limit for the particle density at the hyperbolic space-time scale and obtain the entropy solution to a boundary-driven quasilinear conservation law with a relaxation term. Different from the usual boundary conditions introduced in [Bardos, Roux, and Nedelec, (1979), Comm. Part. Diff. Equ], discontinuity (boundary layer) does not formulate at the boundaries due to the strong relaxation scheme.

math.PR↗

NTIRE 2024 Challenge on Night Photography Rendering

This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year's competition, the challenge images were collected with a mobile phone and the speed of algorithms was also measured alongside the quality of their output. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. There were 2 nominations: quality and efficiency. Top 5 solutions in terms of output quality were sorted by evaluation time (see Fig. 1). The top ranking participants' solutions effectively represent the state-of-the-art in nighttime photography rendering. More results can be found at https://nightimaging.org.

cs.CV↗

PROXYQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models

Large Language Models (LLMs) have succeeded remarkably in understanding long-form contents. However, exploring their capability for generating long-form contents, such as reports and articles, has been relatively unexplored and inadequately assessed by existing benchmarks. The prevalent evaluation methods, which predominantly rely on crowdsourcing, are recognized for their labor-intensive nature and lack of efficiency, whereas automated metrics, such as the ROUGE score, demonstrate discordance with human judgment criteria. In this paper, we propose ProxyQA, an innovative framework dedicated to assessing long-text generation. ProxyQA comprises in-depth human-curated meta-questions spanning various domains, each accompanied by specific proxy-questions with pre-annotated answers. LLMs are tasked to generate extensive content in response to these meta-questions, by engaging an evaluator and incorporating the generated texts as contextual background, ProxyQA assesses the generated content's quality through the evaluator's accuracy in addressing the proxy-questions. We examine multiple LLMs, emphasizing ProxyQA's demanding nature as a high-quality assessment tool. Human evaluation demonstrates that the proxy-question method is notably self-consistent and aligns closely with human evaluative standards. The dataset and leaderboard is available at \url{https://proxy-qa.com}.

cs.CL↗

Sparsity- and Hybridity-Inspired Visual Parameter-Efficient Fine-Tuning for Medical Diagnosis

The success of Large Vision Models (LVMs) is accompanied by vast data volumes, which are prohibitively expensive in medical diagnosis.To address this, recent efforts exploit Parameter-Efficient Fine-Tuning (PEFT), which trains a small number of weights while freezing the rest.However, they typically assign trainable weights to the same positions in LVMs in a heuristic manner, regardless of task differences, making them suboptimal for professional applications like medical diagnosis.To address this, we statistically reveal the nature of sparsity and hybridity during diagnostic-targeted fine-tuning, i.e., a small portion of key weights significantly impacts performance, and these key weights are hybrid, including both task-specific and task-agnostic parts.Based on this, we propose a novel Sparsity- and Hybridity-inspired Parameter Efficient Fine-Tuning (SH-PEFT).It selects and trains a small portion of weights based on their importance, which is innovatively estimated by hybridizing both task-specific and task-agnostic strategies.Validated on six medical datasets of different modalities, we demonstrate that SH-PEFT achieves state-of-the-art performance in transferring LVMs to medical diagnosis in terms of accuracy. By tuning around 0.01% number of weights, it outperforms full model fine-tuning.Moreover, SH-PEFT also achieves comparable performance to other models deliberately optimized for specific medical tasks.Extensive experiments demonstrate the effectiveness of each design and reveal that large model transfer holds great potential in medical diagnosis.

cs.CV↗