Publications (247)
Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration
Jonathan E. Lee, Min Zhu, Ziqiao Xi +3
Geological carbon sequestration (GCS) involves injecting CO into subsurface geological formations for permanent storage. Numerical simulations could guide decisions in GCS proj…
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Min Zhu, Handi Zhang, Anran Jiao +2
Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equ…
Enhancing searches for astrophysical neutrino sources in IceCube with machine learning and improved spatial modeling
Leo Seen, Tianlu Yuan, Lu Lu +2
Searches for astrophysical neutrino sources in IceCube rely on an unbinned likelihood that consists of an energy and spatial component. Accurate modeling of the detector, ice, and…
D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators
Zecheng Zhang, Christian Moya, Lu Lu +2
Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems. Ho…
Active operator learning with predictive uncertainty quantification for partial differential equations
Nick Winovich, Mitchell Daneker, Lu Lu +1
With the increased prevalence of neural operators being used to provide rapid solutions to partial differential equations (PDEs), understanding the accuracy of model predictions an…
Can Large Language Models Understand Spatial Audio?
Changli Tang, Wenyi Yu, Guangzhi Sun +8
This paper explores enabling large language models (LLMs) to understand spatial information from multichannel audio, a skill currently lacking in auditory LLMs. By leveraging LLMs'…
Set-membership NLMS algorithm based on bias-compensated and regression noise variance estimation for noisy inputs
Kaili Yin, Haiquan Zhao, Lu Lu
The bias-compensated set-membership normalised LMS (BCSMNLMS) algorithm is proposed based on the concept of set-membership filtering, which incorporates the bias-compensation techn…
Connecting Speech Encoder and Large Language Model for ASR
Wenyi Yu, Changli Tang, Guangzhi Sun +6
The impressive capability and versatility of large language models (LLMs) have aroused increasing attention in automatic speech recognition (ASR), with several pioneering studies a…
Recursive Geman-McClure method for implementing second-order Volterra filter
Lu Lu, Wenyuan Wang, Xiaomin Yang +2
The second-order Volterra (SOV) filter is a powerful tool for modeling the nonlinear system. The Geman-McClure estimator, whose loss function is non-convex and has been proven to b…
DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning
Zecheng Zhang, Christian Moya, Lu Lu +2
We propose a novel fine-tuning method to achieve multi-operator learning through training a distributed neural operator with diverse function data and then zero-shot fine-tuning th…
One-shot learning for solution operators of partial differential equations
Anran Jiao, Haiyang He, Rishikesh Ranade +2
Learning and solving governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in a variety of areas of scien…
Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators
Weihang Ouyang, Yeonjong Shin, Si-Wei Liu +1
The finite element method (FEM) is a well-established numerical method for solving partial differential equations (PDEs). However, its mesh-based nature gives rise to substantial c…
Dynamic gNodeB Sleep Control for Energy-Conserving 5G Radio Access Network
Pengfei Shen, Yulin Shao, Qi Cao +1
5G radio access network (RAN) is consuming much more energy than legacy RAN due to the denser deployments of gNodeBs (gNBs) and higher single-gNB power consumption. In an effort to…
Building a Scalable, Reproducible, Evaluatable, and Closed-Loop Simulation Environment Foundation for Embodied Intelligence
Junwu Xiong, Yongjian Guo, Mingxi Luo +17
This paper presents a cloud-native simulation infrastructure framework for embodied intelligence that supports large-scale training, standardized evaluation, and simulation-based d…
Mayawaves: Python Library for Interacting with the Einstein Toolkit and the MAYA Catalog
Deborah Ferguson, Surendra Anne, Miguel Gracia-Linares +9
Numerical relativity simulations are crucial for studying black holes and have been instrumental in the detection of gravitational waves by the LVK. However, these simulations prod…
A graph discretization of vector Laplace operator
Shu Li, Lu Lu, Jianfeng Wang
In this paper, we study the graph-theoretic analogues of vector Laplacian (or Helmholtz operator) and vector Laplace equation. We determine the graph matrix representation of vecto…
Solla: Towards a Speech-Oriented LLM That Hears Acoustic Context
Junyi Ao, Dekun Chen, Xiaohai Tian +6
Large Language Models (LLMs) have recently shown remarkable ability to process not only text but also multimodal inputs such as speech and audio. However, most existing models prim…
Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
Philip Anastassiou, Jiawei Chen, Jitong Chen +43
We introduce Seed-TTS, a family of large-scale autoregressive text-to-speech (TTS) models capable of generating speech that is virtually indistinguishable from human speech. Seed-T…
Robust Andrew's sine estimate adaptive filtering
Lu Lu, Yi Yu, Zongsheng Zheng +2
The Andrew's sine function is a robust estimator, which has been used in outlier rejection and robust statistics. However, the performance of such estimator does not receive attent…
Measuring the Astrophysical Galactic Plane Neutrino Flux and Searching for Galactic PeVatrons using the IceCube Multi-Flavor Astrophysical Neutrino Sample
Matthias Thiesmeyer, Tianlu Yuan, Leo Seen +2
The IceCube Neutrino Observatory has provided new insights into the high-energy universe, in particular, unveiling neutrinos from the galactic plane. However, galactic neutrino sou…
A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
Lu Lu, Xuhui Meng, Shengze Cai +4
Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applica…
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
Lu Lu, Raphael Pestourie, Steven G. Johnson +1
Deep neural operators can learn operators mapping between infinite-dimensional function spaces via deep neural networks and have become an emerging paradigm of scientific machine l…
Integral Cayley Graphs over Dihedral Groups
Lu Lu, Qiongxiang Huang, Xueyi Huang
In this paper, we give a necessary and sufficient condition for the integrality of Cayley graphs over the dihedral group . Moreover…
Network-Coded Multiple Access
Lu Lu, Lizhao You, Soung Chang Liew
This paper proposes and experimentally demonstrates a first wireless local area network (WLAN) system that jointly exploits physical-layer network coding (PNC) and multiuser decodi…
Collapse of Deep and Narrow Neural Nets
Lu Lu, Yanhui Su, George Em Karniadakis
Recent theoretical work has demonstrated that deep neural networks have superior performance over shallow networks, but their training is more difficult, e.g., they suffer from the…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…
DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks
Shengze Cai, Zhicheng Wang, Lu Lu +2
Electroconvection is a multiphysics problem involving coupling of the flow field with the electric field as well as the cation and anion concentration fields. For small Debye lengt…
Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy
Le Liang, Jiajia Guo, Jun Zhang +6
6G networks will introduce unprecedented complexity, which calls for a paradigm shift in network optimization and management. Artificial intelligence (AI)-based solutions, especial…
SALMONN-omni: A Standalone Speech LLM without Codec Injection for Full-duplex Conversation
Wenyi Yu, Siyin Wang, Xiaoyu Yang +7
In order to enable fluid and natural human-machine speech interaction, existing full-duplex conversational systems often adopt modular architectures with auxiliary components such…
Progressive amorphization of GeSbTe phase-change material under electron beam irradiation
Ting-Ting Jiang, Jiang-Jing Wang, Lu Lu +5
Fast and reversible phase transitions in chalcogenide phase-change materials (PCMs), in particular, Ge-Sb-Te compounds, are not only of fundamental interests, but also make PCMs ba…
Towards General Auditory Intelligence: Large Multimodal Models for Machine Listening and Speaking
Siyin Wang, Zengrui Jin, Changli Tang +26
In the era of large language models (LLMs) and artificial general intelligence (AGI), computer audition must evolve beyond traditional paradigms to fully leverage the capabilities…
An improved limit to the diffuse flux of ultra-high energy neutrinos from the Pierre Auger Observatory
The Pierre Auger Collaboration, Alexander Aab, Pedro Abreu +462
Neutrinos in the cosmic ray flux with energies near 1 EeV and above are detectable with the Surface Detector array of the Pierre Auger Observatory. We report here on searches throu…
DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators
Zhiping Mao, Lu Lu, Olaf Marxen +2
In high-speed flow past a normal shock, the fluid temperature rises rapidly triggering downstream chemical dissociation reactions. The chemical changes lead to appreciable changes…
PI-MFM: Physics-informed multimodal foundation model for solving partial differential equations
Min Zhu, Jingmin Sun, Zecheng Zhang +2
Partial differential equations (PDEs) govern a wide range of physical systems, and recent multimodal foundation models have shown promise for learning PDE solution operators across…
The Pierre Auger Observatory: Contributions to the 33rd International Cosmic Ray Conference (ICRC 2013)
The Pierre Auger Collaboration, Alexander Aab, Pedro Abreu +495
Contributions of the Pierre Auger Collaboration to the 33rd International Cosmic Ray Conference, Rio de Janeiro, Brazil, July 2013
DeepXDE: A deep learning library for solving differential equations
Lu Lu, Xuhui Meng, Zhiping Mao +1
Deep learning has achieved remarkable success in diverse applications; however, its use in solving partial differential equations (PDEs) has emerged only recently. Here, we present…
A unified multichannel far-field speech recognition system: combining neural beamforming with attention based end-to-end model
Dongdi Zhao, Jianbo Ma, Lu Lu +6
Far-field speech recognition is a challenging task that conventionally uses signal processing beamforming to attack noise and interference problem. But the performance has been fou…
Dying ReLU and Initialization: Theory and Numerical Examples
Lu Lu, Yeonjong Shin, Yanhui Su +1
The dying ReLU refers to the problem when ReLU neurons become inactive and only output 0 for any input. There are many empirical and heuristic explanations of why ReLU neurons die.…
Mixed graphs with smallest eigenvalue greater than
Lu Lu, ZhenZhen Lou
The classical problem of characterizing the graphs with bounded eigenvalues may date back to the work of Smith in 1970. Especially, the research on graphs with smallest eigenvalues…
SALMONN: Towards Generic Hearing Abilities for Large Language Models
Changli Tang, Wenyi Yu, Guangzhi Sun +6
Hearing is arguably an essential ability of artificial intelligence (AI) agents in the physical world, which refers to the perception and understanding of general auditory informat…
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
Christian Moya, Amirhossein Mollaali, Zecheng Zhang +2
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for…
Machine Learning for Vehicular Networks
Hao Ye, Le Liang, Geoffrey Ye Li +3
The emerging vehicular networks are expected to make everyday vehicular operation safer, greener, and more efficient, and pave the path to autonomous driving in the advent of the f…
Process-Supervised Reinforcement Learning for Interactive Multimodal Tool-Use Agents
Weiting Tan, Xinghua Qu, Ming Tu +4
Effective interactive tool use requires agents to master Tool Integrated Reasoning (TIR): a complex process involving multi-turn planning and long-context dialogue management. To t…
Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters
Shanbo Cheng, Yu Bao, Qian Cao +23
Multilingual translation stands as a challenging task for large language models (LLMs) to handle intricate language patterns and stilted translations that arise in automated transl…
Noncoherent Detection for Physical-Layer Network Coding
Zhaorui Wang, Soung Chang Liew, Lu Lu
This paper investigates noncoherent detection in a two-way relay channel operated with physical layer network coding (PNC), assuming FSK modulation and short-packet transmissions.…
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training
Minglun Han, Ye Bai, Chen Shen +6
Speech self-supervised pre-training can effectively improve the performance of downstream tasks. However, previous self-supervised learning (SSL) methods for speech, such as HuBERT…
video-SALMONN: Speech-Enhanced Audio-Visual Large Language Models
Guangzhi Sun, Wenyi Yu, Changli Tang +7
Speech understanding as an element of the more generic video understanding using audio-visual large language models (av-LLMs) is a crucial yet understudied aspect. This paper propo…
RED-DiffEq: Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversion
Siming Shan, Min Zhu, Youzuo Lin +1
Partial differential equation (PDE)-governed inverse problems are fundamental across various scientific and engineering applications; yet they face significant challenges due to no…
Diffusion multi-rate LMS algorithm for acoustic sensor networks
Lu Lu, Xiaomin Yang, Rongzhu Zhang
In this paper, we present a diffusion multi-rate least-mean-square (LMS) algorithm, named DMLMS, which is an effective solution for distributed estimation when two or more observat…
Stochastic Operator Network: A Stochastic Maximum Principle Based Approach to Operator Learning
Ryan Bausback, Jingqiao Tang, Lu Lu +2
We develop a novel framework for uncertainty quantification in operator learning, the Stochastic Operator Network (SON). SON combines the stochastic optimal control concepts of the…
Probabilistic modeling of Cherenkov emission from particle showers
Ian Crawshaw, Tianlu Yuan, Emre Yildizci +2
Subatomic particles can interact with target nuclei in matter or decay in flight, and an individual high-energy particle can induce a particle shower composed of numerous, lower-en…
Sampling-based Estimation of the Number of Distinct Values in Distributed Environment
Jiajun Li, Zhewei Wei, Bolin Ding +3
In data mining, estimating the number of distinct values (NDV) is a fundamental problem with various applications. Existing methods for estimating NDV can be broadly classified int…
Physical Layer Network Coding Over Finite And Infinite Fields
Zhang Shengli, Soung chang Liew, Lu Lu
Direct application of network coding at the physical layer - physical layer network coding (PNC) - is a promising technique for two-way relay wireless networks. In a two-way relay…
In situ characterization of vacancy ordering in Ge-Sb-Te phase-change memory alloys
Ting-Ting Jiang, Xu-Dong Wang, Jiang-Jing Wang +7
Tailoring the degree of structural disorder in Ge-Sb-Te alloys is important for the development of non-volatile phase-change memory and neuro-inspired computing. Upon crystallizati…
Statistical Analysis of Modern Reliability Data
Yueyao Wang, I-Chen Lee, Lu Lu +1
Traditional reliability analysis has been using time to event data, degradation data, and recurrent event data, while the associated covariates tend to be simple and constant over…
FunDiff: Diffusion Models over Function Spaces for Physics-Informed Generative Modeling
Sifan Wang, Zehao Dou, Siming Shan +2
Recent advances in generative modeling -- particularly diffusion models and flow matching -- have achieved remarkable success in synthesizing discrete data such as images and video…
Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences
Cheng Song, Lu Lu, Zhen Ke +2
Emotion recognition is an important part of affective computing. Extracting emotional cues from human gaits yields benefits such as natural interaction, a nonintrusive nature, and…
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs
Zhongkai Hao, Jiachen Yao, Chang Su +8
While significant progress has been made on Physics-Informed Neural Networks (PINNs), a comprehensive comparison of these methods across a wide range of Partial Differential Equati…
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Chenxi Wu, Min Zhu, Qinyang Tan +2
Physics-informed neural networks (PINNs) have shown to be an effective tool for solving forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs…
EarlyTom: Early Token Compression Completes Fast Video Understanding
Hesong Wang, Xin Jin, Lu Lu +4
Video large language models (Video-LLMs) have demonstrated strong capabilities in video understanding tasks. However, their practical deployment is still hindered by the inefficien…
Enhancements to the IceCube Extremely High Energy Neutrino Selection using Graph & Transformer Based Neural Networks
Maxwell Nakos, Aske Rosted, Lu Lu
KM3NeT has recently reported the detection of a very high-energy neutrino event, while IceCube has previously set upper limits on the differential neutrino flux above 100 PeV but h…
Convergence rate of DeepONets for learning operators arising from advection-diffusion equations
Beichuan Deng, Yeonjong Shin, Lu Lu +2
We present convergence analysis of operator learning in [Chen and Chen 1995] and [Lu et al. 2020], where continuous operators are approximated by a sum of products of branch and tr…
Q Cache: Visual Attention is Valuable in Less than Half of Decode Layers for Multimodal Large Language Model
Jiedong Zhuang, Lu Lu, Ming Dai +4
Multimodal large language models (MLLMs) are plagued by exorbitant inference costs attributable to the profusion of visual tokens within the vision encoder. The redundant visual to…
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Ye Bai, Jingping Chen, Jitong Chen +52
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific con…
Measurement of the cosmic ray spectrum above eV using inclined events detected with the Pierre Auger Observatory
The Pierre Auger Collaboration, Alexander Aab, Pedro Abreu +461
A measurement of the cosmic-ray spectrum for energies exceeding eV is presented, which is based on the analysis of showers with zenith angles greater than $60^{\…
Speeding up and reducing memory usage for scientific machine learning via mixed precision
Joel Hayford, Jacob Goldman-Wetzler, Eric Wang +1
Scientific machine learning (SciML) has emerged as a versatile approach to address complex computational science and engineering problems. Within this field, physics-informed neura…
High-Energy and Ultra-High-Energy Neutrinos
Markus Ackermann, Sanjib K. Agarwalla, Jaime Alvarez-Muñiz +43
Astrophysical neutrinos are excellent probes of astroparticle physics and high-energy physics. With energies far beyond solar, supernovae, atmospheric, and accelerator neutrinos, h…
FlexiSLM: A Dynamic and Controllable Frame Rate Spoken Language Model
Jiaqi Li, Chaoren Wang, Xiaohai Tian +9
Spoken language models (SLMs) extend LLMs to speech input and output. Existing SLMs represent speech at fixed frame rates (e.g., 25 or 12.5 Hz), ignoring the time-varying informati…
On directed strongly regular Cayley graphs over non-abelian groups with an abelian subgroup of index
Xueyi Huang, Lu Lu, Jongyook Park
In 1988, Duval introduced the concept of directed strongly regular graphs, which can be viewed as a directed graph version of strongly regular graphs. Such directed graphs have sim…
PF-DMD: Physics-fusion dynamic mode decomposition for accurate and robust forecasting of dynamical systems with imperfect data and physics
Yuhui Yin, Chenhui Kou, Shengkun Jia +3
The DMD (Dynamic Mode Decomposition) method has attracted widespread attention as a representative modal-decomposition method and can build a predictive model. However, the DMD may…
Distance-regular Cayley graphs over (pseudo-) semi-dihedral groups
Xueyi Huang, Lu Lu, Xiongfeng Zhan
Distance-regular graphs are a class of regualr graphs with pretty combinatorial symmetry. In 2007, MiklaviÄ and PotoÄnik proposed the problem of charaterizing distance-regular Ca…
MedBench v5: A Dynamic, Process-Oriented, and Hallucination-Aware Benchmark for Clinical Multimodal Models
Jinru Ding, Chuchu Jiang, Lu Lu +12
Existing medical AI benchmarks lack process visibility, atomic skill evaluation, and integrated hallucination detection. We introduce MedBench v5, a redesigned benchmark for clinic…
Reliable Physical-layer Network Coding Supporting Real Applications
Lizhao You, Soung Chang Liew, Lu Lu
This paper presents the first reliable physical-layer network coding (PNC) system that supports real TCP/IP applications for the two-way relay network (TWRN). Theoretically, PNC co…
Modeling Bivariate Geyser Eruption System with Covariate-Adjusted Recurrent Event Process
Zhongnan Jin, Lu Lu, Khaled Bedair +1
Geyser eruption is one of the most popular signature attractions at the Yellowstone National Park. The interdependence of geyser eruptions and impacts of covariates are of interest…
Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition
Yist Y. Lin, Tao Han, Haihua Xu +6
One of limitations in end-to-end automatic speech recognition (ASR) framework is its performance would be compromised if train-test utterance lengths are mismatched. In this paper,…
What Quality Engineers Need to Know about Degradation Models
Jared M. Clark, Jie Min, Mingyang Li +5
Degradation models play a critical role in quality engineering by enabling the assessment and prediction of system reliability based on data. The objective of this paper is to prov…
How to Host a Data Competition: Statistical Advice for Design and Analysis of a Data Competition
Christine M. Anderson-Cook, Kary L. Myers, Lu Lu +3
Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, pa…
Identifying heterogeneous micromechanical properties of biological tissues via physics-informed neural networks
Wensi Wu, Mitchell Daneker, Kevin T. Turner +2
The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field hetero…
DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domains
Minglang Yin, Nicolas Charon, Ryan Brody +3
The solution of a PDE over varying initial/boundary conditions on multiple domains is needed in a wide variety of applications, but it is computationally expensive if the solution…
Application of Surface Coil for Nuclear Magnetic Resonance Studies of Semi-conducting Thin Films
Wencong Liu, Lu Lu, V. F. MitroviÄ
We conduct a comprehensive set of tests of performance of surface coils used for nuclear magnetic resonance (NMR) study of quasi 2-dimensional samples. We report …
LLM-Mini-CEX: Automatic Evaluation of Large Language Model for Diagnostic Conversation
Xiaoming Shi, Jie Xu, Jinru Ding +9
There is an increasing interest in developing LLMs for medical diagnosis to improve diagnosis efficiency. Despite their alluring technological potential, there is no unified and co…
Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate
Lu Lu, Yi Yu, Rodrigo C. de Lamare +1
We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey's biweight M-estimate CG (TbMCG), for system identification in impulsive noise environments. In partic…
Emergence of a neutrino flux above 5 PeV and implications for ultrahigh energy cosmic rays
Marco S. Muzio, Tianlu Yuan, Lu Lu
The rare detections of astrophysical neutrinos with energies above 5~PeV by two neutrino telescopes underscore the existence of a flux at these energies. In addition to over a deca…
Concept Study of a Radio Array Embedded in a Deep Gen2-like Optical Array
Abby Bishop, Lu Lu, Albrecht Karle +1
The IceCube Neutrino Observatory has discovered a diffuse astrophysical flux up to 10 PeV and is now planning a large extension with IceCube-Gen2, including an optical array and a…
Spectral extremal graphs for the bowtie
Yongtao Li, Lu Lu, Yuejian Peng
Let be the (friendship) graph obtained from triangles by sharing a common vertex. The -free graphs of order which attain the maximal spectral radius was firstly…
TCM-3CEval: A Triaxial Benchmark for Assessing Responses from Large Language Models in Traditional Chinese Medicine
Tianai Huang, Lu Lu, Jiayuan Chen +5
Large language models (LLMs) excel in various NLP tasks and modern medicine, but their evaluation in traditional Chinese medicine (TCM) is underexplored. To address this, we introd…
Variational bounds on the energy dissipation rate in body-forced shear flow
Nikola P. Petrov, Lu Lu, Charles R. Doering
A new variational problem for upper bounds on the rate of energy dissipation in body-forced shear flows is formulated by including a balance parameter in the derivation from the Na…
GeoFunFlow: Geometric Function Flow Matching for Inverse Operator Learning over Complex Geometries
Sifan Wang, Zhikai Wu, David van Dijk +1
Inverse problems governed by partial differential equations (PDEs) are crucial in science and engineering. They are particularly challenging due to ill-posedness, data sparsity, an…
Physical-Layer Network Coding: Tutorial, Survey, and Beyond
Soung Chang Liew, Shengli Zhang, Lu Lu
The concept of physical-layer network coding (PNC) was proposed in 2006 for application in wireless networks. Since then it has developed into a subfield of network coding with wid…
Importance is in your attention: agent importance prediction for autonomous driving
Christopher Hazard, Akshay Bhagat, Balarama Raju Buddharaju +5
Trajectory prediction is an important task in autonomous driving. State-of-the-art trajectory prediction models often use attention mechanisms to model the interaction between agen…
Modeling Biological Membrane and Red Blood Cells by Coarse- Grained Particle Method
He Li, Hung-yu Chang, Jun Yang +2
In this work, we review previously developed coarse-grained (CG) particle models for biological membrane and red blood cells (RBCs) and discuss the advantages of the CG particle me…
Text-only Domain Adaptation using Unified Speech-Text Representation in Transducer
Lu Huang, Boyu Li, Jun Zhang +2
Domain adaptation using text-only corpus is challenging in end-to-end(E2E) speech recognition. Adaptation by synthesizing audio from text through TTS is resource-consuming. We pres…
ParaS2S: Benchmarking and Aligning Spoken Language Models for Paralinguistic-aware Speech-to-Speech Interaction
Shu-wen Yang, Ming Tu, Andy T. Liu +5
Speech-to-Speech (S2S) models have shown promising dialogue capabilities, but their ability to handle paralinguistic cues - such as emotion, tone, and speaker attributes - and to r…
Deep Learning for Solving and Estimating Dynamic Macro-Finance Models
Benjamin Fan, Edward Qiao, Anran Jiao +3
We develop a methodology that utilizes deep learning to simultaneously solve and estimate canonical continuous-time general equilibrium models in financial economics. We illustrate…
Diffusion leaky LMS algorithm: analysis and implementation
Lu Lu, Haiquan Zhao
The diffusion least-mean square (dLMS) algorithms have attracted much attention owing to its robustness for distributed estimation problems. However, the performance of such filter…
SALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generation
Wenyi Yu, Siyin Wang, Xiaoyu Yang +7
Full-duplex multimodal large language models (LLMs) provide a unified framework for addressing diverse speech understanding and generation tasks, enabling more natural and seamless…
Infinite classes of strongly regular graphs derived from
Lu Lu, Qiongxiang Huang, Jiangxia Hou
It is known that the automorphism group of the elementary abelian -group is isomorphic to the general linear group of degree over . Let be the c…
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
Min Zhu, Shihang Feng, Youzuo Lin +1
Full waveform inversion (FWI) infers the subsurface structure information from seismic waveform data by solving a non-convex optimization problem. Data-driven FWI has been increasi…
OpenRBC: A Fast Simulator of Red Blood Cells at Protein Resolution
Yu-Hang Tang, Lu Lu, He Li +4
We present OpenRBC, a coarse-grained molecular dynamics code, which is capable of performing an unprecedented in silico experiment --- simulating an entire mammal red blood cell li…
MINT: Boosting Audio-Language Model via Multi-Target Pre-Training and Instruction Tuning
Hang Zhao, Yifei Xin, Zhesong Yu +3
In the realm of audio-language pre-training (ALP), the challenge of achieving cross-modal alignment is significant. Moreover, the integration of audio inputs with diverse distribut…