arXiv subjects

Yi Huang

Publications and source records attributed to Yi Huang.

At least 127 records · Page 7Linked to original sources

arXiv2024

Bound state-continuum resonance transition in a shallow quantum well

We show a transition from a bound state to a continuum resonance in a shallow quantum well (QW) by electrostatic gating to bend the conduction band edge. This bound state-continuum resonance (BSCR) transition is particularly relevant in topological quantum computing platforms where shallow InAs QWs are used. We predict the observed capacitance jump and the parallel metal-insulator transition accompanied the BSCR transition. An experimental puzzle in shallow InAs QWs is the mobility drop at an electron density smaller than expected for the bound-state second subband occupation. We explain this puzzle as a result of intersubband scattering involving a level-broadened continuum resonance, mediated by screened Coulomb impurities.

cond-mat.mes-hall↗

arXiv2024

Multi-scale Conditional Generative Modeling for Microscopic Image Restoration

The advance of diffusion-based generative models in recent years has revolutionized state-of-the-art (SOTA) techniques in a wide variety of image analysis and synthesis tasks, whereas their adaptation on image restoration, particularly within computational microscopy remains theoretically and empirically underexplored. In this research, we introduce a multi-scale generative model that enhances conditional image restoration through a novel exploitation of the Brownian Bridge process within wavelet domain. By initiating the Brownian Bridge diffusion process specifically at the lowest-frequency subband and applying generative adversarial networks at subsequent multi-scale high-frequency subbands in the wavelet domain, our method provides significant acceleration during training and sampling while sustaining a high image generation quality and diversity on par with SOTA diffusion models. Experimental results on various computational microscopy and imaging tasks confirm our method's robust performance and its considerable reduction in its sampling steps and time. This pioneering technique offers an efficient image restoration framework that harmonizes efficiency with quality, signifying a major stride in incorporating cutting-edge generative models into computational microscopy workflows.

eess.IV↗

arXiv2024

Energy efficiency analysis of ammonia-fueled power systems for vehicles considering residual heat recovery

Ammonia, known as a good hydrogen carrier, shows great potential for use as a zero-carbon fuel for vehicles. However, both the internal combustion engine (ICE) and the proton exchange membrane fuel cell (PEMFC), the currently available engines used by the vehicle, require hydrogen decomposed from ammonia. On-board hydrogen production is an energy-intensive process that significantly reduces system efficiency. Therefore, energy recovery from the system's residual heat is essential to promote system efficiency. ICEs and FCs require different amounts of hydrogen, and they produce residual heat of different quality and quantity, so the system efficiency is not only determined by the engine operating point, but also by the measures and ratios of residual heat recovery. To thoroughly understand the relationships between system energy efficiency and system configuration as well as system parameters, this paper takes three typical power systems with different configurations as our objects. Models of three systems are set up for system energy efficiency analysis, and carry out simulations under different conditions to conduct system output power and energy efficiency. By analyzing the simulation results, the factors that most significantly impact the system efficiency are identified, the guidelines for system design and parameter optimization are proposed.

eess.SY↗

arXiv2024

Elastic CRFs for Open-ontology Slot Filling

Slot filling is a crucial component in task-oriented dialog systems that is used to parse (user) utterances into semantic concepts called slots. An ontology is defined by the collection of slots and the values that each slot can take. The most widely used practice of treating slot filling as a sequence labeling task suffers from two main drawbacks. First, the ontology is usually pre-defined and fixed and therefore is not able to detect new labels for unseen slots. Second, the one-hot encoding of slot labels ignores the correlations between slots with similar semantics, which makes it difficult to share knowledge learned across different domains. To address these problems, we propose a new model called elastic conditional random field (eCRF), where each slot is represented by the embedding of its natural language description and modeled by a CRF layer. New slot values can be detected by eCRF whenever a language description is available for the slot. In our experiment, we show that eCRFs outperform existing models in both in-domain and cross-domain tasks, especially in predicting unseen slots and values.

cs.CL↗

arXiv2024

Automatic diagnosis of cardiac magnetic resonance images based on semi-supervised learning

Cardiac magnetic resonance imaging (MRI) is a pivotal tool for assessing cardiac function. Precise segmentation of cardiac structures is imperative for accurate cardiac functional evaluation. This paper introduces a semi-supervised model for automatic segmentation of cardiac images and auxiliary diagnosis. By harnessing cardiac MRI images and necessitating only a small portion of annotated image data, the model achieves fully automated, high-precision segmentation of cardiac images, extraction of features, calculation of clinical indices, and prediction of diseases. The provided segmentation results, clinical indices, and prediction outcomes can aid physicians in diagnosis, thereby serving as auxiliary diagnostic tools. Experimental results showcase that this semi-supervised model for automatic segmentation of cardiac images and auxiliary diagnosis attains high accuracy in segmentation and correctness in prediction, demonstrating substantial practical guidance and application value.

eess.IV↗

arXiv2024

MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections

3D Gaussian Splatting showcases notable advancements in photo-realistic and real-time novel view synthesis. However, it faces challenges in modeling mirror reflections, which exhibit substantial appearance variations from different viewpoints. To tackle this problem, we present MirrorGaussian, the first method for mirror scene reconstruction with real-time rendering based on 3D Gaussian Splatting. The key insight is grounded on the mirror symmetry between the real-world space and the virtual mirror space. We introduce an intuitive dual-rendering strategy that enables differentiable rasterization of both the real-world 3D Gaussians and the mirrored counterpart obtained by reflecting the former about the mirror plane. All 3D Gaussians are jointly optimized with the mirror plane in an end-to-end framework. MirrorGaussian achieves high-quality and real-time rendering in scenes with mirrors, empowering scene editing like adding new mirrors and objects. Comprehensive experiments on multiple datasets demonstrate that our approach significantly outperforms existing methods, achieving state-of-the-art results. Project page: https://mirror-gaussian.github.io/.

cs.CV↗

arXiv2024

The earthquake metric on Teichm{ü}ller space

This is the first paper to systematically study the earthquake metric, an asymmetric Finsler metric on Teichm{ü}ller space introduced by Thurston. We provide proofs for several assertions of Thurston and establish new properties of this metric, among which are incompleteness, asymptotic distance to the boundary and comparisons with the Thurston metric and the Weil--Petersson metric. In doing so, we propose a novel asymmetric generalisation of the notion of completion for symmetric metrics, which we call the FD-completion, and prove that for the earthquake metric the FD-completion and various symmetrised metric completions coincide with the Weil--Petersson completion. We also answer a question of Thurston by giving an interpretation of this metric arising from a global minimisation problem, namely, the earthquake magnitude minimisation problem. At several points of this paper, we formulate a certain number of open problems which will show that the earthquake metric constitutes a promising subject.

math.GT↗

arXiv2024

GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning

Recent advances in text-to-video generation have harnessed the power of diffusion models to create visually compelling content conditioned on text prompts. However, they usually encounter high computational costs and often struggle to produce videos with coherent physical motions. To tackle these issues, we propose GPT4Motion, a training-free framework that leverages the planning capability of large language models such as GPT, the physical simulation strength of Blender, and the excellent image generation ability of text-to-image diffusion models to enhance the quality of video synthesis. Specifically, GPT4Motion employs GPT-4 to generate a Blender script based on a user textual prompt, which commands Blender's built-in physics engine to craft fundamental scene components that encapsulate coherent physical motions across frames. Then these components are inputted into Stable Diffusion to generate a video aligned with the textual prompt. Experimental results on three basic physical motion scenarios, including rigid object drop and collision, cloth draping and swinging, and liquid flow, demonstrate that GPT4Motion can generate high-quality videos efficiently in maintaining motion coherency and entity consistency. GPT4Motion offers new insights in text-to-video research, enhancing its quality and broadening its horizon for further explorations.

cs.CV↗

arXiv2024

An Alternative Method to Identify the Susceptibility Threshold Level of Device under Test in a Reverberation Chamber

By counting the number of pass/fail occurrences of a DUT (Device under Test) in the stirring process in a reverberation chamber (RC), the threshold electric field (E-field) level can be well estimated without tuning the input power and repeating the whole testing many times. The Monte-Carlo method is used to verify the results. Estimated values and uncertainties are given for Rayleigh distributed fields and for Rice distributed fields with different K-factors.

eess.SP↗

arXiv2024

Electronic transport, metal-insulator transition, and Wigner crystallization in transition metal dichalcogenide monolayers

Two recent electronic transport experiments from Columbia University and Harvard University have reported record high mobility and low channel densities in transition metal dichalcogenide (TMD) WSe$_2$ monolayers [J. Pack, et al., arXiv:2310.19782; A. Y. Joe, et al., Phys. Rev. Lett. 132, 056303 (2024)]. A two-dimensional (2D) metal-insulator transition (MIT) is demonstrated in the Columbia sample at low densities, a regime where the formation of a Wigner crystal (WC) is theoretically anticipated in the absence of disorder. We employ the finite-temperature Boltzmann theory to understand the low-temperature transport properties of monolayer TMDs, taking into account realistic disorder scattering. We analyze the experimental results, focusing on the 2D MIT behavior and the influence of temperature and density on mobility and resistivity in the metallic phase. We provide a discussion of the nontrivial carrier density dependence of our transport results. Our analysis elucidates the linear-in-$T$ resistivity in the metallic phase, attributing it to Friedel oscillations associated with screened charged impurities. Furthermore, we explore whether Coulomb disorder could lead to the MIT through either a quantum Anderson localization transition or a classical percolation transition. Our theoretical estimates of the disorder-induced MIT critical densities, although smaller, are within a factor of ~2 of the experimental critical density. We examine the exceptionally high melting temperature ~10 K of WCs observed experimentally in the MoSe$_2$ systems at low density, an order of magnitude larger than the pristine melting temperature. This suggests that the observed 2D low-density MIT behavior is likely a result of the complex interplay between disorder effects and interaction-driven WC physics, offering a comprehensive understanding of the low-temperature transport phenomena in TMD monolayers.

cond-mat.mes-hall↗

arXiv2024

WaveDM: Wavelet-Based Diffusion Models for Image Restoration

Latest diffusion-based methods for many image restoration tasks outperform traditional models, but they encounter the long-time inference problem. To tackle it, this paper proposes a Wavelet-Based Diffusion Model (WaveDM). WaveDM learns the distribution of clean images in the wavelet domain conditioned on the wavelet spectrum of degraded images after wavelet transform, which is more time-saving in each step of sampling than modeling in the spatial domain. To ensure restoration performance, a unique training strategy is proposed where the low-frequency and high-frequency spectrums are learned using distinct modules. In addition, an Efficient Conditional Sampling (ECS) strategy is developed from experiments, which reduces the number of total sampling steps to around 5. Evaluations on twelve benchmark datasets including image raindrop removal, rain steaks removal, dehazing, defocus deblurring, demoiréing, and denoising demonstrate that WaveDM achieves state-of-the-art performance with the efficiency that is comparable to traditional one-pass methods and over 100$\times$ faster than existing image restoration methods using vanilla diffusion models.

cs.CV↗

arXiv2024

Localization of Lindbladian Fermions

We study a Lindbladian generalization of the Anderson model of localization that describes disordered free fermions coupled to a disordered environment. From finite size scaling of both eigenvalue statistics and participation ratio, we identify localization transitions in both the non-Hermitian Lindbladian spectrum, which governs transient relaxation dynamics, and in the Hermitian stationary state density matrix. These localization transitions occur at different critical values of Hamiltonian and dissipative disorder strength, implying the existence of atypical phases with a mixture of localized and delocalized features. We find this phenomenon is robust to changes to the value of the dissipative spectral gap.

cond-mat.dis-nn↗

arXiv2024

Orion-14B: Open-source Multilingual Large Language Models

In this study, we introduce Orion-14B, a collection of multilingual large language models with 14 billion parameters. We utilize a data scheduling approach to train a foundational model on a diverse corpus of 2.5 trillion tokens, sourced from texts in English, Chinese, Japanese, Korean, and other languages. Additionally, we fine-tuned a series of models tailored for conversational applications and other specific use cases. Our evaluation results demonstrate that Orion-14B achieves state-of-the-art performance across a broad spectrum of tasks. We make the Orion-14B model family and its associated code publicly accessible https://github.com/OrionStarAI/Orion, aiming to inspire future research and practical applications in the field.

cs.CL↗

arXiv2024

The infinitesimal and global Thurston geometry of Teichm{ü}ller space

We undertake a systematic study of the infinitesimal geometry of the Thurston metric, showing that the topology, convex geometry and metric geometry of the tangent and cotangent spheres based at any marked hyperbolic surface representing a point in Teichm{ü}ller space can recover the marking and geometry of this marked surface. We then translate the results concerning the infinitesimal structures to global geometric statements for the Thurston metric, most notably deriving rigidity statements for the Thurston metric analogous to the celebrated Royden theorem.

math.GT↗

arXiv2024

USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis

Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address these issues. Nevertheless, the development of such foundational models encounters intrinsic challenges in US analysis, i.e., insufficient databases, low quality, and ineffective features. In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis. First, a large-scale Multi-organ, Multi-center, and Multi-device US database was built, comprehensively containing over two million US images. Organ-balanced sampling was employed for unbiased learning. Then, USFM is self-supervised pre-trained on the sufficient US database. To extract the effective features from low-quality US images, we proposed a spatial-frequency dual masked image modeling method. A productive spatial noise addition-recovery approach was designed to learn meaningful US information robustly, while a novel frequency band-stop masking learning approach was also employed to extract complex, implicit grayscale distribution and textural variations. Extensive experiments were conducted on the various tasks of segmentation, classification, and image enhancement from diverse organs and diseases. Comparisons with representative US image analysis models illustrate the universality and effectiveness of USFM. The label efficiency experiments suggest the USFM obtains robust performance with only 20% annotation, laying the groundwork for the rapid development of US models in clinical practices.

eess.IV↗

arXiv2023

Understanding disorder in Silicon quantum computing platforms: Scattering mechanisms in Si/SiGe quantum wells

Motivated by recent experiments on Si/SiGe quantum wells with a co-design of high electron mobility and large valley splitting [B. Paquelet Wuetz, et al., Nature Communications 14, 1385 (2023); D. D. Esposti, et al., arXiv:2309.02832], suitable for a Si-based spin qubit quantum computing platform, we examine the role of disorder by theoretically calculating mobility and quantum mobility from various scattering mechanisms and their dependence on the electron density. At low electron densities $n_e < 4 \times 10^{11}$ cm$^{-2}$, we find that mobility is limited by remote Coulomb impurities in the capping layer, whereas interface roughness becomes the significant limiting factor at higher densities. We also find that alloy disorder scattering is not a limiting mechanism in the reported high-mobility structures. We estimate the critical density of the disorder-driven low-density metal-insulator transition using the Anderson-Ioffe-Regel localization criterion and qualitatively explain the breakdown of the Boltzmann-Born theory at low densities. We also estimate the critical density by considering inhomogeneous density fluctuations induced by long-range Coulomb disorder in the system, and find a larger critical density compared to the one obtained from the Anderson-Ioffe-Regel criterion. For quantum mobility, our calculation suggests that remote and distant background impurities are likely the limiting scattering sources across all density. Future measurements of quantum mobility should provide more information on the distribution of background impurities inside the SiGe barriers. Moreover, we extend our theoretical analysis to the effect of quantum degeneracy on transport properties and predict the mobility and the critical density for the metal-insulator transition in spin-polarized high-mobility structures under an external parallel magnetic field.

cond-mat.mes-hall↗

arXiv2023

Prompt Pool based Class-Incremental Continual Learning for Dialog State Tracking

Continual learning is crucial for dialog state tracking (DST) in dialog systems, since requirements from users for new functionalities are often encountered. However, most of existing continual learning methods for DST require task identities during testing, which is a severe limit in real-world applications. In this paper, we aim to address continual learning of DST in the class-incremental scenario (namely the task identity is unknown in testing). Inspired by the recently emerging prompt tuning method that performs well on dialog systems, we propose to use the prompt pool method, where we maintain a pool of key-value paired prompts and select prompts from the pool according to the distance between the dialog history and the prompt keys. The proposed method can automatically identify tasks and select appropriate prompts during testing. We conduct experiments on Schema-Guided Dialog dataset (SGD) and another dataset collected from a real-world dialog application. Experiment results show that the prompt pool method achieves much higher joint goal accuracy than the baseline. After combining with a rehearsal buffer, the model performance can be further improved.

cs.CL↗

arXiv2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high-energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of a three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous high-performance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3x speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76-79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2.

stat.ML↗