arXiv subjects

Jing Li

Publications and source records attributed to Jing Li.

At least 433 records · Page 24Linked to original sources

arXiv2024

Topologically Protected Strong-Interaction of Photonics with Free Electron

We propose a robust scheme of studying the strong interactions between free electrons and photons using topological photonics. Our study reveals that the topological corner state can be used to enhance the interaction between light and a free electron significantly. The quality factor of the topological cavity can exceed 20 000 and the corner state has a very long lifetime even after the pump pulse is off. And thus, the platform enables us to achieve a strong interaction without the need for zero delay and phase matching as in traditional photon-induced near-field electron microscopy (PINEM). This work provides the new perspective that the topological photonic structures can be utilized as a platform to shape free electron wave packets, which facilitates the control of quantum electrodynamical (QED) processes and quantum optics with free electrons in the future.

physics.optics↗

arXiv2024

VIALM: A Survey and Benchmark of Visually Impaired Assistance with Large Models

Visually Impaired Assistance (VIA) aims to automatically help the visually impaired (VI) handle daily activities. The advancement of VIA primarily depends on developments in Computer Vision (CV) and Natural Language Processing (NLP), both of which exhibit cutting-edge paradigms with large models (LMs). Furthermore, LMs have shown exceptional multimodal abilities to tackle challenging physically-grounded tasks such as embodied robots. To investigate the potential and limitations of state-of-the-art (SOTA) LMs' capabilities in VIA applications, we present an extensive study for the task of VIA with LMs (VIALM). In this task, given an image illustrating the physical environments and a linguistic request from a VI user, VIALM aims to output step-by-step guidance to assist the VI user in fulfilling the request grounded in the environment. The study consists of a survey reviewing recent LM research and benchmark experiments examining selected LMs' capabilities in VIA. The results indicate that while LMs can potentially benefit VIA, their output cannot be well environment-grounded (i.e., 25.7% GPT-4's responses) and lacks fine-grained guidance (i.e., 32.1% GPT-4's responses).

cs.CL↗

arXiv2024

Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network

The opioid epidemic, referring to the growing hospitalizations and deaths because of overdose of opioid usage and addiction, has become a severe health problem in the United States. Many strategies have been developed by the federal and local governments and health communities to combat this crisis. Among them, improving our understanding of the epidemic through better health surveillance is one of the top priorities. In addition to direct testing, machine learning approaches may also allow us to detect opioid users by analyzing data from social media because many opioid users may choose not to do the tests but may share their experiences on social media anonymously. In this paper, we take advantage of recent advances in machine learning, collect and analyze user posts from a popular social network Reddit with the goal to identify opioid users. Posts from more than 1,000 users who have posted on three sub-reddits over a period of one month have been collected. In addition to the ones that contain keywords such as opioid, opiate, or heroin, we have also collected posts that contain slang words of opioid such as black or chocolate. We apply an attention-based bidirectional long short memory model to identify opioid users. Experimental results show that the approaches significantly outperform competitive algorithms in terms of F1-score. Furthermore, the model allows us to extract most informative words, such as opiate, opioid, and black, from posts via the attention layer, which provides more insights on how the machine learning algorithm works in distinguishing drug users from non-drug users.

cs.CL↗

arXiv2024

Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind

When reading a story, humans can quickly understand new fictional characters with a few observations, mainly by drawing analogies to fictional and real people they already know. This reflects the few-shot and meta-learning essence of humans' inference of characters' mental states, i.e., theory-of-mind (ToM), which is largely ignored in existing research. We fill this gap with a novel NLP dataset, ToM-in-AMC, the first assessment of machines' meta-learning of ToM in a realistic narrative understanding scenario. Our dataset consists of ~1,000 parsed movie scripts, each corresponding to a few-shot character understanding task that requires models to mimic humans' ability of fast digesting characters with a few starting scenes in a new movie. We propose a novel ToM prompting approach designed to explicitly assess the influence of multiple ToM dimensions. It surpasses existing baseline models, underscoring the significance of modeling multiple ToM dimensions for our task. Our extensive human study verifies that humans are capable of solving our problem by inferring characters' mental states based on their previously seen movies. In comparison, our systems based on either state-of-the-art large language models (GPT-4) or meta-learning algorithms lags >20% behind, highlighting a notable limitation in existing approaches' ToM capabilities.

cs.CL↗

arXiv2024

IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators

This study focuses on media bias detection, crucial in today's era of influential social media platforms shaping individual attitudes and opinions. In contrast to prior work that primarily relies on training specific models tailored to particular datasets, resulting in limited adaptability and subpar performance on out-of-domain data, we introduce a general bias detection framework, IndiVec, built upon large language models. IndiVec begins by constructing a fine-grained media bias database, leveraging the robust instruction-following capabilities of large language models and vector database techniques. When confronted with new input for bias detection, our framework automatically selects the most relevant indicator from the vector database and employs majority voting to determine the input's bias label. IndiVec excels compared to previous methods due to its adaptability (demonstrating consistent performance across diverse datasets from various sources) and explainability (providing explicit top-k indicators to interpret bias predictions). Experimental results on four political bias datasets highlight IndiVec's significant superiority over baselines. Furthermore, additional experiments and analysis provide profound insights into the framework's effectiveness.

cs.CL↗

arXiv2024

Controlling atom-photon bound states in a coupled resonator array with a two-level quantum emitter

We consider a one-dimensional (1D) coupled-resonator array (CRA), where a two-level quantum emitter (2LE) is electric-dipole coupled to the modes of two adjacent resonators. We investigate the energy spectrum, the photon probability distribution of the bound states and the emission process of the 2LE into the CRA vacuum. A quantum phase transition is found which is characterized by the change of the number of the out-of-band discrete levels. The condition for this change is also presented. The photon wave functions of bound states are found to be asymmetry around the position of the 2LE when the coupling strengths between the 2LE and the resonator are not equal, and they have the same preferred directions which are primary determined by the larger one among the coupling strengths. The presence of the atom-photon bound states is manifested in the form of a stationary oscillation or a non-vanishing constant in the long enough time.

quant-ph↗

arXiv2024

Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine learning.

cs.LG↗

arXiv2024

Extending Whisper with prompt tuning to target-speaker ASR

Target-speaker automatic speech recognition (ASR) aims to transcribe the desired speech of a target speaker from multi-talker overlapped utterances. Most of the existing target-speaker ASR (TS-ASR) methods involve either training from scratch or fully fine-tuning a pre-trained model, leading to significant training costs and becoming inapplicable to large foundation models. This work leverages prompt tuning, a parameter-efficient fine-tuning approach, to extend Whisper, a large-scale single-talker ASR model, to TS-ASR. Variants of prompt tuning approaches along with their configurations are explored and optimized for TS-ASR.Experimental results show that prompt tuning can achieve performance comparable to state-of-the-art full training approaches while only requiring about 1\% of task-specific model parameters. Notably, the original Whisper's features, such as inverse text normalization and timestamp tagging, are retained in target-speaker ASR, keeping the generated transcriptions natural and informative.

cs.CL↗

arXiv2024

Distinguishing the Topological Charge of Vortex Beam via Fourier Back Plane Imaging with Chiral Gammadion Structure

In recent years, research on the interaction between Orbital Angular Momentum (OAM) and matter has seen a continuous influx of investigations. OAM possesses distinct properties, such as additional degrees of freedom, vortex characteristics, and topological properties, which expand its applications in optical communication, optical sensing, and optical force. Through experiments involving the interaction of a chiral metal swastika structure with a SAM-OAM beam generated by a q-plate, we have observed a phenomenon does not present in pure SAM beams. Fourier back focal plane (FBP) imaging under SAM beam excitation easily identifies the chirality and geometric properties of the structure. When the SAM-OAM beam excites the structure, FBP not only identifies its chirality and geometric properties but also distinguishes different OAM topological charges and signs, as well as the degree of elliptic polarization. The stokes parametric FBP imaging reveals asymmetric polarization distribution resulting from the interaction between a vortex beam and the chiral structure. Moreover, it clearly reflects the conversion process of SAM to OAM. The experimental results align well with simulation results. These findings hold valuable insights for the advancement of optical information storage and communication using OAM, opening up new possibilities for further exploration in this field.

physics.optics↗

arXiv2023

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcomes. We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA, and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. The classification accuracy of each gene was compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.

cs.LG↗

arXiv2023

Align on the Fly: Adapting Chatbot Behavior to Established Norms

In this paper, we aim to align large language models with the ever-changing, complex, and diverse human values (e.g., social norms) across time and locations. This presents a challenge to existing alignment techniques, such as supervised fine-tuning, which internalize values within model parameters. To overcome this, we propose an On-the-fly Preference Optimization (OPO) method, which is a real-time alignment that works in a streaming way. It employs an external memory to store established rules for alignment, which can constrain LLMs' behaviors without further training, allowing for convenient updates and customization of human values. We also introduce a scalable evaluation to assess the proposed method more effectively. Experimental results on both human-annotated and auto-generated questions from legal and moral domains indicate the effectiveness of the proposed OPO method. Our code and data are released at https://github.com/GAIR-NLP/OPO.

cs.CL↗

arXiv2023

Chemo-dynamical Nature of the Anticenter Stream and Monoceros Ring

In the epoch of deep photometric surveys, a large number of substructures, e.g., over-densities, streams, were identified. With the help of astrometry and spectroscopy, the community revealed a complex picture of our Milky Way (MW) after investigating their origins. Off-plane substructures Anticenter Stream (ACS) and Monoceros Ring (MNC), once considered as dissolving dwarf galaxies, were later found to share similar kinematics and metallicity with the Galactic outer thin disk. In this work, we aim to chemically tag ACS and MNC with high-accuracy abundances from the APOGEE survey. By extrapolating chemical abundance trends in the outer thin disk region (10 < Rgc < 18 kpc, 0 < |Zgc| < 3kpc), we found that ACS and MNC stars show consistent chemical abundances as the extrapolating values for 12 elements, including C, N, O, Mg, Al, Si, K, Ca, Cr, Mn, Co and Ni. The similar chemical patterns indicate that ACS and MNC have similar star formation history as the MW outer thin disk, meanwhile, we also excluded their dwarf galaxy association, as they are distinctive in multiple chemical spaces. The ages of ACS and MNC stars are consistent with the time of the first Sgr dSph passage, indicating their possible connection.

astro-ph.GA↗

arXiv2023

A Novel Hybrid Ordinal Learning Model with Health Care Application

Ordinal learning (OL) is a type of machine learning models with broad utility in health care applications such as diagnosis of different grades of a disease (e.g., mild, modest, severe) and prediction of the speed of disease progression (e.g., very fast, fast, moderate, slow). This paper aims to tackle a situation when precisely labeled samples are limited in the training set due to cost or availability constraints, whereas there could be an abundance of samples with imprecise labels. We focus on imprecise labels that are intervals, i.e., one can know that a sample belongs to an interval of labels but cannot know which unique label it has. This situation is quite common in health care datasets due to limitations of the diagnostic instrument, sparse clinical visits, or/and patient dropout. Limited research has been done to develop OL models with imprecise/interval labels. We propose a new Hybrid Ordinal Learner (HOL) to integrate samples with both precise and interval labels to train a robust OL model. We also develop a tractable and efficient optimization algorithm to solve the HOL formulation. We compare HOL with several recently developed OL methods on four benchmarking datasets, which demonstrate the superior performance of HOL. Finally, we apply HOL to a real-world dataset for predicting the speed of progressing to Alzheimer's Disease (AD) for individuals with Mild Cognitive Impairment (MCI) based on a combination of multi-modality neuroimaging and demographic/clinical datasets. HOL achieves high accuracy in the prediction and outperforms existing methods. The capability of accurately predicting the speed of progression to AD for each individual with MCI has the potential for helping facilitate more individually-optimized interventional strategies.

cs.LG↗

arXiv2023

Linear-in-momentum spin orbit interactions in planar Ge/GeSi heterostructures and spin qubits

We investigate the existence of linear-in-momentum spin-orbit interactions in the valence band of Ge/GeSi heterostructures using an atomistic tight-binding method. We show that symmetry breaking at the Ge/GeSi interfaces gives rise to a linear Dresselhaus-type interaction for heavy-holes. This interaction results from the heavy-hole/light-hole mixings induced by the interfaces and can be captured by a suitable correction to the minimal Luttinger-Kohn, four bands $\vec{k}\cdot\vec{p}$ Hamiltonian. It is dependent on the steepness of the Ge/GeSi interfaces, and is suppressed if interdiffusion is strong enough. Besides the Dresselhaus interaction, the Ge/GeSi interfaces also make a contribution to the in-plane gyromagnetic $g$-factors of the holes. The tight-binding calculations also highlight the existence of a small linear Rashba interaction resulting from the couplings between the heavy-hole/light-hole manifold and the conduction band enabled by the low structural symmetry of Ge/GeSi heterostructures. These interactions can be leveraged to drive the hole spin. The linear Dresselhaus interaction may, in particular, dominate the physics of the devices for out-of-plane magnetic fields. When the magnetic field lies in-plane, it is, however, usually far less efficient than the $g$-tensor modulation mechanisms arising from the motion of the dot in non-separable, inhomogeneous electric fields and strains.

cond-mat.mes-hall↗

arXiv2023

Cellular uptake of active nonspherical nanoparticles

Due to the potential applications in biomedical engineering, it becomes more and more important to understand the process of engulfment and internalization of nanoparticles (NPs) by cell membranes. Despite the fact that the interaction between cell membranes and passive particles has been widely studied, the interaction between cell membranes and self-propelled nonspherical NPs remains to be elucidated. Here we present a theoretical model to systematically investigate the influence of the active force, aspect ratio of NPs, particle size and membrane properties (adhesion energy density and membrane tension) on the cellular uptake of a nonspherical nanoparticle. It is found that the active force generated by an NP can trigger a type of first-order wrapping transition from a small partial wrapping state to a large one. In addition, the phase diagram in the force-aspect ratio (particle size, adhesion energy density and membrane tension) space displays more complex behaviors compared with that for the passive wrapping mediated merely by adhesion. These results may provide a useful guidance to the study of activity-driven cellular entry of active particles into cells.

physics.bio-ph↗

arXiv2023

AutoRepo: A general framework for multi-modal LLM-based automated construction reporting

Ensuring the safety, quality, and timely completion of construction projects is paramount, with construction inspections serving as a vital instrument towards these goals. Nevertheless, the predominantly manual approach of present-day inspections frequently results in inefficiencies and inadequate information management. Such methods often fall short of providing holistic, exhaustive assessments, consequently engendering regulatory oversights and potential safety hazards. To address this issue, this paper presents a novel framework named AutoRepo for automated generation of construction inspection reports. The unmanned vehicles efficiently perform construction inspections and collect scene information, while the multimodal large language models (LLMs) are leveraged to automatically generate the inspection reports. The framework was applied and tested on a real-world construction site, demonstrating its potential to expedite the inspection process, significantly reduce resource allocation, and produce high-quality, regulatory standard-compliant inspection reports. This research thus underscores the immense potential of multimodal large language models in revolutionizing construction inspection practices, signaling a significant leap forward towards a more efficient and safer construction management paradigm.

cs.AI↗

arXiv2023

Throughput Maximization for Intelligent Refracting Surface Assisted mmWave High-Speed Train Communications

With the increasing demands from passengers for data-intensive services, millimeter-wave (mmWave) communication is considered as an effective technique to release the transmission pressure on high speed train (HST) networks. However, mmWave signals ncounter severe losses when passing through the carriage, which decreases the quality of services on board. In this paper, we investigate an intelligent refracting surface (IRS)-assisted HST communication system. Herein, an IRS is deployed on the train window to dynamically reconfigure the propagation environment, and a hybrid time division multiple access-nonorthogonal multiple access scheme is leveraged for interference mitigation. We aim to maximize the overall throughput while taking into account the constraints imposed by base station beamforming, IRS discrete phase shifts and transmit power. To obtain a practical solution, we employ an alternating optimization method and propose a two-stage algorithm. In the first stage, the successive convex approximation method and branch and bound algorithm are leveraged for IRS phase shift design. In the second stage, the Lagrangian multiplier method is utilized for power allocation. Simulation results demonstrate the benefits of IRS adoption and power allocation for throughput improvement in mmWave HST networks.

cs.IT↗

arXiv2023

Power System Capacity Planning Considering Seasonal Hydrogen Storage by Salt Caverns

In China, air conditioning in summer and electric heating in winter lead to seasonal volatility in load power. Therefore, it is urgent to develop economic and efficient long-term energy storage systems to enhance peak regulation. Power-to-hydrogen technology is a perspective solution to balance seasonal power fluctuation. However, current hydrogen storage methods have shortcomings such as small storage capacity, high levelized cost and low operation safety, which the salt cavern hydrogen storage could overcome. This paper considers the use of hydrogen storage in salt caverns as a means of peak shaving. To minimize the overall operating cost, a comprehensive power system capacity planning model is proposed with the consideration of hydrogen storage in salt caverns, which is implemented by adopting an improved fast unit commitment method. Considering the seasonal characteristics of the load power in Jiangsu Province, the capacity of the power system in 2050 has been planned. According to the case study, after the optimal deployment of the salt cavern hydrogen storage system (SCHSS), the construction capacity of renewable units (especially wind power) will be significantly increased with environmental friendliness and lower costs. Compared with the current energy storage method, the overall cost of SCHSS-incorporated power system will be reduced by 22.2% with a carbon emission reduction of 24.4%, and the amount of curtailed wind and solar power will be reduced by 27.0% and 13.6%, respectively.

eess.SY↗