arXiv subjects

Xiangyu Li

Publications and source records attributed to Xiangyu Li.

At least 127 records · Page 7Linked to original sources

arXiv2024

Phase-field analysis for brittle fracture in ferroelectric materials with flexoelectric effect

Understanding the nature of brittle failure in ferroelectric materials is essential, but difficult due to the complex interaction between mechanical and electrical concentrated fields near the crack tip. In this work, an extended phase-field model incorporating multiple order parameters is constructed to analyze the coupled evolution of fracture and domain behavior in ferroelectric materials. The strain gradient is incorporated into the governing equations to evaluate the impact of the flexoelectric effect during the crack propagation process. Our advanced phase-field model demonstrated that, with the consideration of the flexoelectric effect, both the crack extension rate and crack path are related to the initial polarization direction. This phenomenon is associated with the eigenstrain induced by the flexoelectric effect. This study provides in-depth insight into the fracture behavior of ferroelectric materials. The developed model framework can also be employed to investigate electromechanical coupling failures in more complex ferroelectric structures.

cond-mat.mtrl-sci↗

arXiv2024

Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security

Since the advent of personal computing devices, intelligent personal assistants (IPAs) have been one of the key technologies that researchers and engineers have focused on, aiming to help users efficiently obtain information and execute tasks, and provide users with more intelligent, convenient, and rich interaction experiences. With the development of smartphones and IoT, computing and sensing devices have become ubiquitous, greatly expanding the boundaries of IPAs. However, due to the lack of capabilities such as user intent understanding, task planning, tool using, and personal data management etc., existing IPAs still have limited practicality and scalability. Recently, the emergence of foundation models, represented by large language models (LLMs), brings new opportunities for the development of IPAs. With the powerful semantic understanding and reasoning capabilities, LLM can enable intelligent agents to solve complex problems autonomously. In this paper, we focus on Personal LLM Agents, which are LLM-based agents that are deeply integrated with personal data and personal devices and used for personal assistance. We envision that Personal LLM Agents will become a major software paradigm for end-users in the upcoming era. To realize this vision, we take the first step to discuss several important questions about Personal LLM Agents, including their architecture, capability, efficiency and security. We start by summarizing the key components and design choices in the architecture of Personal LLM Agents, followed by an in-depth analysis of the opinions collected from domain experts. Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.

cs.HC↗

arXiv2024

Potential quantum advantage for simulation of fluid dynamics

Numerical simulation of turbulent fluid dynamics needs to either parameterize turbulence-which introduces large uncertainties-or explicitly resolve the smallest scales-which is prohibitively expensive. Here we provide evidence through analytic bounds and numerical studies that a potential quantum exponential speedup can be achieved to simulate the Navier-Stokes equations governing turbulence using quantum computing. Specifically, we provide a formulation of the lattice Boltzmann equation for which we give evidence that low-order Carleman linearization is much more accurate than previously believed for these systems and that for computationally interesting examples. This is achieved via a combination of reformulating the nonlinearity and accurately linearizing the dynamical equations, effectively trading nonlinearity for additional degrees of freedom that add negligible expense in the quantum solver. Based on this we apply a quantum algorithm for simulating the Carleman-linearized lattice Boltzmann equation and provide evidence that its cost scales logarithmically with system size, compared to polynomial scaling in the best known classical algorithms. This work suggests that an exponential quantum advantage may exist for simulating fluid dynamics, paving the way for simulating nonlinear multiscale transport phenomena in a wide range of disciplines using quantum computing.

quant-ph↗

arXiv2024

RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict

Fact-checking is the task of verifying the factuality of a given claim by examining the available evidence. High-quality evidence plays a vital role in enhancing fact-checking systems and facilitating the generation of explanations that are understandable to humans. However, the provision of both sufficient and relevant evidence for explainable fact-checking systems poses a challenge. To tackle this challenge, we propose a method based on a Large Language Model to automatically retrieve and summarize evidence from the Web. Furthermore, we construct RU22Fact, a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022 of 16K samples, each containing real-world claims, optimized evidence, and referenced explanation. To establish a baseline for our dataset, we also develop an end-to-end explainable fact-checking system to verify claims and generate explanations. Experimental results demonstrate the prospect of optimized evidence in increasing fact-checking performance and also indicate the possibility of further progress in the end-to-end claim verification and explanation generation tasks.

cs.CL↗

arXiv2024

FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model

The task of stock earnings forecasting has received considerable attention due to the demand investors in real-world scenarios. However, compared with financial institutions, it is not easy for ordinary investors to mine factors and analyze news. On the other hand, although large language models in the financial field can serve users in the form of dialogue robots, it still requires users to have financial knowledge to ask reasonable questions. To serve the user experience, we aim to build an automatic system, FinReport, for ordinary investors to collect information, analyze it, and generate reports after summarizing. Specifically, our FinReport is based on financial news announcements and a multi-factor model to ensure the professionalism of the report. The FinReport consists of three modules: news factorization module, return forecasting module, risk assessment module. The news factorization module involves understanding news information and combining it with stock factors, the return forecasting module aim to analysis the impact of news on market sentiment, and the risk assessment module is adopted to control investment risk. Extensive experiments on real-world datasets have well verified the effectiveness and explainability of our proposed FinReport. Our codes and datasets are available at https://github.com/frinkleko/FinReport.

cs.CL↗

arXiv2024

Sensing-Aided Peer-to-Peer Millimeter-Wave Communication

One of the bottlenecks of modern communications is to enable sensing and mutual communication simultaneously without causing scheduling conflicts, and how sensing may be leveraged to help directional communication accuracy. To this end, we propose and implement a novel peer-to-peer (P2P) millimeter-wave communication system to jointly achieve beamforming and sensing. A radar and IMU-assisted tracking and beamforming algorithm is designed and tested. The results show that a robust tracking capacity with an overall higher throughput can be obtained. It is also hopeful that based on our proposed system, our design and implementation can be deployed in a more scalable manner for future extensions.

eess.SP↗

arXiv2023

Precheck Sequence Based False Base Station Detection During Handover: A Physical Layer Security Scheme

False Base Station (FBS) attack has been a severe security problem for the cellular network since 2G era. During handover, the user equipment (UE) periodically receives state information from surrounding base stations (BSs) and uploads it to the source BS. The source BS compares the uploaded signal power and shifts UE to another BS that can provide the strongest signal. An FBS can transmit signal with the proper power and attract UE to connect to it. In this paper, based on the 3GPP standard, a Precheck Sequence-based Detection (PSD) Scheme is proposed to secure the transition of legal base station (LBS) for UE. This scheme first analyzes the structure of received signals in blocks and symbols. Several additional symbols are added to the current signal sequence for verification. By designing a long table of symbol sequence, every UE which needs handover will be allocated a specific sequence from this table. The simulation results show that the performance of this PSD Scheme is better than that of any existing ones, even when a specific transmit power is designed for FBS.

eess.SP↗

arXiv2023

AIGC In China: Current Developments And Future Outlook

The increasing attention given to AI Generated Content (AIGC) has brought a profound impact on various aspects of daily life, industrial manufacturing, and the academic sector. Recognizing the global trends and competitiveness in AIGC development, this study aims to analyze China's current status in the field. The investigation begins with an overview of the foundational technologies and current applications of AIGC. Subsequently, the study delves into the market status, policy landscape, and development trajectory of AIGC in China, utilizing keyword searches to identify relevant scholarly papers. Furthermore, the paper provides a comprehensive examination of AIGC products and their corresponding ecosystem, emphasizing the ecological construction of AIGC. Finally, this paper discusses the challenges and risks faced by the AIGC industry while presenting a forward-looking perspective on the industry's future based on competitive insights in AIGC.

cs.AI↗

arXiv2023

The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge

Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores the anisotropic nature of the NCCT, and are evaluated on different in-house datasets with distinct metrics, making it highly challenging to improve segmentation performance and perform objective comparisons among different methods. The INSTANCE 2022 was a grand challenge held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing. The INSTANCE released a training set of 100 cases with ground-truth and a validation set with 30 cases without ground-truth labels that were available to the participants. A held-out testing set with 70 cases is utilized for the final evaluation and ranking. The methods from different participants are ranked based on four metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), Relative Volume Difference (RVD) and Normalized Surface Dice (NSD). A total of 13 teams submitted distinct solutions to resolve the challenges, making several baseline models, pre-processing strategies and anisotropic data processing techniques available to future researchers. The winner method achieved an average DSC of 0.6925, demonstrating a significant growth over our proposed baseline method. To the best of our knowledge, the proposed INSTANCE challenge releases the first intracranial hemorrhage segmentation benchmark, and is also the first challenge that intended to resolve the anisotropic problem in 3D medical image segmentation, which provides new alternatives in these research fields.

eess.IV↗

arXiv2022

STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing

In this paper, we propose a novel SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing, which leverages contextual information to enrich natural language (NL) utterance and table schema representations for text-to-SQL conversations. Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation. In addition, we construct a high-quality large-scale context-dependent text-to-SQL conversation corpus to pre-train STAR. Extensive experiments show that STAR achieves new state-of-the-art performance on two downstream benchmarks (SParC and CoSQL), significantly outperforming previous pre-training methods and ranking first on the leaderboard. We believe the release of the constructed corpus, codebase and pre-trained STAR checkpoints would push forward the research in this area. For reproducibility, we release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/star.

cs.CL↗

arXiv2022

DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation

Scalable 6D pose estimation for rigid objects from RGB images aims at handling multiple objects and generalizing to novel objects. Building on a well-known auto-encoding framework to cope with object symmetry and the lack of labeled training data, we achieve scalability by disentangling the latent representation of auto-encoder into shape and pose sub-spaces. The latent shape space models the similarity of different objects through contrastive metric learning, and the latent pose code is compared with canonical rotations for rotation retrieval. Because different object symmetries induce inconsistent latent pose spaces, we re-entangle the shape representation with canonical rotations to generate shape-dependent pose codebooks for rotation retrieval. We show state-of-the-art performance on two benchmarks containing textureless CAD objects without category and daily objects with categories respectively, and further demonstrate improved scalability by extending to a more challenging setting of daily objects across categories.

cs.CV↗

arXiv2022

From Bitcoin to Solana -- Innovating Blockchain towards Enterprise Applications

This survey presents a comprehensive study of recent advances in block-chain technologies, focusing on how issues that affecting the enterprise adoption were progressively addressed from the original Bitcoin system to Ethereum, to Solana etc. Key issues preventing the wide adoption are scala-bility and performance, while recent advances in Solana has clearly demon-strated that it is possible to significantly improve on those issues by innovat-ing on data structure, processes and algorithms by consolidating various time-consuming algorithms and security enforcements, and differentiate and balance users and their responsibilities and rights, while maintaining the re-quired security and integrity that blockchain systems inherently offer.

cs.CR↗

arXiv2022

Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions formed from seen state and object during training. Since the same state may be various in the visual appearance while entangled with different objects, CZSL is still a challenging task. Some methods recognize state and object with two trained classifiers, ignoring the impact of the interaction between object and state; the other methods try to learn the joint representation of the state-object compositions, leading to the domain gap between seen and unseen composition sets. In this paper, we propose a novel Siamese Contrastive Embedding Network (SCEN) (Code: https://github.com/XDUxyLi/SCEN-master) for unseen composition recognition. Considering the entanglement between state and object, we embed the visual feature into a Siamese Contrastive Space to capture prototypes of them separately, alleviating the interaction between state and object. In addition, we design a State Transition Module (STM) to increase the diversity of training compositions, improving the robustness of the recognition model. Extensive experiments indicate that our method significantly outperforms the state-of-the-art approaches on three challenging benchmark datasets, including the recent proposed C-QGA dataset.

cs.CV↗

arXiv2022

ULTRA: Uncertainty-aware Label Distribution Learning for Breast Tumor Cellularity Assessment

Neoadjuvant therapy (NAT) for breast cancer is a common treatment option in clinical practice. Tumor cellularity (TC), which represents the percentage of invasive tumors in the tumor bed, has been widely used to quantify the response of breast cancer to NAT. Therefore, automatic TC estimation is significant in clinical practice. However, existing state-of-the-art methods usually take it as a TC score regression problem, which ignores the ambiguity of TC labels caused by subjective assessment or multiple raters. In this paper, to efficiently leverage the label ambiguities, we proposed an Uncertainty-aware Label disTRibution leArning (ULTRA) framework for automatic TC estimation. The proposed ULTRA first converted the single-value TC labels to discrete label distributions, which effectively models the ambiguity among all possible TC labels. Furthermore, the network learned TC label distributions by minimizing the Kullback-Leibler (KL) divergence between the predicted and ground-truth TC label distributions, which better supervised the model to leverage the ambiguity of TC labels. Moreover, the ULTRA mimicked the multi-rater fusion process in clinical practice with a multi-branch feature fusion module to further explore the uncertainties of TC labels. We evaluated the ULTRA on the public BreastPathQ dataset. The experimental results demonstrate that the ULTRA outperformed the regression-based methods for a large margin and achieved state-of-the-art results. The code will be available from https://github.com/PerceptionComputingLab/ULTRA

eess.IV↗

arXiv2021

Zero-field spin noise spectrum of an alkali vapor with strong spin-exchange coupling

We study the zero-field optical spin noise spectra (OSN) for a thermal state hot 87Rb vapor with strong spin exchange coupling. Our main findings are: (1) The OSN spectrum consists of two components representing a positive and a negative hyperfine spin correlation (HSC), the relative power of which varies dramatically with the detuning frequency of the probe. (2) There exist two polar frequencies at which the OSN spectrum is completely polarized with one type of HSC. (3) At the limit of far detuning, the power ratio of the positive and negative HSC component of the OSN is 4:5. (4) The total power of the OSN is independent of the strength of SE coupling. (5) We give a simple way of deriving the OSN using the eigensolution of the density matrix equation.

quant-ph↗

arXiv2021

Atomistic metrics of BaSO$_4$ as an ultra-efficient radiative cooling material: a first-principles prediction

Radiative cooling has recently revived due to its significant potential as an environmentally friendly cooling technology. However, the design of particle-matrix cooling nanocomposites was generally carried out via tedious trial-and-error approaches, and the atomistic physics for efficient radiative cooling was not well understood. In this work, we identify the atomistic metrics of Barium Sulfate (BaSO$_4$) nanocomposite, which is an ultra-efficient radiative cooling material, using a predictive first-principles approach coupled with Monte Carlo simulations. Our results show that BaSO$_4$-acrylic nanocomposites not only attain high total solar reflectance of 92.5% (0.28 - 4.0 um), but also simultaneously demonstrate high normal emittance of 96.0% in the sky window region (8 - 13 um), outperforming the commonly used $α$-quartz ($α$-SiO$_2$). We identify two pertinent characters of ultra-efficient radiative cooling paints: i) a balanced band gap and refractive index, which enables strong scattering while negating absorption in the solar spectrum, and ii) a sufficient number of infrared-active optical resonance phonon modes resulting in abundant Reststrahlen bands and high emissivity in the sky window. The first principles approach and the resulted physical insights in this work pave the way for further search of ultra-efficient radiative cooling materials.

cond-mat.mtrl-sci↗

arXiv2021

Trear: Transformer-based RGB-D Egocentric Action Recognition

In this paper, we propose a \textbf{Tr}ansformer-based RGB-D \textbf{e}gocentric \textbf{a}ction \textbf{r}ecognition framework, called Trear. It consists of two modules, inter-frame attention encoder and mutual-attentional fusion block. Instead of using optical flow or recurrent units, we adopt self-attention mechanism to model the temporal structure of the data from different modalities. Input frames are cropped randomly to mitigate the effect of the data redundancy. Features from each modality are interacted through the proposed fusion block and combined through a simple yet effective fusion operation to produce a joint RGB-D representation. Empirical experiments on two large egocentric RGB-D datasets, THU-READ and FPHA, and one small dataset, WCVS, have shown that the proposed method outperforms the state-of-the-art results by a large margin.

cs.CV↗

arXiv2020

Transformer Guided Geometry Model for Flow-Based Unsupervised Visual Odometry

Existing unsupervised visual odometry (VO) methods either match pairwise images or integrate the temporal information using recurrent neural networks over a long sequence of images. They are either not accurate, time-consuming in training or error accumulative. In this paper, we propose a method consisting of two camera pose estimators that deal with the information from pairwise images and a short sequence of images respectively. For image sequences, a Transformer-like structure is adopted to build a geometry model over a local temporal window, referred to as Transformer-based Auxiliary Pose Estimator (TAPE). Meanwhile, a Flow-to-Flow Pose Estimator (F2FPE) is proposed to exploit the relationship between pairwise images. The two estimators are constrained through a simple yet effective consistency loss in training. Empirical evaluation has shown that the proposed method outperforms the state-of-the-art unsupervised learning-based methods by a large margin and performs comparably to supervised and traditional ones on the KITTI and Malaga dataset.

cs.CV↗