arXiv subjects

Xin Ma

Publications and source records attributed to Xin Ma.

At least 91 records · Page 5Linked to original sources

arXiv2024

Enhancing Autism Spectrum Disorder Early Detection with the Parent-Child Dyads Block-Play Protocol and an Attention-enhanced GCN-xLSTM Hybrid Deep Learning Framework

Autism Spectrum Disorder (ASD) is a rapidly growing neurodevelopmental disorder. Performing a timely intervention is crucial for the growth of young children with ASD, but traditional clinical screening methods lack objectivity. This study introduces an innovative approach to early detection of ASD. The contributions are threefold. First, this work proposes a novel Parent-Child Dyads Block-Play (PCB) protocol, grounded in kinesiological and neuroscientific research, to identify behavioral patterns distinguishing ASD from typically developing (TD) toddlers. Second, we have compiled a substantial video dataset, featuring 40 ASD and 89 TD toddlers engaged in block play with parents. This dataset exceeds previous efforts on both the scale of participants and the length of individual sessions. Third, our approach to action analysis in videos employs a hybrid deep learning framework, integrating a two-stream graph convolution network with attention-enhanced xLSTM (2sGCN-AxLSTM). This framework is adept at capturing dynamic interactions between toddlers and parents by extracting spatial features correlated with upper body and head movements and focusing on global contextual information of action sequences over time. By learning these global features with spatio-temporal correlations, our 2sGCN-AxLSTM effectively analyzes dynamic human behavior patterns and demonstrates an unprecedented accuracy of 89.6\% in early detection of ASD. Our approach shows strong potential for enhancing early ASD diagnosis by accurately analyzing parent-child interactions, providing a critical tool to support timely and informed clinical decision-making.

cs.CV↗

arXiv2024

China's plug-in hybrid electric vehicle transition: an operational carbon perspective

Assessing the emissions of plug-in hybrid electric vehicle (PHEV) operations is crucial for accelerating the carbon-neutral transition in the passenger car sector. This study is the first to adopt a bottom-up model to measure the real-world energy use and carbon dioxide emissions of China's top twenty selling PHEV models across different regions from 2020 to 2022. The results indicate that (1) the actual electricity intensity of the best-selling PHEV models (20.2-38.2 kWh/100 km) was 30-40% higher than the New European Driving Cycle values, and the actual gasoline intensity (4.7-23.5 L/100 km) was 3-6 times greater than the New European Driving Cycle values. (2) The overall energy use of the best-selling models varied among different regions, and the energy use from 2020 to 2022 in Southern China was double that Northern China and the Yangtze River Middle Reach. (3) The top-selling models emitted 4.7 megatons of carbon dioxide nationwide from 2020 to 2022, with 1.9 megatons released by electricity consumption and 2.8 megatons released by gasoline combustion. Furthermore, targeted policy implications for expediting the carbon-neutral transition within the passenger car sector are proposed. In essence, this study explores and compares benchmark data at both the national and regional levels, along with performance metrics associated with PHEV operations. The main objective is to aid nationwide decarbonization efforts, focusing on carbon reduction and promoting the rapid transition of road transportation toward a net-zero carbon future.

econ.GN↗

arXiv2024

Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis

Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct research. This study aims to provide a comprehensive overview of LLM applications in BHI, highlighting their transformative potential and addressing the associated ethical and practical challenges. We reviewed 1,698 research articles from January 2022 to December 2023, categorizing them by research themes and diagnostic categories. Additionally, we conducted network analysis to map scholarly collaborations and research dynamics. Our findings reveal a substantial increase in the potential applications of LLMs to a variety of BHI tasks, including clinical decision support, patient interaction, and medical document analysis. Notably, LLMs are expected to be instrumental in enhancing the accuracy of diagnostic tools and patient care protocols. The network analysis highlights dense and dynamically evolving collaborations across institutions, underscoring the interdisciplinary nature of LLM research in BHI. A significant trend was the application of LLMs in managing specific disease categories such as mental health and neurological disorders, demonstrating their potential to influence personalized medicine and public health strategies. LLMs hold promising potential to further transform biomedical research and healthcare delivery. While promising, the ethical implications and challenges of model validation call for rigorous scrutiny to optimize their benefits in clinical settings. This survey serves as a resource for stakeholders in healthcare, including researchers, clinicians, and policymakers, to understand the current state and future potential of LLMs in BHI.

cs.DL↗

arXiv2024

Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models

Diffusion models have achieved great progress in image animation due to powerful generative capabilities. However, maintaining spatio-temporal consistency with detailed information from the input static image over time (e.g., style, background, and object of the input static image) and ensuring smoothness in animated video narratives guided by textual prompts still remains challenging. In this paper, we introduce Cinemo, a novel image animation approach towards achieving better motion controllability, as well as stronger temporal consistency and smoothness. In general, we propose three effective strategies at the training and inference stages of Cinemo to accomplish our goal. At the training stage, Cinemo focuses on learning the distribution of motion residuals, rather than directly predicting subsequent via a motion diffusion model. Additionally, a structural similarity index-based strategy is proposed to enable Cinemo to have better controllability of motion intensity. At the inference stage, a noise refinement technique based on discrete cosine transformation is introduced to mitigate sudden motion changes. Such three strategies enable Cinemo to produce highly consistent, smooth, and motion-controllable results. Compared to previous methods, Cinemo offers simpler and more precise user controllability. Extensive experiments against several state-of-the-art methods, including both commercial tools and research approaches, across multiple metrics, demonstrate the effectiveness and superiority of our proposed approach.

cs.CV↗

arXiv2024

Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation

Using 286 research papers collected from Web of Science, ScienceDirect, SpringerLink, arXiv, and Google Scholar databases, a systematic review methodology was adopted to review and summarize the current challenges and potential future developments in data, algorithms, and evaluation aspects of RSs. It was found that RSs involve five major research topics, namely algorithmic improvement, domain applications, user behavior & cognition, data processing & modeling, and social impact & ethics. Collaborative filtering and hybrid recommendation techniques are mainstream. The performance of RSs is jointly limited by four types of eight data issues, two types of twelve algorithmic issues, and two evaluation issues. Notably, data-related issues such as cold start, data sparsity, and data poisoning, algorithmic issues like interest drift, device-cloud collaboration, non-causal driven, and multitask conflicts, along with evaluation issues such as offline data leakage and multi-objective balancing, have prominent impacts. Fusing physiological signals for multimodal modeling, defending against data poisoning through user information behavior, evaluating generative recommendations via social experiments, fine-tuning pre-trained large models to schedule device-cloud resource, enhancing causal inference with deep reinforcement learning, training multi-task models based on probability distributions, using cross-temporal dataset partitioning, and evaluating recommendation objectives across the full lifecycle are feasible solutions to address the aforementioned prominent challenges and unlock the power and value of RSs.The collected literature is mainly based on major international databases, and future research will further expand upon it.

cs.IR↗

arXiv2024

TCMBench: A Comprehensive Benchmark for Evaluating Large Language Models in Traditional Chinese Medicine

Large language models (LLMs) have performed remarkably well in various natural language processing tasks by benchmarking, including in the Western medical domain. However, the professional evaluation benchmarks for LLMs have yet to be covered in the traditional Chinese medicine(TCM) domain, which has a profound history and vast influence. To address this research gap, we introduce TCM-Bench, an comprehensive benchmark for evaluating LLM performance in TCM. It comprises the TCM-ED dataset, consisting of 5,473 questions sourced from the TCM Licensing Exam (TCMLE), including 1,300 questions with authoritative analysis. It covers the core components of TCMLE, including TCM basis and clinical practice. To evaluate LLMs beyond accuracy of question answering, we propose TCMScore, a metric tailored for evaluating the quality of answers generated by LLMs for TCM related questions. It comprehensively considers the consistency of TCM semantics and knowledge. After conducting comprehensive experimental analyses from diverse perspectives, we can obtain the following findings: (1) The unsatisfactory performance of LLMs on this benchmark underscores their significant room for improvement in TCM. (2) Introducing domain knowledge can enhance LLMs' performance. However, for in-domain models like ZhongJing-TCM, the quality of generated analysis text has decreased, and we hypothesize that their fine-tuning process affects the basic LLM capabilities. (3) Traditional metrics for text generation quality like Rouge and BertScore are susceptible to text length and surface semantic ambiguity, while domain-specific metrics such as TCMScore can further supplement and explain their evaluation results. These findings highlight the capabilities and limitations of LLMs in the TCM and aim to provide a more profound assistance to medical research.

cs.CL↗

arXiv2024

Non-free almost finite actions for locally finite-by-virtually $\mathbb{Z}$ groups

In this paper, we study almost finiteness and almost finiteness in measure of non-free actions. Let $α:G\curvearrowright X$ be a minimal action of a locally finite-by-virtually $\mathbb{Z}$ group $G$ on the Cantor set $X$. We prove that under certain assumptions, the action $α$ is almost finite in measure if and only if $α$ is essentially free. As an application, we obtain that any minimal topologically free action of a virtually $\mathbb{Z}$ group on an infinite compact metrizable space with the small boundary property is almost finite. This is the first general result, assuming only topological freeness, in this direction, and these lead to new results on uniform property $Γ$ and $\mathcal{Z}$-stability for their crossed product $C^*$-algebras. Some concrete examples of minimal topological free (but non-free) subshifts are provided.

math.OA↗

arXiv2024

Semantic-embedded Similarity Prototype for Scene Recognition

Due to the high inter-class similarity caused by the complex composition and the co-existing objects across scenes, numerous studies have explored object semantic knowledge within scenes to improve scene recognition. However, a resulting challenge emerges as object information extraction techniques require heavy computational costs, thereby burdening the network considerably. This limitation often renders object-assisted approaches incompatible with edge devices in practical deployment. In contrast, this paper proposes a semantic knowledge-based similarity prototype, which can help the scene recognition network achieve superior accuracy without increasing the computational cost in practice. It is simple and can be plug-and-played into existing pipelines. More specifically, a statistical strategy is introduced to depict semantic knowledge in scenes as class-level semantic representations. These representations are used to explore correlations between scene classes, ultimately constructing a similarity prototype. Furthermore, we propose to leverage the similarity prototype to support network training from the perspective of Gradient Label Softening and Batch-level Contrastive Loss, respectively. Comprehensive evaluations on multiple benchmarks show that our similarity prototype enhances the performance of existing networks, all while avoiding any additional computational burden in practical deployments. Code and the statistical similarity prototype will be available at https://github.com/ChuanxinSong/SimilarityPrototype

cs.CV↗

arXiv2024

A Minimal Set of Parameters Based Depth-Dependent Distortion Model and Its Calibration Method for Stereo Vision Systems

Depth position highly affects lens distortion, especially in close-range photography, which limits the measurement accuracy of existing stereo vision systems. Moreover, traditional depth-dependent distortion models and their calibration methods have remained complicated. In this work, we propose a minimal set of parameters based depth-dependent distortion model (MDM), which considers the radial and decentering distortions of the lens to improve the accuracy of stereo vision systems and simplify their calibration process. In addition, we present an easy and flexible calibration method for the MDM of stereo vision systems with a commonly used planar pattern, which requires cameras to observe the planar pattern in different orientations. The proposed technique is easy to use and flexible compared with classical calibration techniques for depth-dependent distortion models in which the lens must be perpendicular to the planar pattern. The experimental validation of the MDM and its calibration method showed that the MDM improved the calibration accuracy by 56.55% and 74.15% compared with the Li's distortion model and traditional Brown's distortion model. Besides, an iteration-based reconstruction method is proposed to iteratively estimate the depth information in the MDM during three-dimensional reconstruction. The results showed that the accuracy of the iteration-based reconstruction method was improved by 9.08% compared with that of the non-iteration reconstruction method.

cs.CV↗

arXiv2024

Ac$_3$Ni$_2$O$_7$ and La$_2$$Ae$Ni$_2$O$_6$F ($Ae$ = Sr, Ba): Benchmark Materials for Bilayer Nickelate Superconductivity

We theoretically propose Ac$_3$Ni$_2$O$_7$, La$_2$BaNi$_2$O$_6$F, and La$_2$SrNi$_2$O$_6$F compounds to be benchmark materials for bilayer nickelate superconductivity. The stable phase of Ac$_3$Ni$_2$O$_7$ and La$_2$BaNi$_2$O$_6$F are found to be $I4/mmm$ without the lattice distortion caused by octahedra rotation at ambient pressure, where as the lattice distortion in La$_2$SrNi$_2$O$_6$F can be suppressed with relatively small external pressure of 4 GPa. The magnetism, electronic structure and spin susceptibilities of Ac$_3$Ni$_2$O$_7$ are extremely close to those of La$_3$Ni$_2$O$_7$ at 30 GPa. The ground state of La$_2$BaNi$_2$O$_6$F and La$_2$SrNi$_2$O$_6$F are antiferromagnetically coupled checkerboard bilayer with sizable magnetic moment on Ni. In addition, the inter-layer coupling $J_{\perp}$ between Ni-bilayers in La$_2$BaNi$_2$O$_6$F or La$_2$SrNi$_2$O$_6$F is only $\sim$ 1/10 of that in Ac$_3$Ni$_2$O$_7$ or La$_3$Ni$_2$O$_7$ at 30 GPa. We argue that these compounds may serve as superconducting candidates at ambient pressure and can be employed to testify theoretical proposals for bilayer nickelate superconductivity.

cond-mat.supr-con↗

arXiv2024

Inter-object Discriminative Graph Modeling for Indoor Scene Recognition

Variable scene layouts and coexisting objects across scenes make indoor scene recognition still a challenging task. Leveraging object information within scenes to enhance the distinguishability of feature representations has emerged as a key approach in this domain. Currently, most object-assisted methods use a separate branch to process object information, combining object and scene features heuristically. However, few of them pay attention to interpretably handle the hidden discriminative knowledge within object information. In this paper, we propose to leverage discriminative object knowledge to enhance scene feature representations. Initially, we capture the object-scene discriminative relationships from a probabilistic perspective, which are transformed into an Inter-Object Discriminative Prototype (IODP). Given the abundant prior knowledge from IODP, we subsequently construct a Discriminative Graph Network (DGN), in which pixel-level scene features are defined as nodes and the discriminative relationships between node features are encoded as edges. DGN aims to incorporate inter-object discriminative knowledge into the image representation through graph convolution and mapping operations (GCN). With the proposed IODP and DGN, we obtain state-of-the-art results on several widely used scene datasets, demonstrating the effectiveness of the proposed approach.

cs.CV↗

arXiv2024

Deformable Object Manipulation With Constraints Using Path Set Planning and Tracking

In robotic deformable object manipulation (DOM) applications, constraints arise commonly from environments and task-specific requirements. Enabling DOM with constraints is therefore crucial for its deployment in practice. However, dealing with constraints turns out to be challenging due to many inherent factors such as inaccessible deformation models of deformable objects (DOs) and varying environmental setups. This article presents a systematic manipulation framework for DOM subject to constraints by proposing a novel path set planning and tracking scheme. First, constrained DOM tasks are formulated into a versatile optimization formalism which enables dynamic constraint imposition. Because of the lack of the local optimization objective and high state dimensionality, the formulated problem is not analytically solvable. To address this, planning of the path set, which collects paths of DO feedback points, is proposed subsequently to offer feasible path and motion references for DO in constrained setups. Both theoretical analyses and computationally efficient algorithmic implementation of path set planning are discussed. Lastly, a control architecture combining path set tracking and constraint handling is designed for task execution. The effectiveness of our methods is validated in a variety of DOM tasks with constrained experimental settings.

cs.RO↗

arXiv2024

Distinct pressure evolution of superconductivity and charge-density-wave in kagome superconductor CsV$_3$Sb$_5$ thin flakes

It is intriguing to explore the coexistence and (or) competition between charge-density-wave (CDW) and superconductivity (SC) in many correlated electron systems, such as cuprates, organic superconductors and dichacolgenides. Among them, the recently discovered $\mathbb{Z} _2$ topological kagome metals AV$_3$Sb$_5$ (A=K, Rb, Cs) serve as an ideal platform to study the intricate relation between them. Here, we report the electrical resistance measurements on CsV$_3$Sb$_5$ thin flakes ($\approx$ 60 nm) under hydrostatic pressure up to 2.12 GPa to compare its pressure phase diagram of CDW and SC with its bulk form. Even though the CDW transition temperature (T$_{CDW}$) in CsV$_3$Sb$_5$ thin flakes is still monotonically suppressed under pressure and totally vanishes at P$_2$=1.83 GPa similar to the bulk, the superconducting transition temperature (T$_c$) shows an initial decrease and consequent increase up to its maximum $\sim$ 8.03 K at P$_2$, in sharp contrast with the M-shaped double domes in the bulk CsV$_3$Sb$_5$. Our results suggest the important role of reduced dimensionality on the CDW state and its interplay with the SC, offering a new perspective to explore the exotic nature of CsV$_3$Sb$_5$.

cond-mat.supr-con↗

arXiv2024

Photochemical Upcycling of Ultrastrong Polyethylene Nanomembranes into Fibrous Carbon at Ambient Conditions

The escalating global issue of plastic waste accumulation, specifically polyolefins, necessitates an urgent solution for upcycling these materials into beneficial compounds. Yet, achieving such upcycling without introducing carbon dioxide into the environment remains a formidable challenge. In this study, we demonstrate an eco-friendly approach for the photochemical conversion of ultrastrong, ultratransparent, and ultrathin polyethylene membrane into fibrous carbon nanomembrane at ambient conditions. The membrane was sputter-coated with platinum and cuprous oxide nanoparticles and exposed to simulated sunlight, resulting in a porous carbon membrane decorated with Pt nanoparticles. The new carbonized nanomembrane maintained the pristine membrane's morphology. The membrane exhibited high activity (2.11 mA/cm2) for electrochemical ethanol oxidation with stability over 1000 cycles. This work holds significance for sustainable plastic waste management and the design of new polyolefin materials in a circular economy.

physics.app-ph↗

arXiv2024

InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understanding and generation. The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words. Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby showcasing its efficacy in learning video-language representation at scale. Specifically, we utilize a multi-scale approach to generate video-related descriptions. Furthermore, we introduce ViCLIP, a video-text representation learning model based on ViT-L. Learned on InternVid via contrastive learning, this model demonstrates leading zero-shot action recognition and competitive video retrieval performance. Beyond basic video understanding tasks like recognition and retrieval, our dataset and model have broad applications. They are particularly beneficial for generating interleaved video-text data for learning a video-centric dialogue system, advancing video-to-text and text-to-video generation research. These proposed resources provide a tool for researchers and practitioners interested in multimodal video understanding and generation.

cs.CV↗

arXiv2023

SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction

Recently video generation has achieved substantial progress with realistic results. Nevertheless, existing AI-generated videos are usually very short clips ("shot-level") depicting a single scene. To deliver a coherent long video ("story-level"), it is desirable to have creative transition and prediction effects across different clips. This paper presents a short-to-long video diffusion model, SEINE, that focuses on generative transition and prediction. The goal is to generate high-quality long videos with smooth and creative transitions between scenes and varying lengths of shot-level videos. Specifically, we propose a random-mask video diffusion model to automatically generate transitions based on textual descriptions. By providing the images of different scenes as inputs, combined with text-based control, our model generates transition videos that ensure coherence and visual quality. Furthermore, the model can be readily extended to various tasks such as image-to-video animation and autoregressive video prediction. To conduct a comprehensive evaluation of this new generative task, we propose three assessing criteria for smooth and creative transition: temporal consistency, semantic similarity, and video-text semantic alignment. Extensive experiments validate the effectiveness of our approach over existing methods for generative transition and prediction, enabling the creation of story-level long videos. Project page: https://vchitect.github.io/SEINE-project/ .

cs.CV↗

arXiv2023

Dimensions and cotorsion pairs in recollements of extriangulated categories

Let $(\mathcal A, \mathcal B, \mathcal C)$ be a recollement of extriangulated categories. In this paper, we provide bounds on the coresolution dimensions of the subcategories involved in $\mathcal A$, $\mathcal B$ and $\mathcal C$. We show that a hereditary cotorsion pair in $\mathcal B$ can induce hereditary cotorsion pairs in $\mathcal A$ and $\mathcal C$ under certain conditions. Besides, we construct recollements of abelian categories from a recollement of extriangulated categories with respect to the hearts of cotorsion pairs.

math.RT↗

arXiv2023

LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models

This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task to simultaneously a) accomplish the synthesis of visually realistic and temporally coherent videos while b) preserving the strong creative generation nature of the pre-trained T2I model. To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model. Our key insights are two-fold: 1) We reveal that the incorporation of simple temporal self-attentions, coupled with rotary positional encoding, adequately captures the temporal correlations inherent in video data. 2) Additionally, we validate that the process of joint image-video fine-tuning plays a pivotal role in producing high-quality and creative outcomes. To enhance the performance of LaVie, we contribute a comprehensive and diverse video dataset named Vimeo25M, consisting of 25 million text-video pairs that prioritize quality, diversity, and aesthetic appeal. Extensive experiments demonstrate that LaVie achieves state-of-the-art performance both quantitatively and qualitatively. Furthermore, we showcase the versatility of pre-trained LaVie models in various long video generation and personalized video synthesis applications.

cs.CV↗