arXiv subjects

Yan Zhang

Publications and source records attributed to Yan Zhang.

At least 325 records · Page 18Linked to original sources

arXiv2024

Evolutionary Retrosynthetic Route Planning

Molecular retrosynthesis is a significant and complex problem in the field of chemistry, however, traditional manual synthesis methods not only need well-trained experts but also are time-consuming. With the development of big data and machine learning, artificial intelligence (AI) based retrosynthesis is attracting more attention and has become a valuable tool for molecular retrosynthesis. At present, Monte Carlo tree search is a mainstream search framework employed to address this problem. Nevertheless, its search efficiency is compromised by its large search space. Therefore, this paper proposes a novel approach for retrosynthetic route planning based on evolutionary optimization, marking the first use of Evolutionary Algorithm (EA) in the field of multi-step retrosynthesis. The proposed method involves modeling the retrosynthetic problem into an optimization problem, defining the search space and operators. Additionally, to improve the search efficiency, a parallel strategy is implemented. The new approach is applied to four case products and compared with Monte Carlo tree search. The experimental results show that, in comparison to the Monte Carlo tree search algorithm, EA significantly reduces the number of calling single-step model by an average of 53.9%. The time required to search three solutions decreases by an average of 83.9%, and the number of feasible search routes increases by 1.38 times. The source code is available at https://github.com/ilog-ecnu/EvoRRP.

cs.AI↗

arXiv2024

Towards Context-aware Support for Color Vision Deficiency: An Approach Integrating LLM and AR

People with color vision deficiency often face challenges in distinguishing colors such as red and green, which can complicate daily tasks and require the use of assistive tools or environmental adjustments. Current support tools mainly focus on presentation-based aids, like the color vision modes found in iPhone accessibility settings. However, offering context-aware support, like indicating the doneness of meat, remains a challenge since task-specific solutions are not cost-effective for all possible scenarios. To address this, our paper proposes an application that provides contextual and autonomous assistance. This application is mainly composed of: (i) an augmented reality interface that efficiently captures context; and (ii) a multi-modal large language model-based reasoner that serves to cognitize the context and then reason about the appropriate support contents. Preliminary user experiments with two color vision deficient users across five different scenarios have demonstrated the effectiveness and universality of our application.

cs.CV↗

arXiv2024

Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks: Design Optimization and Analysis

This paper investigates autonomous driving safety improvement via task offloading from cellular vehicles (CVs) to a multi-access edge computing (MEC) server using vehicle-to-infrastructure (V2I) links. Considering that the latter links can be reused by vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of the V2I link may suffer from severe interference that can cause outages during the task offloading. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) whose computationally capable metamaterials are leveraged to jointly enable V2I reflective links as well as to implement interference cancellation at the V2V links. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices to maximize an autonomous driving safety task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems solved via an alternate approximation method. Our simulation results showcase that the proposed RICS-assisted offloading framework significantly improves the safety of the considered autonomous driving network, yielding a nearly 34\% improvement in the safety coefficient of the CVs. In addition, it is demonstrated that the V2V data rate can be improved by around 60\% indicating that the RICS-induced adjustment of the signals can effectively mitigate interference at the V2V link.

cs.DC↗

arXiv2024

DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models

Large language models (LLMs) have demonstrated emergent capabilities across diverse reasoning tasks via popular Chains-of-Thought (COT) prompting. However, such a simple and fast COT approach often encounters limitations in dealing with complicated problems, while a thorough method, which considers multiple reasoning pathways and verifies each step carefully, results in slower inference. This paper addresses the challenge of enabling LLMs to autonomously select between fast and slow inference methods, thereby optimizing both efficiency and effectiveness. We introduce a dynamic decision-making framework that categorizes tasks into two distinct pathways: 'Fast', designated for tasks where the LLM quickly identifies a high-confidence solution, and 'Slow', allocated for tasks that the LLM perceives as complex and for which it has low confidence in immediate solutions as well as requiring more reasoning paths to verify. Experiments on five popular reasoning benchmarks demonstrated the superiority of the DynaThink over baselines.

cs.CL↗

arXiv2024

FLY-TTS: Fast, Lightweight and High-Quality End-to-End Text-to-Speech Synthesis

While recent advances in Text-To-Speech synthesis have yielded remarkable improvements in generating high-quality speech, research on lightweight and fast models is limited. This paper introduces FLY-TTS, a new fast, lightweight and high-quality speech synthesis system based on VITS. Specifically, 1) We replace the decoder with ConvNeXt blocks that generate Fourier spectral coefficients followed by the inverse short-time Fourier transform to synthesize waveforms; 2) To compress the model size, we introduce the grouped parameter-sharing mechanism to the text encoder and flow-based model; 3) We further employ the large pre-trained WavLM model for adversarial training to improve synthesis quality. Experimental results show that our model achieves a real-time factor of 0.0139 on an Intel Core i9 CPU, 8.8x faster than the baseline (0.1221), with a 1.6x parameter compression. Objective and subjective evaluations indicate that FLY-TTS exhibits comparable speech quality to the strong baseline.

eess.AS↗

arXiv2024

CELLO: Causal Evaluation of Large Vision-Language Models

Causal reasoning is fundamental to human intelligence and crucial for effective decision-making in real-world environments. Despite recent advancements in large vision-language models (LVLMs), their ability to comprehend causality remains unclear. Previous work typically focuses on commonsense causality between events and/or actions, which is insufficient for applications like embodied agents and lacks the explicitly defined causal graphs required for formal causal reasoning. To overcome these limitations, we introduce a fine-grained and unified definition of causality involving interactions between humans and/or objects. Building on the definition, we construct a novel dataset, CELLO, consisting of 14,094 causal questions across all four levels of causality: discovery, association, intervention, and counterfactual. This dataset surpasses traditional commonsense causality by including explicit causal graphs that detail the interactions between humans and objects. Extensive experiments on CELLO reveal that current LVLMs still struggle with causal reasoning tasks, but they can benefit significantly from our proposed CELLO-CoT, a causally inspired chain-of-thought prompting strategy. Both quantitative and qualitative analyses from this study provide valuable insights for future research. Our project page is at https://github.com/OpenCausaLab/CELLO.

cs.CV↗

arXiv2024

Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environments

Learning from Demonstration (LfD) stands as an efficient framework for imparting human-like skills to robots. Nevertheless, designing an LfD framework capable of seamlessly imitating, generalizing, and reacting to disturbances for long-horizon manipulation tasks in dynamic environments remains a challenge. To tackle this challenge, we present Logic Dynamic Movement Primitives (Logic-DMP), which combines Task and Motion Planning (TAMP) with an optimal control formulation of DMP, allowing us to incorporate motion-level via-point specifications and to handle task-level variations or disturbances in dynamic environments. We conduct a comparative analysis of our proposed approach against several baselines, evaluating its generalization ability and reactivity across three long-horizon manipulation tasks. Our experiment demonstrates the fast generalization and reactivity of Logic-DMP for handling task-level variants and disturbances in long-horizon manipulation tasks.

cs.RO↗

arXiv2024

TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement

Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). However, careful evaluations by human reveal that the translations produced by LLMs still contain multiple errors. Importantly, feeding back such error information into the LLMs can lead to self-refinement and result in improved translation performance. Motivated by these insights, we introduce a systematic LLM-based self-refinement translation framework, named \textbf{TEaR}, which stands for \textbf{T}ranslate, \textbf{E}stimate, \textbf{a}nd \textbf{R}efine, marking a significant step forward in this direction. Our findings demonstrate that 1) our self-refinement framework successfully assists LLMs in improving their translation quality across a wide range of languages, whether it's from high-resource languages to low-resource ones or whether it's English-centric or centered around other languages; 2) TEaR exhibits superior systematicity and interpretability; 3) different estimation strategies yield varied impacts, directly affecting the effectiveness of the final corrections. Additionally, traditional neural translation models and evaluation models operate separately, often focusing on singular tasks due to their limited capabilities, while general-purpose LLMs possess the capability to undertake both tasks simultaneously. We further conduct cross-model correction experiments to investigate the potential relationship between the translation and evaluation capabilities of general-purpose LLMs. Our code and data are available at https://github.com/fzp0424/self_correct_mt

cs.CL↗

arXiv2024

AI-Empowered Multiple Access for 6G: A Survey of Spectrum Sensing, Protocol Designs, and Optimizations

With the rapidly increasing number of bandwidth-intensive terminals capable of intelligent computing and communication, such as smart devices equipped with shallow neural network models, the complexity of multiple access for these intelligent terminals is increasing due to the dynamic network environment and ubiquitous connectivity in 6G systems. Traditional multiple access (MA) design and optimization methods are gradually losing ground to artificial intelligence (AI) techniques that have proven their superiority in handling complexity. AI-empowered MA and its optimization strategies aimed at achieving high Quality-of-Service (QoS) are attracting more attention, especially in the area of latency-sensitive applications in 6G systems. In this work, we aim to: 1) present the development and comparative evaluation of AI-enabled MA; 2) provide a timely survey focusing on spectrum sensing, protocol design, and optimization for AI-empowered MA; and 3) explore the potential use cases of AI-empowered MA in the typical application scenarios within 6G systems. Specifically, we first present a unified framework of AI-empowered MA for 6G systems by incorporating various promising machine learning techniques in spectrum sensing, resource allocation, MA protocol design, and optimization. We then introduce AI-empowered MA spectrum sensing related to spectrum sharing and spectrum interference management. Next, we discuss the AI-empowered MA protocol designs and implementation methods by reviewing and comparing the state-of-the-art, and we further explore the optimization algorithms related to dynamic resource management, parameter adjustment, and access scheme switching. Finally, we discuss the current challenges, point out open issues, and outline potential future research directions in this field.

cs.NI↗

arXiv2024

Degrees of Freedom Matter: Inferring Dynamics from Point Trajectories

Understanding the dynamics of generic 3D scenes is fundamentally challenging in computer vision, essential in enhancing applications related to scene reconstruction, motion tracking, and avatar creation. In this work, we address the task as the problem of inferring dense, long-range motion of 3D points. By observing a set of point trajectories, we aim to learn an implicit motion field parameterized by a neural network to predict the movement of novel points within the same domain, without relying on any data-driven or scene-specific priors. To achieve this, our approach builds upon the recently introduced dynamic point field model that learns smooth deformation fields between the canonical frame and individual observation frames. However, temporal consistency between consecutive frames is neglected, and the number of required parameters increases linearly with the sequence length due to per-frame modeling. To address these shortcomings, we exploit the intrinsic regularization provided by SIREN, and modify the input layer to produce a spatiotemporally smooth motion field. Additionally, we analyze the motion field Jacobian matrix, and discover that the motion degrees of freedom (DOFs) in an infinitesimal area around a point and the network hidden variables have different behaviors to affect the model's representational power. This enables us to improve the model representation capability while retaining the model compactness. Furthermore, to reduce the risk of overfitting, we introduce a regularization term based on the assumption of piece-wise motion smoothness. Our experiments assess the model's performance in predicting unseen point trajectories and its application in temporal mesh alignment with guidance. The results demonstrate its superiority and effectiveness. The code and data for the project are publicly available: \url{https://yz-cnsdqz.github.io/eigenmotion/DOMA/}

cs.CV↗

arXiv2024

CAMEL. II. A 3D Coronal Mass Ejection Catalog Based on Coronal Mass Ejection Automatic Detection with Deep Learning

Coronal mass ejections (CMEs) are major drivers of geomagnetic storms, which may cause severe space weather effects. Automating the detection, tracking, and three-dimensional (3D) reconstruction of CMEs is important for operational predictions of CME arrivals. The COR1 coronagraphs on board the Solar Terrestrial Relations Observatory spacecraft have facilitated extensive polarization observations, which are very suitable for the establishment of a 3D CME system. We have developed such a 3D system comprising four modules: classification, segmentation, tracking, and 3D reconstructions. We generalize our previously pretrained classification model to classify COR1 coronagraph images. Subsequently, as there are no publicly available CME segmentation data sets, we manually annotate the structural regions of CMEs using Large Angle and Spectrometric Coronagraph C2 observations. Leveraging transformer-based models, we achieve state-of-the-art results in CME segmentation. Furthermore, we improve the tracking algorithm to solve the difficult separation task of multiple CMEs. In the final module, tracking results, combined with the polarization ratio technique are used to develop the first single-view 3D CME catalog without requiring manual mask annotation. Our method provides higher precision in automatic 2D CME catalog and more reliable physical parameters of CMEs, including 3D propagation direction and speed. The aforementioned 3D CME system can be applied to any coronagraph data with the capability of polarization measurements.

astro-ph.SR↗

arXiv2024

A Study of the Latest Updates of the Readout System for the Hybird-Pixel Detector at HEPS

The High Energy Photon Source (HEPS) represents a fourth-generation light source. This facility has made unprecedented advancements in accelerator technology, necessitating the development of new detectors to satisfy physical requirements such as single-photon resolution, large dynamic range, and high frame rates. Since 2016, the Institute of High Energy Physics has introduced the first user-experimental hybrid pixel detector, progressing to the fourth-generation million-pixel detector designed for challenging conditions, with the dual-threshold single-photon detector HEPS-Beijing PIXel (HEPS-BPIX) set as the next-generation target. HEPS-BPIX will employ the entirely new Application-Specific Integrated Circuit (ASIC) BP40 for pixel information readout. Data flow will be managed and controlled through readout electronics based on a two-tier Field-Programmable Gate Array (FPGA) system: the Front-End Electronics (FEE) and the Input-Output Board (IOB) handle the fan-out for 12 ASICs, and the u4FCP is tasked with processing serial data on high-speed links, transferring pixel-level data to the back-end RTM and uTCA chassis, or independently outputting through a network port, enabling remote control of the entire detector. The new HEPS-BPIX firmware has undergone a comprehensive redesign and update to meet the electronic characteristics of the new chip and to improve the overall performance of the detector. We provide an overview of the core subunits of HEPS-BPIX, emphasizing the readout system, evaluating the new hardware and firmware, and highlighting some of its innovative features and characteristics.

physics.ins-det↗

arXiv2024

Benchmarking Micro-action Recognition: Dataset, Methods, and Applications

Micro-action is an imperceptible non-verbal behaviour characterised by low-intensity movement. It offers insights into the feelings and intentions of individuals and is important for human-oriented applications such as emotion recognition and psychological assessment. However, the identification, differentiation, and understanding of micro-actions pose challenges due to the imperceptible and inaccessible nature of these subtle human behaviors in everyday life. In this study, we innovatively collect a new micro-action dataset designated as Micro-action-52 (MA-52), and propose a benchmark named micro-action network (MANet) for micro-action recognition (MAR) task. Uniquely, MA-52 provides the whole-body perspective including gestures, upper- and lower-limb movements, attempting to reveal comprehensive micro-action cues. In detail, MA-52 contains 52 micro-action categories along with seven body part labels, and encompasses a full array of realistic and natural micro-actions, accounting for 205 participants and 22,422 video instances collated from the psychological interviews. Based on the proposed dataset, we assess MANet and other nine prevalent action recognition methods. MANet incorporates squeeze-and excitation (SE) and temporal shift module (TSM) into the ResNet architecture for modeling the spatiotemporal characteristics of micro-actions. Then a joint-embedding loss is designed for semantic matching between video and action labels; the loss is used to better distinguish between visually similar yet distinct micro-action categories. The extended application in emotion recognition has demonstrated one of the important values of our proposed dataset and method. In the future, further exploration of human behaviour, emotion, and psychological assessment will be conducted in depth. The dataset and source code are released at https://github.com/VUT-HFUT/Micro-Action.

cs.CV↗

arXiv2024

Mott insulating phase and coherent-incoherent crossover across magnetic phase transition in 2D antiferromagnetic CrSBr

In two-dimensional van der Waals magnetic materials, the interplay between magnetism and electron correlation can give rise to new ground states and lead to novel transport and optical properties. A fundamental question in these materials is how the electron correlation manifests and interacts with the magnetic orders. In this study, we demonstrate that the recently discovered 2D antiferromagnetic material, CrSBr, is a Mott insulator, through the combined use of resonant and temperature-dependent angle-resolved photoemission spectroscopy techiniques, supplemented by dynamical mean-field theory analysis. Intriguingly, we found that as the system transitions from the antiferromagnetic to the paramagnetic phases, its Mott bands undergo a reconfiguration, and a coherent-incoherent crossover, driven by the dissolution of the magnetic order. Our findings reveal a distinctive evolution of band structure associated with magnetic phase transitions, shedding light on the investigation of intricate interplay between correlation and magnetic orders in strongly correlated van der Waals magnetic materials.

cond-mat.str-el↗

arXiv2024

Non-Driving-Related Tasks Influencing Drivers' Takeover Time: A Meta-Analysis

Before the era of fully automated vehicles, human is consistently an indispensable part of the driving system. Various studies have investigated drivers' cooperation with the vehicle under different conditions. In this article, we analyzed how non-driving-related tasks (NDRT) influence takeover time (TOT) by conducting a meta-analysis on 37 related papers. NDRTs were transcoded into combinations of five basic dimensions to unify and demonstrate their effects on drivers. In order to interpret experimental data comprehensively, we implemented three methods. A synthetical analysis was conducted to compare the effect size between each study and subgroup. Studies with eligible control groups have been examined by the two-group analysis, followed by moderator analysis on seven variables. The results from the two-group analysis showed that both visual-mental-motoric and visual-mental tasks have significant negative effects on the takeover time and the previous type had a larger effect than the latter one. Moreover, the subgroup comparison and meta-regression in the meta-analysis part revealed the correlation between moderators and the effect size, in which the Driving Experience and the Automation Level affected the relation between NDRT and TOT. The findings of this paper can contribute to the improvement and new directions for further scientific research and engineering design.

cs.HC↗

arXiv2024

Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation

The "pre-training then fine-tuning (FT)" paradigm is widely adopted to boost the model performance of deep learning-based methods for medical volumetric segmentation. However, conventional full FT incurs high computational and memory costs. Thus, it is of increasing importance to fine-tune pre-trained models for medical volumetric segmentation tasks in a both effective and parameter-efficient manner. In this paper, we introduce a new framework named Med-Tuning to realize parameter-efficient tuning (PET) for medical volumetric segmentation task and an efficient plug-and-play module named Med-Adapter for task-specific feature extraction. With a small number of tuned parameters, our framework enhances the 2D baselines's precision on segmentation tasks, which are pre-trained on natural images. Extensive experiments on three benchmark datasets (CT and MRI modalities) show that our method achieves better results than previous PET methods on volumetric segmentation tasks. Compared to full FT, Med-Tuning reduces the fine-tuned model parameters by up to 4x, with even better segmentation performance. Our project webpage is at \url{https://rubics-xuan.github.io/Med-Tuning/}.

cs.CV↗

arXiv2024

Operators' cognitive performance under extreme hot-humid exposure and its physiological-psychological mechanism based on ECG, fNIRS, and Eye Tracking

Operators' cognitive functions are impaired significantly under extreme heat stress, potentially resulting in more severe secondary disasters. This research investigated the impact of elevated temperature and humidity (25 60%RH, 30 70%RH, 35 80%RH, 40 90%RH) on the cognitive functions and performance of operators. Meanwhile, we explored the psychological-physiological mechanism underlying the change in performance by electrocardiogram (ECG), functional near-infrared spectroscopy (fNIRS), and eye tracking physiologically. Psychological aspects such as situation awareness, workload, and working memory were assessed. Eventually, we verified and extended the maximal adaptability model to the extreme condition. Unexpectedly, a temporary improvement in simple reaction tasks but rapid impairment in advanced cognitive functions (i.e. situation awareness, communication, working memory) was obtained above 35 WBGT. The best performance in a suitable environment was due to more effective activation in the prefrontal cortex (PFC). With temperature increasing, more mistakes occurred and comprehension was impaired due to drowsiness and lower arousal levels, according to evidence of compensatory effect in fNIRS. In the extreme environment, the enhanced PFC cooperation with higher functional connectivity resulted in a temporary improvement, while depressed activation in PFC, heavy physical load, and poor regulation of the cardiovascular system restricted it. Our results provide a detailed study of the process of operators' performance and cognitive functions when encountering increasing heat stress, as well as its underlying mechanisms from a neuroergonomics perspective. This can contribute to a better understanding of the interaction between operators' performance and workplace conditions, and help to achieve a more reliable human-centered production system in the promising era of Industry 5.0.

q-bio.NC↗

arXiv2024

Exploring the Impact of Synthetic Data for Aerial-view Human Detection

Aerial-view human detection has a large demand for large-scale data to capture more diverse human appearances compared to ground-view human detection. Therefore, synthetic data can be a good resource to expand data, but the domain gap with real-world data is the biggest obstacle to its use in training. As a common solution to deal with the domain gap, the sim2real transformation is used, and its quality is affected by three factors: i) the real data serving as a reference when calculating the domain gap, ii) the synthetic data chosen to avoid the transformation quality degradation, and iii) the synthetic data pool from which the synthetic data is selected. In this paper, we investigate the impact of these factors on maximizing the effectiveness of synthetic data in training in terms of improving learning performance and acquiring domain generalization ability--two main benefits expected of using synthetic data. As an evaluation metric for the second benefit, we introduce a method for measuring the distribution gap between two datasets, which is derived as the normalized sum of the Mahalanobis distances of all test data. As a result, we have discovered several important findings that have never been investigated or have been used previously without accurate understanding. We expect that these findings can break the current trend of either naively using or being hesitant to use synthetic data in machine learning due to the lack of understanding, leading to more appropriate use in future research.

cs.CV↗