arXiv subjects

Hao Wang

Publications and source records attributed to Hao Wang.

At least 937 records · Page 52Linked to original sources

arXiv2024

The signal synchronization function of myelin

The myelinated axons are widely present in both central and peripheral nervous systems. Its unique compact spiraling structure poses significant challenges to understanding its biological functions and developmental mechanisms. Conventionally, myelin is considered as an insulating layer to achieve saltatory conduction for the enhancement of the neural signal speed, which serves as the foundation of neuroscience. However, this insulating hypothesis is inadequate to account for various experimental observations, especially the long unmyelinated tract observed in the cortex. We here show non-random distributions in three ultrastructural features of myelin: the non-random spiraling directions, the localization preferences of myelin outer tongues, and the radial components along boundaries between oppositely spiraled myelin sheaths. These phenomena are predicted by a novel concept of myelin biological function, which we propose as the signal synchronization function. Our findings demonstrate that cytoplasmic channels within myelin may act as coiled inductors, facilitating electromagnetic induction between adjacent myelin sheaths, and thereby promoting signal synchronization between axons. This, in turn, explains the non-random ultrastructural features observed. We believe these insights lay the foundation for a new understanding of myelin inductive function.

q-bio.NC↗

arXiv2024

ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models

There is a growing interest in the role that LLMs play in chemistry which lead to an increased focus on the development of LLMs benchmarks tailored to chemical domains to assess the performance of LLMs across a spectrum of chemical tasks varying in type and complexity. However, existing benchmarks in this domain fail to adequately meet the specific requirements of chemical research professionals. To this end, we propose \textbf{\textit{ChemEval}}, which provides a comprehensive assessment of the capabilities of LLMs across a wide range of chemical domain tasks. Specifically, ChemEval identified 4 crucial progressive levels in chemistry, assessing 12 dimensions of LLMs across 42 distinct chemical tasks which are informed by open-source data and the data meticulously crafted by chemical experts, ensuring that the tasks have practical value and can effectively evaluate the capabilities of LLMs. In the experiment, we evaluate 12 mainstream LLMs on ChemEval under zero-shot and few-shot learning contexts, which included carefully selected demonstration examples and carefully designed prompts. The results show that while general LLMs like GPT-4 and Claude-3.5 excel in literature understanding and instruction following, they fall short in tasks demanding advanced chemical knowledge. Conversely, specialized LLMs exhibit enhanced chemical competencies, albeit with reduced literary comprehension. This suggests that LLMs have significant potential for enhancement when tackling sophisticated tasks in the field of chemistry. We believe our work will facilitate the exploration of their potential to drive progress in chemistry. Our benchmark and analysis will be available at {\color{blue} \url{https://github.com/USTC-StarTeam/ChemEval}}.

cs.CL↗

arXiv2024

Analysis of vessel traffic flow characteristics in inland restricted waterways using multi-source data

To effectively manage vessel traffic and alleviate congestion on busy inland waterways, a comprehensive understanding of vessel traffic flow characteristics is crucial. However, limited data availability has resulted in minimal research on the traffic flow characteristics of inland waterway vessels. This study addresses this gap by conducting vessel-following experiments and fixed-point video monitoring in inland waterways, collecting multi-source data to analyze vessel traffic flow characteristics. First, the analysis of vessel speed distribution identifies the economic speed for vessels operating in these environments. Next, the relationship between microscopic vessel speed and gap distance is examined, with the logarithmic model emerging as the most accurate among various tested models. Additionally, the study explores the relationships among macroscopic speed, density, and flow rate, proposing a novel piecewise fundamental diagram model to describe these relationships. Lastly, the inland vessel traffic states are categorized using K-means clustering algorithm and applied to vessel navigation services. These findings provide valuable insights for enhancing inland waterway transportation and advancing the development of an integrated waterway transportation system.

cs.CE↗

arXiv2024

Evaluation of waterway lock service quality in Yangtze Delta: from the perspectives of customer and supplier

In recent decades, the waterway locks in the Yangtze Delta, China, have become major traffic bottlenecks. To gain a comprehensive understanding of the crew's perspectives and primary concerns regarding lock services during vessel lockage, and to enhance customer satisfaction and improve vessel lockage efficiency, it is necessary to assess the waterway lock service quality (WLSQ). This paper presents an evaluation system for WLSQ from various stakeholders' viewpoints. Firstly, by employing questionnaire surveys and the structural equation model method, in conjunction with factor analysis, the WLSQ and its influencing factors in the Yangtze River Delta region are analyzed from a customer perspective. Secondly, the Analytic Hierarchy Process method is utilized, along with a dedicated questionnaire for service suppliers, to examine their concerns regarding the performance of vessel lock services. The findings indicate that there exists a cognitive bias towards factors influencing the WLSQ. Crew members express the greatest concern over vessel lockage delays, whereas vessel lockage safety is the primary concern for management department administrators. Furthermore, enhancing the supporting facilities of waterway locks can significantly increase crew members' satisfaction during vessel lockage. Improving staff skills, and safety conditions can also greatly enhance customers' tolerance for lockage delays. The results of this study will provide valuable insights for the lock management department, operators, and the government in formulating relevant policies to improve WLSQ and implementing ongoing service quality evaluations.

cs.CY↗

arXiv2024

EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models

Emotional intelligence in large language models (LLMs) is of great importance in Natural Language Processing. However, the previous research mainly focus on basic sentiment analysis tasks, such as emotion recognition, which is not enough to evaluate LLMs' overall emotional intelligence. Therefore, this paper presents a novel framework named EmotionQueen for evaluating the emotional intelligence of LLMs. The framework includes four distinctive tasks: Key Event Recognition, Mixed Event Recognition, Implicit Emotional Recognition, and Intention Recognition. LLMs are requested to recognize important event or implicit emotions and generate empathetic response. We also design two metrics to evaluate LLMs' capabilities in recognition and response for emotion-related statements. Experiments yield significant conclusions about LLMs' capabilities and limitations in emotion intelligence.

cs.CL↗

arXiv2024

Multi-Floor Zero-Shot Object Navigation Policy

Object navigation in multi-floor environments presents a formidable challenge in robotics, requiring sophisticated spatial reasoning and adaptive exploration strategies. Traditional approaches have primarily focused on single-floor scenarios, overlooking the complexities introduced by multi-floor structures. To address these challenges, we first propose a Multi-floor Navigation Policy (MFNP) and implement it in Zero-Shot object navigation tasks. Our framework comprises three key components: (i) Multi-floor Navigation Policy, which enables an agent to explore across multiple floors; (ii) Multi-modal Large Language Models (MLLMs) for reasoning in the navigation process; and (iii) Inter-Floor Navigation, ensuring efficient floor transitions. We evaluate MFNP on the Habitat-Matterport 3D (HM3D) and Matterport 3D (MP3D) datasets, both include multi-floor scenes. Our experiment results demonstrate that MFNP significantly outperforms all the existing methods in Zero-Shot object navigation, achieving higher success rates and improved exploration efficiency. Ablation studies further highlight the effectiveness of each component in addressing the unique challenges of multi-floor navigation. Meanwhile, we conducted real-world experiments to evaluate the feasibility of our policy. Upon deployment of MFNP, the Unitree quadruped robot demonstrated successful multi-floor navigation and found the target object in a completely unseen environment. By introducing MFNP, we offer a new paradigm for tackling complex, multi-floor environments in object navigation tasks, opening avenues for future research in visual-based navigation in realistic, multi-floor settings.

cs.RO↗

arXiv2024

D2Vformer: A Flexible Time Series Prediction Model Based on Time Position Embedding

Time position embeddings capture the positional information of time steps, often serving as auxiliary inputs to enhance the predictive capabilities of time series models. However, existing models exhibit limitations in capturing intricate time positional information and effectively utilizing these embeddings. To address these limitations, this paper proposes a novel model called D2Vformer. Unlike typical prediction methods that rely on RNNs or Transformers, this approach can directly handle scenarios where the predicted sequence is not adjacent to the input sequence or where its length dynamically changes. In comparison to conventional methods, D2Vformer undoubtedly saves a significant amount of training resources. In D2Vformer, the Date2Vec module uses the timestamp information and feature sequences to generate time position embeddings. Afterward, D2Vformer introduces a new fusion block that utilizes an attention mechanism to explore the similarity in time positions between the embeddings of the input sequence and the predicted sequence, thereby generating predictions based on this similarity. Through extensive experiments on six datasets, we demonstrate that Date2Vec outperforms other time position embedding methods, and D2Vformer surpasses state-of-the-art methods in both fixed-length and variable-length prediction tasks.

cs.LG↗

arXiv2024

First Indication of Solar $^8$B Neutrino Flux through Coherent Elastic Neutrino-Nucleus Scattering in PandaX-4T

The PandaX-4T liquid xenon detector at the China Jinping Underground Laboratory is used to measure the solar $^8$B neutrino flux by detecting neutrinos through coherent scattering with xenon nuclei. Data samples requiring the coincidence of scintillation and ionization signals (paired), as well as unpaired ionization-only signals (US2), are selected with energy threshold of approximately 1.1 keV (0.33 keV) nuclear recoil energy. Combining the commissioning run and the first science run of PandaX-4T, a total exposure of 1.20 and 1.04 tonne$\cdot$year are collected for the paired and US2, respectively. After unblinding, 3 and 332 events are observed with an expectation of 2.8$\pm$0.5 and 251$\pm$32 background events, for the paired and US2 data, respectively. A combined analysis yields a best-fit $^8$B neutrino signal of 3.5 (75) events from the paired (US2) data sample, with $\sim$37\% uncertainty, and the background-only hypothesis is disfavored at 2.64$σ$ significance. This gives a solar $^8$B neutrino flux of ($8.4\pm3.1$)$\times$10$^6$ cm$^{-2}$s$^{-1}$, consistent with the standard solar model prediction. It is also the first indication of solar $^8$B neutrino ``fog'' in a dark matter direct detection experiment.

hep-ex↗

arXiv2024

Can recent DESI BAO measurements accommodate a negative cosmological constant?

Anti-de Sitter vacuum, which correspond to a negative cosmological constant (CC), is theoretically important and well-motivated. However, whether it exists in reality or not has always been a controversial issue. In this paper, we perform the search for the negative CC using recent Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillation measurements combined with Planck cosmic microwave background and Pantheon Plus supernova data. Though we did not find the evidence for negative CC, we observed the indication for it, the negative CC is preferred at $>68\%$ significance level, while such a negative CC can make the state equation of evolving dark energy component (coexisting with negative CC) $w\geqslant -1$. Our work highlights the potential of upcoming cosmological surveys to search for the negative CC.

astro-ph.CO↗

arXiv2024

Dataset Regeneration for Sequential Recommendation

The sequential recommender (SR) system is a crucial component of modern recommender systems, as it aims to capture the evolving preferences of users. Significant efforts have been made to enhance the capabilities of SR systems. These methods typically follow the model-centric paradigm, which involves developing effective models based on fixed datasets. However, this approach often overlooks potential quality issues and flaws inherent in the data. Driven by the potential of data-centric AI, we propose a novel data-centric paradigm for developing an ideal training dataset using a model-agnostic dataset regeneration framework called DR4SR. This framework enables the regeneration of a dataset with exceptional cross-architecture generalizability. Additionally, we introduce the DR4SR+ framework, which incorporates a model-aware dataset personalizer to tailor the regenerated dataset specifically for a target model. To demonstrate the effectiveness of the data-centric paradigm, we integrate our framework with various model-centric methods and observe significant performance improvements across four widely adopted datasets. Furthermore, we conduct in-depth analyses to explore the potential of the data-centric paradigm and provide valuable insights. The code can be found at https://github.com/USTC-StarTeam/DR4SR.

cs.IR↗

arXiv2024

Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement

Currently, the mainstream restoration tasks under adverse weather conditions have predominantly focused on single-weather scenarios. However, in reality, multiple weather conditions always coexist and their degree of mixing is usually unknown. Under such complex and diverse weather conditions, single-weather restoration models struggle to meet practical demands. This is particularly critical in fields such as autonomous driving, where there is an urgent need for a model capable of effectively handling mixed weather conditions and enhancing image quality in an automated manner. In this paper, we propose a Task Sequence Generator module that, in conjunction with the Task Intra-patch Block, effectively extracts task-specific features embedded in degraded images. The Task Intra-patch Block introduces an external learnable sequence that aids the network in capturing task-specific information. Additionally, we employ a histogram-based transformer module as the backbone of our network, enabling the capture of both global and local dynamic range features. Our proposed model achieves state-of-the-art performance on public datasets.

cs.CV↗

arXiv2024

Providing Safety Assurances for Systems with Unknown Dynamics

As autonomous systems become more complex and integral in our society, the need to accurately model and safely control these systems has increased significantly. In the past decade, there has been tremendous success in using deep learning techniques to model and control systems that are difficult to model using first principles. However, providing safety assurances for such systems remains difficult, partially due to the uncertainty in the learned model. In this work, we aim to provide safety assurances for systems whose dynamics are not readily derived from first principles and, hence, are more advantageous to be learned using deep learning techniques. Given the system of interest and safety constraints, we learn an ensemble model of the system dynamics from data. Leveraging ensemble uncertainty as a measure of uncertainty in the learned dynamics model, we compute a maximal robust control invariant set, starting from which the system is guaranteed to satisfy the safety constraints under the condition that realized model uncertainties are contained in the predefined set of admissible model uncertainty. We demonstrate the effectiveness of our method using a simulated case study with an inverted pendulum and a hardware experiment with a TurtleBot. The experiments show that our method robustifies the control actions of the system against model uncertainty and generates safe behaviors without being overly restrictive. The codes and accompanying videos can be found on the project website.

cs.RO↗

arXiv2024

Multiple weather images restoration using the task transformer and adaptive mixup strategy

The current state-of-the-art in severe weather removal predominantly focuses on single-task applications, such as rain removal, haze removal, and snow removal. However, real-world weather conditions often consist of a mixture of several weather types, and the degree of weather mixing in autonomous driving scenarios remains unknown. In the presence of complex and diverse weather conditions, a single weather removal model often encounters challenges in producing clear images from severe weather images. Therefore, there is a need for the development of multi-task severe weather removal models that can effectively handle mixed weather conditions and improve image quality in autonomous driving scenarios. In this paper, we introduce a novel multi-task severe weather removal model that can effectively handle complex weather conditions in an adaptive manner. Our model incorporates a weather task sequence generator, enabling the self-attention mechanism to selectively focus on features specific to different weather types. To tackle the challenge of repairing large areas of weather degradation, we introduce Fast Fourier Convolution (FFC) to increase the receptive field. Additionally, we propose an adaptive upsampling technique that effectively processes both the weather task information and underlying image features by selectively retaining relevant information. Our proposed model has achieved state-of-the-art performance on the publicly available dataset.

cs.CV↗

arXiv2024

Zeroshot Listwise Learning to Rank Algorithm for Recommendation

Learning to rank is a rare technology compared with other techniques such as deep neural networks. The number of experts in the field is roughly 1/6 of the number of professionals in deep learning. Being an effective ranking methodology, learning to rank has been widely used in the field of information retrieval. However, in recent years, learning to rank as a recommendation approach has been on decline. In this paper, we take full advantage of order statistic approximation and power law distribution to design a zeroshot listwise learning to rank algorithm for recommendation. We prove in the experiment section that our approach is both accurate and fair.

cs.IR↗

arXiv2024

Construction of topological quantum magnets from atomic spins on surfaces

Artificial quantum systems have emerged as indispensable platforms to realize exotic topological matter in a well-controlled manner. Here, we demonstrate topological quantum Heisenberg spin lattices, engineered with spin chains and two-dimensional spin arrays using spin 1/2 atoms on insulating films in a scanning tunnelling microscope (STM). We engineered with atomic precision both topological and trivial phases of the quantum spin model, realizing first- and second-order topological quantum magnets. Their many-body excitations were probed by single-atom electron spin resonance with ultrahigh energy resolution. The atomically-localized magnetic field of the STM tip allows us to directly visualize various topological bound modes including topological edge states, topological defects, and higher-order corner modes. Our results provide an important bottom-up approach to simulating exotic quantum many-body phases of interacting spins.

cond-mat.mes-hall↗

arXiv2024

BrainVis: Exploring the Bridge between Brain and Visual Signals via Image Reconstruction

Analyzing and reconstructing visual stimuli from brain signals effectively advances the understanding of human visual system. However, the EEG signals are complex and contain significant noise. This leads to substantial limitations in existing works of visual stimuli reconstruction from EEG, such as difficulties in aligning EEG embeddings with the fine-grained semantic information and a heavy reliance on additional large self-collected dataset for training. To address these challenges, we propose a novel approach called BrainVis. Firstly, we divide the EEG signals into various units and apply a self-supervised approach on them to obtain EEG time-domain features, in an attempt to ease the training difficulty. Additionally, we also propose to utilize the frequency-domain features to enhance the EEG representations. Then, we simultaneously align EEG time-frequency embeddings with the interpolation of the coarse and fine-grained semantics in the CLIP space, to highlight the primary visual components and reduce the cross-modal alignment difficulty. Finally, we adopt the cascaded diffusion models to reconstruct images. Using only 10\% training data of the previous work, our proposed BrainVis outperforms state of the arts in both semantic fidelity reconstruction and generation quality. The code is available at https://github.com/RomGai/BrainVis.

cs.CV↗

arXiv2024

Interference-Cancellation-Based Channel Knowledge Map Construction and Its Applications to Channel Estimation

Channel knowledge map (CKM) is viewed as a digital twin of wireless channels, providing location-specific channel knowledge for environment-aware communications. A fundamental problem in CKM-assisted communications is how to construct the CKM efficiently. Current research focuses on interpolating or predicting channel knowledge based on error-free channel knowledge from measured regions, ignoring the extraction of channel knowledge. This paper addresses this gap by unifying the extraction and representation of channel knowledge. We propose a novel CKM construction framework that leverages the received signals of the base station (BS) as online and low-cost data. Specifically, we partition the BS coverage area into spatial grids. The channel knowledge per grid is represented by a set of multi-path powers, delays, and angles, based on the principle of spatial consistency. In the extraction of these channel parameters, the challenges lie in strong inter-cell interferences and non-linear relationship between received signals and channel parameters. To address these issues, we formulate the problem of CKM construction into a problem of Bayesian inference, employing a block-sparsity prior model to characterize the path-loss differences of interferers. Under the Bayesian inference framework, we develop a hybrid message-passing algorithm for the interference-cancellation-based CKM construction. Based on the CKM, we obtain the joint frequency-space covariance of user channel and design a CKM-assisted Bayesian channel estimator. The computational complexity of the channel estimator is substantially reduced by exploiting the CKM-derived covariance structure. Numerical results show that the proposed CKM provides accurate channel parameters at low signal-to-interference-plus-noise ratio (SINR) and that the CKM-assisted channel estimator significantly outperforms state-of-the-art counterparts.

cs.IT↗

arXiv2024

Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models

Sequential recommendation has attracted increasing attention due to its ability to accurately capture the dynamic changes in user interests. We have noticed that generative models, especially diffusion models, which have achieved significant results in fields like image and audio, hold considerable promise in the field of sequential recommendation. However, existing sequential recommendation methods based on diffusion models are constrained by a prior distribution limited to Gaussian distribution, hindering the possibility of introducing user-specific information for each recommendation and leading to information loss. To address these issues, we introduce the Schrödinger Bridge into diffusion-based sequential recommendation models, creating the SdifRec model. This allows us to replace the Gaussian prior of the diffusion model with the user's current state, directly modeling the process from a user's current state to the target recommendation. Additionally, to better utilize collaborative information in recommendations, we propose an extended version of SdifRec called con-SdifRec, which utilizes user clustering information as a guiding condition to further enhance the posterior distribution. Finally, extensive experiments on multiple public benchmark datasets have demonstrated the effectiveness of SdifRec and con-SdifRec through comparison with several state-of-the-art methods. Further in-depth analysis has validated their efficiency and robustness.

cs.IR↗