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Spectrometric Characterization of Suspension Liquid and Light Extinction Model Update

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SSRN

SSRN 2022年

作者： Liu, Sheng Shen, Yang Gao, Zihui Gao, Hongwen School of Computer Science and Technology Huaibei Normal University Huaibei235000 China State Key Laboratory of Enviromental Pollution Control and Resource Reuse Tongji University Shanghai200092 China Shanghai GreenEmpire Environmental Science and Technology Corporation Shanghai200092 China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei235000 China

On the basis of the classical light scattering models, the light extinction model is the first to establish as [[EQUATION]] (, N and γ – average diameter in μm, number and relative refractive index of the suspending particles, λ , A and δ – incident light wavelength in μm, absorbance and optical path in cm of the suspension liquid) by spectrometric characterization of ten standard suspension liquids. It has been used to determine the suspending particles in the calcium oxalate, Formazine, soil, milk and sewage suspension water samples. As the result, the light extinction model method brought out less than 12% error of and 18% error of the suspending particles’ quality by comparing with the conventional methods. It provides a simple and reliable spectroptometric determination of a suspension liquid. Also, it is very potential to in-situ monitor the growth and working state of the suspending particles using in synthesis of materials, culture of cells, treatment of wastewater and safety of drinking water and foods. © 2022, The Authors. All rights reserved.

关键词： Suspensions (fluids)

Simulation and assimilation of the digital human brain

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arXiv 2022年

作者： Lu, Wenlian Du, Xin Wang, Jiexiang Zeng, Longbin Ye, Leijun Xiang, Shitong Zheng, Qibao Zhang, Jie Xu, Ningsheng Feng, Jianfeng Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence [Fudan University Ministry of Education China Shanghai Centre for Mathematical Sciences Fudan University Shanghai China School of Computer Science Fudan University Shanghai China School of Software Technology Zhejiang University Hangzhou China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China Department of Computer Science University of Warwick Coventry United Kingdom Zhangjiang Fudan International Innovation Centre Shanghai China

Here, we present the Digital Brain (DB), a platform for simulating spiking neuronal networks at the large neuron scale of the human brain based on personalized magnetic-resonance-imaging data and biological constraints. The DB aims to reproduce both the resting state and certain aspects of the action of the human brain. An architecture with up to 86 billion neurons and 14,012 GPUs, including a two-level routing scheme between GPUs to accelerate spike transmission up to 47.8 trillion neuronal synapses, was implemented as part of the simulations. We show that the DB can reproduce blood-oxygen-level-dependent signals of the resting-state of the human brain with a high correlation coefficient, as well as interact with its perceptual input, as demonstrated in a visual task. These results indicate the feasibility of implementing a digital representation of the human brain, which can open the door to a broad range of potential applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Neurons

Revolutionizing Intrusive Neural Interfaces: Vascular Stent-Based Neural Electrodes

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Revolutionizing Intrusive Neural Interfaces: Vascular Stent-...

International Winter Workshop on Brain-computer Interface (BCI)

作者： Liang Shen Bo Wen Zihua Chen Minjie Wang Linlin Han Bo Shen Kai Cao Lihua Zhang Jianjun Wu Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of AI & Robotics Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders State Key Laboratory of Medical Neurobiology Huashan Hospital Fudan University Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China

This review delves into the emerging field of endovascular stent electrodes in the realm of brain-computer interfaces, highlighting advancements in their fabrication methods, signal acquisition performance, electrical stimulation effects, and research progress in electrochemical characteristics, mechanical stability, and biocompatibility. In comparison to traditional invasive procedures, endovascular stent electrodes, implanted through minimally invasive means, mitigate the risks associated with extensive trauma and tissue inflammation, demonstrating commendable signal acquisition capabilities. While exhibiting a slight deficiency in visual evoked potentials, they showcase accuracy in the acquisition and decoding of motor perception signals, rivaling or surpassing traditional surgical methods. Additionally, the technology shows potential therapeutic efficacy in electrical stimulation applications. Nevertheless, challenges such as electrical stimulation thresholds and visibility issues warrant further research and refinement. In summary, endovascular stent electrodes, as an emerging avenue in brain-computer interfaces, hold promise for significant breakthroughs in future research, opening up new possibilities in the field of neuroscience.

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A semantic-based method for unsupervised commonsense question answering

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arXiv 2021年

作者： Niu, Yilin Huang, Fei Liang, Jiaming Chen, Wenkai Zhu, Xiaoyan Huang, Minlie The CoAI group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China School of Computer Science and Technology Beijing University of Posts and Telecommunications

Unsupervised commonsense question answering is appealing since it does not rely on any labeled task data. Among existing work, a popular solution is to use pre-trained language models to score candidate choices directly conditioned on the question or context. However, such scores from language models can be easily affected by irrelevant factors, such as word frequencies, sentence structures, etc. These distracting factors may not only mislead the model to choose a wrong answer but also make it oversensitive to lexical perturbations in candidate answers. In this paper, we present a novel SEmantic-based Question Answering method (SEQA) for unsupervised commonsense question answering. Instead of directly scoring each answer choice, our method first generates a set of plausible answers with generative models (e.g., GPT-2), and then uses these plausible answers to select the correct choice by considering the semantic similarity between each plausible answer and each choice. We devise a simple, yet sound formalism for this idea and verify its effectiveness and robustness with extensive experiments. We evaluate the proposed method on four benchmark datasets, and our method achieves the best results in unsupervised settings. Moreover, when attacked by TextFooler (Jin et al., 2020) with synonym replacement, SEQA demonstrates much less performance drops than baselines, thereby indicating stronger robustness. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

AnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild

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arXiv 2022年

作者： Zhang, Libo Gao, Junyuan Xiao, Zhen Fan, Heng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China Nanjing Institute of Software Technology Nanjing China Department of Computer Science and Engineering University of North Texas Denton United States

Multi-animal tracking (MAT), a multi-object tracking (MOT) problem, is crucial for animal motion and behavior analysis and has many crucial applications such as biology, ecology and animal conservation. Despite its importance, MAT is largely under-explored compared to other MOT problems such as multi-human tracking due to the scarcity of dedicated benchmarks. To address this problem, we introduce AnimalTrack, a dedicated benchmark for multi-animal tracking in the wild. Specifically, AnimalTrack consists of 58 sequences from a diverse selection of 10 common animal categories. On average, each sequence comprises of 33 target objects for tracking. In order to ensure high quality, every frame in AnimalTrack is manually labeled with careful inspection and refinement. To our best knowledge, AnimalTrack is the first benchmark dedicated to multi-animal tracking. In addition, to understand how existing MOT algorithms perform on AnimalTrack and provide baselines for future comparison, we extensively evaluate 14 state-of-the-art representative trackers. The evaluation results demonstrate that, not surprisingly, most of these trackers become degenerated due to the differences between pedestrians and animals in various aspects (e.g., pose, motion, and appearance), and more efforts are desired to improve multi-animal tracking. We hope that AnimalTrack together with evaluation and analysis will foster further progress on multi-animal tracking. The dataset and evaluation as well as our analysis will be made available at https:// ***/projects/AnimalTrack/. Copyright © 2022, The Authors. All rights reserved.

关键词： Petroleum reservoir evaluation

Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation

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arXiv 2024年

作者： Zhou, Yangxuan Zhao, Sha Wang, Jiquan Jiang, Haiteng Li, Shijian Luo, Benyan Li, Tao Pan, Gang State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Neurobiology Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Zhejiang University School of Medicine Hangzhou China MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Hangzhou China The First Affiliated Hospital College of Medicine Zhejiang University Hangzhou China

Sleep staging is important for monitoring sleep quality and diagnosing sleep-related disorders. Recently, numerous deep learning-based models have been proposed for automatic sleep staging using polysomnography recordings. Most of them are trained and tested on the same labeled datasets which results in poor generalization to unseen target domains. However, they regard the subjects in the target domains as a whole and overlook the individual discrepancies, which limits the model’s generalization ability to new patients (i.e., unseen subjects) and plug-and-play applicability in clinics. To address this, we propose a novel Source-Free Unsupervised Individual Domain Adaptation (SF-UIDA) framework for sleep staging, leveraging sequential cross-view contrasting and pseudo-label based fine-tuning. It is actually a two-step subject-specific adaptation scheme, which enables the source model to effectively adapt to newly appeared unlabeled individual without access to the source data. It meets the practical needs in real-world scenarios, where the personalized customization can be plug-and-play applied to new ones. Our framework is applied to three classic sleep staging models and evaluated on three public sleep datasets, achieving the state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Sleep research

Multi-span style extraction for generative reading comprehension

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Multi-span style extraction for generative reading comprehen...

2021 Workshop on Scientific Document Understanding, SDU 2021

作者： Yang, Junjie Zhang, Zhuosheng Zhao, Hai SJTU-ParisTech Elite Institute of Technology Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Generative machine reading comprehension (MRC) requires a model to generate well-formed answers. For this type of MRC, answer generation method is crucial to the model performance. However, generative models, which are supposed to be the right model for the task, in generally perform poorly. At the same time, single-span extraction models have been proven effective for extractive MRC, where the answer is constrained to a single span in the passage. Nevertheless, they generally suffer from generating incomplete answers or introducing redundant words when applied to the generative MRC. Thus, we extend the single-span extraction method to multi-span, proposing a new framework which enables generative MRC to be smoothly solved as multi-span extraction. Thorough experiments demonstrate that this novel approach can alleviate the dilemma between generative models and single-span models and produce answers with better-formed syntax and semantics. Copyright © 2021for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Extraction

Experimental demonstration of reconstructing quantum states with generative models

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arXiv 2024年

作者： Li, Xuegang Jiang, Wenjie Hua, Ziyue Wang, Weiting Pan, Xiaoxuan Cai, Weizhou Lu, Zhide Han, Jiaxiu Wu, Rebing Zou, Chang-Ling Deng, Dong-Ling Sun, Luyan Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China Hefei National Laboratory Hefei230088 China Key Laboratory of Quantum Information CAS University of Science and Technology of China Anhui Hefei230026 China Shanghai Qi Zhi Institute 41th Floor AI Tower No. 701 Yunjin Road Xuhui District Shanghai200232 China

Quantum state tomography, a process that reconstructs a quantum state from measurements on an ensemble of identically prepared copies, plays a crucial role in benchmarking quantum devices. However, brute-force approaches to quantum state tomography would become impractical for large systems, as the required resources scale exponentially with the system size. Here, we explore a machine learning approach and report an experimental demonstration of reconstructing quantum states based on neural network generative models with an array of programmable superconducting transmon qubits. In particular, we experimentally prepare the Greenberger-Horne-Zeilinger states and random states up to five qubits and demonstrate that the machine learning approach can efficiently reconstruct these states with the number of required experimental samples scaling linearly with system size. Our results experimentally showcase the intriguing potential for exploiting machine learning techniques in validating and characterizing complex quantum devices, offering a valuable guide for the future development of quantum technologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

Customer relationship management systems in clinical laboratories: A systematic review

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Informatics in Medicine Unlocked 2025年 53卷

作者： Bation, Jonel Jaro, Mary Ann Nery, Lheyniel Jane Mudjahidin, Mudjahidin Aristio, Andre Parvian Palad, Eddie Bouy Chavez, Jason Velasco, Lemuel Clark Department of Information Technology College of Computer Studies Mindanao State University-Iligan Institute of Technology Iligan City 9200 Philippines Department of Information Systems Faculty of Electrical Technology and Intelligent Informatics Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia School of Business Administration Zamboanga Peninsula Polytechnic State University Zamboanga City 7000 Philippines Department of Industrial and Information Management College of Management National Cheng Kung University Tainan City 701 Taiwan Center for Computational Analytics and Modelling Premiere Research Institute of Science and Mathematics MSU-IIT Iligan City 9200 Philippines

The implementation of Customer Relationship Management (CRM) systems in clinical laboratories is crucial in improving customer relationships, service quality, and operational efficiency that aligns with a patient-centric care model. This study utilizes the PRISMA guidelines in reviewing and synthesizing 26 journal articles using the People, Process, technology (PPT) framework to analyze the roles of people involved in clinical settings, the processes by which laboratory services were delivered, and the technological considerations enhancing patient care. Results revealed that the successful implementation of CRM systems in clinical laboratories depends on the aligned efforts of both developers and end-users. Subsequently, marketing processes and customer service were then found out to be crucial for the successful utilization of CRM systems in clinical laboratories. The features and the system integration techniques of CRM systems were found out to be vital in developing efficient operations, enhancing data analysis, and extending accessibility. The research gap analysis, on the one hand, shows that the effectiveness of CRM systems on the patients, the lack of qualitative methods, and the development of corrective actions to increase patient satisfaction are relevant areas of research concerns to optimize the effectiveness of implementing different CRM systems. © 2025

关键词： Clinical laboratories CRM Customer relationship management systems