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A passive,reusable,and resonating wearable sensing system for ondemand,non-invasive,and wireless molecular stress biomarker detection

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Nano Research 2024年第8期17卷 7542-7556页

作者： Shingirirai Chakoma Xiaochang Pei Huiting Qin Anita Ghandehari Sahar NajafiKhoshnoo Jerome Rajendran Rahim Esfandyarpour Department of Electrical Engineering and Computer Science University of CaliforniaIrvineCA 92697USA Laboratory for Integrated Nano BioElectronics Innovation The Henry Samueli School of EngineeringUniversity of CaliforniaIrvineCA 92697USA Henry Samueli School of Engineering University of CaliforniaIrvineCA 92697USA

The significant impact of stress on health necessitates accurate assessment methods,where traditional questionnaires lack reliability and *** advancements like wearables with electrocardiogram(ECG)and galvanic skin response(GSR)sensors face accuracy and artifact *** biosensors detecting cortisol,a critical stress hormone,present a promising ***,existing cortisol assays,requiring saliva,urine,or blood,are complex,expensive,and unsuitable for continuous *** study introduces a passive,molecularly imprinted polymer-radio-frequency(MIP-RF)wearable sensing system for real-time,non-invasive sweat cortisol *** system is wireless,flexible,battery-free,reusable,environmentally stable,and designed for long-term monitoring,using an inductance-capacitance *** transducer translates cortisol concentrations into resonant frequency shifts with high sensitivity(~160 kHz/(log(μM)))across a physiological range of 0.025–1μ*** with near-field communication(NFC)for wireless and battery-free operation,and threedimensional(3D)-printed microfluidic channel for in-situ sweat collection,it enables daily activity cortisol level *** of cortisol circadian rhythm through morning and evening measurements demonstrates its effectiveness in tracking and monitoring sweat cortisol levels.A 28-day stability test and the use of cost-effective 3D nanomaterials printing enhance its economic viability and *** innovation paves the way for a new era in realistic,on-demand health monitoring outside the laboratory,leveraging wearable technology for molecular stress biomarker detection.

关键词： cortisol wearable flexible sensors three-dimensional(3D)nanomaterials printing reusable environmentally stable

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A Lexicon and Depth-Wise Separable Convolution Based Handwritten Text Recognition System 37th

A Lexicon and Depth-Wise Separable Convolution Based Handwr...

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37th International Conference on Image and Vision Computing New Zealand, IVCNZ 2022

作者： Kumari, Lalita Singh, Sukhdeep Rathore, V.V.S. Sharma, Anuj Department of Computer Science and Applications Panjab University Chandigarh India Moga India Physical Research Laboratory Ahmedabad India

ISBN: (纸本)9783031258244

Cursive handwritten text recognition is a challenging research problem in pattern recognition. The current state-of-the-art approaches include models based on convolutional recurrent neural networks and multi-dimensional long short-term memory recurrent neural network techniques. These methods are highly computationally extensive as well model is complex at the design level. In recent studies, a combination of convolutional neural networks and gated convolutional neural networks based models demonstrated less number of parameters in comparison to convolutional recurrent neural networks based models. In the direction to reduced the total number of parameters to be trained, in this work, we have used depthwise separable convolution in place of standard convolutions with a combination of gated-convolutional neural network and bidirectional gated recurrent unit to reduce the total number of parameters to be trained. Additionally, we have also included a lexicon-based word beam search decoder at the testing step. It also helps in improving the overall accuracy of the model. We have obtained 3.84% character error rate and 9.40% word error rate on IAM dataset, 3.15% character error rate and 11.8% word error rate on RIMES dataset and 4.88% character error rate and 14.56% word error rate in George Washington dataset respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Inductive Relation Prediction by Disentangled Subgraph Structure

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Tsinghua science and Technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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Three-dimensional Target Detection Algorithm for Dangerous Goods in CT Security Inspection

Three-dimensional Target Detection Algorithm for Dangerous G...

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2023 International Conference on Algorithm, Imaging Processing, and Machine Vision, AIPMV 2023

作者： He, Jingze Guo, Yao Song, Qing Department of Computer Science and Technology Tsinghua University Beijing China Pattern Recognition and Intelligent Vision Laboratory Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781510672444

In this paper, a 3D dangerous goods detection method based on RetinaNet is proposed. This method uses the bidirectional feature pyramid network structure of RetinaNet to extract multi-scale features from point cloud data and trains the system using Focal Loss function to achieve fast and accurate detection of dangerous goods. In addition, in order to improve the detection accuracy, this paper also introduces the 3D region proposal network (3D RPN) and non-maximum suppression (NMS) algorithm. The experimental results show that the proposed method performs well on our self-built CT dataset, with high accuracy and low false positive rate, and is suitable for dangerous goods detection tasks in practical scenarios. © 2024 SPIE.

关键词： computerized tomography

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Robust Link Prediction over Noisy Hyper-Relational Knowledge Graphs via Active Learning 24

Robust Link Prediction over Noisy Hyper-Relational Knowledge...

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33rd ACM Web Conference, WWW 2024

作者： Yu, Weijian Yang, Jie Yang, Dingqi State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400701719

Modern Knowledge Graphs (KGs) are inevitably noisy due to the nature of their construction process. Existing robust learning techniques for noisy KGs mostly focus on triple facts, where the fact-wise confidence is straightforward to evaluate. However, hyper-relational facts, where an arbitrary number of key-value pairs are associated with a base triplet, have become increasingly popular in modern KGs, but significantly complicate the confidence assessment of the fact. Against this background, we study the problem of robust link prediction over noisy hyper-relational KGs, and propose NYLON, a \underlineN oise-resistant h\underlineY per-re\underlineL ati\underlineON al link prediction technique via active crowd learning. Specifically, beyond the traditional fact-wise confidence, we first introduce element-wise confidence measuring the fine-grained confidence of each entity or relation of a hyper-relational fact. We connect the element- and fact-wise confidences via a "least confidence'' principle to allow efficient crowd labeling. NYLON is then designed to systematically integrate three key components, where a hyper-relational link predictor uses the fact-wise confidence for robust prediction, a cross-grained confidence evaluator predicts both element- and fact-wise confidences, and an effort-efficient active labeler selects informative facts for crowd annotators to label using an efficient labeling mechanism guided by the element-wise confidence under the "least confidence'' principle and further followed by data augmentation. We evaluate NYLON on three real-world KG datasets against a sizeable collection of baselines. Results show that NYLON achieves superior and robust performance in both link prediction and error detection tasks on noisy KGs, and outperforms best baselines by 2.42-10.93% and 3.46-10.65% in the two tasks, respectively. © 2024 ACM.

关键词： Knowledge graph

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A disk I/O optimized system for concurrent graph processing jobs

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Frontiers of computer science 2024年第3期18卷 13-29页

作者： Xianghao XU Fang WANG Hong JIANG Yongli CHENG Dan FENG Peng FANG School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Computer Science&Engineering University of Texas at ArlingtonArlingtonTX 76019USA College of Computer and Data Science Fuzhou UniversityFuzhou 350108China Zhejiang Lab Hangzhou 311121China

In order to analyze and process the large graphs with high cost efficiency,researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity *** the other hand,with the rapidly growing need of analyzing graphs in the real-world,graph processing systems have to efficiently handle massive concurrent graph processing(CGP)***,due to the inherent design for single graph processing job,existing out-of-core graph processing systems usually incur unnecessary data accesses and severe competition of I/O bandwidth when handling the CGP *** this paper,we propose GraphCP,a disk I/O optimized out-of-core graph processing system that efficiently supports the processing of CGP *** proposes a benefit-aware sharing execution model to share the I/O access and processing of graph data among the CGP jobs and adaptively schedule the graph data loading based on the states of vertices,which efficiently overcomes above challenges faced by existing out-of-core graph processing ***,GraphCP adopts a dependency-based future-vertex updating model so as to reduce disk I/Os in the future *** addition,GraphCP organizes the graph data with a Source-Sorted Sub-Block graph representation for better processing capacity and I/O access *** evaluation results show that GraphCP is 20.5×and 8.9×faster than two out-of-core graph processing systems GridGraph and GraphZ,and 3.5×and 1.7×faster than two state-of-art concurrent graph processing systems Seraph and GraphSO.

关键词： graph processing disk I/O concurrent jobs

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Analysis on DeepFake Dataset, Manipulation and Detection Techniques: A Review

Analysis on DeepFake Dataset, Manipulation and Detection Tec...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Rasool, Aale Katarya, Rahul Big Data Analytics and Web Intelligence Laboratory Department of Computer Science Delhi Technological University New Delhi India

ISBN: (纸本)9789819959730

The ability to spot false news is becoming increasingly important in today’s environment. Modern, rapid communication and easy access to all kinds of information were made possible by social networks and internet media. On the other hand, it opens up new avenues for malicious people to exploit vast amounts of publicly available data. Fake images and videos may now be predicted with greater accuracy because of advances in machine learning, particularly deep learning. Many studies have been conducted in recent years to better understand how deepfakes work, and several deep learning-based algorithms to detect deepfake videos or images have been developed. This study provides a comprehensive summary of the most recent manipulation techniques and datasets utilized in this subject. In addition, we reviewed the recent work of other academics on the subject and provided a roadmap for future study to aid scholars in combating the issue of deep fakes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Convolutional neural networks

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Assuming Carbon Tax in a Simultaneous Production and Maintenance Planning Optimization 6th

Assuming Carbon Tax in a Simultaneous Production and Maint...

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6th conference on Computing Systems and Applications, CSA 2024

作者： Abdi, Hind Berrichi, Ali RECITS Laboratory Faculty of Mathematics USTHB University BP 32 El-Alia Bab-Ezzouar Algiers Algeria LIMOSE Laboratory The Department of Computer Science UMBB University Boumerdes Algeria

ISBN: (纸本)9783031718472

In this paper, we address the challenge of simultaneous production and maintenance planning under carbon emission (CE) regulations, aiming to minimize the combined costs of production, setup, inventory, maintenance, and CE. The study focuses on the multi-item capacitated lot-sizing problem, incorporating carbon emission considerations to ensure compliance with environmental regulations and promote sustainable manufacturing practices. We develop a mixed-integer non-linear program to model the problem and subsequently perform linearization techniques to enable its efficient solution. The linearized model is solved using the CPLEX solver, demonstrating a significant reduction in carbon emissions when faced with high carbon taxes. The results highlight the importance of integrating maintenance and production planning with carbon emission constraints, emphasizing the potential for achieving both economic and environmental benefits in manufacturing operations. The paper contributes to the ongoing efforts to develop sustainable and responsible strategies for production and maintenance planning in the context of increasingly stringent carbon emission regulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Carbon capture and utilization

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Near-Linear Time Approximation Algorithms for k-means with Outliers 41

Near-Linear Time Approximation Algorithms for k-means with O...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Junyu Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha410083 China

The k-means with outliers problem is one of the most extensively studied clustering problems in the field of machine learning, where the goal is to discard up to z outliers and identify a minimum k-means clustering on the remaining data points. Most previous results for this problem have running time dependent on the aspect ratio ∆ (the ratio between the maximum and the minimum pairwise distances) to achieve fast approximations. To address the issue of aspect ratio dependency on the running time, we propose sampling-based algorithms with almost linear running time in the data size, where a crucial component of our approach is an algorithm called Fast-Sampling. Fast-Sampling algorithm can find inliers that well approximate the optimal clustering centers without relying on a guess for the optimal clustering costs, where a 4-approximate solution can be obtained in time O(ndk log log n/∊2) with O(k/∊) centers opened and (1 + ∊)z outliers discarded. To reduce the number of centers opened, we propose a center reduction algorithm, where an O(1/∊)-approximate solution can be obtained in time O(ndk log log n/∊2 + dpoly(k, 1/∊) log(n∆)) with (1 + ∊)z outliers discarded and exactly k centers opened. Empirical experiments suggest that our proposed sampling-based algorithms outperform state-of-the-art algorithms for the k-means with outliers problem. Copyright 2024 by the author(s)

关键词： K-means clustering

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Network link weight optimization based on antisymmetric deep graph networks and reinforcement learning 6

Network link weight optimization based on antisymmetric deep...

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6th International Conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Chen, Yuhan Guo, Yingya Department of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

Route optimization is a key core technology to optimize network traffic distribution, achieve network load balancing, and improve network performance. Traditional distributed networks widely run shortest-path based routing protocols, and the path of traffic is determined by the link weights of the distributed network, so routing optimization methods are usually optimized around the link weights of the network. Heuristics-based link weight optimization methods are widely used. However, heuristic methods rely on manually set rules, which are poorly generalized and cannot adapt well to dynamically changing traffic demands. Compared to heuristics, deep reinforcement learning (DRL) has the advantage of extracting more accurate feature representations in addition to its ability to handle high-dimensional state and action spaces. We propose a network link weight optimization method based on anti-symmetric deep graph networks (A-DGN) and reinforcement learning using a novel GNN framework anti-symmetric deep graph networks, where link weights are adjusted to reduce the network link utilization with the optimization objective of minimizing the maximum link utilization in the network. Experimental results show that the proposed method achieves significant performance improvements in the link weight optimization problem in four real-world network topology scenarios. © 2024 IEEE.

关键词： Deep reinforcement learning

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