检索结果-内蒙古大学图书馆

COULD DROPPING A FEW CELLS CHANGE THE TAKEAWAYS FROM DIFFERENTIAL EXPRESSION?

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Shiffman, Miriam Giordano, Ryan Broderick, Tamara MIT Computational & Systems Biology Program United States MIT Dept. of Electrical Engineering & Computer Science United States UC Berkeley Dept. of Statistics United States

Analysis of differential gene expression plays a fundamental role in biology toward illuminating the molecular mechanisms driving a difference between groups (e.g., due to treatment or disease). While any analysis is run on particular cells or samples, the intent is to generalize to future occurrences of the treatment or disease. Implicitly, generalization is justified under the assumption that present and future samples are independent and identically distributed from the same population. Though this assumption is always false, we might hope that any deviation from the assumption is small enough that A) fundamental conclusions of the analysis still hold, and B) standard tools like standard error, significance, and power still reflect generalizability. Conversely, we might worry about these deviations, and reliance on standard statistical tools, if conclusions could be substantively changed by dropping a very small fraction of observations. While checking every small fraction is computationally intractable, recent work develops an approximation to identify when such an influential subset exists. Building on this work, we develop a metric for dropping-data robustness of differential expression;namely, we cast the analysis in a differentiable form suitable to the approximation, extend the approximation to models whose hyperparameters depend on the full dataset, and extend the notion of a data point from a single cell to a pseudobulk observation. We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets. We use our tool to assess the robustness of differential expression for published single-cell RNA-seq data, and discover that thousands of genes can have their results flipped by dropping Copyright © 2023, The Authors. All rights reserved.

关键词： Diseases

IoT–based tire pressure monitoring system for air and temperature pressure using MPX5500D and LM35 sensor

学校读者我要写书评

暂无评论

IoT–based tire pressure monitoring system for air and tempe...

International Conference on Computing, engineering, and Design (ICCED)

作者： Agung Pangestu Iing Sodikin Muhammad Yusro Rum Sapundani Rosyid Ridlo Al Hakim Sinka Wilyanti Electrical Engineering Department Faculty of Engineering and Computer Science Jakarta Global University Depok Indonesia Department of Electronic Engineering Faculty of Engineering State University of Jakarta Jakarta Indonesia Electromedical Engineering Technology Faculty of Health Science University of Muhammadiyah Purwokerto Purwokerto Indonesia Graduate Program in Electrical Engineering Faculty of Engineering and Computer Science Jakarta Global University Depok Indonesia

ISBN: (纸本)9781665453905

Research has been carried out to monitor vehicle tires before they are used and can reduce damage, including overcoming vehicle fuel waste because air pressure is continuously monitored. This research aims to utilize the MPX5500DP sensor as an air pressure device, the LM35 sensor as a temperature reader, and a buzzer based on IoT to build a tire pressure monitoring system (TPMS). The MPX5500DP and LM35 sensor inputs to the Arduino Uno microcontroller are distributed by the NodeMCU, fitted with a Wi-Fi module. The Blynk application sends and displays the data on a smartphone using the IoT-based. Based on this research, data on the percentage of errors in monitoring air pressure and tire temperature on vehicles were obtained by comparing the data to the pressure gauge and thermometer: 1—the results of the average reading of the sensor error value. MPX5500DP air pressure against pressure gauge is 5.3%. 2—the average reading of the LM35 sensor error value on the temperature thermometer is 6.8%. With this research, the air pressure and temperature in the tires can be monitored in real-time via a smartphone using the IoT-based.

关键词： Temperature measurement Temperature distribution Thermometers Microcontrollers Tire pressure Real-time systems Pressure gauges

An Integrated Fire Scene Information System Based on YOLOv4-Tiny and DeepSort Technologies

学校读者我要写书评

暂无评论

An Integrated Fire Scene Information System Based on YOLOv4-...

IEEE International Workshop on Electromagnetics, Applications and Student Innovation (iWEM)

作者： Chien Ho Chen Yu Cheng Wang Shao An Yu Department of Leisure Industry and Health Promotion National Taipei University of Nursing and Health Sciences Taipei City Taiwan Department of Computer Information and Network Engineering & Master Program Lunghwa University of Science and Technology Taoyuan City Taiwan

ISBN: (数字)9798350366976

ISBN: (纸本)9798350366983

This research introduces a pioneering framework for flame, smoke detection, and personnel monitoring, developed to address the increasing frequency of fire-related incidents in Taiwan. These incidents have caused significant loss of life and property damage. To tackle these issues, we employ YOLOv4-Tiny and DeepSort technologies to detect flames, smoke, and monitor personnel within surveillance footage, providing essential information to users. Unlike previous studies, this system integrates flame and smoke detection with personnel monitoring, enhancing rescue efficiency and ensuring personnel safety. During testing, the system effectively analyzed danger levels, monitored personnel, and provided necessary information. However, smoke can reduce the accuracy of personnel monitoring, so future research will aim to improve this issue and optimize the detection and identification of individuals moving between different footage. In summary, this investigation proposes a system that analyzes danger levels and monitors personnel in fire scenes. A survey of firefighters and security personnel concluded that this system helps improve rescue efficiency and reduce personnel loss due to fire incidents.

关键词： Training Adaptation models Accuracy Target tracking Computational modeling Heuristic algorithms Fires

ConvBLS: An Effective and Efficient Incremental Convolutional Broad Learning System for Image Classification

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Lei, Chunyu Chen, C.L. Philip Guo, Jifeng Zhang, Tong The Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Brain and Affective Cognitive Research Center Pazhou Lab Guangzhou510335 China

Deep learning generally suffers from enormous computational resources and time-consuming training processes. Broad Learning System (BLS) and its convolutional variants have been proposed to mitigate these issues and have achieved superb performance in image classification. However, the existing convolutional-based broad learning system (C-BLS) either lacks an efficient training method and incremental learning capability or suffers from poor performance. To this end, we propose a convolutional broad learning system (ConvBLS) based on the spherical K-means (SKM) algorithm and two-stage multi-scale (TSMS) feature fusion, which consists of the convolutional feature (CF) layer, convolutional enhancement (CE) layer, TSMS feature fusion layer, and output layer. First, unlike the current C-BLS, the simple yet efficient SKM algorithm is utilized to learn the weights of CF layers. Compared with random filters, the SKM algorithm makes the CF layer learn more comprehensive spatial features. Second, similar to the vanilla BLS, CE layers are established to expand the feature space. Third, the TSMS feature fusion layer is proposed to extract more effective multi-scale features through the integration of CF layers and CE layers. Thanks to the above design and the pseudo-inverse calculation of the output layer weights, our proposed ConvBLS method is unprecedentedly efficient and effective. Finally, the corresponding incremental learning algorithms are presented for rapid remodeling if the model deems to expand. Experiments and comparisons demonstrate the superiority of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning systems

Exploring Environmentally Friendly Corrosion Inhibitors: Insights from Natural and Biomass Residue-Derived Compounds

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

作者： Othman, Norinsan Kamil Mohammed, Naba Jasim Yusop, Muhammed Rahimi Al-Gburi, Ahmed Jamal Abdullah Materials Science Program Department of Applied Physics Faculty of Science and Technology University Kebangsaan Malaysia Selangor Bangi43600 Malaysia Faculty of Science and Technology School of Chemical Sciences and Food Technology University Kepangsaan Malaysia Selangor Bangi43600 Malaysia Faculty of Electronics and Computer Engineering Universiti Teknikal Malaysia Melaka Hang Tuah Jaya Melaka Durian Tunggal76100 Malaysia

In this investigation, we explored the corrosive effects of date palm seed extracted from natural sources and biomass residues on mild steel in a solution of 0.5 M hydrochloric acid (HCl), employing a combination of experimental and theoretical approaches. Our objective was to scrutinize the interaction of the inhibitor with the metal surface, analyze experimental data, and elucidate how the structural characteristics of the inhibitors influence the corrosion process. The experimental results indicated that as the immersion time and temperature increased, the inhibitory effectiveness declined. Notably, the highest level of inhibition occurred when the concentration of date palm seed extract (DPSE) reached 1100 ppm, showcasing peak inhibitory effects of 95.8% and 97.8% through potentiodynamic polarization and electrochemical impedance spectroscopy techniques, respectively. Additionally, an analysis using X-ray diffraction (XRD) was conducted to scrutinize the chemical composition of the mild steel's surface. The adsorption data were evaluated using adsorption isotherms, revealing that DPSE adhered to the Langmuir adsorption isotherm on the mild steel surface. The Langmuir model exhibited the highest linear regression coefficient value and a slope close to unity. The Gibbs free energy () values suggested that the protection afforded by DPSE involved physical and spontaneous absorption. Examination of the results pertaining to the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) indicated that the carboxyl (COOH) group and the C=C bond were the principal regions exhibiting elevated electron acceptance activity. These components were found to be crucial in the adsorption process on the surface of iron (Fe). © 2024, The Authors. All rights reserved.

关键词： Electrochemical impedance spectroscopy

KISS-Matcher: Fast and Robust Point Cloud Registration Revisited

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Lim, Hyungtae Kim, Daebeom Shin, Gunhee Shi, Jingnan Vizzo, Ignacio Myung, Hyun Park, Jaesik Carlone, Luca Massachusetts Institute of Technology CambridgeMA02139 United States Daejeon34141 Korea Republic of Dexory United Kingdom Computer Science Engineering and Interdisciplinary Program of AI Seoul National University Seoul08826 Korea Republic of

While global point cloud registration systems have advanced significantly in all aspects, many studies have focused on specific components, such as feature extraction, graph-theoretic pruning, or pose solvers. In this paper, we take a holistic view on the registration problem and develop an open-source and versatile C++ library for point cloud registration, called KISS-Matcher. KISS-Matcher combines a novel feature detector, Faster-PFH, that improves over the classical fast point feature histogram (FPFH). Moreover, it adopts a k-core-based graph-theoretic pruning to reduce the time complexity of rejecting outlier correspondences. Finally, it combines these modules in a complete, user-friendly, and ready-to-use pipeline. As verified by extensive experiments, KISS-Matcher has superior scalability and broad applicability, achieving a substantial speedup compared to state-of-the-art outlier-robust registration pipelines while preserving accuracy. Our code will be available at https://***/MIT-SPARK/KISS-Matcher. © 2024, CC BY-NC-ND.

关键词： C++ (programming language)

Predicting RNA-RBP Interactions by Using a Pseudo-Siamese Network

学校读者我要写书评

暂无评论

Predicting RNA-RBP Interactions by Using a Pseudo-Siamese Ne...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wang, Fu Yuan, Liangliang Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9781665401265

Large scale RNA-protein binding data made computational identification of RNA-protein interactions possible, which provides great convenience for revealing the interplay between non-coding RNAs and RNA-binding proteins (RBPs). Various machine learning methods have been applied to the prediction of RNA-protein interactions (RPIs). However, most of the RPI predictors were designed for linear RNAs, while methods for circular RNAs (circRNAs) are few. Besides, most of the predictors use only RNA sequence or only protein sequences as input. Automatic feature learning from RNA and RBP sequences using deep neural networks has great potential to improve RPI predictors. In this paper, we propose a new method, PSi-bind, to predict the binding relationship between RNAs and RBPs, which is featured by a pseudo-Siamese neural network. We feed both RNA and RBP sequence embedding features to the network and perform an end-to-end learning to discover the latent association between RNAs and RBPs. Especially, we train our model on a large-scale circRNA dataset. The experimental results show that the model achieves very high accuracy on independent test set, and outperforms the state-of-the-art RPI predictors by a large margin. © 2021 IEEE.

关键词： RNA

Multi-Class Gaze Detection in a Dynamic Environment

学校读者我要写书评

暂无评论

Multi-Class Gaze Detection in a Dynamic Environment

IEEE Instrumentation and Measurement Technology Conference

作者： Aidan Lochbihler Bruce Wallace Kathleen Van Benthem Chris Herdman Will Sloan Kirsten Brightman Frank Knoefel Shawn Marshall Rafik Goubran Systems and Computer Engineering Carleton University Ottawa Canada Systems and Computer Engineering Carleton University Bruyère Research Institute SAM3 Innovation Hub Ottawa Canada Department of Cognitive Science Carleton University Ottawa Canada Bruyè re Research Institute Elisabeth-Bruyè re Hospital Systems and Computer Engineering Carleton University Ottawa Canada Bruyère Research Institute Elisabeth-Bruyère Hospital Ottawa Hospital Carleton University

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Developing AI tools to identify areas of interest within a dynamic field of view is essential for objective behavioural evaluation of drivers. Video image classification and specifically image segmentation is a key technology to allow for the possibility of physiological and behavioural measurement of drivers. For example, to understand driver attention, one must measure where a driver is looking when driving and this requires segmentation of their field of view into relevant areas of interest, such as windows, mirrors, and dashboard. The present work addresses the challenge of dynamic field of view classification and shows the impact of transfer learning, a new AI tool, on segmentation accuracy. Results from this study demonstrate that transfer learning improves predictive performance by 0.02 to 0.20 Dice when large training sets were used. The resulting performance was >0.80 Dice for all classification tests of driver attention segmentation. This work showed that transfer learning also supported the use of smaller training sets while still providing adequate performance. This finding is key for applications where labeled training data is limited or costly to create. The present results expand the application space for deep learning-based image segmentation models.

关键词： Training Image segmentation Transfer learning Training data Physiology Object recognition Mirrors

Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Yuhas, Michael Easwaran, Arvind School of Computer Science and Engineering Singapore Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore

In a cyber-physical system such as an autonomous vehicle (AV), machine learning (ML) models can be used to navigate and identify objects that may interfere with the vehicle’s operation. However, ML models are unlikely to make accurate decisions when presented with data outside their training distribution. Out-of-distribution (OOD) detection can act as a safety monitor for ML models by identifying such samples at run time. However, in safety critical systems like AVs, OOD detection needs to satisfy real-time constraints in addition to functional requirements. In this demonstration, we use a mobile robot as a surrogate for an AV and use an OOD detector to identify potentially hazardous samples. The robot navigates a miniature town using image data and a YOLO object detection network. We show that our OOD detector is capable of identifying OOD images in real-time on an embedded platform concurrently performing object detection and lane following. We also show that it can be used to successfully stop the vehicle in the presence of unknown, novel samples. Copyright © 2022, The Authors. All rights reserved.

关键词： Mobile robots