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Object search by a concept-conditioned object detector

Neural Computing and Applications 2024年第26期36卷 16001-16021页

作者： Rigoni, Davide Serafini, Luciano Sperduti, Alessandro Department of Mathematics "Tullio Levi-Civita" University of Padova Via Trieste 63 PD Padua35131 Italy Human Inspired Technology Research Centre University of Padova Via Luzzatti 4 PD Padua35121 Italy Augmented Intelligence Center Bruno Kessler Foundation Via Sommarive 18 TN Povo38123 Italy Department of Pharmaceutical and Pharmacological Sciences University of Padova Via Marzolo 5 PD Padua35131 Italy Department of Information Engineering and Computer Science University of Trento Via Sommarive 9 TN Povo38123 Italy

Object detectors are used for searching all objects belonging to a pre-defined set of categories contained in a given picture. However, users are often not interested in finding all objects, but only those that pertain to a small set of categories or concepts. Nowadays, the standard approach to solve this task involves initially employing an object detector to identify all objects within the image, followed by refining the outcomes to retain only the ones of interest. Nevertheless, the object detector does not take advantage of the user’s prior intent that, when used, can potentially improve the detection performance of the model. This work presents a method to condition an existing object detector with the user’s intent, encoded as one or more concepts from the WordNet graph, to find just those objects of interest. The proposed approach takes advantage of existing datasets for object detection without the need for new annotations, and it allows to adapt the already existing object detector models with minor changes. The evaluation, performed on the COCO and the Visual Genome datasets considering several object detector architectures, shows that conditioning the search on concepts is actually beneficial. The code and the pre-trained model weights are released at: https://***/drigoni/Concept-Conditioned-Object-Detector. © The Author(s) 2024.

关键词： Object detection

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Microgrid Optimal Sizing and Scheduling for Feasibility Studies

Microgrid Optimal Sizing and Scheduling for Feasibility Stud...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Simon Abongmbo Fatima Ezzahra El Aidos Driss Benhaddou Lei Fan Carlos Gamarra Tianrun Zhang Jian Shi Harish Krishnamoorthy Electrical and Computer Engineering Department University of Houston Houston TX USA Computer Science Department University of Houston Houston TX USA Engineering Technology Department University of Houston Houston TX USA Clean Energy Technology Houston Advanced Research Center Houston TX USA

Community microgrids play a significant role in the decarbonization of the power grid while providing support for resilience, backup, and stability, due to their high penetration of renewables and Distributed Energy Resources. However, the feasibility analysis of community microgrids is a complex process often requiring multiple building scenarios, complicated technology models, weather forecasting, and a high level of technical expertise. This paper aims to abstract the complexity of the optimal planning process by presenting a comprehensive mathematical formulation and optimization technique used for sizing and scheduling of power generation, CHP, and storage technologies. The problem involves a dual formulation utilizing a metaheuristic Genetic Algorithm for sizing of the technology components under specified constraints while minimizing total investment cost, and a Mixed Integer Linear Programming problem for optimal scheduling of generation and storage technologies, ensuring compliance with system-related constraints. This model has been implemented as part of an online microgrid and district energy planning tool developed in a collaboration between the University of Houston, Fugro and, the Houston advanced research center (HARC) through a grant funded by the US Department of Energy (DOE).

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Synthesized Acoustic Vortex-Frequency Comb via Rotational Doppler Effect

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Physical Review Letters 2025年第13期134卷 137001-137001页

作者： Yurou Jia Xuan Zhang Suying Zhang Guoqiang Xu Taoyu Chen Hongtao Zhou Yechao Bai Ying Cheng Dajian Wu Department of Physics MOE Key Laboratory of Modern Acoustics Collaborative Innovation Center of Advanced Microstructures Jiangsu Physical Science Research Center Nanjing University Nanjing 210093 China Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Mechanics School of Mechanical Engineering Tianjin University Tianjin 300350 China School of Electronic Science and Engineering Nanjing University Nanjing 210023 China Jiangsu Key Lab on Opto-Electronic Technology School of Physics and Technology Nanjing Normal University Nanjing 210023 China

The synergistic control of light’s frequency and orbital angular momentum (OAM) via integrated nonlinear ring microresonators is crucial for creating spatiotemporal optical waveforms and advancing optical metrology. A direct analog of this capability in acoustics is challenging due to the nonlinearity-based mechanisms’ dependence on external driving and limited frequency conversion efficiency. Here, we present an acoustic synthesized comb structure derived from spatiotemporal metasurfaces, concurrently controlling frequency and OAM characteristics with high conversion efficiency and removing the constraints of critical driving frequency and power level in nonlinear mechanisms. By optimizing comb structures, energy distributions among OAM modes are adjusted to different frequency lines. We finally propose spatiotemporal metalens devices based on vortex-frequency combs, enabling synchronously multiple-order spatial differential operations for parallel data processing. This Letter is groundbreaking in integrating spectral- and spatial-domain acoustic waves, promising multifunctional imaging and advantages in spatiotemporal beams.

关键词： Vortex flow

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QAUST:Protein Function Prediction Using Structure Similarity,Protein Interaction,and Functional Motifs

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Genomics, Proteomics & Bioinformatics 2021年第6期19卷 998-1011页

作者： Fatima Zohra Smaili Shuye Tian Ambrish Roy Meshari Alazmi Stefan T.Arold Srayanta Mukherjee P.Scott Hefty Wei Chen Xin Gao Computational Bioscience Research Center(CBRC) ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia Department of Biology Southern University of Science and Technology of China(SUSTC)Shenzhen 518055China Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48109USA College of Computer Science and Engineering University of Ha’ilHa'il 55476Saudi Arabia Biological and Environmental Sciences and Engineering(BESE)Division King Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia Department of Molecular Bioscience University of KansasLawrenceKS 66047USA

The number of available protein sequences in public databases is increasing ***,a sig-nificant percentage of these sequences lack functional annotation,which is essential for the understanding of how bio-logical systems ***,we propose a novel method,Quantitative Annotation of Unknown STructure(QAUST),to infer protein functions,specifically Gene Ontology(GO)terms and Enzyme Commission(EC)*** uses three sources of information:structure information encoded by global and local structure similarity search,biological network information inferred by protein–protein interaction data,and sequence information extracted from functionally discriminative sequence *** three pieces of information are combined by consensus averaging to make the final *** approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation(CAFA)benchmark *** results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or *** further demonstrate that a previously unknown function of human tripartite motif-containing 22(TRIM22)protein predicted by QAUST can be experimentally validated.

关键词： Protein function prediction GO term EC number Protein structure similarity Functionally discriminative motif

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Brain Region Knowledge Based Dual-Stream Transformer for EEG Emotion Recognition

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IEEE Transactions on Consumer Electronics 2025年

作者： Lin, Yichuan Xu, Geng-Xin Liang, Haotian Wang, Yishan Wan, Feng Li, Ye Southern University of Science and Technology Shenzhen518055 China Research Center for Biomedical Information Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Hangzhou Institute of Advanced Technology Chinese Academy of Sciences China Department of Electrical and Computer Engineering The Faculty of Science and Technology The Centre for Cognitive and Brain Sciences The Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau China

In consumer electronics, affective computing allows devices to recognize and respond to the emotional states of individuals, thereby enhancing personalization and satisfaction. Electroencephalogram (EEG)-based emotion recognition methods have garnered attention for their capability to provide real-time, objective insights into emotional and cognitive states. Existing research mainly focuses on extracting temporal and spectral features from EEG signals for emotion recognition. However, electrode shifts may occur across different experiments during data acquisition, resulting in models with limited robustness and constrained classification performance. To address this challenge, this paper introduces a Brain Region Knowledge based Dual-Stream network (BRKDSnet) for emotion recognition. It employs a dual-stream architecture to effectively fuse the temporal, spectral, and spatial features of EEG signals. By incorporating prior brain region partition knowledge, it efficiently aggregates channel features into distinct brain region features. Such a brain region based partition mitigates noise issues caused by electrode shifts during user wearing in consumer electronic applications, and leading to learn more robust feature representations. The experiments conducted on the widely used emotion recognition datasets SEED and SEED-IV shows that BRKDSnet achieves state-of-the-art results, with accuracies of 97.81% (improved by 0.64%-3.57% compared to existing works) and 90.76% (improved by 3.13%-11.39%) respectively. Finally, through analysis of the visualization results, it is found that EEG channels located in the temporal lobe contributed the most to emotion recognition classification, which also provides the potential for future applications in wearable devices. © 2025 IEEE.

关键词： Electroencephalography

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A Novel advanced Algorithm in Automation of Food and Health Monitoring System using IOT 8

A Novel Advanced Algorithm in Automation of Food and Health ...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Sampath Kumar, Pattem. Vijayasree, J. Saikumar, K. Ayad Alkhafaji, Mohammed Obaid, Ahmed J. Ali Zearah, Sajad Malla Reddy Institute of Engineering & Technology Hyderabad India Department of Electronic and Communication India Koneru Lakshmaiah Education Foundation Hyderabad Dept of ECE R.V.S Nagar Moinabad-Chilkur Rd near AP Police Academy Telangana Aziznagar India National University of Science and Technology College of Engineering Technology Department of Medical Device Industry Engineering Dhi Qar Iraq University of Kufa Faculty of Computer Science and Mathematics Iraq Al-ayen University Scientific Research Center Thi-Qar Iraq

ISBN: (纸本)9798350334012

Food is one of the essential factors in human growth and development. The recent results of the internet of things and robots have already shown the beginnings of a new food monitorization. Automation in the restaurants and food sector did not originate technically, so humanoid-related IoT platforms are necessary. In this research, an advanced monitoring algorithm for the automation of food & health system performs using the (IoT). The suggested system develop using food quality sensing devices. Each detector was equipped with the above qualities and connected using breadboards and relay boards. There is tremendous fear and motivation in the thought that there may not be enough food to go around as the human population expands exponentially. To construct a monitoring system that can identify the presence of people, gases, and temperature differences, we employed website creation in combination with the (IoT). The designed application has attained a sensitivity of 97.53%, an accuracy of 98.83%, a recall of 97.98%, and a detection rate of 98.91%. The implemented technology is beneficial to researchers and society. The proposed IoT-based automation of food & health monitoring system compete with present technology and outperforms the methodology. © 2023 IEEE.

关键词： Internet of things

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Electron microscopy observations of the diversity of Ryugu organic matter and its relationship to minerals at the micro- to nano-scale

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Meteoritics and Planetary science 2024年第8期59卷 2023-2043页

作者： Stroud, Rhonda M. Barosch, Jens Bonal, Lydie Burgess, Katherine Cody, George D. De Gregorio, Bradley T. Daly, Luke Dartois, Emmanuel Dobrică, Elena Duprat, Jean Engrand, Cecile Harries, Dennis Hashiguchi, Minako Ishii, Hope Kebukawa, Yoko Kilcoyne, A. David Langenhorst, Falko Lee, Martin R. Nittler, Larry R. Quirico, Eric Okumura, Taiga Remusat, Laurent Sandford, Scott Yabuta, Hikaru Abe, Masanao Abreu, Neyda M. Bagot, Paul A. J. Beck, Pierre Bejach, Laure Bland, Phil A. Bridges, John C. Cymes, Brittany A. Dazzi, Alexandre de la Peña, Francisco Deniset-Besseau, Ariane Enju, Satomi Enokido, Yuma Frank, David R. Gray, Jennifer Haruta, Mitsutaka Hata, Satoshi Hicks, Leon Igami, Yohei Jacob, Damien Kamide, Kanami Komatsu, Mutsumi Laforet, Sylvain Leroux, Hugues Le Guillou, Corentin Martins, Zita Marinova, Maya Martinez, James Mathurin, Jérémie Matsumoto, Megumi Matsumoto, Toru Matsuno, Junya McFadzean, Samuel Michikami, Tatsuhiro Mitsukawa, Itaru Miyake, Akira Miyahara, Masaaki Miyazaki, Akiko Montagnac, Gilles Mostefaoui, Smail Nakamura, Tomoki Nakato, Aiko Naraoka, Hiroshi Nakauchi, Yusuke Nakazawa, Satoru Nishimura, Masahiro Noguchi, Takaaki Ohtaki, Kenta Ohigashi, Takuji Okada, Tatsuaki Okumura, Shota Okazaki, Ryuji Phan, Thi H. V. Rebois, Rolando Sakamoto, Kanako Saiki, Takanao Saito, Hikaru Seto, Yusuke Shigenaka, Miho Smith, William Suga, Hiroki Sun, Mingqi Tachibana, Shogo Takahashi, Yoshio Takeichi, Yasuo Takeuchi, Akihisa Takigawa, Aki Tamenori, Yusuke Tanaka, Satoshi Terui, Fuyuto Thompson, Michelle S. Tomioka, Naotaka Tsuchiyama, Akira Tsuda, Yuichi Uesugi, Kentaro Uesugi, Masayuki Usui, Tomohiro Verdier-Paoletti, Maximilien Wakabayashi, Daisuke Watanabe, Sei-ichiro Yada, Toru Yamashita, Shohei Yasutake, Masahiro Yogata, Kasumi Yoshikawa, Makoto Yurimoto, Hisayoshi Zanetta, Pierre-M. Zega, Thomas Zolensky, Michael E. School of Earth and Space Exploration Arizona State University Materials Science and Technology Division US Naval Research Laboratory Earth and Planets Laboratory Carnegie Institution for Science Institut de Planétologie et d'Astrophysique Université Grenoble Alpes School of Geographical and Earth Sciences University of Glasgow Australian Centre for Microscopy and Microanalysis University of Sydney Department of Materials University of Oxford Institut des Sciences Moléculaires d'Orsay Université Paris-Saclay Centre National de la Recherche Scientifique Hawai‘i Institute of Geophysics and Planetology the University of Hawai‘i at Mānoa Institut de Mineralogie Physique des Materiaux et Cosmochimie Museum National d'Histoire Naturelle Centre National de la Recherche Scientifique UMR 7590 Sorbonne Université Laboratoire de Physique des 2 Infinis Irène Joliot-Curie Centre National de la Recherche Scientifique Université Paris-Saclay Luxembourg Institute of Science and Technology European Space Resources Innovation Centre Graduate School of Environmental Studies Nagoya University Department of Chemistry and Life Science Yokohama National University Institute of Geoscience Friedrich Schiller University Jena Department of Earth and Planetary Science The University of Tokyo NASA Ames Research Center Department of Earth and Planetary Systems Science Hiroshima University Institute of Space and Astronautical Science Japan Aerospace Exploration Agency The Graduate University for Advanced Studies SOKENDAI Langley Research Center NASA School of Earth and Planetary Sciences Curtin University School of Physics and Astronomy University of Leicester Institut Chimie Physique Centre National de la Recherche Scientifique Université Paris-Saclay Université de Lille CNRS INRAE Centrale Lille Université Artois FR 2638-IMEC-Institut Michel-Eugène Chevreul Department of Mathematics Physics and Earth Science Ehime University Department of Earth Science Tohoku University Materials Characterization Lab Th

Transmission electron microscopy analyses of Hayabusa2 samples show that Ryugu organic matter exhibits a range of morphologies, elemental compositions, and carbon functional chemistries consistent with those of carbonaceous chondrites that have experienced low-temperature aqueous alteration. Both nanoglobules and diffuse organic matter are abundant. Non-globular organic particles are also present, and including some that contain nanodiamond clusters. Diffuse organic matter is finely distributed in and around phyllosilicates, forms coatings on other minerals, and is also preserved in vesicles in secondary minerals such as carbonate and pyrrhotite. The average elemental compositions determined by energy-dispersive spectroscopy of extracted, demineralized insoluble organic matter samples A0107 and C0106 are C₁₀₀N₃O₉S₁ and C₁₀₀N₃O₇S₁, respectively, with the difference in O/C slightly outside the difference in the standard error of the mean. The functional chemistry of the nanoglobules varies from mostly aromatic C=C to mixtures of aromatic C=C, ketone C=O, aliphatic (CH_n), and carboxyl (COOH) groups. Diffuse organic matter associated with phyllosilicates has variable aromatic C, ketone and carboxyl groups, and some localized aliphatics, but is dominated by molecular carbonate (CO₃) absorption, comparable to prior observations of clay-bound organic matter in CI meteorites. © 2024 The Authors. Meteoritics & Planetary science published by Wiley Periodicals LLC on behalf of The Meteoritical Society.

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Nonlinear topology network routing shortest path based on improved particle swarm optimization algorithm

Nonlinear topology network routing shortest path based on im...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Hui Zheng Jin Jin Dongshuai Jin Yifan Wang Guoquan Zhang Shenguo Zhang College of Electrical Engineering Gansu Engineering Research Center for Eco-Environmental Intelligent Networking Northwest Minzu University Lanzhou China College of Mathematics and Computer Science Northwest Minzu University Lanzhou China

In the nonlinear topology network routing, through the research on the routing algorithm that guarantees QoS in the network, the different performance characteristics of the network are fully considered, and the network bandwidth, network delay, packet loss rate, demonstration jitter and other indicators are comprehensively considered constraints, citing the respective advantages of particle swarm optimization and simulated annealing algorithms in modern meta-heuristic algorithms, an improved particle swarm optimization algorithm SAPSO is applied to a multi-constrained nonlinear topology network routing model. The algorithm can be successfully applied to this problem through simulation verification. The introduction of simulated annealing algorithm can well prevent falling into local optimal solution. Using the SAPSO algorithm can reduce the comprehensive cost of the network in a multi-constrained nonlinear topology network.

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SRAD-CLF: Squeak and Rattle Anomaly Detection via Contrastive Learning Framework on Real Industrial Noise Recordings

SRAD-CLF: Squeak and Rattle Anomaly Detection via Contrastiv...

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European Signal Processing Conference (EUSIPCO)

作者： Zhaoyi Liu Álvaro López-Chilet Dading Chong Sam Michiels Jon Ander Gómez Friedrich Wolf-Monheim David Newton Danny Hughes imec-Distrinet Computer Science KU Leuven Leuven Belgium Ford Research & Advanced Engineering Aachen Germany PRHLT Research Center Universitat Politécnica de Valéncia Valencia Spain School of Electronic and Computer Engineering Peking University China Ford Office of the CIO - Security & Strategy Cologne Germany

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Vehicle manufacturing monitoring places significant emphasis on special noises from vehicles, encompassing Squeaking and Rattling (SR) noises as a pivotal aspect. Existing SR Anomaly Detection (SR-AD) studies still suffer from several challenges under actual industrial scenarios: lengthy and diverse real-recordings, costly manual labelling, and subtle distinction in road track noises. For the first time, we proposed SRAD-CLF, a novel framework that leverages contrastive learning on Real Industrial Noise Recordings (RINR) to tackle these challenges. Specifically, we initiate the process by segmenting a complete audio sample into distinct road-surface-type audio clips, subsequently assigning a surface label to each clip. Then, a contrastive learning-based framework employing encoder perturbations is implemented to enhance representation, effectively mitigating risks associated with data augmentation, like context loss and the incorporation of irrelevant augmentation details into the features. To enhance the efficacy of SRAD-CLF, we integrate a classifier based on road surface-type information within a self-supervised learning framework, fortifying the performance of the SR-AD task. Our proposed SRAD-CLF achieves promising results for SR-AD on real-world industrial datasets collected from the Ford Manutacturing Plant in Valencia (Spain).

关键词： Roads Perturbation methods Noise Contrastive learning Manuals Recording Manufacturing Anomaly detection Surface treatment Monitoring

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Unsupervised Representation Learning with Hebbian Synaptic and Structural Plasticity in Brain-Like Feedforward Neural Networks

SSRN

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SSRN 2023年

作者： Ravichandran, Naresh Lansner, Anders Herman, Pawel Division of Computational Science and Technology School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Swedish e-Science Research Centre Stockholm Sweden Department of Mathematics Stockholm University Stockholm Sweden Digital Futures KTH Royal Institute of Technology Stockholm Sweden

Neural networks that can capture key principles underlying brain computation offer exciting new opportunities for developing artificial intelligence and brain-like computing algorithms. Such networks remain biologically plausible while leveraging localized forms of synaptic learning rules and modular network architecture found in the neocortex. Compared to backprop-driven deep learning approches, they provide more suitable models for deploying on neuromorphic hardware and have greater potential for scalability on large-scale computing clusters. The development of such brain-like neural networks depends on having a learning procedure that can build effective internal representations from data. In this work, we introduce and evaluate a brain-like neural network model capable of unsupervised representation learning. It builds on the Bayesian Confidence Propagation Neural Network (BCPNN), which has earlier been implemented as abstract as well as biophyscially detailed recurrent attractor neural networks explaining various cortical associative memory phenomena. Here we developed a feedforward BCPNN model to perform representation learning by incorporating a range of brain-like attributes derived from neocortical circuits such as cortical columns, divisive normalization, Hebbian synaptic plasticity, structural plasticity, sparse activity, and sparse patchy connectivity. The model was tested on a diverse set of popular machine learning benchmarks: grayscale images (MNIST, Fashion-MNIST), RGB natural images (SVHN, CIFAR-10), QSAR (MUV, HIV), and malware detection (EMBER). The performance of the model when using a linear classifier to predict the class labels fared competitively with conventional multi-layer perceptrons and other state-of-the-art brain-like neural networks. © 2023, The Authors. All rights reserved.

关键词： Brain

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