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International Joint Conference on Neural Networks (IJCNN)

作者： G.G. Lendaris NW Computational Intelligence Laboratory (NWCIL) & Systems Science Ph.D. Program Portland State University Portland OR USA

In AI systems so far developed, more knowledge (typically stored as "rules") entails slower processing; in the case of humans, the more knowledge attained (in the form of experience), the speed/efficiency of performing new related tasks is improved. Experience-based (EB) identification and control is explored with the objective of achieving more human-like processes for 'intelligent' computing agents. The notion of experience is being successfully addressed via a novel concept for applying reinforcement learning (RL), called HLLA -higher level learning algorithm. The key idea is to re-purpose the RL method (to a "higher level") such that instead of creating an optimal controller for a given task, an already achieved collection of such solutions for a variety of related contexts is provided (as an experience repository), and HLLA creates a strategy for optimally selecting a solution from the repository. The selection process is triggered by the agent becoming aware that a change in context has occurred, followed by the agent seeking information about what changed -a process here called context discernment - and finally, by selection. Typically, context discernment entails a form of system identification (SId); substantial enhancement of SId is also achieved via the EB methods. Examples are given.

关键词： Context awareness Humans Control systems Learning Optimal control System identification Artificial intelligence Context modeling Computational intelligence Laboratories

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Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

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Nature Communications 2025年第1期16卷 1-21页

作者： Hefei Zhang Xuhang Li Shivani Nanda Albertha J. M. Walhout dongyuan Song Jingyi Jessica Li Onur Yukselen Alper Kucukural Manuel Garber Department of Systems Biology University of Massachusetts Chan Medical School Worcester MA USA Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles CA USA Department of Statistics and Data Science Department of Biostatistics Department of Computational Medicine and Department of Human Genetics University of California Los Angeles CA USA Via Scientific Inc. Cambridge MA USA Department of Genomics and Computational Biology University of Massachusetts Chan Medical School Worcester MA USA

Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, however, this is challenging, especially in whole animals. Here, we present ‘Worm Perturb-Seq (WPS)’, a method that provides high-resolution RNA-sequencing profiles for hundreds of replicate perturbations at a time in living animals. WPS introduces multiple experimental advances combining strengths of Caenhorhabditis elegans genetics and multiplexed RNA-sequencing with a novel analytical framework, EmpirdE. EmpirdE leverages the unique power of large transcriptomic datasets and improves statistical rigor by using gene-specific empirical null distributions to identify dEGs. We apply WPS to 103 nuclear hormone receptors (NHRs) and find a striking ‘pairwise modularity’ in which pairs of NHRs regulate shared target genes. We envision the advances of WPS to be useful not only for C. elegans, but broadly for other models, including human cells.

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Application considerations for the dHP methodology

Application considerations for the DHP methodology

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International Joint Conference on Neural Networks (IJCNN)

作者： G.C. Lendaris T. Shannon Systems Science & Electrical Engineering Portland State University Portland OR USA Graduate Student Systems Science Ph. D Program Portland State University Portland OR USA

Six questions are posed that are likely to be of interest to one considering applying the dual heuristic programming (dHP) methodology. Tentative answers to these questions are provided in the last section of the paper. As a basis for these, the paper first describes observations made from a series of explorations into various aspects of using the dHP method to design the controller for the pole-cart benchmark problem. Parameters of the explorations included training gains, plant parameter variations, fidelity of plant model, controller sampling rates, network architectures, training strategies, and generalization tests.

关键词： Neural networks Sampling methods Equations design optimization design methodology Benchmark testing Mathematical programming Process design Analytical models Signal generators

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MontageNet: Annotated dataset of Furniture Components in Real-World Images 5

MontageNet: Annotated Dataset of Furniture Components in Rea...

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5th ACM International Conference on Multimedia in Asia, MMAsia 2023

作者： Fang, Iuan Kai Zhang, Bo Hao Liu, Te Lun Tan, Hao Chen, Wei Syun Lee, Che Rung International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu City Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan Department of Computer Science National Tsing Hua University Hsinchu City Taiwan

ISBN: (纸本)9798400702051

Indoor understanding is currently a topic that is widely studied in the field of machine learning. Furniture is the most common object in indoor scenes, just as various vehicles are most commonly seen in street scenes. Any object is made up of a combination of functional components. Functional component dismantling and reassembly is an important development for industrial manufacturing to improve efficiency and reduce costs. In the context of understanding indoor scenes, we focus on building a dataset that uses real-world furniture images as part labels. Building a large-scale furniture dataset is very challenging, first of all, the existing dataset has too few real images of furniture, mostly 3d model images, but the diversity of furniture in the real world far exceeds that of 3d models, and real images help improve the calculation speed of model training. Most of the published furniture data is poorly aligned with the annotation data, and even fewer materials perform component segmentation labeling using real furniture images. MontageNet has become a rich resource for part-level 3d shape analysis, semantic understanding, instance segmentation and 3d reconstruction, and other research. Our accompanying empirical studies provide an in-depth analysis of dataset characteristics and performance evaluation of several state-of-the-art methods against our benchmarks. © 2023 Copyright held by the owner/author(s).

关键词： Semantics

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Adaptive critic design for intelligent steering and speed control of a 2-axle vehicle

Adaptive critic design for intelligent steering and speed co...

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International Joint Conference on Neural Networks (IJCNN)

作者： G.G. Lendaris L. Schultz T. Shannon NW Computational Intelligence Laboratory Systems Science Ph.D. Program and ECE Department Portland State University Portland OR USA

Selected adaptive critic (AC) methods are known to be capable of designing (approximately) optimal control policies for nonlinear plants. The present research focuses on an AC method known as dual heuristic programming (dHP). In particular it is seen as useful to explore correspondences between the form of a utility function and the resulting controllers designed by the dHP method. Based on the task of designing a steering controller for a 2-axle, terrestrial, autonomous vehicle, the present paper relates a pair of critics to "divide the labor" of training the controller. Improvements in convergence of the training process is realized in this way. The controllers designed by the dHP method are reasonably robust, and demonstrate good performance on disturbances not even trained on encountering a patch of ice during a steering maneuver, and encountering a wind gust perpendicular to direction of travel.

关键词： programmable control Adaptive control Intelligent vehicles Velocity control Roads Computational intelligence Optimal control Ice design methodology Equations

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Automated Classification of Medical Images Using Convolutional Neural Networks 15

Automated Classification of Medical Images Using Convolution...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pillai, N. Muthuvairavan Manimala, S. Rongali, Aruna Sri Gugnani, Anchit Kumar, Senthil A. Sriramakrishnan, G.V. Computer Science and Business Systems R.M.D Engineering College Thiruvallur district Kavarapettai India Computer science and engineering University SJCE JSS Science and technology University Karnataka Mysore India Sr. Software Engineer Department Information Technology Emphasis/Information Technology Ph.D University The country University of The Cumberlands United States Physiotherapy Amity University Rajasthan Jaipur India Artificial Intelligent department Shri Vishnu Engineering College for Women AP Bhimavaram India Tamilnadu Chennai India

ISBN: (纸本)9798350370249

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, Automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. These convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort. ©2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Financial Performance Classification Using an Improved Id3 Algorithm with decision Coherence

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第21期48卷 27-36页

作者： Pei, Wen Pan, Wen-An Huang, Jui-Chan College of Management Chung Hua University Hsinchu30012 Taiwan Ph.D Program of Management Chung Hua University Hsinchu30012 Taiwan Department of Industrial Engineering and Management National Kaohsiung University of Science and Technology Kaohsiung807618 Taiwan

The evaluation of enterprise financial performance is of great significance to investors, creditors and managers. Through the performance evaluation, we can judge the comprehensive strength of the enterprise and then take corresponding countermeasures. The study mainly quantifies the three financial performance factors of profitability, operating ability and solvency in segments. At the same time, variables are constructed for the statement subjects other than the performance factors. In addition, an iterative binary tree three-generation algorithm model based on decision coordination degree is introduced to classify and measure the financial performance of enterprises. The study used 528 financial statements from a manufacturing industry and employed a mean imputation strategy and Z-score standardization method for preprocessing. Ultimately, 19 variables were selected for financial performance evaluation. Using the model's construction time, classification accuracy, and area under the curve as evaluation indicators, verify the performance advantages of the proposed model in financial performance evaluation. The results show that when the sample size is 300, the construction time of the improved model is only 120ms, and out of 50 financial statements, only 7 financial statements with "poor" profitability are misclassified as "excellent". The average classification accuracy of the improved model is 87.57% and the average area under the curve is 0.8369. In the solvency test, the average classification accuracy of the improved model in the solvency indicator is 84.21%. In the test of operating capacity indicators, the mean value of classification accuracy of the improved model proposed by the Institute in operating capacity indicators is as high as 85.16%. The results show that the classification model proposed by the Institute has the least construction time and the highest classification accuracy. It provides reliable technical support for the rational allocation of

关键词： Profitability

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Innovation and Marketing Performance of Womenpreneur in Fashion Industry in Indonesia

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Procedia - Social and Behavioral sciences 2016年 219卷 299-306页

作者： Heny Hendrayati Vanessa Gaffar Ph.D Student of Management Science Study Universitas Pendidikan Indonesia Jl. Dr. Setiabudhi No. 229 Bandung Indonesia Management Study Program UniversitasPendidikan Indonesia Jl. Dr. Setiabudhi No. 229 Bandung Indonesia

Creative industry in Indonesia plays an important role since it gives a great economic contribution to Indonesia's GdP. The existing condition shows the increase of competition in the industry and thus resulting in the decreasing of sales. It becomes a problem since it affects the industry's marketing performance. This research is focusing on women entrepreneur (womenpreneur) who are currently involved in fashion industry. This research will explore marketing performance in fashion industry, as well as analyze the influence of innovation to marketing performance of womanpreneur in fashion industry. Most of the womenpreneur didn’t understand the importance of having an original product that has uniqueness. In terms of marketing performance, sales as well customer growth is going down. There is a positive influence between innovation a marketing performance of womenpreneur in fashion industry.

关键词： Innovation Marketing Performance Womenpreneur Fashion Creative industry

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An integrative systems-biology approach defines mechanisms of Alzheimer’s disease neurodegeneration

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Nature Communications 2025年第1期16卷 1-21页

作者： Leventhal, Matthew J. Zanella, Camila A. Kang, Byunguk Peng, Jiajie Gritsch, david Liao, Zhixiang Bukhari, Hassan Wang, Tao Pao, Ping-Chieh danquah, Serwah Benetatos, Joseph Nehme, Ralda Farhi, Samouil Tsai, Li-Huei dong, Xianjun Scherzer, Clemens R. Feany, Mel B. Fraenkel, Ernest MIT Ph.D. Program in Computational and Systems Biology Cambridge MA United States Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA United States Broad Institute of Harvard and MIT Cambridge MA United States Department of Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA United States Spatial Technology Platform Broad Institute of Harvard and MIT Cambridge MA United States Precision Neurology Program Brigham and Women’s Hospital and Harvard Medical school Boston MA United States APDA Center for Advanced Parkinson’s Disease Research Brigham and Women’s Hospital and Harvard Medical School Boston MA United States Picower Institute for Learning and Memory Massachusetts Institute of Technology Cambridge MA United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA United States Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA United States School of Computer Science Northwestern Polytechnical University Xi’an China Stephen and Denise Adams Center of Yale School of Medicine New Haven CT United States

despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimer’s disease, remain incompletely understood. To define relevant pathways, we conducted an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in drosophila. We also measured proteomics, phosphoproteomics, and metabolomics in drosophila models of Alzheimer’s disease and identified Alzheimer’s genetic variants that modify gene expression in disease-vulnerable neurons in humans. We then used a network model to integrate these data with previously published Alzheimer’s disease proteomics, lipidomics and genomics. Here, we computationally predict and experimentally confirm how HNRNPA2B1 and MEPCE enhance toxicity of the tau protein, a pathological feature of Alzheimer’s disease. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate dNA damage in drosophila and human stem cell-derived neural progenitor cells. Our study identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimer’s disease. © The Author(s) 2025.

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Extraction of c- F os Protein Regions from Mouse Brain Slice Images using Multi-channelization, 1 x 1 Convolution and U -Net 13

Extraction of c- F os Protein Regions from Mouse Brain Slice...

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Wang, Ziyue Okamura, Hibiki Luo, Shijie Hayashi, Yu Takizawa, Hotaka Master's Program in Computer Science University of Tsukuba Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Ph.D. Program in Humanics Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering Academic Computing and Communications Center University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798350373332

This study proposed a novel extraction method of c-Fos protein regions in dAB(3,3'-diaminobenzidine)-stained mouse brain slice images using the U-Net model combined with the multi-channelization and 1×1 convolution techniques. Our U-Net m odel, called 1× 1 conv U-Net, was applied to three dAB- strainedmouse slice images whose resolutions were 1000x1000 pixels. The experimental results demonstrated that the 1×1 conv U-Net outperformed the conventional U-Net. © 2024 IEEE.

关键词： Mammals

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