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Generating Lymphoma Variability Through Adversarial Generation Models: A Limited Resource Approach for Synthetic Histopathology Image Generation and Classification

SSRN

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SSRN 2024年

作者： Tiwari, Ashutosh Saudarshan, Harsh Krisnawati, Dyah Ika Khafid, Muhamad Wardani, Erika Martining Mutalik, Chinmaya Chen, Chi-Long Kuo, Tsung-Rong International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Computer Science and Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495509 India Department of Nursing Faculty of Nursing and Midwifery Universitas Nahdlatul Ulama Surabaya East Java Surabaya60237 Indonesia Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Pathology School of Medicine College of Medicine and Taipei Medical University Hospital Taipei Medical University Taipei11031 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Precision Medicine and Translational Cancer Research Center Taipei Medical University Hospital Taipei11031 Taiwan Tulane University United States Graduate Institute of Clinical Medicine Taiwan

Lymphomas, a diverse group of blood cancers originating from the lymphatic system, present significant challenges for diagnosis and treatment due to their morphological and genetic differences. These variability obscures the development of personalized therapies and impacts disease outcomes. The scarcity of high-quality, diverse images of lymphoma cells is further hindered by patient privacy concerns, the rarity of certain types, and logistical challenges of collecting and processing biomedical images. Additionally, the costs and technical difficulties of microscopic imaging limit the scope of research. This paper addresses these challenges by utilizing generative adversarial networks (GANs) to generate synthetic yet realistic images of lymphoma histopathology and classify them in a low resource setting. Focusing specifically on chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL), we employed GANs to enhance the understanding of lymphoma variability and support advancements in digital cancer biology and pathology. This dual approach of image generation and classification aims to improve disease diagnosis and prognosis, ultimately contributing to better patient outcomes. © 2024, The Authors. All rights reserved.

关键词： Lung cancer

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engineering the directionality of hot carrier tunneling in plasmonic tunneling structures

arXiv

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arXiv 2023年

作者： Abbasi, Mahdiyeh Liao, Shusen Zhu, Yunxuan Natelson, Douglas Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Engineering Rice University HoustonTX77005 United States Department of Materials Science and Nanoengineering Rice University HoustonTX77005 United States

Tunneling metal-insulator-metal (MIM) junctions can exhibit an open-circuit photovoltage (OCPV) response under illumination that may be useful for photodetection. One mechanism for photovoltage generation is hot carrier tunneling, in which photoexcited carriers generate a net photocurrent that must be balanced by a drift current in the open-circuit configuration. We present experiments in electromigrated planar MIM structures, designed with asymmetric plasmonic properties using Au and Pt electrodes. Decay of optically excited local plasmonic modes preferentially creates hot carriers on the Au side of the junction, leading to a clear preferred directionality of the hot electron photocurrent and hence a preferred polarity of the resulting OCPV. In contrast, in an ensemble of symmetric devices constructed from only one Au, polarity of the OCPV has no preferred direction. Copyright © 2023, The Authors. All rights reserved.

关键词： Plasmonics

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Deep Learning Based Facial Emotion Recognition using Multiple Layers Model

Deep Learning Based Facial Emotion Recognition using Multipl...

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Lidia Sandra Yaya Heryadi Lukas Wayan Suparta Antoni Wibowo Psychology Department Krida Wacana Christian University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665499705

A Facial Emotion Recognition (FER) system is an important tool to be implemented in any psychology academic field and beyond. This paper aims to show a system of Facial Emotion Recognition that can be done using the multiple layers model of ResN et50 which is an ANN. The dataset is an array that consists of 7 different emotions represented by the numbers 0–6. The emotion included in the dataset is “anger”, “disgust”, “fear”, “happiness”, “sadness”, “shock”, and “neutrality”. This research found that the highest accuracy in the application of the model was recorded at 65% and the test was 60% to recognize facial emotion from a graphical input.

关键词： Deep learning Emotion recognition Mechatronics Automation Face recognition Electric shock Psychology

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Adaptive Environment-Aware Robotic Arm Reaching Based on a Bio-Inspired Neurodynamical Computational Framework

arXiv

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arXiv 2024年

作者： Chatziparaschis, Dimitrios Zhong, Shan Christopoulos, Vasileios Karydis, Konstantinos Dept. of Electrical and Computer Engineering Univ. of California Riverside 900 University Avenue RiversideCA92521 United States Neuroscience graduate program Univ. of California Riverside 900 University Avenue RiversideCA92521 United States Dept. of Bioengineering Univ. of California Riverside 900 University Avenue RiversideCA92521 United States

Bio-inspired robotic systems are capable of adaptive learning, scalable control, and efficient information processing. Enabling real-time decision-making for such systems is critical to respond to dynamic changes in the environment. We focus on dynamic target tracking in open areas using a robotic six-degree-of-freedom manipulator with a bird-eye view camera for visual feedback, and by deploying the Neurodynamical Computational Framework (NeuCF). NeuCF is a recently developed bio-inspired model for target tracking based on Dynamic Neural Fields (DNFs) and Stochastic Optimal Control (SOC) theory. It has been trained for reaching actions on a planar surface toward localized visual beacons, and it can re-target or generate stop signals on the fly based on changes in the environment (e.g., a new target has emerged, or an existing one has been removed). We evaluated our system over various target-reaching scenarios. In all experiments, NeuCF had high end-effector positional accuracy, generated smooth trajectories, and provided reduced path lengths compared with a baseline cubic polynomial trajectory generator. In all, the developed system offers a robust and dynamic-aware robotic manipulation approach that affords real-time decision-making. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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GraCo: Granularity-Controllable Interactive Segmentation

GraCo: Granularity-Controllable Interactive Segmentation

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yian Zhao Kehan Li Zesen Cheng Pengchong Qiao Xiawu Zheng Rongrong Ji Chang Liu Li Yuan Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Peng Cheng Laboratory Shenzhen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Department of Automation and BNRist Tsinghua University Beijing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Interactive Segmentation (IS) segments specific objects or parts in the image according to user input. Current IS pipelines fall into two categories: single-granularity out-put and multi-granularity output. The latter aims to allevi-ate the spatial ambiguity present in the former. However, the multi-granularity output pipeline suffers from limited interaction flexibility and produces redundant results. In this work, we introduce Granularity-Controllable Interactive Segmentation (GraCo), a novel approach that allows precise control of prediction granularity by introducing ad-ditional parameters to input. This enhances the customization of the interactive system and eliminates redundancy while resolving ambiguity. Nevertheless, the exorbitant cost of annotating multi-granularity masks and the lack of avail-able datasets with granularity annotations make it difficult for models to acquire the necessary guidance to control out-put granularity. To address this problem, we design an any-granularity mask generator that exploits the semantic property of the pre-trained IS model to automatically gen-erate abundant mask-granularity pairs without requiring additional manual annotation. Based on these pairs, we propose a granularity-controllable learning strategy that efficiently imparts the granularity controllability to the IS model. Extensive experiments on intricate scenarios at ob-ject and part levels demonstrate that our GraCo has signifi-cant advantages over previous methods. This highlights the potential of GraCo to be a flexible annotation tool, capable of adapting to diverse segmentation scenarios. The project page: https://***/GraCo.

关键词： Image segmentation Adaptation models Annotations Interactive systems Pipelines Semantics Redundancy

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A weighted multivariate fuzzy time series method for multiple output high-dimensional time series forecasting in IoT applications

A weighted multivariate fuzzy time series method for multipl...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Hugo Vinicius Bitencourt Patrícia de Oliveira Lucas Omid Orang Petrônio Silva Frederico Gadelha Guimarães Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Instituto Federal do Norte de Minas Gerais Salinas MG Brazil Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Instituto Federal do Norte de Minas Gerais Januária MG Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

In Internet of Things (IoT) applications, data flows are continuous streams of high-dimensional time series that aggregate various data sources. In this context, decision-making processes frequently encompass multiple factors and criteria that demand forecasting these time series. This paper introduces MO-WMVFTS, a novel multiple-input multiple-output (MIMO) fuzzy time series (FTS) method for tackling this complex scenario. MO-WMVFTS is a hybrid forecasting method that fuses weighted multivariate FTS (WMVFTS) with embedding transformations, designed for IoT applications. To assess the performance of the proposed method, it was applied to the prediction of energy consumption in smart homes and air quality in smart cities. Here, three real-world datasets are used to assess the validity of our proposed approach, and the results obtained are highly competitive when compared to other existing methods.

关键词： Smart buildings Smart cities Soft sensors Time series analysis Smart homes Predictive models Internet of Things Forecasting Streams MIMO communication

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Statistical Analysis of Intrinsic High-Frequency Characteristic Fluctuation of Emerging Silicon Gate-All-Around Nanosheet (NS) MOSFETs at Sub-3-nm Nodes

Statistical Analysis of Intrinsic High-Frequency Characteris...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Kazuhiko Endo Seiji Samukawa Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Institute of Communications Engineering Department of Electronics and Electrical Engineering Institute of Biomedical Engineering Center for mm Wave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Fluid Science Tohoku University 2-1-1 Katahira Aoba-ku Sendai Japan National Institute of Advanced Industrial Science and Technology Tsukuba Japan

In recent years, the GAA NS Si MOSFET has been explored as a leading technology. However, the intrinsic parameters of GAA NS Si MOSFETs are affected to varying degrees by various fluctuation sources, Statistically independent and identically distributed $(iid)$ assumptions on the aforementioned random variables overestimate the variability of high-frequency characteristics, compared with considering all fluctuation factors simultaneously. Notably, the random nanosized metal grains dominates the variations of voltage gain, cut-off frequency, and 3dB frequency because the random work functions strongly alter the channel surface potential.

关键词： MOSFET Fluctuations Statistical analysis Gallium arsenide Cutoff frequency Metals Logic gates

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Hot Carrier Injection Reliability of Fabricated N- and P-Type Multi FinFETs with Different TiN Stacks

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ECS Journal of Solid State Science and Technology 2023年第3期12卷 035007-035007页

作者： Chen, Yu-Lin Yeh, Wen-Kuan Hsu, Heng-Tung Chen, Ke-Horng Lin, Wen-Chin Yu, Tien-Han Chou, Hung-Ting Godwin Raj, D. Godfrey, D. International College of Semiconductor Technology National Yang Ming Chiao Tung University Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan National Applied Research Laboratories Taiwan Institute of Microelectronics National Cheng Kung University Taiwan National Chung-Shan Institute of Science and Technology Taoyuan Taiwan Dept of ECE Amal Jyothi College of Engineering Kerala India Dept of Electronics and Communication Engineering Dayananda Sagar University Bengaluru India

Device degradation due to hot carrier injection (HCI) in multi-fin 20 nm and 10 nm N- and P-type FinFET devices are thoroughly analyzed. To further understand the HCI reliability of the four FinFET devices, the device is fabricated with a standard Vt base and low Vt base gate stacks with different work functions. It is evident that: (i) The standard Vt device sustains lower effective stress bias due to the difference in threshold voltage, resulting in a more stable threshold voltage than the low Vt base device, and (ii) the transconductance of the single N- and P-type FinFET is more severely degraded than the multi-fin N- and P-type FinFET, mainly because multi N- and P-type Finfet has coupe effect, which effectively reduces the impact of HCI. © 2023 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited

关键词： FinFET

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Double-slit interference with a caliper

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Physics Education 2020年第4期55卷 1-5页

作者： Wendt, Natália Carolina Schvan Sanas Zamboni, Rodrigo Muller, Marcia Fabris, José Luís Department of Physics Universidade Tecnológica Federal do Paraná Curitiba Brazil Graduate Program in Electrical and Computer Engineering Universidade Tecnológica Federal do Paraná Curitiba Brazil

The implementation of a simple double-slit for light interference experiments by using just three off-the-shelf, non-optical components is described. Thanks to the simplicity of the assemblage, the distance between the slits and slit width can be easily modified. The device can be used in teaching laboratories for quantitative experiments, as well as in demonstration classes. © 2020 IOP Publishing Ltd.

关键词： double-slit light interference single-slit

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Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes

arXiv

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arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Chengchi University Taipei11529 Taiwan Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Institute of Information Science Academia Sinica Taipei11529 Taiwan

The emergence of contemporary deepfakes has attracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is difficult to distinguish from genuine content. Currently, machine learning techniques have been extensively studied for automatically detecting deepfakes. However, human perception has been less explored. Malicious deepfakes could ultimately cause public and social problems. Can we humans correctly perceive the authenticity of the content of the videos we watch? The answer is obviously uncertain;therefore, this paper aims to evaluate the human ability to discern deepfake videos through a subjective study. We present our findings by comparing human observers to five state-of-the-art deepfake detection models. To this end, we used gamification concepts to provide 110 participants (55 native English speakers and 55 non-native English speakers) with a web-based platform where they could access a series of 40 videos (20 real and 20 fake) to determine their authenticity. Each participant performed the experiment twice with the same 40 videos in different random orders. The videos are manually selected from the FakeAVCeleb dataset. We found that all AI models performed significantly better than humans when evaluated on the same 40 videos. The study also reveals that while deception is not impossible, humans tend to overestimate their detection capabilities. Our experimental results may help benchmark human versus machine performance, advance forensics analysis, and enable adaptive countermeasures. © 2024, CC BY.

关键词： Audiovisual

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