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A multimodal integrative approach to model transcriptional addiction of thyroid cancer on RUNX2

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Cancer Communications 2022年第9期42卷 892-896页

作者： Emanuele Vitale Elisabetta Sauta Mila Gugnoni Federica Torricelli Veronica Manicardi Alessia Ciarrocchi Laboratory of Translational Research Azienda Unità Sanitaria Locale-IRCCS di Reggio EmiliaReggio Emilia 42123Italy Clinical and Experimental Medicine PhD Program University of Modena and Reggio EmiliaModena 41121Italy Department of Electrical Computer and Biomedical EngineeringUniversity of PaviaPavia 27100Italy

Dear Editor Aberrant gene expression sustains massive proliferation and stress adaptation under the regulation of oncogenic transcription factors(TFs)whose binding across the genome orchestrates in space and time *** dependency defines the addiction of cancer cells to TFs on top of the regulatory hierarchy governing cancer dysregulated programs[1].These factors and their dependent mechanisms are an attractive and unexplored reservoir of potential targets for new anticancer ***,while the transcriptional landscape of many cancer-supportive TFs has been revealed,this information remains purely descriptive and confined to in vitro *** foster transferability,new approaches that integrate clinical data into the transcriptional networks are needed.

关键词： addiction modal reservoir

Fractional Order Sliding Mode Control with Fuzzy Parameter Tuning Applied to a SCARA Robot

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Fractional Order Sliding Mode Control with Fuzzy Parameter T...

Mechatronic Systems Supervision (IW_MSS), IEEE International Workshop on

作者： Aicha Znidi Fatma Abdelhedi Ahmed Said Nouri Nabil Derbel Lab: Numerical Control of Industrial Processes National Engineering School of Gabes University of Gabes Tunisia Department of Electrical and Computer Engineering College of Engineering King Abdulaziz University Jeddah Saudi Arabia Control & Energy Management Laboratory University of Sfax National Engineering School of Sfax Tunisia

ISBN: (数字)9798350378382

ISBN: (纸本)9798350378399

This paper presents the application of Fractional Order Sliding Mode Control (FO-SMC) in order to achieve a robust motion trajectory regulation in dynamic robot systems. The proposed control strategy benefits of both advantages of fractional calculus and sliding mode control to upgrade the robustness and the performances of the controller. Moreover, the paper proposes to use a fuzzy tuning of sliding mode controller in order to satisfy the capabilities of the actuators. The stability of the closed loop system has been demonstrated using the Lyapunov theory. Simulation results demonstrate the efficiency of the proposed method achieving precise trajectory tracking and robust performances, it has been shown that fuzzy tuning of the sliding function parameters significantly reduces control variable peaks, making the control more suitable for actuator application.

关键词： Asymptotic stability Actuators Trajectory tracking Simulation Manipulators Stability analysis Closed loop systems Tuning Sliding mode control Fractional calculus

Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning

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Biomedical engineering Frontiers 2022年第1期3卷 422-436页

作者： Bijie Bai Hongda Wang Yuzhu Li Kevin de Haan Francesco Colonnese Yujie Wan Jingyi Zuo Ngan B.Doan Xiaoran Zhang Yijie Zhang Jingxi Li Xilin Yang Wenjie Dong Morgan Angus Darrow Elham Kamangar Han Sung Lee Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos Angeles 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCAUSA Computer Science Department University of CaliforniaLos AngelesCAUSA Physics and Astronomy Department University of CaliforniaLos AngelesCA 90095USA Translational Pathology Core Laboratory University of CaliforniaLos AngelesCA 90095USA Statistics Department University of CaliforniaLos AngelesCA 90095USA Department of Pathology and Laboratory Medicine University of California at DavisSacramentoCA 95817USA Department of Surgery University of CaliforniaLos AngelesCA 90095USA

The immunohistochemical(IHC)staining of the human epidermal growth factor receptor 2(HER2)biomarker is widely practiced in breast tissue analysis,preclinical studies,and diagnostic decisions,guiding cancer treatment and investigation of ***2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist,which typically takes one day to prepare in a laboratory,increasing analysis time and associated ***,we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images,matching the standard HER2 IHC staining that is chemically performed on the same tissue *** efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis,in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images(WSIs)to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts.A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail,membrane clearness,and absence of staining artifacts with respect to their immunohistochemically stained *** virtual HER2 staining framework bypasses the costly,laborious,and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.

关键词： HER2 consuming Deep

ML-Based Power System Stability Assessment Considering Network Topology Changes: WECC 20,000+ Bus System Case Study

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ML-Based Power System Stability Assessment Considering Netwo...

IEEE Power engineering Society Conference and Exposition in Africa, PowerAfrica

作者： Xinlan Jia Jiaojiao Dong Yinfeng Zhao Jin Tan Yilu Liu Department of Electrical Engineering & Computer Science University of Tennessee Knoxville TN USA National Renewable Energy Laboratory Golden CO USA Oak Ridge National Laboratory Oak Ridge TN USA

Modern power grids are fast-changing and thus require real-time monitoring and online stability assessment. With the rapid development of machine learning (ML) techniques, using data-driven models to provide fast and accurate estimations of power system stability marginal information, such as frequency nadir for frequency stability and critical clearing time (CCT) for transient stability, have become possible. However, despite the numerous research on ML-based methods for frequency nadir and CCT prediction, there is limited work on the impact of different network topology changes. Furthermore, most previous studies only focused on small or synthetic systems, and there is a lack of research on actual large power system models. In this paper, the above issues are addressed by studying the actual U.S. Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. Massive simulations are conducted in PowerWorld Simulator to study the impact of various topology change scenarios on both frequency stability and transient stability. System operating information is extracted from the success dispatch cases of various network topologies to generate a comprehensive dataset for ML-based models. Two ML methods, random forest (RF) and multilayer perceptron (MLP) neural network, are trained and tested for both frequency nadir prediction and CCT prediction. Test results have proven the models are capable of online stability assessment for large power networks such as the WECC system with sufficient accuracy.

关键词：

Wireless Communication with Flexible Reflector: Joint Placement and Rotation Optimization for Coverage Enhancement

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arXiv 2024年

作者： Lu, Haiquan Yu, Zhi Zeng, Yong Ma, Shaodan Jin, Shi Zhang, Rui The National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Passive metal reflectors for communication enhancement have appealing advantages such as ultra low cost, zero energy expenditure, maintenance-free operation, long life span, and full compatibility with legacy wireless systems. To unleash the full potential of passive reflectors for wireless communications, this paper proposes a new passive reflector architecture, termed flexible reflector (FR), for enabling the flexible adjustment of beamforming direction via the FR placement and rotation optimization. We consider the multi-FR aided area coverage enhancement and aim to maximize the minimum expected receive power over all locations within the target coverage area, by jointly optimizing the placement positions and rotation angles of multiple FRs. To gain useful insights, the special case of movable reflector (MR) with fixed rotation is first studied to maximize the expected receive power at a target location, where the optimal single-MR placement positions for electrically large and small reflectors are derived in closed-form, respectively. It is shown that the reflector should be placed at the specular reflection point for electrically large reflector. While for area coverage enhancement, the optimal placement is obtained for the single-MR case and a sequential placement algorithm is proposed for the multi-MR case. Moreover, for the general case of FR, joint placement and rotation design is considered for the single-/multiFR aided coverage enhancement, respectively. Numerical results are presented which demonstrate significant performance gains of FRs over various benchmark schemes under different practical setups in terms of receive power enhancement. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning

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arXiv 2024年

作者： Wu, Kun Zhao, Yinuo Xu, Zhiyuan Che, Zhengping Yin, Chengxiang Liu, Chi Harold Feng, Feifei Tang, Jian Department of Electrical Engineering and Computer Science Syracuse University SyracuseNY13244 USE United States Beijing Institute of Technology Beijing100811 China Beijing Innovation Center of Humanoid Robotics Beijing101111 China Midea Group Shanghai201700 China

Offline Reinforcement Learning (RL), which operates solely on static datasets without further interactions with the environment, provides an appealing alternative to learning a safe and promising control policy. The prevailing methods typically learn a conservative policy to mitigate the problem of Q-value overestimation, but it is prone to overdo it, leading to an overly conservative policy. Moreover, they optimize all samples equally with fixed constraints, lacking the nuanced ability to control conservative levels in a fine-grained manner. Consequently, this limitation results in a performance decline. To address the above two challenges in a united way, we propose a framework, Adaptive Conservative Level in Q-Learning (ACL-QL), which limits the Q-values in a mild range and enables adaptive control on the conservative level over each state-action pair, i.e., lifting the Q-values more for good transitions and less for bad transitions. We theoretically analyze the conditions under which the conservative level of the learned Q-function can be limited in a mild range and how to optimize each transition adaptively. Motivated by the theoretical analysis, we propose a novel algorithm, ACL-QL, which uses two learnable adaptive weight functions to control the conservative level over each transition. Subsequently, we design a monotonicity loss and surrogate losses to train the adaptive weight functions, Q-function, and policy network alternatively. We evaluate ACL-QL on the commonly used D4RL benchmark and conduct extensive ablation studies to illustrate the effectiveness and state-of-the-art performance compared to existing offline DRL baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

Efficient and Distributed Large-Scale 3D Map Registration using Tomographic Features

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arXiv 2024年

作者： Unlu, Halil Utku Tzes, Anthony Krishnamurthy, Prashanth Khorrami, Farshad Electrical & Computer Engineering Department 6 MetroTech Center BrooklynNY11201 United States Saadiyat Island Abu Dhabi129188 United Arab Emirates Center for Artificial Intelligence and Robotics NYUAD Saadiyat Island Abu Dhabi129188 United Arab Emirates

A robust, resource-efficient, distributed, and minimally parameterized 3D map matching and merging algorithm is proposed. The suggested algorithm utilizes tomographic features from 2D projections of horizontal cross-sections of gravity-aligned local maps, and matches these projection slices at all possible height differences, enabling the estimation of four degrees of freedom in an efficient and parallelizable manner. The advocated algorithm improves state-of-the-art feature extraction and registration pipelines by an order of magnitude in memory use and execution time. Experimental studies are offered to investigate the efficiency of this 3D map merging scheme. © 2024, CC BY-NC-ND.

关键词： Degrees of freedom (mechanics)

Robust Co-Optimization of Distribution Network Hardening and Mobile Resource Scheduling with Decision-Dependent Uncertainty

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Chen, Chen Zhai, Qiaozhu Jia, Qing-Shan Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

This paper studies the robust co-planning of proactive network hardening and mobile hydrogen energy resources (MHERs) scheduling, which is to enhance the resilience of power distribution network (PDN) against the disastrous events. A decision-dependent robust optimization model is formulated with min − max resilience constraint and discrete recourse structure, which helps achieve the load survivability target considering endogenous uncertainties. Different from the traditional model with a fixed uncertainty set, we adopt a dynamic representation that explicitly captures the endogenous uncertainties of network contingency as well as the available hydrogen storage levels of MHERs, which induces a decision-dependent uncertainty (DDU) set. Also, the multi-period adaptive routing and energy scheduling of MHERs are modeled as a mixed-integer recourse problem for further decreasing the resilience cost. Then, a nested parametric column-and-constraint generation (N-PC&CG) algorithm is customized and developed to solve this challenging formulation. By leveraging the structural property of the DDU set as well as the combination of discrete recourse decisions and the corresponding extreme points, we derive a strengthened solution scheme with nontrivial enhancement strategies to realize efficient and exact computation. Numerical results on 14-bus test system and 56-bus real-world distribution network demonstrate the resilience benefits and economical feasibility of the proposed method under different damage severity levels. Moreover, the enhanced NPC&CG shows a superior solution capability to support prompt decisions for resilient planning with DDU models. Copyright © 2025, The Authors. All rights reserved.

关键词： Hardening

Hyper-Skin: A Hyperspectral Dataset for Reconstructing Facial Skin-Spectra from RGB Images

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arXiv 2023年

作者： Ng, Pai Chet Chi, Zhixiang Verdie, Yannick Lu, Juwei Plataniotis, Konstantinos N. The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Canada Huawei Noah's Ark Laboratory Huawei Canada Canada

We introduce Hyper-Skin, a hyperspectral dataset covering wide range of wavelengths from visible (VIS) spectrum (400nm - 700nm) to near-infrared (NIR) spectrum (700nm - 1000nm), uniquely designed to facilitate research on facial skin-spectra reconstruction. By reconstructing skin spectra from RGB images, our dataset enables the study of hyperspectral skin analysis, such as melanin and hemoglobin concentrations, directly on the consumer device. Overcoming limitations of existing datasets, Hyper-Skin consists of diverse facial skin data collected with a pushbroom hyperspectral camera. With 330 hyperspectral cubes from 51 subjects, the dataset covers the facial skin from different angles and facial poses. Each hyperspectral cube has dimensions of 1024×1024×448, resulting in millions of spectra vectors per image. The dataset, carefully curated in adherence to ethical guidelines, includes paired hyperspectral images and synthetic RGB images generated using real camera responses. We demonstrate the efficacy of our dataset by showcasing skin spectra reconstruction using state-of-the-art models on 31 bands of hyperspectral data resampled in the VIS and NIR spectrum. This Hyper-Skin dataset would be a valuable resource to NeurIPS community, encouraging the development of novel algorithms for skin spectral reconstruction while fostering interdisciplinary collaboration in hyperspectral skin analysis related to cosmetology and skin's well-being. Instructions to request the data and the related benchmarking codes are publicly available at: https://***/hyperspectral-skin/Hyper-Skin-2023. © 2023, CC BY.

关键词： Cameras