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ASSESSING REUSABILITY OF DEEP LEARNING-BASED MONOTHERAPY DRUG RESPONSE PREDICTION MODELS TRAINED WITH OMICS DATA

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arXiv 2024年

作者： Overbeek, Jamie C. Partin, Alexander Brettin, Thomas S. Chia, Nicholas Narykov, Oleksandr Vasanthakumari, Priyanka Wilke, Andreas Zhu, Yitan Clyde, Austin Jones, Sara Gnanaolivu, Rohan Liu, Yuanhang Jiang, Jun Wang, Chen Knutson, Carter McNaughton, Andrew Kumar, Neeraj Fernando, Gayara Demini Ghosh, Souparno Sanchez-Villalobos, Cesar Zhang, Ruibo Pal, Ranadip Ryan Weil, M. Stevens, Rick L. Data Science and Learning Division Argonne National Laboratory LemontIL United States Computing Environment and Life Sciences Argonne National Laboratory LemontIL United States Department of Computer Science The University of Chicago ChicagoIL United States Frederick National Laboratory for Cancer Research FrederickMD United States Department of Quantitative Health Sciences Mayo Clinic RochesterMN United States Pacific Northwest National Laboratory RichlandWA United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States

Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, models that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment response will likely emerge from collaborative research efforts. This highlights the need for reusable and adaptable models that can be improved and tested by the wider scientific community. In this study, we present a scoring system for assessing the reusability of prediction DRP models, and apply it to 17 peer-reviewed DL-based DRP models. As part of the IMPROVE (Innovative Methodologies and New Data for Predictive Oncology Model Evaluation) project, which aims to develop methods for systematic evaluation and comparison DL models across scientific domains, we analyzed these 17 DRP models focusing on three key categories: software environment, code modularity, and data availability and preprocessing. While not the primary focus, we also attempted to reproduce key performance metrics to verify model behavior and adaptability. Our assessment of 17 DRP models reveals both strengths and shortcomings in model reusability. Issues like underspecified software environments, lack of workflow documentation, and omission of data preprocessing scripts create challenges for users attempting to reproduce or build upon these models. To promote rigorous practices and open-source sharing, we offer recommendations for developing and sharing prediction models. Following these recommendations can address many of the issues identified in this study, improving model reusability without adding significant burdens on researchers. This work offers the first comprehensive assessment of reusability and reproducibility across diverse DRP models, providing insights into current model sharing practices and promoting standards within the DRP a

关键词： Prediction models

Energy-Efficient Probabilistic Semantic Communication over Space-Air-Ground Integrated Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Zhao, Zhouxiang Yang, Zhaohui Chen, Mingzhe Zhu, Chen Xu, Wei Zhang, Zhaoyang Huang, Kaibin Zhejiang University College of Information Science and Electronic Engineering China Zhejiang Provincial Key Laboratory of Info. Proc. Commun Hangzhou310027 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Zhejiang University Polytechnic Institute Hangzhou310015 China Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

Space-air-ground integrated networks (SAGINs) are emerging as a pivotal element in the evolution of future wireless networks. Despite their potential, the joint design of communication and computation within SAGINs remains a formidable challenge. In this paper, the problem of energy efficiency in SAGIN-enabled probabilistic semantic communication (PSCom) system is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSCom technique to compress the transmitting data, while the GTs can automatically recover the missing information. The PSCom is underpinned by shared probabilistic graphs that serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Through analysis, the computation overhead function in PSCom is a piecewise function with respect to the semantic compression ratio. Therefore, it is important to make a balance between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, power, computation capacity, bandwidth, semantic compression ratio, and UAV location constraints. To solve this non-convex non-smooth problem, we propose an iterative algorithm where the closed-form solutions for computation capacity allocation and UAV altitude are obtained at each iteration. Numerical results show the effectiveness of the proposed algorithm. © 2002-2012 IEEE.

关键词： Drones

Touchstone benchmark: are we on the right way for evaluating AI algorithms for medical segmentation? 24

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Touchstone benchmark: are we on the right way for evaluating...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

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A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks

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arXiv 2023年

作者： Chen, Xinyi Yang, Qu Wu, Jibin Li, Haizhou Tan, Kay Chen Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen [CUHK- Shenzhen China

Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. Given that each neural coding scheme possesses its own merits and drawbacks, these SNNs encounter challenges in achieving optimal performance such as accuracy, response time, efficiency, and robustness, all of which are crucial for practical applications. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes. As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience. Additionally, it incorporates a flexible neural coding assignment strategy to accommodate task-specific requirements, along with novel layer-wise learning methods to effectively implement hybrid coding SNNs. We demonstrate the superiority of the proposed framework on image classification and sound localization tasks. Specifically, the proposed hybrid coding SNNs achieve comparable accuracy to state-of-the-art SNNs, while exhibiting significantly reduced inference latency and energy consumption, as well as high noise robustness. This study yields valuable insights into hybrid neural coding designs, paving the way for developing high-performance neuromorphic systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Pattern recognition

H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture

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arXiv 2022年

作者： Zhang, Chengming Geng, Tong Guo, Anqi Tian, Jiannan Herbordt, Martin Li, Ang Tao, Dingwen School of Electrical Engineering and Computer Science Washington State University PullmanWA United States Mathematics and Computer Science Division Pacific Northwest National Laboratory RichlandWA United States Department of Electrical and Computer Engineering Boston University BostonMA United States Department of Electrical and Computer Engineering University of Rochester RochesterNY United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs. Compared with other Machine Learning (ML) modalities, the acceleration of Graph Neural Networks (GNNs) is more challenging due to the irregularity and heterogeneity derived from graph typologies. Existing efforts, however, have focused mainly on handling graphs’ irregularity and have not studied their heterogeneity. To this end we propose H-GCN, a PL (Programmable Logic) and AIE (AI Engine) based hybrid accelerator that leverages the emerging heterogeneity of Xilinx Versal Adaptive Compute Acceleration Platforms (ACAPs) to achieve high-performance GNN inference. In particular, H-GCN partitions each graph into three subgraphs based on its inherent heterogeneity, and processes them using PL and AIE, respectively. To further improve performance, we explore the sparsity support of AIE and develop an efficient density-aware method to automatically map tiles of sparse matrix-matrix multiplication (SpMM) onto the systolic tensor array. Compared with state-of-the-art GCN accelerators, H-GCN achieves, on average, speedups of 1.1∼2.3×. © 2022, CC BY.

关键词： Graph neural networks

Deep Learning Based Transceiver Design for Additive Non-Gaussian Impulsive Noise Channels

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhao, Chengjie Wang, Jun Peng, Qihang Huang, Wei Chen, Xiaonan Zhao, Zexue Le-Ngoc, Tho University of Electronic Science and Technology of China National Key Laboratory on Wireless Communications Sichuan611731 China University of Electronic Science and Technology of China School of Information and Communication Engineering Sichuan611731 China China Ship Research and Development Academy Beijing100101 China McGill University Department of Electrical and Computer Engineering MontrealQCH3A 0E9 Canada

This paper presents a deep-learning (DL)-based transceiver design on additive non-Gaussian impulsive noise (IN) channels. At first, an impulsive generative adversarial network (IGAN) with specific regularization terms is proposed to capture the IN behaviours and used as the channel noise simulator (CNS). Subsequently, the transmitter and receiver are jointly optimized to develop both optimal transmit signal and detection. Furthermore, to enhance the detection performance, the multi-level wavelet signal recovery network (MWSRN) is applied to construct a preprocessor to combat both IN and channel fading without pilots. Illustrative simulation results show that the proposed scheme can achieve better convergence and bit-error rate (BER) performance under different IN settings than various existing approaches. © 2015 IEEE.

关键词： Impulse noise

Movable-Antenna Position Optimization: A Graph-based Approach

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arXiv 2024年

作者： Mei, Weidong Wei, Xin Ning, Boyu Chen, Zhi Zhang, Rui National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Fluid antennas (FAs) and movable antennas (MAs) have emerged as promising technologies in wireless communications, which offer the flexibility to improve channel conditions by adjusting transmit/receive antenna positions within a spatial region. In this letter, we focus on an MA-enhanced multiple-input single-output (MISO) communication system, aiming to optimize the positions of multiple transmit MAs to maximize the received signal power. Unlike the prior works on continuously searching for the optimal MA positions, we propose to sample the transmit region into discrete points, such that the continuous antenna position optimization problem is transformed to a discrete sampling point selection problem based on the point-wise channel information. However, such a point selection problem is combinatory and challenging to be optimally solved. To tackle this challenge, we ingeniously recast it as an equivalent fixed-hop shortest path problem in graph theory and propose a customized algorithm to solve it optimally in polynomial time. To further reduce the complexity, a linear-time sequential update algorithm is also proposed to obtain a high-quality suboptimal solution. Numerical results demonstrate that the proposed algorithms can yield considerable performance gains over the conventional fixed-position antennas with/without antenna selection. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph theory

Evolution of 1/f Flux Noise in Superconducting Qubits with Weak Magnetic Fields

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Physical Review Letters 2023年第22期130卷 220602-220602页

作者： David A. Rower Lamia Ateshian Lauren H. Li Max Hays Dolev Bluvstein Leon Ding Bharath Kannan Aziza Almanakly Jochen Braumüller David K. Kim Alexander Melville Bethany M. Niedzielski Mollie E. Schwartz Jonilyn L. Yoder Terry P. Orlando Joel I-Jan Wang Simon Gustavsson Jeffrey A. Grover Kyle Serniak Riccardo Comin William D. Oliver Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Harvard University Cambridge Massachusetts 02139 USA MIT Lincoln Laboratory Lexington Massachusetts 02421 USA

The microscopic description of 1/f magnetic flux noise in superconducting circuits has remained an open question for several decades despite extensive experimental and theoretical investigation. Recent progress in superconducting devices for quantum information has highlighted the need to mitigate sources of qubit decoherence, driving a renewed interest in understanding the underlying noise mechanism(s). Though a consensus has emerged attributing flux noise to surface spins, their identity and interaction mechanisms remain unclear, prompting further study. Here, we apply weak in-plane magnetic fields to a capacitively shunted flux qubit (where the Zeeman splitting of surface spins lies below the device temperature) and study the flux-noise-limited qubit dephasing, revealing previously unexplored trends that may shed light on the dynamics behind the emergent 1/f noise. Notably, we observe an enhancement (suppression) of the spin-echo (Ramsey) pure-dephasing time in fields up to B=100 G. With direct noise spectroscopy, we further observe a transition from a 1/f to approximately Lorentzian frequency dependence below 10 Hz and a reduction of the noise above 1 MHz with increasing magnetic field. We suggest that these trends are qualitatively consistent with an increase of spin cluster sizes with magnetic field. These results should help to inform a complete microscopic theory of 1/f flux noise in superconducting circuits.

关键词： Fluctuations & noise Open quantum systems & decoherence SQUID Superconducting qubits

Towards Generalized Multi-stage Clustering: Multi-view Self-distillation

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arXiv 2023年

作者： Wang, Jiatai Xu, Zhiwei Wang, Xin Li, Tao College of Computer Science Nankai University Tianjin300350 China Xingchuang Haihe Laboratory Tianjin300459 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering Stony Brook University New York11794 United States

Existing multi-stage clustering methods independently learn the salient features from multiple views and then perform the clustering task. Particularly, multi-view clustering (MVC) has attracted a lot of attention in multi-view or multimodal scenarios. MVC aims at exploring common semantics and pseudo-labels from multiple views and clustering in a selfsupervised manner. However, limited by noisy data and inadequate feature learning, such a clustering paradigm generates overconfident pseudo-labels that mis-guide the model to produce inaccurate predictions. Therefore, it is desirable to have a method that can correct this pseudo-label mistraction in multi-stage clustering to avoid the bias accumulation. To alleviate the effect of overconfident pseudo-labels and improve the generalization ability of the model, this paper proposes a novel multi-stage deep MVC framework where multi-view self-distillation (DistilMVC) is introduced to distill dark knowledge of label distribution. Specifically, in the feature subspace at different hierarchies, we explore the common semantics of multiple views through contrastive learning and obtain pseudo-labels by maximizing the mutual information between views. Additionally, a teacher network is responsible for distilling pseudo-labels into dark knowledge, supervising the student network and improving its predictive capabilities to enhance the robustness. Extensive experiments on real-world multi-view datasets show that our method has better clustering performance than state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation