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arXiv 2024年

作者： Liao, Weibin Zhu, Yinghao Zhang, Zhongji Wang, Yuhang Wang, Zixiang Chu, Xu Wang, Yasha Ma, Liantao Peking University School of Computer Science Beijing China Ministry of Education Key Laboratory of High Confidence Software Technologies Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China Peking University School of Software and Microelectronics Beijing China Peking University School of Computer Science Center on Frontiers of Computing Studies Beijing China Tianjin China

Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep neural networks (DNNs) to directly model the patient’s health status. Existing DNNs training protocols, including Impute-then-Regress Procedure and Jointly Optimizing of Impute-n-Regress Procedure, require the additional imputation models to reconstruction missing values. However, Impute-then-Regress Procedure introduces the risk of injecting imputed, non-real data into downstream clinical prediction tasks, resulting in power loss, biased estimation, and poorly performing models, while Jointly Optimizing of Impute-n-Regress Procedure is also difficult to generalize due to the complex optimization space and demanding data requirements. Inspired by the recent advanced literature of learnable prompt in the fields of NLP and CV, in this work, we rethought the necessity of the imputation model in downstream clinical tasks, and proposed Learnable Prompt as Pseudo-Imputation (PAI) as a new training protocol to assist EHR analysis. PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all state-of-the-arts EHR analysis models on four real-world datasets across two clinical prediction tasks. Further experimental analysis indicates that PAI exhibits higher robustness in situations of data insufficiency and high missing rates. More importantly, as a plug-and-play protocol, PAI can be easily integrated into any existing or even imperceptible future EHR analysis models. The code of this work is deployed publicly available at https://***/MrBlankness/PAI to help the research community reproduce the results and assist the EHR analysis tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning

arXiv

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arXiv 2024年

作者： Xu, Yongxin Zhang, Ruizhe Jiang, Xinke Feng, Yujie Xiao, Yuzhen Ma, Xinyu Zhu, Runchuan Chu, Xu Zhao, Junfeng Wang, Yasha School of Computer Science School of Software & Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China Center on Frontiers of Computing Studies Peking University Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong

Retrieval-Augmented Generation (RAG) offers an effective solution to the issues faced by Large Language Models (LLMs) in hallucination generation and knowledge obsolescence by incorporating externally retrieved knowledge. However, due to potential conflicts between internal and external knowledge, as well as retrieval noise, LLMs often struggle to effectively integrate external evidence, leading to a decline in performance. Although existing methods attempt to tackle these challenges, they often struggle to strike a balance between model adherence and robustness, resulting in significant learning variance. Inspired by human cognitive processes, we propose Parenting, a novel framework that decouples adherence and robustness within the parameter space of LLMs. Specifically, Parenting utilizes a key parameter mining method based on forward activation gain to identify and isolate the crucial parameter units that are strongly linked to adherence and robustness. Then, Parenting employs a type-guided tailored tuning strategy, applying specific and appropriate fine-tuning methods to parameter units representing different capabilities, aiming to achieve a balanced enhancement of adherence and robustness. Extensive experiments on various datasets and models validate the effectiveness and generalizability of our methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Obsolescence

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PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud computing

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Procedia computer science 2024年 238卷 385-395页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios.

关键词： cloud computing edge computing dataset generation energy-efficiency green computing machine learning internet of things power consumption modeling resource management scheduling

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Learnable Prompt as Pseudo-Imputation: Rethinking the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction 25

Learnable Prompt as Pseudo-Imputation: Rethinking the Necess...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Weibin Liao Yinghao Zhu Zhongji Zhang Yuhang Wang Zixiang Wang Xu Chu Yasha Wang Liantao Ma School of Computer Science Peking University Beijing China & Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China School of Software and Microelectronics Peking University Beijing China & Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China School of Computer Science Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China & Peking University Information Technology Institute (Tianjin Binhai) Peking University Tianjin China National Engineering Research Center for Software Engineering Peking University Beijing China & Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China

ISBN: (纸本)9798400712456

Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep neural networks (DNNs) to directly model the patient's health status. Existing DNNs training protocols, including Impute-then-Regress Procedure and Jointly Optimizing of Impute-n-Regress Procedure, require the additional imputation models to reconstruction missing values. However, Impute-then-Regress Procedure introduces the risk of injecting imputed, non-real data into downstream clinical prediction tasks, resulting in power loss, biased estimation, and poorly performing models, while Jointly Optimizing of Impute-n-Regress Procedure is also difficult to generalize due to the complex optimization space and demanding data requirements. Inspired by the recent advanced literature of learnable prompt in the fields of NLP and CV, in this work, we rethought the necessity of the imputation model in downstream clinical tasks, and proposed Learnable Prompt as Pseudo-Imputation (PAI) as a new training protocol to assist EHR analysis. PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all state-of-the-arts EHR analysis models on four real-world datasets across two clinical prediction tasks. Further experimental analysis indicates that PAI exhibits higher robustness in situations of data insufficiency and high missing rates. More importantly, as a plug-and-play protocol, PAI can be easily integrated into any existing or even imperceptible future EHR analysis models. The code of this work is deployed publicly available at https://***/MrBlankness/PAI to help the research community reproduce the results and assist the EHR analysis tasks.

关键词： electronic health records

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ExplSched: Maximizing Deep Learning Cluster Efficiency for Exploratory Jobs

ExplSched: Maximizing Deep Learning Cluster Efficiency for E...

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IEEE International Conference on Cluster computing

作者： Hongliang Li Hairui Zhao Zhewen Xu Xiang Li Haixiao Xu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun China High Performance Computing Center Jilin University China

Resource management for Deep Learning (DL) clusters is essential for system efficiency and model training quality. Existing schedulers provided by DL frameworks are mostly adaptations from traditional HPC clusters and usually work on jobs’ makespan, assuming that DL training jobs finish completely. Unfortunately, it is reported that a fair amount of training jobs are exploratory jobs and often finish unsuccessfully (over 30%) in production clusters. This is due to the distinct characteristic of Deep Neural Network (DNN) training that it is an exploratory process of frequent user interventions, such as adjusting model structures, tuning hyperparameters, and exploring feature validity. Existing DL cluster schedulers using offline algorithms are not suitable for exploratory jobs when unexpected early terminations can cause noticeable resource waste. Moreover, DL training jobs are iterative and usually yield diminishing returns as they progress. Equally allocating resource among training iterations is not efficient, especially when dealing with exploratory jobs where it can worsen the degradation of system efficiency. The fundamental goal of a DL training job is to gain model quality improvement, usually indicated by the loss reduction (job profit) of a DNN model. This paper introduces a novel scheduling problem for exploratory jobs that seeks to maximize the overall training profit of a DL cluster. We propose ExplSched, an online scheduling solution based on the primal-dual framework, resulting in a competitive ratio of 2α that belongs to O(ln n). It uses a resource price function that emphasizes the importance of job profit to resource consumption ratio to make quick resource allocation decisions. Experimental results show that ExplSched achieved an average system utility improvement of 87.28% compared with other related work.

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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and performance Evaluation 14

An Adaptive Decision-Making Approach for Better Selection of...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds. © 2023 Elsevier B.V.. All rights reserved.

关键词： Smart contract

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Modeling Neural Networks Training Process with Markov Decision Process 2

Modeling Neural Networks Training Process with Markov Decisi...

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2nd International Conference on Artificial Intelligence and computer engineering, ICAICE 2021

作者： Bai, Yantao Liu, Wanwei Mao, Xinjun Liang, Zhen National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha China College of Computer Science National University of Defense Technology Changsha China Institute for Quantum Information National University of Defense Technology State Key Laboratory of High Performance Computing Changsha China

ISBN: (纸本)9781665421867

With the development of computer technology, statistics-based machine learning method has made great break-throughs, and also improved the development of artificial intelligence. Nevertheless, as a very influential model, neural networks are still treated as 'black boxes'. The results of neural networks are extremely sensitive to the training samples, which lead to great challenges to the controllability of the algorithm. With the wide application of machine learning, demand for interpretability and controllability of neural networks algorithms is increasing. As a result, various scholars have tried to explain and verify neural networks algorithms based on formal methods in recent years. In this paper, a method (called MNNTP) is presented to model the training process of neural networks by using a Markov decision process (MDP). Through MNNTP, the neural networks are abstracted into the form of MDP, which makes notable contributions for verifying some mathematical properties of the neural networks. © 2021 IEEE.

关键词： Markov processes

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An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks

arXiv

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arXiv 2023年

作者： Tao, Ye Liu, Wanwei Song, Fu Liang, Zhen Wang, Ji Zhu, Hongxu College of Computer Science and Technology National University of Defense Technology China School of Information Science and Technology ShanghaiTech University China Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Automotive Software Innovation Center China Institute for Quantum Information State Key Laboratory for High Performance Computing National University of Defense Technology China

Deep neural networks, (DNNs, a.k.a. NNs), have been widely used in various tasks and have been proven to be successful. However, the accompanied expensive computing and storage costs make the deployments in resource-constrained devices a significant concern. To solve this issue, quantization has emerged as an effective way to reduce the costs of DNNs with little accuracy degradation by quantizing floating-point numbers to low-width fixed-point representations. Quantized neural networks (QNNs) have been developed, with binarized neural networks (BNNs) restricted to binary values as a special case. Another concern about neural networks is their vulnerability and lack of interpretability. Despite the active research on trustworthy of DNNs, few approaches have been proposed to QNNs. To this end, this paper presents an automata-theoretic approach to synthesizing BNNs that meet designated properties. More specifically, we define a temporal logic, called BLTL, as the specification language. We show that each BLTL formula can be transformed into an automaton on finite words. To deal with the state-explosion problem, we provide a tableau-based approach in real implementation. For the synthesis procedure, we utilize SMT solvers to detect the existence of a model (i.e., a BNN) in the construction process. Notably, synthesis provides a way to determine the hyper-parameters of the network before training. Moreover, we experimentally evaluate our approach and demonstrate its effectiveness in improving the individual fairness and local robustness of BNNs while maintaining accuracy to a great extent. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

A Novel Deep Learning Approach Featuring Graph-Based Algorit...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Keliang Niyogisubizo, Jovial Xiao, Linxia Pan, Yi Wei, Dongqing Rosiyadi, Didi Wei, Yanjie Chinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Shenzhen518055 China Shanghai Jiao Tong University State Key Laboratory of Microbial Metabolism School of Life Sciences and Biotechnology Shanghai200240 China National Research and Innovation Agency Research Center for Artificial Intelligence and Cybersecurity Bandung Indonesia

ISBN: (纸本)9798350337488

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs. © 2023 IEEE.

关键词： cell segmentation cell tracking deep learning Deeplabv3+ instance segmentation iPS cell reprogramming

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Preparing a commercial quantum key distribution system for certification against implementation loopholes

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Physical Review Applied 2024年第4期22卷 044076页

作者： Vadim Makarov Alexey Abrikosov Poompong Chaiwongkhot Aleksey K. Fedorov Anqi Huang Evgeny Kiktenko Mikhail Petrov Anastasiya Ponosova Daria Ruzhitskaya Andrey Tayduganov Daniil Trefilov Konstantin Zaitsev Skolkovo Moscow 121205 Russia Vigo Quantum Communication Center NTI Center for Quantum Communications Department of Physics Faculty of Science Institute for Quantum Computing Department of Physics and Astronomy Quantum technology foundation (Thailand) Bangkok 10110 Thailand QRate Skolkovo Moscow 143026 Russia Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology Moscow 119991 Russia atlanTTic Research Center School of Telecommunication Engineering Department of Signal Theory and Communications Moscow 101000 Russia

A commercial quantum key distribution (QKD) system needs to be formally certified to enable its wide deployment. The certification should include the system’s robustness against known implementation loopholes and attacks that exploit them. Here we ready a fiber-optic QKD system for this procedure. The system has a prepare-and-measure scheme with decoy-state BB84 protocol, polarization encoding, a qubit source rate of 312.5 MHz, and is manufactured by QRate. We detail its hardware and postprocessing. We analyze the hardware for known implementation loopholes, search for possible new loopholes, and discuss countermeasures. We then amend the system design to address the highest-risk loopholes identified. We also work out technical requirements on the certification lab and outline its possible structure.

关键词： Quantum communication Quantum cryptography Quantum protocols

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