查看更多>>摘要：UreteroPelvic Junction Obstruction(UPJO)is a common hydronephrosis disease in children that can result in an even progressive loss of renal function.Ultrasonography is an economical,radiationless,noninvasive,and high noise preliminary diagnostic step for UPJO.Artificial intelligence has been widely applied to medical fields and can greatly assist doctors'diagnostic abilities.The demand for a highly secure network environment in transferring electronic medical data online,therefore,has led to the development of blockchain technology.In this study,we built and tested a framework that integrates a deep learning diagnosis model with blockchain technology.Our diagnosis model is a combination of an attention-based pyramid semantic segmentation network and a discrete wavelet transformation-processed residual classification network.We also compared the performance between benchmark models and our models.Our diagnosis model outperformed benchmarks on the segmentation task and classification task with MIoU=87.93,MPA=93.52,and accuracy=91.77％.For the blockchain system,we applied the InterPlanetary File System protocol to build a secure and private sharing environment.This framework can automatically grade the severity of UPJO using ultrasound images,guarantee secure medical data sharing,assist in doctors'diagnostic ability,relieve patients'burden,and provide technical support for future federated learning and linkage of the Internet of Medical Things(IoMT).
查看更多>>摘要：Alzheimer's disease(AD)is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function,but the etiology of AD is still unclear.With the explosive growth of electronic health data,the application of artificial intelligence(AI)in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches,and improving the disease's diagnostic and prognostic outcome.This paper first briefly introduces Al technologies and applications in medicine,and then presents a comprehensive review of Al in AD.In simple,it includes etiology discovery based on genetic data,computer-aided diagnosis(CAD),computer-aided prognosis(CAP)of AD using multi-modality data(genetic,neuroimaging and linguistic data),and pharmacological or non-pharmacological approaches for treating AD.Later,some popular publicly available AD datasets are introduced,which are important for advancing Al technologies in AD analysis.Finally,core research challenges and future research directions are discussed.
查看更多>>摘要：With severe acute respiratory syndrome coronavirus 2 spreading globally and causing 2019 coronavirus disease(COVID-19),a challenge that we unprepared for was about how to optimally plan and distribute limited top-medical resources for patients in need of urgent care.To address this challenge,physicians desperately needed a scientific tool to methodically differentiate between cases with varying severity.In this study,the unique data of COVID-19 intensive care unit(ICU)patients provided by the national medical team in Wuhan were classified into discrete and continuous variable types.All continuous data were discretized using an entropy-based method and transformed into serial information margins,in which each information margin is related to a specific symptom or clinical meaning.Finally,all these native and processed discrete data were used to configure a readable scorecard through logistic regression,which is the desired scientific tool aforementioned.A total of 322 ICU patients(age:[median:64,interquartile range:54-75],males:178[55.28％],and death:72[22.36％])were included in the study.Probabilities of mortality in COVID-19 patients can be evaluated using a scorecard model(calibration slope:1.343,Brier:0.048,Dxy=0.972,and population stability index=0.071),with desired model performances(accuracy=0.948,area under curve=0.99,sensitivity=1,and specificity=0.939).This new model can interpret clinical meanings from complex data,and compare it with existing machine learning methods through a black-box mechanism.This new data-information model answers a critical question of how a computing algorithm produces clinically meaningful results that will help physicians logically allocate medical resources for COVID-19 patients.Notably,this tool has limitations,giving that this research is a retrospective study.Hopefully,this tool will be tested further and optimized for adaptation to similar clinical cases in the future.
查看更多>>摘要：We investigate the problem of maximizing the sum of submodular and supermodular functions under a fairness constraint.This sum function is non-submodular in general.For an offline model,we introduce two approximation algorithms:A greedy algorithm and a threshold greedy algorithm.For a streaming model,we propose a one-pass streaming algorithm.We also analyze the approximation ratios of these algorithms,which all depend on the total curvature of the supermodular function.The total curvature is computable in polynomial time and widely utilized in the literature.
查看更多>>摘要：Omicron,the new mutant coronavirus,has spread rapidly globally,attracting close attention from different stakeholders worldwide.The complex and constantly changing epidemic situation has had a new impact on the world.Therefore,this paper focuses on the characteristics of the rapid spread of the COVID-19 variant strain.Generally,epidemic prevention experts conduct preliminary screening as part of the existing epidemic plan database according to the current local situation,after which they sort the alternatives deemed more suitable for the situation.Then the decision-makers identify the most divergent expert group,plan for consultation and adjustments,and finally obtain the plan with the smallest divergence.This article aims to integrate the experts'opinions with the method of minimizing the differences,which can maximize the expert consensus and help organize the schemes that best meet the epidemic situation.The experts'negotiation and iteration of the differences in the initial plan align with the current complex and dynamic epidemic situation and are of great significance to the rapid formulation of plans to achieve effective prevention and control.
查看更多>>摘要：Min-max disagreements are an important generalization of the correlation clustering problem(CorCP).It can be defined as follows.Given a marked complete graph G=(V,E),each edge in the graph is marked by a positive label"+"or a negative label"-"based on the similarity of the connected vertices.The goal is to find a clustering C of vertices V,so as to minimize the number of disagreements at the vertex with the most disagreements.Here,the disagreements are the positive cut edges and the negative non-cut edges produced by clustering C.This paper considers two robust min-max disagreements:min-max disagreements with outliers and min-max disagreements with penalties.Given parameter 8 e(0,1/14),we first provide a threshold-based iterative clustering algorithm based on LP-rounding technique,which is a(1/8,7/(1-14δ))-bi-criteria approximation algorithm for both the min-max disagreements with outliers and the min-max disagreements with outliers on one-sided complete bipartite graphs.Next,we verify that the above algorithm can achieve an approximation ratio of 21 for min-max disagreements with penalties when we set parameter 8=1/21.
查看更多>>摘要：In this work,we study a k-Cardinality Constrained Regularized Submodular Maximization(k-CCRSM)problem,in which the objective utility is expressed as the difference between a non-negative submodular and a modular function.No multiplicative approximation algorithm exists for the regularized model,and most works have focused on designing weak approximation algorithms for this problem.In this study,we consider the k-CCRSM problem in a streaming fashion,wherein the elements are assumed to be visited individually and cannot be entirely stored in memory.We propose two multipass streaming algorithms with theoretical guarantees for the above problem,wherein submodular terms are monotonic and nonmonotonic.
查看更多>>摘要：Edge computing platforms enable application developers and content providers to provide context-aware services(such as service recommendations)using real-time wireless access network information.How to recommend the most suitable candidate from these numerous available services is an urgent task.Click-through rate(CTR)prediction is a core task of traditional service recommendation.However,many existing service recommender systems do not exploit user mobility for prediction,particularly in an edge computing environment.In this paper,we propose a model named long and short-term user preferences modeling with a multi-interest network based on user behavior.It uses a logarithmic network to capture multiple interests in different fields,enriching the representations of user short-term preferences.In terms of long-term preferences,users'comprehensive preferences are extracted in different periods and are fused using a nonlocal network.Extensive experiments on three datasets demonstrate that our model relying on user mobility can substantially improve the accuracy of service recommendation in edge computing compared with the state-of-the-art models.
查看更多>>摘要：Air pollution is a severe environmental problem in urban areas.Accurate air quality prediction can help governments and individuals make proper decisions to cope with potential air pollution.As a classic time series forecasting model,the AutoRegressive Integrated Moving Average(ARIMA)has been widely adopted in air quality prediction.However,because of the volatility of air quality and the lack of additional context information,i.e.,the spatial relationships among monitor stations,traditional ARIMA models suffer from unstable prediction performance.Though some deep networks can achieve higher accuracy,a mass of training data,heavy computing,and time cost are required.In this paper,we propose a hybrid model to simultaneously predict seven air pollution indicators from multiple monitoring stations.The proposed model consists of three components:(1)an extended ARIMA to predict matrix series of multiple air quality indicators from several adjacent monitoring stations;(2)the Empirical Mode Decomposition(EMD)to decompose the air quality time series data into multiple smooth sub-series;and(3)the truncated Singular Value Decomposition(SVD)to compress and denoise the expanded matrix.Experimental results on the public dataset show that our proposed model outperforms the state-of-art air quality forecasting models in both accuracy and time cost.
查看更多>>摘要：When deploying workflows in cloud environments,the use of Spot Instances(Sis)is intriguing as they are much cheaper than on-demand ones.However,Sis are volatile and may be revoked at any time,which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling techniques.Although some scheduling methods for Sis have been proposed,most of them are no more applicable to the latest Sis,as they have evolved by eliminating bidding and simplifying the pricing model.This study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile Sis in cloud environments.Based on Monte Carlo simulation and list scheduling,a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this problem.With the Monte Carlo simulation framework,MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling,and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection criteria.Experimental results show that the performance of MCLS is more competitive compared with traditional algorithms.
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