Edge computing has been an efficient way to provide prompt and near-data services for resource-and-delay sensitive IoT applications via computation offloading. Effective offloading strategies need comprehensively cope with several major issues, including 1) the allocation of dynamic communication computational resources, 2) delay constraints heterogeneous tasks, 3) requirements computationally inexpensive distributed algorithms. However, most existing works mainly focus on part these which...

Federated meta-learning (FML) has emerged as a promising paradigm to cope with the data limitation and heterogeneity challenges in today's edge learning arena. However, its performance is often limited by slow convergence corresponding low communication efficiency. In addition, since available radio spectrum IoT devices' energy capacity are usually insufficient, it crucial control resource allocation consumption when deploying FML practical wireless networks. To overcome challenges, this...

To accommodate ever-increasing computational workloads while satisfying the requirements of delay-sensitive tasks, mobile edge computing (MEC) is proposed to offload tasks nearby servers. Nevertheless, it can introduce new technical issues in terms transmission and computation overheads affected by underlying offloading decisions. In this paper, we investigate problem a hierarchical network architecture, where be offloaded micro-BS further forwarded macro-BS equipped with an MEC server....

Task offloading is a widely used technology in Edge Computing (EC), which declines the makespan of user task with aid resourceful edge servers. How to solve competition for computation and communication resources among tasks fundamental issue offloading. Besides, real-life often comprise multiple interdependent subtasks. Dependencies subtasks significantly raises complexity offloading, makes it difficult propose generalized approaches scenarios different size. In this paper, we study...

Introduction: We report a case of relapse and refractory acute B lymphoblastic leukemia (r/r B-ALL) apparent turned to myeloid (AML), which is occasional fatal during CAR-T treatment. There are still limited data clarify the molecular mechanism blast populations proliferation Case Presentation: A 21-year-old man with an established history r/r B-ALL complex chromosome karyotype renal extramedullary infiltration presented our institution. He exhibited rapid monocytic 19 days after CD19 cell...

Freshness-aware computation offloading has garnered increasing attention recently in the realm of edge computing, driven by need to promptly obtain up-to-date information and mitigate transmission outdated data. However, most existing works assume that channels are reliable, neglecting intrinsic fluctuations uncertainty wireless communication. More importantly, tasks typically have diverse freshness requirements. Accommodation various task priorities context freshness-aware scheduling...

As an emerging energy replenishment technique, radio-frequency (RF) transfer has been considered as a promising solution to power the low-end wireless sensor networks (WSNs). However, in RF-powered orthogonal frequency-division multiple accessing (OFDMA) WSNs, both provisioning and data transmission are easily impacted by highly dynamic unpredictable channels, making channel assignment management closely coupled. Furthermore, due lack of priori knowledge stochastic conditions, network...

Abstract We aimed to comprehensively investigate the proteomic profile and underlying biological function of exosomal proteins associated with B-cell acute lymphoblastic leukemia. Exosomes were isolated from plasma samples collected five patients B-ALL healthy individuals, their protein content was quantitatively analyzed by liquid chromatography tandem mass spectrometry. A total 342 differentially expressed identified in B-ALL. The DEPs mainly metabolic processes activity regulation...

Federated learning (FL) has emerged as a popular paradigm for distributed machine among vast clients. Unfortunately, resource-constrained clients often fail to participate in FL because they cannot pay the memory resources required model training due their limited or bandwidth. Split federated (SFL) is novel framework which commit intermediate results of cloud server client-server collaborative models, making also eligible FL. However, existing SFL frameworks mostly require frequent...

Abstract Interstitial lung diseases (ILDs), or diffuse pulmonary disease, are a subset of that primarily affect alveoli and the space around interstitial tissue bronchioles. It clinically manifests as progressive dyspnea, patients often exhibit varied decrease in diffusion function. Recently, variants telomere biology-related genes have been identified genetic lesions ILDs. Here, we enrolled 82 with pneumonia from 2017 to 2021 our hospital explore candidate gene mutations these via...

After the financial crisis, market capitalization of Industrial and Commercial Bank China(ICBC), China(BOC) China Construction Bank(CCB) rose to top three global business, fully representative China's overall economic growth, while exposure banking industry’s high risks. In order identify compare credit risk listed banks, using KMV model calculate default distance banks then rate: First, use stock data point, distance, on this basis analyze banks; Finally, reasons for differences risk. An...

This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where learned function may fail explain task correctly and misguide agent unseen environments due intrinsic covariate shift. Leveraging both expert data lower-quality diverse data, we devise principled algorithm (namely CLARE) that solves IRL efficiently via integrating "conservatism" into utilizing an estimated dynamics model. Our theoretical analysis provides...

A central question in edge intelligence is "how can an device learn its local model with limited data and constrained computing capacity?" In this study, we explore the approach where a global initialization first obtained by running federated learning (FL) across multiple devices, based on which semi-supervised algorithm devised for single to carry out quick adaptation data. Specifically, account heterogeneity resource constraints, trained via FL, each conducts updates only customized...

Federated meta-learning (FML) has emerged as a promising paradigm to cope with the data limitation and heterogeneity challenges in today's edge learning arena. However, its performance is often limited by slow convergence corresponding low communication efficiency. In addition, since available radio spectrum IoT devices' energy capacity are usually insufficient, it crucial control resource allocation consumption when deploying FML practical wireless networks. To overcome challenges, this...

In many areas, practitioners need to analyze large datasets that challenge conventional single-machine computing. To scale up data analysis, distributed and parallel computing approaches are increasingly needed. Here we study a fundamental highly important problem in this area: How do ridge regression environment? Ridge is an extremely popular method for supervised learning, has several optimality properties, thus it study. We one-shot methods construct weighted combinations of estimators...

In order to meet the requirements for performance, safety, and latency in many IoT applications, intelligent decisions must be made right here now at network edge. However, constrained resources limited local data amount pose significant challenges development of edge AI. To overcome these challenges, we explore continual learning capable leveraging knowledge transfer from previous tasks. Aiming achieve fast learning, propose a platform-aided federated meta-learning architecture where nodes...

Federated Reinforcement Learning (FRL) has garnered increasing attention recently. However, due to the intrinsic spatio-temporal non-stationarity of data distributions, current approaches typically suffer from high interaction and communication costs. In this paper, we introduce a new FRL algorithm, named $\texttt{MFPO}$, that utilizes momentum, importance sampling, additional server-side adjustment control shift stochastic policy gradients enhance efficiency utilization. We prove by proper...

Federated Reinforcement Learning (FRL) has been deemed as a promising solution for intelligent decision-making in the era of Artificial Internet Things. However, existing FRL approaches often entail repeated interactions with environment during local updating, which can be prohibitively expensive or even infeasible many real-world domains. To overcome this challenge, paper proposes novel offline federated policy optimization algorithm, named $\texttt{DRPO}$, enables distributed agents to...