Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27\% compared to baseline algorithms

Improved language identification using deep bottleneck network

Effective representation plays an important role in automatic spoken language identification (LID). Recently, several representations that employ a pre-trained deep neural network (DNN) as the front-end feature extractor, have achieved state-of-the-art performance. However the performance is still far from satisfactory for dialect and short-duration utterance identification tasks, due to the deficiency of existing representations. To address this issue, this paper proposes the improved representations to exploit the information extracted from different layers of the DNN structure. This is conceptually motivated by regarding the DNN as a bridge between low-level acoustic input and high-level phonetic output features. Specifically, we employ deep bottleneck network (DBN), a DNN with an internal bottleneck layer acting as a feature extractor. We extract representations from two layers of this single network, i.e. DBN-TopLayer and DBN-MidLayer. Evaluations on the NIST LRE2009 dataset, as well as the more specific dialect recognition task, show that each representation can achieve an incremental performance gain. Furthermore, a simple fusion of the representations is shown to exceed current state-of-the-art performance

New Perspectives on Host-Parasite Interplay by Comparative Transcriptomic and Proteomic Analyses of Schistosoma japonicum

Schistosomiasis remains a serious public health problem with an estimated 200 million people infected in 76 countries. Here we isolated ~ 8,400 potential protein-encoding cDNA contigs from Schistosoma japonicum after sequencing circa 84,000 expressed sequence tags. In tandem, we undertook a high-throughput proteomics approach to characterize the protein expression profiles of a number of developmental stages (cercariae, hepatic schistosomula, female and male adults, eggs, and miracidia) and tissues at the host-parasite interface (eggshell and tegument) by interrogating the protein database deduced from the contigs. Comparative analysis of these transcriptomic and proteomic data, the latter including 3,260 proteins with putative identities, revealed differential expression of genes among the various developmental stages and sexes of S. japonicum and localization of putative secretory and membrane antigens, enzymes, and other gene products on the adult tegument and eggshell, many of which displayed genetic polymorphisms. Numerous S. japonicum genes exhibited high levels of identity with those of their mammalian hosts, whereas many others appeared to be conserved only across the genus Schistosoma or Phylum Platyhelminthes. These findings are expected to provide new insights into the pathophysiology of schistosomiasis and for the development of improved interventions for disease control and will facilitate a more fundamental understanding of schistosome biology, evolution, and the host-parasite interplay

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes

High Diversity of Tick-associated Microbiota from Five Tick Species in Yunnan, China

Ticks are obligate blood-sucking vectors for multiple zoonotic diseases. In this study, tick samples were collected from Yunnan Province, China, which is well-known as the “Global Biodiversity Hotspot” in the world. This study aimed to clarify the microbial populations, including pathogens, associated with ticks and to identify the diversity of tick-borne microbiota in this region. The 16S rRNA full-length sequencing from pooled tick DNA samples and PCR amplification of pathogenic genera from individual samples were performed to understand tick-associated microbiota in this region. A total of 191 adult ticks of 5 tick species were included and revealed 11 phyla and 126 genera bacteria, including pathogenic Anaplasma , Ehrlichia , Candidatus Neoehrlichia, Rickettsia , Borrelia , and Babesia . Further identification suggested that Rickettsia sp. YN01 was a variant strain of Rickettsia spp. IG-1, but Rickettsia sp. YN02 and Rickettsia sp. YN03, were potentially two new SFGR species. This study revealed the complexity of ecological interactions between host and microbe and provided insight for the biological control of ticks. A high microbial diversity in ticks from Yunnan was identified, and more investigation should be undertaken to elucidate the pathogenicity in the area

An Estimation-Range Extended Autocorrelation-Based Frequency Estimator

We address the problem of autocorrelation-based single-tone frequency estimation. It has been shown that the frequency can be estimated from the phase of the available signal's autocorrelation with fixed lag. A large lag results in better performance but at the same time limits the estimation range. New methods have been proposed to extend the estimation range. In this paper, a new estimator which is a robust hybrid of periodogram-based and autocorrelation-based frequency estimators is presented. We propose to calculate the autocorrelation function with spectral lines inside the available signal's main lobe spectrum. We show that the new estimator obtains full estimation range of [−π,π). The theoretical performance bound is also deduced. Performance analysis and simulations demonstrate that the proposed estimator approaches the CRLB

FISSION YIELD UNCERTAINTY PROPAGATION IN MULTI-PASS REFUELING PEBBLE-BED HTGR

Multi-pass refueling scheme is a highlighted feature of pebble bed HTGR which spatially mixes the burnup calculation inside core. Such refueling scheme relate burnup calculation in one region of the core to others and thus affects the uncertainty propagation of nuclear data, e.g. fission product yield. In this work, thermal neutron induced U-235 fission product yield uncertainties are propagated in HTR-PM models with various refueling schemes in V.S.O.P. code. And the effect of multi-pass refueling scheme is studied. Bayesian method is applied to estimate the covariance of fission product yield based on ENDF/B-VII.1 fission yield sub-library. Uncertainty quantification is performed with stochastic sampling method and log-normal based correlated sampling method is used to generate reasonable and self-consistent fission product yield samples. The analyzed results indicate that multi-pass refueling scheme could affect the uncertainty propagation of reactor local responses

ID repair for trajectories with transition graphs

In many surveillance applications, capture devices are set on fixed locations to track entities, leading to valuable spatio-temporal trajectories. However, sometimes the IDs of the entities in these trajectories are incorrectly identified due to various reasons (e.g., illumination conditions and partial occlusion). Since very often the movements of the entities are constrained by certain restrictions imposed by the application (e.g., vehicles must move along the given road network), we consider how to repair the erroneous IDs using transition graphs derived from such restrictions. Roughly speaking, the occurrence of erroneous IDs can cause a valid trajectory to be broken into trajectory fragments that violate some movement constraints imposed by the transition graph, and we aim to repair them by rewriting the IDs and merging the fragments. This problem is practically challenging since it is not easy to judge which IDs in the dataset are correct, and also there may be multiple candidates as the correct value for a single error. We formulate the repair process as an optimization problem and propose a two-phase repair paradigm, which includes candidate repair generation and compatible repair selection, to maximize the quality improvement estimated by a designed objective function. Though both phases are intractable, we propose effective algorithms to solve them through exploiting the locality and sparsity of trajectories. We further devise an index structure, as well as a pruning method to make the repair process more efficient. Experiments on both real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed methods

FISSION YIELD UNCERTAINTY PROPAGATION IN MULTI-PASS REFUELING PEBBLE-BED HTGR

Multi-pass refueling scheme is a highlighted feature of pebble bed HTGR which spatially mixes the burnup calculation inside core. Such refueling scheme relate burnup calculation in one region of the core to others and thus affects the uncertainty propagation of nuclear data, e.g. fission product yield. In this work, thermal neutron induced U-235 fission product yield uncertainties are propagated in HTR-PM models with various refueling schemes in V.S.O.P. code. And the effect of multi-pass refueling scheme is studied. Bayesian method is applied to estimate the covariance of fission product yield based on ENDF/B-VII.1 fission yield sub-library. Uncertainty quantification is performed with stochastic sampling method and log-normal based correlated sampling method is used to generate reasonable and self-consistent fission product yield samples. The analyzed results indicate that multi-pass refueling scheme could affect the uncertainty propagation of reactor local responses