JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution

Recent years have witnessed a rapid growth of deep-network based services and applications. A practical and critical problem thus has emerged: how to effectively deploy the deep neural network models such that they can be executed efficiently. Conventional cloud-based approaches usually run the deep models in data center servers, causing large latency because a significant amount of data has to be transferred from the edge of network to the data center. In this paper, we propose JALAD, a joint accuracy- and latency-aware execution framework, which decouples a deep neural network so that a part of it will run at edge devices and the other part inside the conventional cloud, while only a minimum amount of data has to be transferred between them. Though the idea seems straightforward, we are facing challenges including i) how to find the best partition of a deep structure; ii) how to deploy the component at an edge device that only has limited computation power; and iii) how to minimize the overall execution latency. Our answers to these questions are a set of strategies in JALAD, including 1) A normalization based in-layer data compression strategy by jointly considering compression rate and model accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall execution latency; and 3) An edge-cloud structure adaptation strategy that dynamically changes the decoupling for different network conditions. Experiments demonstrate that our solution can significantly reduce the execution latency: it speeds up the overall inference execution with a guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE

A Stochastic Adjustment Strategy for Coordination Process in Distributed Networks

Cloud computing has become a popular basis that integrated into amount of large platforms to support applications (e.g., multimedia, vehicle traffic, and IoT). It is critical to focus on coordinating the part of these applications that execute in the cloud to provide reliable, scalable and available services. Nevertheless, the problem of optimally coordinating the applications is rarely addressed. In this paper, we develop a stochastic model to analyze the fundamental characteristics that occur in ZooKeeper during the coordination process. The model primarily addresses two aspects: demands of followers and the load of a leader. Then, we derive the optimal strategy for provision with deployment of coordinated servers to achieve load balancing based on various factors (e.g. server capacity and network load), so that the overall network performance is optimized. We evaluate our algorithm under realistic settings and reveal the trend of factors such as CPU, memory utilization and network bandwidth with the increasing number of requests. We propose the algorithm that considers how many servers should be deployed and when. Our results demonstrate that the strategy guarantees the performance by making suitable deployment adjustment

Towards Identifying Social Bias in Dialog Systems: Frame, Datasets, and Benchmarks

The research of open-domain dialog systems has been greatly prospered by neural models trained on large-scale corpora, however, such corpora often introduce various safety problems (e.g., offensive languages, biases, and toxic behaviors) that significantly hinder the deployment of dialog systems in practice. Among all these unsafe issues, addressing social bias is more complex as its negative impact on marginalized populations is usually expressed implicitly, thus requiring normative reasoning and rigorous analysis. In this paper, we focus our investigation on social bias detection of dialog safety problems. We first propose a novel Dial-Bias Frame for analyzing the social bias in conversations pragmatically, which considers more comprehensive bias-related analyses rather than simple dichotomy annotations. Based on the proposed framework, we further introduce CDail-Bias Dataset that, to our knowledge, is the first well-annotated Chinese social bias dialog dataset. In addition, we establish several dialog bias detection benchmarks at different label granularities and input types (utterance-level and context-level). We show that the proposed in-depth analyses together with these benchmarks in our Dial-Bias Frame are necessary and essential to bias detection tasks and can benefit building safe dialog systems in practice

Population genetics, diversity and forensic characteristics of Tai–Kadai-speaking Bouyei revealed by insertion/deletions markers

Abstract(#br)China, inhabited by over 1.3 billion people and known for its genetic, cultural and linguistic diversity, is considered to be indispensable for understanding the association between language families and genetic diversity. In order to get a better understanding of the genetic diversity and forensic characteristics of Tai–Kadai-speaking populations in Southwest China, we genotyped 30 insertion/deletion (InDel) markers and amelogenin in 205 individuals from Tai–Kadai-speaking Bouyei people using the Qiagen Investigator DIPplex amplification kit. We carried out a comprehensive population genetic relationship investigation among 14,303 individuals from 84 worldwide populations based on allele frequency correlation and 4907 genotypes of 30 InDels from 36 populations distributed in..

Genomic Insights Into the Admixture History of Mongolic- and Tungusic-Speaking Populations From Southwestern East Asia

As a major part of the modern Trans-Eurasian or Altaic language family, most of the Mongolic and Tungusic languages were mainly spoken in northern China, Mongolia, and southern Siberia, but some were also found in southern China. Previous genetic surveys only focused on the dissection of genetic structure of northern Altaic-speaking populations; however, the ancestral origin and genomic diversification of Mongolic and Tungusic–speaking populations from southwestern East Asia remain poorly understood because of the paucity of high-density sampling and genome-wide data. Here, we generated genome-wide data at nearly 700,000 single-nucleotide polymorphisms (SNPs) in 26 Mongolians and 55 Manchus collected from Guizhou province in southwestern China. We applied principal component analysis (PCA), ADMIXTURE, f statistics, qpWave/qpAdm analysis, qpGraph, TreeMix, Fst, and ALDER to infer the fine-scale population genetic structure and admixture history. We found significant genetic differentiation between northern and southern Mongolic and Tungusic speakers, as one specific genetic cline of Manchu and Mongolian was identified in Guizhou province. Further results from ADMIXTURE and f statistics showed that the studied Guizhou Mongolians and Manchus had a strong genetic affinity with southern East Asians, especially for inland southern East Asians. The qpAdm-based estimates of ancestry admixture proportion demonstrated that Guizhou Mongolians and Manchus people could be modeled as the admixtures of one northern ancestry related to northern Tungusic/Mongolic speakers or Yellow River farmers and one southern ancestry associated with Austronesian, Tai-Kadai, and Austroasiatic speakers. The qpGraph-based phylogeny and neighbor-joining tree further confirmed that Guizhou Manchus and Mongolians derived approximately half of the ancestry from their northern ancestors and the other half from southern Indigenous East Asians. The estimated admixture time ranged from 600 to 1,000 years ago, which further confirmed the admixture events were mediated via the Mongolians Empire expansion during the formation of the Yuan dynasty

Lessons learned: Symbiotic autonomous robot ecosystem for nuclear environments

Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Cleanout (POCO) around nuclear facilities each year, resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning phases. The UK Nuclear Decommissioning Authority supports the view that human-in-the-loop robotic deployments are a solution to improve procedures and reduce risks within radiation haracterisation of nuclear sites. We present a novel implementation of a Cyber-Physical System (CPS) deployed in an analogue nuclear environment, comprised of a multi-robot team coordinated by a human-in-the-loop operator through a digital twin interface. The development of the CPS created efficient partnerships across systems including robots, digital systems and human. This was presented as a multi-staged mission within an inspection scenario for the heterogeneous Symbiotic Multi-Robot Fleet (SMuRF). Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together where a single robot would face challenges in full characterisation of radiation. Key contributions include the demonstration of symbiotic autonomy and query-based learning of an autonomous mission supporting scalable autonomy and autonomy as a service. The coordination of the CPS was a success and displayed further challenges and improvements related to future multi-robot fleets

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve