PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data

Emergence of smartphone and the participatory sensing (PS) paradigm have paved the way for a new variant of pervasive computing. In PS, human user performs sensing tasks and generates notifications, typically in lieu of incentives. These notifications are real-time, large-volume, and multi-modal, which are eventually fused by the PS platform to generate a summary. One major limitation with PS is the sparsity of notifications owing to lack of active participation, thus inhibiting large scale real-life experiments for the research community. On the flip side, research community always needs ground truth to validate the efficacy of the proposed models and algorithms. Most of the PS applications involve human mobility and report generation following sensing of any event of interest in the adjacent environment. This work is an attempt to study and empirically model human participation behavior and event occurrence distributions through development of a location-sensitive data simulation framework, called PS-Sim. From extensive experiments it has been observed that the synthetic data generated by PS-Sim replicates real participation and event occurrence behaviors in PS applications, which may be considered for validation purpose in absence of the groundtruth. As a proof-of-concept, we have used real-life dataset from a vehicular traffic management application to train the models in PS-Sim and cross-validated the simulated data with other parts of the same dataset.Comment: Published and Appeared in Proceedings of IEEE International Conference on Smart Computing (SMARTCOMP-2018

Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system

<p>Abstract</p> <p>Background</p> <p>The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the “in silico” stochastic event based modeling approach to find the molecular dynamics of the system.</p> <p>Results</p> <p>In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular <it>Mg</it>²⁺ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the <it>Mg</it>²⁺ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system.</p> <p>Conclusions</p> <p>Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics.</p

Volunteer Selection in Collaborative Crowdsourcing with Adaptive Common Working Time Slots

Skill-based volunteering is an expanding branch of crowdsourcing where one may acquire sustainable services, solutions, and ideas from the crowd by connecting with them online. The optimal mapping between volunteers and tasks with collaboration becomes challenging for complex tasks demanding greater skills and cognitive ability. Unlike traditional crowdsourcing, volunteers like to work on their own schedule and locations. To address this problem, we propose a novel two-phase framework consisting of Initial Volunteer-Task Mapping (i-VTM) and Adaptive Common Slot Finding (a-CSF) algorithms. The i-VTM algorithm assigns volunteers to the tasks based on their skills and spatial proximity, whereas the a-CSF algorithm recommends appropriate common working time slots for successful volunteer collaboration. Both the algorithms aim to maximize the overall utility of the crowdsourcing platform. Experimenting with the UpWork dataset demonstrates the efficacy of our framework over existing state-of-the-art methods

Advances in Virus-Directed Therapeutics against Epstein-Barr Virus-Associated Malignancies

Epstein-Barr virus (EBV) is the causal agent in the etiology of Burkitt's lymphoma and nasopharyngeal carcinoma and is also associated with multiple human malignancies, including Hodgkin's and non-Hodgkin's lymphoma, and posttransplantation lymphoproliferative disease, as well as sporadic cancers of other tissues. A causal relationship of EBV to these latter malignancies remains controversial, although the episomic EBV genome in most of these cancers is clonal, suggesting infection very early in the development of the tumor and a possible role for EBV in the genesis of these diseases. Furthermore, the prognosis of these tumors is invariably poor when EBV is present, compared to their EBV-negative counterparts. The physical presence of EBV in these tumors represents a potential “tumor-specific” target for therapeutic approaches. While treatment options for other types of herpesvirus infections have evolved and improved over the last two decades, however, therapies directed at EBV have lagged. A major constraint to pharmacological intervention is the shift from lytic infection to a latent pattern of gene expression, which persists in those tumors associated with the virus. In this paper we provide a brief account of new virus-targeted therapeutic approaches against EBV-associated malignancies

Motifs Enable Communication Efficiency and Fault-Tolerance in Transcriptional Networks

Analysis of the topology of transcriptional regulatory networks (TRNs) is an effective way to study the regulatory interactions between the transcription factors (TFs) and the target genes. TRNs are characterized by the abundance of motifs such as feed forward loops (FFLs), which contribute to their structural and functional properties. In this paper, we focus on the role of motifs (specifically, FFLs) in signal propagation in TRNs and the organization of the TRN topology with FFLs as building blocks. To this end, we classify nodes participating in FFLs (termed motif central nodes) into three distinct roles (namely, roles A, B and C), and contrast them with TRN nodes having high connectivity on the basis of their potential for information dissemination, using metrics such as network efficiency, path enumeration, epidemic models and standard graph centrality measures. We also present the notion of a three tier architecture and how it can help study the structural properties of TRN based on connectivity and clustering tendency of motif central nodes. Finally, we motivate the potential implication of the structural properties of motif centrality in design of efficient protocols of information routing in communication networks as well as their functional properties in global regulation and stress response to study specific disease conditions and identification of drug targets

Deep Meta Q-Learning based Multi-Task Offloading in Edge-Cloud Systems

Resource-Constrained Edge Devices Can Not Efficiently Handle the Explosive Growth of Mobile Data and the Increasing Computational Demand of Modern-Day User Applications. Task Offloading Allows the Migration of Complex Tasks from User Devices to the Remote Edge-Cloud Servers Thereby Reducing their Computational Burden and Energy Consumption While Also Improving the Efficiency of Task Processing. However, Obtaining the Optimal Offloading Strategy in a Multi-Task Offloading Decision-Making Process is an NP-Hard Problem. Existing Deep Learning Techniques with Slow Learning Rates and Weak Adaptability Are Not Suitable for Dynamic Multi-User Scenarios. in This Article, We Propose a Novel Deep Meta-Reinforcement Learning-Based Approach to the Multi-Task Offloading Problem using a Combination of First-Order Meta-Learning and Deep Q-Learning Methods. We Establish the Meta-Generalization Bounds for the Proposed Algorithm and Demonstrate that It Can Reduce the Time and Energy Consumption of IoT Applications by Up to 15%. through Rigorous Simulations, We Show that Our Method Achieves Near-Optimal Offloading Solutions While Also Being Able to Adapt to Dynamic Edge-Cloud Environments

Geo-distributed Multi-tier Workload Migration Over Multi-timescale Electricity Markets

Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications\u27 power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the most feasible destination. For this, we use the variation in the electricity price at the ISPs to decide the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. As finding an optimal relocation is $\mathcal {NP}$-Hard, we propose an Ant Colony Optimization (ACO) based bi-objective optimization technique to strike a balance between migration delay and migration power. A thorough simulation analysis of the proposed approach shows that the proposed model can reduce the migration time by $25\%$–$30\%$ and electricity cost by approximately $25\%$ compared to the baseline

Leukemia virus long terminal repeat activates NFκB pathway by a TLR3-dependent mechanism

AbstractThe long terminal repeat (LTR) region of leukemia viruses plays a critical role in tissue tropism and pathogenic potential of the viruses. We have previously reported that U3-LTR from Moloney murine and feline leukemia viruses (Mo-MuLV and FeLV) upregulates specific cellular genes in trans in an integration-independent way. The U3-LTR region necessary for this action does not encode a protein but instead makes a specific RNA transcript. Because several cellular genes transactivated by the U3-LTR can also be activated by NFκB, and because the antiapoptotic and growth promoting activities of NFκB have been implicated in leukemogenesis, we investigated whether FeLV U3-LTR can activate NFκB signaling. Here, we demonstrate that FeLV U3-LTR indeed upregulates the NFκB signaling pathway via activation of Ras-Raf-IκB kinase (IKK) and degradation of IκB. LTR-mediated transcriptional activation of genes did not require new protein synthesis suggesting an active role of the LTR transcript in the process. Using Toll-like receptor (TLR) deficient HEK293 cells and PKR−/− mouse embryo fibroblasts, we further demonstrate that although dsRNA-activated protein kinase R (PKR) is not necessary, TLR3 is required for the activation of NFκB by the LTR. Our study thus demonstrates involvement of a TLR3-dependent but PKR-independent dsRNA-mediated signaling pathway for NFκB activation and thus provides a new mechanistic explanation of LTR-mediated cellular gene transactivation