77 research outputs found
Online Influence Maximization in Non-Stationary Social Networks
Social networks have been popular platforms for information propagation. An
important use case is viral marketing: given a promotion budget, an advertiser
can choose some influential users as the seed set and provide them free or
discounted sample products; in this way, the advertiser hopes to increase the
popularity of the product in the users' friend circles by the world-of-mouth
effect, and thus maximizes the number of users that information of the
production can reach. There has been a body of literature studying the
influence maximization problem. Nevertheless, the existing studies mostly
investigate the problem on a one-off basis, assuming fixed known influence
probabilities among users, or the knowledge of the exact social network
topology. In practice, the social network topology and the influence
probabilities are typically unknown to the advertiser, which can be varying
over time, i.e., in cases of newly established, strengthened or weakened social
ties. In this paper, we focus on a dynamic non-stationary social network and
design a randomized algorithm, RSB, based on multi-armed bandit optimization,
to maximize influence propagation over time. The algorithm produces a sequence
of online decisions and calibrates its explore-exploit strategy utilizing
outcomes of previous decisions. It is rigorously proven to achieve an
upper-bounded regret in reward and applicable to large-scale social networks.
Practical effectiveness of the algorithm is evaluated using both synthetic and
real-world datasets, which demonstrates that our algorithm outperforms previous
stationary methods under non-stationary conditions.Comment: 10 pages. To appear in IEEE/ACM IWQoS 2016. Full versio
Online Job Scheduling in Distributed Machine Learning Clusters
Nowadays large-scale distributed machine learning systems have been deployed
to support various analytics and intelligence services in IT firms. To train a
large dataset and derive the prediction/inference model, e.g., a deep neural
network, multiple workers are run in parallel to train partitions of the input
dataset, and update shared model parameters. In a shared cluster handling
multiple training jobs, a fundamental issue is how to efficiently schedule jobs
and set the number of concurrent workers to run for each job, such that server
resources are maximally utilized and model training can be completed in time.
Targeting a distributed machine learning system using the parameter server
framework, we design an online algorithm for scheduling the arriving jobs and
deciding the adjusted numbers of concurrent workers and parameter servers for
each job over its course, to maximize overall utility of all jobs, contingent
on their completion times. Our online algorithm design utilizes a primal-dual
framework coupled with efficient dual subroutines, achieving good long-term
performance guarantees with polynomial time complexity. Practical effectiveness
of the online algorithm is evaluated using trace-driven simulation and testbed
experiments, which demonstrate its outperformance as compared to commonly
adopted scheduling algorithms in today's cloud systems
Experimental Investigation of Forchheimer Coefficients for Non-Darcy Flow in Conglomerate-Confined Aquifer
The research is financially supported by the National Key Research and Development Program of China (No. 2016YFC0801401 and No. 2016YFC0600708), Major Consulting Project of Chinese Academy of Engineering (No. 2017-ZD-2), Yue Qi Distinguished Scholar Project of China University of Mining & Technology (Beijing), and Fundamental Research Funds for the Central Universities (No. 2009QM01).Peer reviewedPublisher PD
miR-212-3p attenuates neuroinflammation of rats with Alzheimer's disease via regulating the SP1/BACE1/NLRP3/Caspase-1 signaling pathway
Alzheimer's disease (AD) ranks as the leading cause of dementia. MicroRNA (miR)-212-3p has been identified to exert neuroprotective effects on brain disorders. The current study analyzed the protective role of miR-212-3p in AD rats via regulating the nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 3 (NLRP3)/Caspase-1 signaling pathway. The AD rat model was established via injection of amyloid-β 1-42 (Aβ1-42), followed by the Morris water maze test. The morphology and functions of neurons were observed. Furthermore, miR-212-3p, NLRP3, cleaved Caspase-1, gasdermin D N-terminus, interleukin (IL)-1β and IL-18 expressions were measured. H19-7 cells were treated with Aβ1-42 to establish the AD cell model, followed by an assessment of cell viability and pyroptosis. Downstream targets of miR-212-3p and specificity protein 1 (SP1), as well as beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) were predicted by databases and testified using dual-luciferase and chromatin immunoprecipitation assays. miR-212-3p was weakly expressed in AD rats. miR-212-3p overexpression was linked to improved learning and memory capacities of AD rats and reduced neuronal pyroptosis linked to neuroinflammation attenuation. In vitro, miR-212-3p improved viability and suppressed pyroptosis of neurons via inhibiting NLRP3/Caspase-1. Overall, miR-212-3p inhibited SP1 expression to block BACE1-induced activation of NLRP3/Caspase-1, thereby attenuating neuroinflammation of AD rats
Towards Data-centric Graph Machine Learning: Review and Outlook
Data-centric AI, with its primary focus on the collection, management, and
utilization of data to drive AI models and applications, has attracted
increasing attention in recent years. In this article, we conduct an in-depth
and comprehensive review, offering a forward-looking outlook on the current
efforts in data-centric AI pertaining to graph data-the fundamental data
structure for representing and capturing intricate dependencies among massive
and diverse real-life entities. We introduce a systematic framework,
Data-centric Graph Machine Learning (DC-GML), that encompasses all stages of
the graph data lifecycle, including graph data collection, exploration,
improvement, exploitation, and maintenance. A thorough taxonomy of each stage
is presented to answer three critical graph-centric questions: (1) how to
enhance graph data availability and quality; (2) how to learn from graph data
with limited-availability and low-quality; (3) how to build graph MLOps systems
from the graph data-centric view. Lastly, we pinpoint the future prospects of
the DC-GML domain, providing insights to navigate its advancements and
applications.Comment: 42 pages, 9 figure
Polystyrene nanoplastics mediated the toxicity of silver nanoparticles in zebrafish embryos
The widespread distribution of nanoplastics and nanomaterials in aquatic environments is of great concern. Nanoplastics have been found to modulate the toxicity of other environmental pollutants in organisms, while few studies have focused on their influences on nanomaterials. Thus, this study evaluated the influences of polystyrene (PS) nanoplastics on the toxicity of silver nanoparticles (AgNPs) to zebrafish (Danio rerio) embryos, including acute toxicity, oxidative stress, apoptosis, immunotoxicity, and metabolic capability. The results showed that the presence of PS nanoplastics could act as a carrier of the co-existing AgNPs in waters. The release ratio of Ag+ from AgNPs was up to 4.23%. The lethal effects of AgNPs on zebrafish embryos were not significantly changed by the co-added PS nanoplastics. Whereas, the alterations in gene expression related to antioxidant and metabolic capability in zebrafish (sod1, cat, mt2, mtf-1, and cox1) caused by AgNPs were significantly enhanced by the presence of PS nanoplastics, which simultaneously lowered the apoptosis and immunotoxicity (caspase9, nfkβ, cebp, and il-1β) induced by AgNPs. It suggests the presence of PS nanoplastics suppressed the AgNPs-induced genotoxicity in zebrafish. The released Ag+ from AgNPs may be responsible for the toxicity of AgNPs in zebrafish, while the subsequent absorption and agglomeration of AgNPs and the released Ag+ on PS nanoplastics may alleviate the toxicity
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