4,734 research outputs found
2D Proactive Uplink Resource Allocation Algorithm for Event Based MTC Applications
We propose a two dimension (2D) proactive uplink resource allocation
(2D-PURA) algorithm that aims to reduce the delay/latency in event-based
machine-type communications (MTC) applications. Specifically, when an event of
interest occurs at a device, it tends to spread to the neighboring devices.
Consequently, when a device has data to send to the base station (BS), its
neighbors later are highly likely to transmit. Thus, we propose to cluster
devices in the neighborhood around the event, also referred to as the
disturbance region, into rings based on the distance from the original event.
To reduce the uplink latency, we then proactively allocate resources for these
rings. To evaluate the proposed algorithm, we analytically derive the mean
uplink delay, the proportion of resource conservation due to successful
allocations, and the proportion of uplink resource wastage due to unsuccessful
allocations for 2D-PURA algorithm. Numerical results demonstrate that the
proposed method can save over 16.5 and 27 percent of mean uplink delay,
compared with the 1D algorithm and the standard method, respectively.Comment: 6 pages, 6 figures, Published in 2018 IEEE Wireless Communications
and Networking Conference (WCNC
Outward Influence and Cascade Size Estimation in Billion-scale Networks
Estimating cascade size and nodes' influence is a fundamental task in social,
technological, and biological networks. Yet this task is extremely challenging
due to the sheer size and the structural heterogeneity of networks. We
investigate a new influence measure, termed outward influence (OI), defined as
the (expected) number of nodes that a subset of nodes will activate,
excluding the nodes in S. Thus, OI equals, the de facto standard measure,
influence spread of S minus |S|. OI is not only more informative for nodes with
small influence, but also, critical in designing new effective sampling and
statistical estimation methods.
Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence
spread/outward influence at scale and with rigorous theoretical guarantees. The
proposed methods are built on two novel components 1) IICP an important
sampling method for outward influence, and 2) RSA, a robust mean estimation
method that minimize the number of samples through analyzing variance and range
of random variables. Compared to the state-of-the art for influence estimation,
SIEA is times faster in theory and up to several orders of
magnitude faster in practice. For the first time, influence of nodes in the
networks of billions of edges can be estimated with high accuracy within a few
minutes. Our comprehensive experiments on real-world networks also give
evidence against the popular practice of using a fixed number, e.g. 10K or 20K,
of samples to compute the "ground truth" for influence spread.Comment: 16 pages, SIGMETRICS 201
EMaP: Explainable AI with Manifold-based Perturbations
In the last few years, many explanation methods based on the perturbations of
input data have been introduced to improve our understanding of decisions made
by black-box models. The goal of this work is to introduce a novel perturbation
scheme so that more faithful and robust explanations can be obtained. Our study
focuses on the impact of perturbing directions on the data topology. We show
that perturbing along the orthogonal directions of the input manifold better
preserves the data topology, both in the worst-case analysis of the discrete
Gromov-Hausdorff distance and in the average-case analysis via persistent
homology. From those results, we introduce EMaP algorithm, realizing the
orthogonal perturbation scheme. Our experiments show that EMaP not only
improves the explainers' performance but also helps them overcome a
recently-developed attack against perturbation-based methods.Comment: 29 page
NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee
Despite recent studies on understanding deep neural networks (DNNs), there
exists numerous questions on how DNNs generate their predictions. Especially,
given similar predictions on different input samples, are the underlying
mechanisms generating those predictions the same? In this work, we propose
NeuCEPT, a method to locally discover critical neurons that play a major role
in the model's predictions and identify model's mechanisms in generating those
predictions. We first formulate a critical neurons identification problem as
maximizing a sequence of mutual-information objectives and provide a
theoretical framework to efficiently solve for critical neurons while keeping
the precision under control. NeuCEPT next heuristically learns different
model's mechanisms in an unsupervised manner. Our experimental results show
that neurons identified by NeuCEPT not only have strong influence on the
model's predictions but also hold meaningful information about model's
mechanisms.Comment: 6 main page
The software for oceanographic data management: VODC for PC 2.0
To manage and process a large amount of oceanographic data, users must have powerful tools that simplify these tasks. The VODC for PC is software designed to assist in managing oceanographic data. It based on 32 bits Windows operation system and used Microsoft Access database management system. With VODC for PC users can update data simply, convert to some international data formats, combine some VODC databases to one, calculate average, min, max fields for some types of data, check for valid data
Doxorubicin selectively induces apoptosis through the inhibition of a novel isoform of Bcl‑2 in acute myeloid leukaemia MOLM‑13 cells with reduced Beclin 1 expression
The overexpression of anti-apoptotic Bcl-2 in acute myeloid leukaemia (AML) may contribute to difficulties in eradicating these cells during chemotherapy. In the present study, doxorubicin (Dox) was evaluated for its potential to induce selective apoptotic cell death in AML MOLM-13 cells and to modulate autophagy through Bcl-2 and Beclin 1 protein expression. Annexin V/propidium iodide and 5(6)-carboxyfluorescein diacetate succinimidyl ester (CFSE) flow cytometric analyses were conducted to determine the effects of Dox on cell death and cell proliferation, respectively, following 48 h of co-incubation with AML MOLM-13 or U-937 monocytic cells. The protein expression levels of Bcl-2 and Beclin 1 in untreated and treated cells were quantified by western blot analysis. Dox reduced the viability of MOLM-13 cells partly by inhibiting cell division and inducing cell apoptosis. Dox demonstrated a level of selectivity in its cytotoxicity against MOLM-13 compared to U-937 cells (P<0.05). Dox induced a significant decrease in Beclin 1 protein levels in MOLM-13 cells without significantly affecting the protein levels in U-937 monocytes. A novel Bcl-2 15-20 kDa (p15-20-Bcl-2) isoform was found to be selectively expressed in AML MOLM-13 cells (but absent in the leukaemic cell lines tested, OCI-AML2, CML K562 and U-937). Dox induced a highly significant inhibition of p15-20-Bcl-2 at concentrations of 0.5, 0.75 and 1 µM (P<0.01). However, the usual 26 kDa Bcl-2 (p26-Bcl-2-α) isoform protein expression was not affected by the drug in either the MOLM-13 or U-937 cells. It was thus postulated that Dox exhibited some selectivity by targeting the p15-20-Bcl-2 isoform in MOLM-13 cells and activating Beclin 1 to induce cell death
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