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 $S$ 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 $\Omega(\log^4 n)$ 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