DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives

We present a new parallel algorithm for probabilistic graphical model optimization. The algorithm relies on data-parallel primitives (DPPs), which provide portable performance over hardware architecture. We evaluate results on CPUs and GPUs for an image segmentation problem. Compared to a serial baseline, we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare our performance to a reference, OpenMP-based algorithm, and find speedups of up to 7X (CPU).Comment: LDAV 2018, October 201

Performance Analysis of Traditional and Data-Parallel Primitive Implementations of Visualization and Analysis Kernels

Measurements of absolute runtime are useful as a summary of performance when studying parallel visualization and analysis methods on computational platforms of increasing concurrency and complexity. We can obtain even more insights by measuring and examining more detailed measures from hardware performance counters, such as the number of instructions executed by an algorithm implemented in a particular way, the amount of data moved to/from memory, memory hierarchy utilization levels via cache hit/miss ratios, and so forth. This work focuses on performance analysis on modern multi-core platforms of three different visualization and analysis kernels that are implemented in different ways: one is "traditional", using combinations of C++ and VTK, and the other uses a data-parallel approach using VTK-m. Our performance study consists of measurement and reporting of several different hardware performance counters on two different multi-core CPU platforms. The results reveal interesting performance differences between these two different approaches for implementing these kernels, results that would not be apparent using runtime as the only metric

Analysis of the Ex Vivo and In Vivo Antiretroviral Activity of Gemcitabine

Replication of retroviral and host genomes requires ribonucleotide reductase to convert rNTPs to dNTPs, which are then used as substrates for DNA synthesis. Inhibition of ribonucleotide reductase by hydroxyurea (HU) has been previously used to treat cancers as well as HIV. However, the use of HU as an antiretroviral is limited by its associated toxicities such as myelosuppression and hepatotoxicity. In this study, we examined the ribonucleotide reductase inhibitor, gemcitabine, both in cell culture and in C57Bl/6 mice infected with LP-BM5 murine leukemia virus (LP-BM5 MuLV, a murine AIDS model). Gemcitabine decreased infectivity of MuLV in cell culture with an EC50 in the low nanomolar range with no detectable cytotoxicity. Similarly, gemcitabine significantly decreased disease progression in mice infected with LP-BM5. Specifically, gemcitabine treatment decreased spleen size, plasma IgM, and provirus levels compared to LP-BM5 MuLV infected, untreated mice. Gemcitabine efficacy was observed at doses as low as 1 mg/kg/day in the absence of toxicity. Higher doses of gemcitabine (3 mg/kg/day and higher) were associated with toxicity as determined by a loss in body mass. In summary, our findings demonstrate that gemcitabine has antiretroviral activity ex vivo and in vivo in the LP-BM5 MuLV model. These observations together with a recent ex vivo study with HIV-1[1], suggest that gemcitabine has broad antiretroviral activity and could be particularly useful in vivo when used in combination drug therapy

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Detection of Intelligent Intruders in Wireless Sensor Networks

Most of the existing research works on the intrusion detection problem in a wireless sensor network (WSN) assume linear or random mobility patterns in abstracting intruders’ models in traversing the WSN field. However, in real-life WSN applications, an intruder is usually an intelligent mobile robot with environment learning and detection avoidance capability (i.e., the capability to avoid surrounding sensors). Due to this, the literature results based on the linear or random mobility models may not be applied to the real-life WSN design and deployment for efficient and effective intrusion detection in practice. This motivates us to investigate the impact of intruder’s intelligence on the intrusion detection problem in a WSN for various applications. To be specific, we propose two intrusion algorithms, the pinball and flood-fill algorithms, to mimic the intelligent motion and behaviors of a mobile intruder in detecting and circumventing nearby sensors for detection avoidance while heading for its destination. The two proposed algorithms are integrated into a WSN framework for intrusion detection analysis in various circumstances. Monte Carlo simulations are conducted, and the results indicate that: (1) the performance of a WSN drastically changes as a result of the intruder’s intelligence in avoiding sensor detections and intrusion algorithms; (2) network parameters, including node density, sensing range and communication range, play a crucial part in the effectiveness of the intruder’s intrusion algorithms; and (3) it is imperative to integrate intruder’s intelligence in the WSN research for intruder detection problems under various application circumstances