Fast Video Classification via Adaptive Cascading of Deep Models

Recent advances have enabled "oracle" classifiers that can classify across many classes and input distributions with high accuracy without retraining. However, these classifiers are relatively heavyweight, so that applying them to classify video is costly. We show that day-to-day video exhibits highly skewed class distributions over the short term, and that these distributions can be classified by much simpler models. We formulate the problem of detecting the short-term skews online and exploiting models based on it as a new sequential decision making problem dubbed the Online Bandit Problem, and present a new algorithm to solve it. When applied to recognizing faces in TV shows and movies, we realize end-to-end classification speedups of 2.4-7.8x/2.6-11.2x (on GPU/CPU) relative to a state-of-the-art convolutional neural network, at competitive accuracy.Comment: Accepted at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

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

System for Serving Deep Neural Networks Efficiently

Thesis (Ph.D.)--University of Washington, 2019Today, Deep Neural Networks (DNNs) can recognize faces, detect objects, and transcribe speech, with (almost) human performance. DNN-based applications have become an important workload for both edge and cloud computing. However, it is challenging to design a DNN serving system that satisfies various constraints such as latency, energy, and cost, and still achieve high efficiency. First, though server-class accelerators provide significant computing power, it is hard to achieve high efficiency and utilization due to limits on batching induced by the latency constraint. Second, resource management is a challenging problem for mobile-cloud applications because DNN inference strains device battery capacities and cloud cost budgets. Third, model optimization allows systems to trade off accuracy for lower computation demand. Yet it introduces a model selection problem regarding which optimized model to use and when to use it. This dissertation provides techniques to improve the throughput and reduce the cost and energy consumption significantly while meeting all sorts of constraints via better scheduling and resource management algorithms in the serving system. We present the design, implementation, and evaluation of three systems: (a) Nexus, a serving system on a cluster of accelerators in the cloud that includes a batch-aware scheduler and a query analyzer for complex query; (b) MCDNN, an approximation-based execution framework across mobile devices and the cloud that manages resource budgets proportionally to their frequency of use and systematically trades off accuracy for lower resource use; (c) Sequential specialization that generates and exploits low-cost and high-accuracy models at runtime once it detects temporal locality in the streaming applications. Our evaluation on realistic workloads shows that these systems can achieve significant improvements in cost, accuracy, and utilization

Synthesis of X-Zeolite from Waste Basalt Powder and its Influencing Factors and Synthesis Mechanism

Traditional hydrothermal method (TH) and alkali fusion-assisted hydrothermal method (AFH) were evaluated for the preparation of zeolites from waste basalt powder by using NaOH as the activation reagent in this study. The synthesized products were characterized by BET, XRD, FTIR and SEM. The effects of acid treatment, alkali/basalt ratio, calcination temperature and crystallization temperature on the synthesis process were studied. The results showed that AFH successfully synthesized zeolite X with higher crystallinity and no zeolite was formed by TH. The specific surface area of synthetic zeolite X was 486.46 m2·g−1, which was much larger than that of original basalt powder (12.12 m2·g−1). Acid treatment and calcination temperature had no effect on zeolite types, but acid treatment improved the yield and quality of zeolite. Alkali/basalt ratio and crystallization temperature not only affected the crystallinity of synthesized zeolites but also affected its type. The optimum synthesis condition of zeolite X are as follows: acid treatment of 5 wt% HCl solution, NaOH/basalt ratio of 1:1, a calcination temperature of 650 °C and crystallization temperature of 120 °C. The work shows that basalt can be used as a raw material to prepare zeolite