Thompson Sampling with Virtual Helping Agents

We address the problem of online sequential decision making, i.e., balancing the trade-off between exploiting the current knowledge to maximize immediate performance and exploring the new information to gain long-term benefits using the multi-armed bandit framework. Thompson sampling is one of the heuristics for choosing actions that address this exploration-exploitation dilemma. We first propose a general framework that helps heuristically tune the exploration versus exploitation trade-off in Thompson sampling using multiple samples from the posterior distribution. Utilizing this framework, we propose two algorithms for the multi-armed bandit problem and provide theoretical bounds on the cumulative regret. Next, we demonstrate the empirical improvement in the cumulative regret performance of the proposed algorithm over Thompson Sampling. We also show the effectiveness of the proposed algorithm on real-world datasets. Contrary to the existing methods, our framework provides a mechanism to vary the amount of exploration/ exploitation based on the task at hand. Towards this end, we extend our framework for two additional problems, i.e., best arm identification and time-sensitive learning in bandits and compare our algorithm with existing methods.Comment: 14 pages, 8 figure

eCNN: A Block-Based and Highly-Parallel CNN Accelerator for Edge Inference

Convolutional neural networks (CNNs) have recently demonstrated superior quality for computational imaging applications. Therefore, they have great potential to revolutionize the image pipelines on cameras and displays. However, it is difficult for conventional CNN accelerators to support ultra-high-resolution videos at the edge due to their considerable DRAM bandwidth and power consumption. Therefore, finding a further memory- and computation-efficient microarchitecture is crucial to speed up this coming revolution. In this paper, we approach this goal by considering the inference flow, network model, instruction set, and processor design jointly to optimize hardware performance and image quality. We apply a block-based inference flow which can eliminate all the DRAM bandwidth for feature maps and accordingly propose a hardware-oriented network model, ERNet, to optimize image quality based on hardware constraints. Then we devise a coarse-grained instruction set architecture, FBISA, to support power-hungry convolution by massive parallelism. Finally,we implement an embedded processor---eCNN---which accommodates to ERNet and FBISA with a flexible processing architecture. Layout results show that it can support high-quality ERNets for super-resolution and denoising at up to 4K Ultra-HD 30 fps while using only DDR-400 and consuming 6.94W on average. By comparison, the state-of-the-art Diffy uses dual-channel DDR3-2133 and consumes 54.3W to support lower-quality VDSR at Full HD 30 fps. Lastly, we will also present application examples of high-performance style transfer and object recognition to demonstrate the flexibility of eCNN.Comment: 14 pages; appearing in IEEE/ACM International Symposium on Microarchitecture (MICRO), 201

Safety and efficacy of an Anti-CD20 Monoclonal antibody (RedituxTM) In Indian patients with seropositive rheumatoid arthritis

Background: Rituximab (RTX), an anti-CD 20 monoclonal antibody is one of the first line biological disease-modifying anti-rheumatoid drug indicated for the treatment of rheumatoid arthritis (RA) in patient's refractory to conventional Synthetic DMARDs (csDMARDs). Limited data are available about the safety and efficacy of biosimilar version of this molecule. In this study, we assessed the clinical efficacy and safety profile of biosimilar RTX, RedituxTM. Methods: In this prospective study, 36 adults with moderate disease activity measured by the European League Against Rheumatism (EULAR) disease activity score (DAS28-erythrocyte sedimentation rate [ESR] ≥3.2), who had failed conventional therapy with at least 2 csDMARDs were initiated on RTX, 1000 mg, given on day 0 and day 15, after taking informed consent. Biomarkers including ESR, C reactive protein, Immunoglobulin G and M (IgG, IgM) and DAS28-ESR scores were measured at baseline and repeated at 2, 4, 8, 12, 16, 20, and 24 weeks. The primary endpoint was attaining EULAR good/moderate response. Results: DAS28-ESR score showed a statistically significant decline at 24 weeks (P < 0.001). Seventy-five percent patients showed an EULAR moderate response while 25% showed no EULAR response at 24 weeks posttreatment. IgG and IgM levels declined by 24.9% (P < 0.001) and 37% (P = 0.020) at end of 24 wks. However, there were no infections noted during the period of study. The most common adverse event was infusion reaction seen in 16.6% of patients. Conclusions: RTX is a safe and effective drug for the management of seropositive RA with results comparable with the original molecule. No serious adverse effects were noted in the study except for mild infusion reactions and fall in immunoglobulin levels