Downlink resource auction in a tree topology structured wireless mesh network

We analyze the problem of downlink resource allocation in a non-cooperative multi-level tree topology structured wireless mesh network in which a selfish mesh router (MR) may refuse to relay other MRs' traffic so as to improve its own performance at the cost of overall system performance. Based on game theory, we propose an auction framework, where the parent MR serves as the auctioneer while its children MRs act as bidders and compete for time-slots. We derive a payment function from radio resource used for relaying traffic instead of money, so as to simplify the implementation and avoid the possible security problems from monetary payment. We prove the existence and uniqueness of Nash Equilibrium and propose a stochastic best response updating algorithm to allow the bids to iteratively converge to NE in a practical distributed fashion. Simulation results show the proposed auction algorithm greatly outperforms traditional algorithms in non-cooperative environments. © 2010 IEEE.published_or_final_versio

Focused quantization for sparse CNNs

Deep convolutional neural networks (CNNs) are powerful tools for a wide range of vision tasks, but the enormous amount of memory and compute resources required by CNNs pose a challenge in deploying them on constrained devices. Existing compression techniques, while excelling at reducing model sizes, struggle to be computationally friendly. In this paper, we attend to the statistical properties of sparse CNNs and present focused quantization, a novel quantization strategy based on power-of-two values, which exploits the weight distributions after fine-grained pruning. The proposed method dynamically discovers the most effective numerical representation for weights in layers with varying sparsities, significantly reducing model sizes. Multiplications in quantized CNNs are replaced with much cheaper bit-shift operations for efficient inference. Coupled with lossless encoding, we built a compression pipeline that provides CNNs with high compression ratios (CR), low computation cost and minimal loss in accuracy. In ResNet-50, we achieved a 18.08x CR with only 0.24% loss in top-5 accuracy, outperforming existing compression methods. We fully compressed a ResNet-18 and found that it is not only higher in CR and top-5 accuracy, but also more hardware efficient as it requires fewer logic gates to implement when compared to other state-of-the-art quantization methods assuming the same throughput.This work is supported in part by the National Key R&D Program of China (No. 2018YFB1004804), the National Natural Science Foundation of China (No. 61806192). We thank EPSRC for providing Yiren Zhao his doctoral scholarship

Automatic generation of multi-precision multi-arithmetic CNN accelerators for FPGAs

Modern deep Convolutional Neural Networks (CNNs) are computationally demanding, yet real applications often require high throughput and low latency. To help tackle these problems, we propose Tomato, a framework designed to automate the process of generating efficient CNN accelerators. The generated design is pipelined and each convolution layer uses different arithmetics at various precisions. Using Tomato, we showcase state-of-the-art multi-precision multi-arithmetic networks, including MobileNet-V1, running on FPGAs. To our knowledge, this is the first multi-precision multi-arithmetic auto-generation framework for CNNs. In software, Tomato fine-tunes pretrained networks to use a mixture of short powers-of-2 and fixed-point weights with a minimal loss in classification accuracy. The fine-tuned parameters are combined with the templated hardware designs to automatically produce efficient inference circuits in FPGAs. We demonstrate how our approach significantly reduces model sizes and computation complexities, and permits us to pack a complete ImageNet network onto a single FPGA without accessing off-chip memories for the first time. Furthermore, we show how Tomato produces implementations of networks with various sizes running on single or multiple FPGAs. To the best of our knowledge, our automatically generated accelerators outperform closest FPGA-based competitors by at least 2-4x for lantency and throughput; the generated accelerator runs ImageNet classification at a rate of more than 3000 frames per second.EPSRC Doctoral Scholarship Peterhouse Graduate Studentshi

An environmentally friendly solution-processed ZrLaO gate dielectric for large-area applications in the harsh radiation environment

In this work, an eco-friendly aqueous solution-processed ZrLaO dielectric is demonstrated for large-area application in the harsh radiation environment. Appropriate La doping (10% La) into ZrOx could suppress the formation of Vo and improve the InOx/ZrLaO interface. The Zr0.9La0.1Oy thin films remained stable under 144 krad (SiO2) gamma-ray irradiation, no distinct composition variation or property degradation were observed. The resistor-loaded inverter based on InOx/Zr0.9La0.1Oy TFT demonstrated full swing characteristics with a gain of 13.3 at 4 V and remained 91% gain after 103 krad (SiO2) irradiation

Oral microbiota and vitamin D impact on oropharyngeal squamous cell carcinogenesis: a narrative literature review

An emerging body of research is revealing the microbiota pivotal involvement in determining the health or disease state of several human niches, and that of vitamin D also in extra-skeletal regions. Nevertheless, much of the oral microbiota and vitamin D reciprocal impact in oropharyngeal squamous cell carcinogenesis (OPSCC) is still mostly unknown. On this premise, starting from an in-depth scientific bibliographic analysis, this narrative literature review aims to show a detailed view of the state of the art on their contribution in the pathogenesis of this cancer type. Significant differences in the oral microbiota species quantity and quality have been detected in OPSCC affected patients; in particular, mainly high-risk human papillomaviruses (HR-HPVs), Fusobacterium nucleatum, Porphyromonas gingivalis, Pseudomonas aeruginosa and Candida spp. seem to be highly represented. Vitamin D prevents and fights infections promoted by the above identified pathogens, thus confirming its homeostatic function on the microbiota balance. However, its antimicrobial and antitumoral actions, well-described for the gut, have not been fully documented for the oropharynx yet. Deeper investigations of the mechanisms that link vitamin D levels, oral microbial diversity and inflammatory processes will lead to a better definition of OPSCC risk factors for the optimization of specific prevention and treatment strategies

BLM and RMI1 alleviate RPA inhibition of topoIIIα decatenase activity

RPA is a single-stranded DNA binding protein that physically associates with the BLM complex. RPA stimulates BLM helicase activity as well as the double Holliday junction dissolution activity of the BLM-topoisomerase IIIα complex. We investigated the effect of RPA on the ssDNA decatenase activity of topoisomerase IIIα. We found that RPA and other ssDNA binding proteins inhibit decatenation by topoisomerase IIIα. Complex formation between BLM, TopoIIIα, and RMI1 ablates inhibition of decatenation by ssDNA binding proteins. Together, these data indicate that inhibition by RPA does not involve species-specific interactions between RPA and BLM-TopoIIIα-RMI1, which contrasts with RPA modulation of double Holliday junction dissolution. We propose that topoisomerase IIIα and RPA compete to bind to single-stranded regions of catenanes. Interactions with BLM and RMI1 enhance toposiomerase IIIα activity, promoting decatenation in the presence of RPA