Supported-BinaryNet: Bitcell Array-based Weight Supports for Dynamic Accuracy-Latency Trade-offs in SRAM-based Binarized Neural Network. (arXiv:1911.08518v1 [cs.NE])

In this work, we introduce bitcell array-based support parameters to improve the prediction accuracy of SRAM-based binarized neural network (SRAM-BNN). Our approach enhances the training weight space of SRAM-BNN while requiring minimal overheads to a typical design. More flexibility of the weight space leads to higher prediction accuracy in our design. We adapt row digital-to-analog…

Deep Motion Blur Removal Using Noisy/Blurry Image Pairs. (arXiv:1911.08541v1 [cs.CV])

Removing spatially variant motion blur from a blurry image is a challenging problem as blur sources are complicated and difficult to model accurately. Recent progress in deep neural networks suggests that kernel free single image deblurring can be efficiently performed, but questions about deblurring performance persist. Thus, we propose to restore a sharp image by…

Six Degree-of-Freedom Hovering using LIDAR Altimetry via Reinforcement Meta-Learning. (arXiv:1911.08553v1 [eess.SY])

We optimize a six degrees of freedom hovering policy using reinforcement meta-learning. The policy maps flash LIDAR measurements directly to on/off spacecraft body-frame thrust commands, allowing hovering at a fixed position and attitude in the asteroid body-fixed reference frame. Importantly, the policy does not require position and velocity estimates, and can operate in environments with…

Topological properties of secure wireless sensor networks under the q-composite key predistribution scheme with unreliable links. (arXiv:1911.08513v1 [cs.NI])

Security is an important issue in wireless sensor networks (WSNs), which are often deployed in hostile environments. The q-composite key predistribution scheme has been recognized as a suitable approach to secure WSNs. Although the q-composite scheme has received much attention in the literature, there is still a lack of rigorous analysis for secure WSNs operating…

Audita: A Blockchain-based Auditing Framework for Off-chain Storage. (arXiv:1911.08515v1 [cs.CR])

The cloud changed the way we manage and store data. Today, cloud storage services offer clients an infrastructure that allows them a convenient source to store, replicate, and secure data online. However, with these new capabilities also come limitations, such as lack of transparency, limited decentralization, and challenges with privacy and security. And, as the…

Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning. (arXiv:1911.08517v1 [cs.AI])

This paper considers the problem of resource allocation in stream processing, where continuous data flows must be processed in real time in a large distributed system. To maximize system throughput, the resource allocation strategy that partitions the computation tasks of a stream processing graph onto computing devices must simultaneously balance workload distribution and minimize communication.…

Supported-BinaryNet: Bitcell Array-based Weight Supports for Dynamic Accuracy-Latency Trade-offs in SRAM-based Binarized Neural Network. (arXiv:1911.08518v1 [cs.NE])

In this work, we introduce bitcell array-based support parameters to improve the prediction accuracy of SRAM-based binarized neural network (SRAM-BNN). Our approach enhances the training weight space of SRAM-BNN while requiring minimal overheads to a typical design. More flexibility of the weight space leads to higher prediction accuracy in our design. We adapt row digital-to-analog…

MicroCash: Practical Concurrent Processing of Micropayments. (arXiv:1911.08520v1 [cs.CR])

Micropayments are increasingly being adopted by a large number of applications. However, processing micropayments individually can be expensive, with transaction fees exceeding the payment value itself. By aggregating these small transactions into a few larger ones, and using cryptocurrencies, today’s decentralized probabilistic micropayment schemes can reduce these fees. Unfortunately, existing solutions force micropayments to be…

Forbidden knowledge in machine learning — Reflections on the limits of research and publication. (arXiv:1911.08603v1 [cs.LG])

Certain research strands can yield “forbidden knowledge”. This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to…