Another Look at Privacy-Preserving Automated Contact Tracing

In the current COVID-19 pandemic, manual contact tracing has been proven to be very helpful to reach close contacts of infected users and slow down spread of the virus. To improve its scalability, a number of automated contact tracing (ACT) solutions have been proposed, and some of them have been deployed. Despite the dedicated efforts, security and privacy issues of these solutions are still open and under intensive debate. In this article, we examine the ACT concept from a broader perspective, by focusing on not only security and privacy issues but also functional issues such as interface, usability, and coverage. We first elaborate on these issues and particularly point out the inevitable privacy leakages in existing Bluetooth Low Energy based ACT solutions, including centralized and decentralized ones. In addition, we examine the existing venue-based ACT solutions and identify their privacy and security concerns. Then, we propose a generic venue-based ACT solution and a concrete instantiation based on Bluetooth Low Energy technology. Our solution monitors users’ contacting history only in virus-spreading-prone venues and offers higher-level protection for both security and privacy than its predecessors. Finally, we evaluate our solution from security, privacy, and efficiency perspectives, and also highlight how to reduce false positives in some specific indoor environments.

COSMO: Computing with Stochastic Numbers in Memory

Stochastic computing (SC) reduces the complexity of computation by representing numbers with long streams of independent bits. However, increasing performance in SC comes with either an increase in area or a loss in accuracy. Processing in memory (PIM) computes data in-place while having high memory density and supporting bit-parallel operations with low energy consumption. In this article, we propose COSMO, an architecture for co mputing with s tochastic numbers in me mo ry, which enables SC in memory. The proposed architecture is general and can be used for a wide range of applications. It is a highly dense and parallel architecture that supports most SC encodings and operations in memory. It maximizes the performance and energy efficiency of SC by introducing several innovations: (i) in-memory parallel stochastic number generation, (ii) efficient implication-based logic in memory, (iii) novel memory bit line segmenting, (iv) a new memory-compatible SC addition operation, and (v) enabling flexible block allocation. To show the generality and efficiency of our stochastic architecture, we implement image processing, deep neural networks (DNNs), and hyperdimensional (HD) computing on the proposed hardware. Our evaluations show that running DNN inference on COSMO is 141× faster and 80× more energy efficient as compared to GPU.

Tuning the Electrochemical and Photophysical Properties of Osmium-Based Photoredox Catalysts

The use of low-energy deep-red (DR) and near-infrared (NIR) light to excite chromophores enables catalysis to ensue across barriers such as materials and tissues. Herein, we report the detailed photophysical characterization of a library of OsII polypyridyl photosensitizers that absorb low-energy light. By tuning ligand scaffold and electron density, we access a range of synthetically useful excited state energies and redox potentials.1 Introduction1.1 Scope1.2 Measuring Ground-State Redox Potentials1.3 Measuring Photophysical Properties1.4 Synthesis of Osmium Complexes2 Properties of Osmium Complexes2.1 Redox Potentials of Os(L)2-Type Complexes2.2 Redox Potentials of Os(L)3-Type Complexes2.3 UV/Vis Absorption and Emission Spectroscopy3 Conclusions