Track D Social Science, Human Rights and Political Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd

In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and Perspectives

International audienceMining big data to make predictions or decisions is the main goal of modern artificial intelligence (AI) and machine learning (ML) applications. Vast innovation in algorithms, their software implementations and data management has enabled great progress to date, but wide adoption has been slowed by limited capabilities of existing computing hardware. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing (e.g., in GPUs) help alleviate the data communication bottleneck to some extent, but paradigm-shifting concepts are required. In-memory computing has emerged as a prime candidate to eliminate this bottleneck by co-locating the memory and processing. In this context, resistive switching (RS) memory devices is a key promising choice, due to their unique intrinsic device-level properties enabling both storing and computing with a small, massively-parallel footprint at a low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. We present a qualitative and quantitative analysis of several key existing challenges in implementing high-capacity, high-volume RS memories for accelerating the most computationally demanding computation in ML inference – that of vector-matrix multiplication (VMM). Monolithic integration of RS memories with CMOS integrated circuits is presented as the core underlying technology. We review key existing design choices in terms of device-level physical implementation, circuit-level design, and system-level considerations, and provide an outlook for future directions

Innovative biocatalysts as tools to detect and inactive nerve agents

Abstract Pesticides and warfare nerve agents are frequently organophosphates (OPs) or related compounds. Their acute toxicity highlighted more than ever the need to explore applicable strategies for the sensing, decontamination and/or detoxification of these compounds. Herein, we report the use of two different thermostable enzyme families capable to detect and inactivate OPs. In particular, mutants of carboxylesterase-2 from Alicyclobacillus acidocaldarius and of phosphotriesterase-like lactonases from Sulfolobus solfataricus and Sulfolobus acidocaldarius, have been selected and assembled in an optimized format for the development of an electrochemical biosensor and a decontamination formulation, respectively. The features of the developed tools have been tested in an ad-hoc fabricated chamber, to mimic an alarming situation of exposure to a nerve agent. Choosing ethyl-paraoxon as nerve agent simulant, a limit of detection (LOD) of 0.4 nM, after 5 s of exposure time was obtained. Furthermore, an optimized enzymatic formulation was used for a fast and efficient environmental detoxification (>99%) of the nebulized nerve agent simulants in the air and on surfaces. Crucial, large-scale experiments have been possible thanks to production of grams amounts of pure (>90%) enzymes

Improving the clinical potential of ultra-high field fMRI using a model-free analysis method based on response consistency

OBJECTIVE To develop an analysis method that is sensitive to non-model-conform responses often encountered in ultra-high field presurgical planning fMRI. Using the consistency of time courses over a number of experiment repetitions, it should exclude low quality runs and generate activation maps that reflect the reliability of responses. MATERIALS AND METHODS 7 T fMRI data were acquired from six healthy volunteers: three performing purely motor tasks and three a visuomotor task. These were analysed with the proposed approach (UNBIASED) and the GLM. RESULTS UNBIASED results were generally less affected by false positive results than the GLM. Runs that were identified as being of low quality were confirmed to contain little or no activation. In two cases, regions were identified as activated in UNBIASED but not GLM results. Signal changes in these areas were time-locked to the task, but were delayed or transient. CONCLUSION UNBIASED is shown to be a reliable means of identifying consistent task-related signal changes regardless of response timing. In presurgical planning, UNBIASED could be used to rapidly generate reliable maps of the consistency with which eloquent brain regions are activated without recourse to task timing and despite modified hemodynamics

Representation of adverse childhood experiences is associated with lower public stigma towards people who use drugs: an exploratory experimental study

Background: Stigmatising attitudes towards people who use drugs are pervasive amongst the public. We investigated whether public stigma was affected by presentation of a history of adversity, and how substance use was described. Methods : A cross-sectional online study using a convenience sample, with a randomised 2 x 2 x 2 factorial design. Participants read one of eight randomly presented vignettes that described a fictional case history of substance use. In each vignette the gender of the subject (male or female), description of the subject’s substance use (‘addict’ vs substance use disorder), and life history (‘tough life’ vs description of four adverse childhood experiences (ACEs)) were varied. Participants then completed an adapted version of the attribution questionnaire (AQ-9), which assessed stigmatising beliefs. Results: Data were obtained from 502 participants (53.0% Female; mean age 36.5 ± 13.5 years). There was a significant effect of life history on AQ-9 scores (p = .012), and presentation of ACEs was associated with lower stigmatising attitudes. Conclusion: Our findings suggest that describing the life histories of people who have experienced problems with substances may lead to less stigmatising public attitudes. Further research should explore the best ways to utilise this information to develop public-targeted anti-stigma intervention