DEVELOPMENT OF E-SRS (ENVIRONMENT-SENSING RESPONSE SYSTEM) AS A NOVEL METHOD TO DISTINGUISH GENETIC ENVIRONMENTS AND RESOLVE CLOSELY RELATED NUCLEIC ACID SEQUENCES

Master'sMASTER OF SCIENC

ReDas: Supporting Fine-Grained Reshaping and Multiple Dataflows on Systolic Array

Current systolic arrays still suffer from low performance and PE utilization on many real workloads due to the mismatch between the fixed array topology and diverse DNN kernels. We present ReDas, a flexible and lightweight systolic array that can adapt to various DNN models by supporting dynamic fine-grained reshaping and multiple dataflows. The key idea is to construct reconfigurable roundabout data paths using only the short connections between neighbor PEs. The array with 128$\times$128 size supports 129 different logical shapes and 3 dataflows (IS/OS/WS). Experiments on DNN models of MLPerf demonstrate that ReDas can achieve 3.09x speedup on average compared to state-of-the-art work.Comment: 7 pages, 11 figures, conferenc

Personality Openness Predicts Driver Trust in Automated Driving

Maintaining an appropriate level of trust in automated driving (AD) is critical to safe driving. However, few studies have explored factors affecting trust in AD in general, and no study, as far as is known, has directly investigated whether driver personality influences driver trust in an AD system. The current study investigates the relation between driver personality and driver trust in AD, focusing on Level 2 AD. Participants were required to perform a period of AD in a driving simulator, during which their gaze and driving behavior were recorded, as well as their subjective trust scores after driving. In three distinct measures, a significant correlation between Openness and driver trust in the AD system is found: participants with higher Openness traits tend to have less trust in the AD system. No significant correlations between driver trust in AD and other personality traits are found. The findings suggest that driver personality has an impact on driver trust in AD. Theoretical and practical implications of this finding are discussed

Combined Structure-Based Pharmacophore and 3D-QSAR Studies on Phenylalanine Series Compounds as TPH1 Inhibitors

Tryptophan hydroxylase-1 (TPH1) is a key enzyme in the synthesis of serotonin. As a neurotransmitter, serotonin plays important physiological roles both peripherally and centrally. In this study, a combination of ligand-based and structure-based methods is used to clarify the essential quantitative structure-activity relationship (QSAR) of known TPH1 inhibitors. A multicomplex-based pharmacophore (MCBP) guided method has been suggested to generate a comprehensive pharmacophore of TPH1 kinase based on three crystal structures of TPH1-inhibitor complex. This model has been successfully used to identify the bioactive conformation and align 32 structurally diverse substituted phenylalanine derivatives. The QSAR analyses have been performed on these TPH1 inhibitors based on the MCBP guided alignment. These results may provide important information for further design and virtual screening of novel TPH1 inhibitors

Discovery of a potent and selective CDKL5/GSK3 chemical probe that is neuroprotective

Despite mediating several essential processes in the brain, including during development, cyclin-dependent kinase-like 5 (CDKL5) remains a poorly characterized human protein kinase. Accordingly, its substrates, functions, and regulatory mechanisms have not been fully described. We realized that availability of a potent and selective small molecule probe targeting CDKL5 could enable illumination of its roles in normal development as well as in diseases where it has become aberrant due to mutation. We prepared analogs of AT-7519, a compound that has advanced to phase II clinical trials and is a known inhibitor of several cyclin-dependent kinases (CDKs) and cyclin-dependent kinase-like kinases (CDKLs). We identified analog 2 as a highly potent and cell-active chemical probe for CDKL5/GSK3 (glycogen synthase kinase 3). Evaluation of its kinome-wide selectivity confirmed that analog 2 demonstrates excellent selectivity and only retains GSK3α/β affinity. We next demonstrated the inhibition of downstream CDKL5 and GSK3α/β signaling and solved a co-crystal structure of analog 2 bound to human CDKL5. A structurally similar analog (4) proved to lack CDKL5 affinity and maintain potent and selective inhibition of GSK3α/β, making it a suitable negative control. Finally, we used our chemical probe pair (2 and 4) to demonstrate that inhibition of CDKL5 and/or GSK3α/β promotes the survival of human motor neurons exposed to endoplasmic reticulum stress. We have demonstrated a neuroprotective phenotype elicited by our chemical probe pair and exemplified the utility of our compounds to characterize the role of CDKL5/GSK3 in neurons and beyond

The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting condi-tions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting condi-tions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed