Proxima: Near-storage Acceleration for Graph-based Approximate Nearest Neighbor Search in 3D NAND

Approximate nearest neighbor search (ANNS) plays an indispensable role in a wide variety of applications, including recommendation systems, information retrieval, and semantic search. Among the cutting-edge ANNS algorithms, graph-based approaches provide superior accuracy and scalability on massive datasets. However, the best-performing graph-based ANN search solutions incur tens of hundreds of memory footprints as well as costly distance computation, thus hindering their efficient deployment at scale. The 3D NAND flash is emerging as a promising device for data-intensive applications due to its high density and nonvolatility. In this work, we present the near-storage processing (NSP)-based ANNS solution Proxima, to accelerate graph-based ANNS with algorithm-hardware co-design in 3D NAND flash. Proxima significantly reduces the complexity of graph search by leveraging the distance approximation and early termination. On top of the algorithmic enhancement, we implement Proxima search algorithm in 3D NAND flash using the heterogeneous integration technique. To maximize 3D NAND's bandwidth utilization, we present customized dataflow and optimized data allocation scheme. Our evaluation results show that: compared to graph ANNS on CPU and GPU, Proxima achieves a magnitude improvement in throughput or energy efficiency. Proxima yields 7x to 13x speedup over existing ASIC designs. Furthermore, Proxima achieves a good balance between accuracy, efficiency and storage density compared to previous NSP-based accelerators

Approaches for attaining clean bacterial fractions from complex environmental samples

Marine bacteria have been targeted by industry and pharmaceutics as genetic resources for highly active enzymes or novel lead compounds. Although numerous techniques have been introduced to isolate useful bacteria from the environment, we are still highly dependent on the conventional direct cultivation method to attain pure cultures. However, efficient bacterial isolation is hindered by several factors, including the presence of impurities. In this work, to demonstrate the significance of removing impurities and their impact on bacterial isolation, we employed two approaches: dielectrophoresis (DEP) and fluorescent D-amino acids (FDAA). We successfully attained clean bacterial fractions applicable for downstream processing using these approaches, uniquely designed to identify bacteria based on their characteristics and features. The diversity of bacteria attained by both approaches was investigated using 16S rRNA sequencing and compared to that attained by the standard differential centrifugation method. In addition, the viability of the isolates was also determined via direct cultivation. As a result, the separation of bacteria from impurities allowed for the identification of novel and useful bacteria unique to each approach. Successful cultivation also suggested that both approaches were applicable for attaining viable bacteria. In conclusion, removing impurities to attain clean bacterial fractions promotes the isolation of novel bacteria and thus could aid in the successful isolation of useful bacteria within complex environmental samples

Quantitative Understanding of Probabilistic Behavior of Living Cells Operated by Vibrant Intracellular Networks

For quantitative understanding of probabilistic behaviors of living cells, it is essential to construct a correct mathematical description of intracellular networks interacting with complex cell environments, which has been a formidable task. Here, we present a novel model and stochastic kinetics for an intracellular network interacting with hidden cell environments, employing a complete description of cell state dynamics and its coupling to the system network. Our analysis reveals that various environmental effects on the product number fluctuation of intracellular reaction networks can be collectively characterized by Laplace transform of the time-correlation function of the product creation rate fluctuation with the Laplace variable being the product decay rate. On the basis of the latter result, we propose an efficient method for quantitative analysis of the chemical fluctuation produced by intracellular networks coupled to hidden cell environments. By applying the present approach to the gene expression network, we obtain simple analytic results for the gene expression variability and the environment-induced correlations between the expression levels of mutually noninteracting genes. The theoretical results compose a unified framework for quantitative understanding of various gene expression statistics observed across a number of different systems with a small number of adjustable parameters with clear physical meanings.National Research Foundation of Korea (Grant 2011-0016412)National Research Foundation of Korea (Priority Research Center Program 2009-0093817

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Peer reviewe

Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize a graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image; (2) region adjacency graph conversion; and (3) the graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements, such as walls, doors, and symbols, as well as spatial elements, such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data

Stability of a Quartic Functional Equation in Restricted Domains

Let X be a real normed space and Y a Banach space and f:X→Y. We prove the Ulam-Hyers stability theorem for the quartic functional equation f(2x+y)+f(2x-y)-4f(x+y)-4f(x-y)-24f(x)+6f(y)=0 in restricted domains. As a consequence we consider a measure zero stability problem of the above inequality when f:R→Y

Fault Detection and Isolation for a Cooling System of Fuel Cell via Model-based Analysis

The development of fuel cell electric vehicles in recent years has led to increased interest in the use of fuel cells as sources of renewable energy. To achieve successful commercialization of fuel cell vehicles, it will be necessary to guarantee the safety, reliability, and lifetime of fuel cell systems by predictive fault detection and isolation (FDI). In this study, the parity equation, an observer, and a Kalman filter are employed together to compare the characteristics of FDI, focusing on the sensors of the thermal management system. Residuals corresponding to the difference between temperature outputs of linear models under driving cycles and nonlinear temperature outputs are used to isolate faults. Then, assessment of three model-based sensor FDI schemes is used to isolate sensor faults using the Cumulative Sum Control Chart (CUSUM) method. Generated residuals are evaluated by CUSUM to detect the presence of a sensor fault. As a result, isolated sensor faults are assessed

Enhancing catalytic activity of TiO2 nanoparticles through acid treatment in Eosin-Y sensitized photohydrogen evolution reaction system

Light-driven water splitting has gained increasing attention as an eco-friendly method for hydrogen production. There is a pressing need to enhance the performance of catalysts for the commercial viability of this reaction. Many methods have been proposed to improve catalyst performance; however, an economical and straightforward approach remains a priority. This paper presents an uncomplicated technique called acid treatment, which augments the catalytic performance of nanoparticles. The method promotes a change in the catalytic reactivity by causing a deficit in electron density of Ti and O on the surface of TiO2 nanoparticles without altering their size, morphology, or crystal structure. In the Eosin Y sensitized photocatalytic hydrogen production system, nitric acid treated TiO2 (16.95 μmol/g) exhibited 1.5 times the hydrogen production compared to bare TiO2 (11.15 μmol/g)