A device-level characterization approach to quantify the impacts of different random variation sources in FinFET technology

A simple device-level characterization approach to quantitatively evaluate the impacts of different random variation sources in FinFETs is proposed. The impacts of random dopant fluctuation are negligible for FinFETs with lightly doped channel, leaving metal gate granularity and line-edge roughness as the two major random variation sources. The variations of Vth induced by these two major categories are theoretically decomposed based on the distinction in physical mechanisms and their influences on different electrical characteristics. The effectiveness of the proposed method is confirmed through both TCAD simulations and experimental results. This letter can provide helpful guidelines for variation-aware technology development

Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

Though deep learning-based object detection methods have achieved promising results on the conventional datasets, it is still challenging to locate objects from the low-quality images captured in adverse weather conditions. The existing methods either have difficulties in balancing the tasks of image enhancement and object detection, or often ignore the latent information beneficial for detection. To alleviate this problem, we propose a novel Image-Adaptive YOLO (IA-YOLO) framework, where each image can be adaptively enhanced for better detection performance. Specifically, a differentiable image processing (DIP) module is presented to take into account the adverse weather conditions for YOLO detector, whose parameters are predicted by a small convolutional neural net-work (CNN-PP). We learn CNN-PP and YOLOv3 jointly in an end-to-end fashion, which ensures that CNN-PP can learn an appropriate DIP to enhance the image for detection in a weakly supervised manner. Our proposed IA-YOLO approach can adaptively process images in both normal and adverse weather conditions. The experimental results are very encouraging, demonstrating the effectiveness of our proposed IA-YOLO method in both foggy and low-light scenarios.Comment: AAAI 2022, Preprint version with Appendi

Rethinking Closed-loop Training for Autonomous Driving

Recent advances in high-fidelity simulators have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v.s. deployment and allowing training to be scaled both safely and cheaply. However, there is a lack of understanding of how to build effective training benchmarks for closed-loop training. In this work, we present the first empirical study which analyzes the effects of different training benchmark designs on the success of learning agents, such as how to design traffic scenarios and scale training environments. Furthermore, we show that many popular RL algorithms cannot achieve satisfactory performance in the context of autonomous driving, as they lack long-term planning and take an extremely long time to train. To address these issues, we propose trajectory value learning (TRAVL), an RL-based driving agent that performs planning with multistep look-ahead and exploits cheaply generated imagined data for efficient learning. Our experiments show that TRAVL can learn much faster and produce safer maneuvers compared to all the baselines. For more information, visit the project website: https://waabi.ai/research/travlComment: ECCV 202

Genome-scale identification of Caenorhabditis elegans regulatory elements by tiling-array mapping of DNase I hypersensitive sites

<p>Abstract</p> <p>Background</p> <p>A major goal of post-genomics research is the integrated analysis of genes, regulatory elements and the chromatin architecture on a genome-wide scale. Mapping DNase I hypersensitive sites within the nuclear chromatin is a powerful and well-established method of identifying regulatory element candidates.</p> <p>Results</p> <p>Here, we report the first genome-wide analysis of DNase I hypersensitive sites (DHSs) in <it>Caenorhabditis elegans</it>. The data was obtained by hybridizing DNase I-treated and end-captured material from young adult worms to a high-resolution tiling microarray. The data show that <it>C. elegans </it>DHSs were significantly enriched within intergenic regions located 2 kb upstream and downstream of coding genes, and also that a considerable fraction of all DHSs mapped to intergenic positions distant to annotated coding genes. Annotated transcribed loci were generally depleted in DHSs relative to intergenic regions, but DHSs were nonetheless enriched in coding exons and UTRs, whereas introns were significantly depleted in DHSs. Many DHSs appeared to be associated with annotated non-coding RNAs and recently detected transcripts of unknown function. It has been reported that nematode highly conserved non-coding elements were associated with cis-regulatory elements, and we also found that DHSs, particularly distal intergenic DHSs, were significantly enriched in regions that were conserved between the <it>C. elegans </it>and <it>C. briggsae </it>genomes.</p> <p>Conclusion</p> <p>We describe the first genome-wide analysis of <it>C. elegans </it>DHSs, and show that the distribution of DHSs is strongly associated with functional elements in the genome.</p

Differential expression of miRNAs related to caste differentiation in the honey bee, Apis mellifera

International audienceAbstractHoney bees are very important eusocial insects and are involved in the pollination of many plants. Queen bees and worker bees can develop from the same fertilized eggs and are thus genetically identical despite their substantial behavioral and physiological differences. The mechanism governing developmental differences between worker and queen bees has always attracted much interest. While there are several reports on messenger RNA (mRNA) expression related to caste differentiation or microRNA (miRNA) expression in one time point of caste differentiation, no systematic investigation of the dynamic expression of small RNAs along with these two caste development has, thus far, been carried out. In this study, we present the dynamic expression profiles of queen and worker bee small RNAs and show caste-specific miRNA expression patterns between them, indicating that miRNAs may be related to the differential development of worker and queen bee larvae. Results presented here will make a valuable contribution to understanding of the caste switch between worker and queen bees

Recipe for a Busy Bee: MicroRNAs in Honey Bee Caste Determination

Social caste determination in the honey bee is assumed to be determined by the dietary status of the young larvae and translated into physiological and epigenetic changes through nutrient-sensing pathways. We have employed Illumina/Solexa sequencing to examine the small RNA content in the bee larval food, and show that worker jelly is enriched in miRNA complexity and abundance relative to royal jelly. The miRNA levels in worker jelly were 7-215 fold higher than in royal jelly, and both jellies showed dynamic changes in miRNA content during the 4(th) to 6(th) day of larval development. Adding specific miRNAs to royal jelly elicited significant changes in queen larval mRNA expression and morphological characters of the emerging adult queen bee. We propose that miRNAs in the nurse bee secretions constitute an additional element in the regulatory control of caste determination in the honey bee.The research was supported by National Sciences Foundation of China Grant No.30630040; National Key Basic Research & Development Program 973 under Grant Nos. 2009CB825401 and 2007CB946901 to RSC, the earmarked fund for Modern Agro-industry Technology Research System (No. CARS-45-KXJ3), and a grant of the National Natural Science Foundation of China (NSFC 30571409) to SKS, and the Nature and Science Foundation Commission of Zhejiang Province (R3080306) to SKS. Zhang was supported by the Australian Research Council through the ARC Centre of Excellence in Vision Science (CE0561903). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Investigation on the amplitude of random telegraph noise (RTN) in nanoscale MOSFETs: Scaling limit of “Hole in the inversion layer” model

In this paper, the widely adopted “hole in the inversion layer” (HIL) model for predicting the amplitude of random telegraph noise (RTN) in nanoscale MOSFETs, is theoretically revisited with focusing on its scaling limit and validation range. It is found that this simple physical model fail to apply on ultra-scaled devices with L&lt;;20nm and/or W&lt;;10nm, due to the non-negligible impact from source/drain and the failure of assumed equivalence to resistor network in ultra-scaled devices. This work provides a deeper understanding to this model and is helpful for accurate prediction of RTN amplitude in nanoscale devices and circuits

Correlation between inflammatory marker and lipid metabolism in patients with uterine leiomyomas

IntroductionObesity is a risk factor for the development of uterine leiomyoma (UL), and the inflammatory response plays a key role in the pathogenesis of UL. Our objective was to assess whether there was an independent relationship between inflammatory markers and triglycerides (TG) in patients with UL.Methods1,477 UL participants who were hospitalized at the Jining Medical University between January 2016 and December 2022 were included in this cross-sectional study. The independent and dependent variables measured at baseline were inflammatory markers and TG levels, respectively. The covariates were age, body mass index (BMI), UL and menstrual status. Based on the number of fibroids, the study population was divided into Single-group and Multiple-group.ResultsUnivariate and multiple regression analyses and stratified analyses revealed significant positive correlations between neutrophil-lymphocyte ratio and systemic immune inflammation index and TG, and significant negative correlations between monocyte-lymphocyte ratio and TG.ConclusionThe findings show a significant correlation between the inflammatory response and lipid metabolism levels in UL patients. This provides direction for further research into the pathophysiology of UL and also helps to formulate hypotheses for predictive models of UL

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.Comment: 32 pages, 16 figures. Homepage: https://atm22.grand-challenge.org/. Submitte