Energy-based Out-of-distribution Detection

Determining whether inputs are out-of-distribution (OOD) is an essential building block for safely deploying machine learning models in the open world. However, previous methods relying on the softmax confidence score suffer from overconfident posterior distributions for OOD data. We propose a unified framework for OOD detection that uses an energy score. We show that energy scores better distinguish in- and out-of-distribution samples than the traditional approach using the softmax scores. Unlike softmax confidence scores, energy scores are theoretically aligned with the probability density of the inputs and are less susceptible to the overconfidence issue. Within this framework, energy can be flexibly used as a scoring function for any pre-trained neural classifier as well as a trainable cost function to shape the energy surface explicitly for OOD detection. On a CIFAR-10 pre-trained WideResNet, using the energy score reduces the average FPR (at TPR 95%) by 18.03% compared to the softmax confidence score. With energy-based training, our method outperforms the state-of-the-art on common benchmarks

Gunrock: GPU Graph Analytics

For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock's overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries such as Ligra and Galois, and better performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing (TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance Graph Processing Library on the GPU

Transcriptome Profiling to Identify Genes Involved in Mesosulfuron-Methyl Resistance in Alopecurus aequalis

Non-target-site resistance (NTSR) to herbicides is a worldwide concern for weed control. However, as the dominant NTSR mechanism in weeds, metabolic resistance is not yet well-characterized at the genetic level. For this study, we have identified a shortawn foxtail (Alopecurus aequalis Sobol.) population displaying both TSR and NTSR to mesosulfuron-methyl and fenoxaprop-P-ethyl, yet the molecular basis for this NTSR remains unclear. To investigate the mechanisms of metabolic resistance, an RNA-Seq transcriptome analysis was used to find candidate genes that may confer metabolic resistance to the herbicide mesosulfuron-methyl in this plant population. The RNA-Seq libraries generated 831,846,736 clean reads. The de novo transcriptome assembly yielded 95,479 unigenes (averaging 944 bp in length) that were assigned putative annotations. Among these, a total of 29,889 unigenes were assigned to 67 GO terms that contained three main categories, and 14,246 unigenes assigned to 32 predicted KEGG metabolic pathways. Global gene expression was measured using the reads generated from the untreated control (CK), water-only control (WCK), and mesosulfuron-methyl treatment (T) of R and susceptible (S). Contigs that showed expression differences between mesosulfuron-methyl-treated R and S biotypes, and between mesosulfuron-methyl-treated, water-treated and untreated R plants were selected for further quantitative real-time PCR (qRT-PCR) validation analyses. Seventeen contigs were consistently highly expressed in the resistant A. aequalis plants, including four cytochrome P450 monooxygenase (CytP450) genes, two glutathione S-transferase (GST) genes, two glucosyltransferase (GT) genes, two ATP-binding cassette (ABC) transporter genes, and seven additional contigs with functional annotations related to oxidation, hydrolysis, and plant stress physiology. These 17 contigs could serve as major candidate genes for contributing to metabolic mesosulfuron-methyl resistance; hence they deserve further functional study. This is the first large-scale transcriptome-sequencing study to identify NTSR genes in A. aequalis that uses the Illumina platform. This work demonstrates that NTSR is likely driven by the differences in the expression patterns of a set of genes. The assembled transcriptome data presented here provide a valuable resource for A. aequalis biology, and should facilitate the study of herbicide resistance at the molecular level in this and other weed species

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data