10 research outputs found
A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition
The joint task of Dialog Sentiment Classification (DSC) and Act Recognition
(DAR) aims to predict the sentiment label and act label for each utterance in a
dialog simultaneously. However, current methods encode the dialog context in
only one direction, which limits their ability to thoroughly comprehend the
context. Moreover, these methods overlook the explicit correlations between
sentiment and act labels, which leads to an insufficient ability to capture
rich sentiment and act clues and hinders effective and accurate reasoning. To
address these issues, we propose a Bi-directional Multi-hop Inference Model
(BMIM) that leverages a feature selection network and a bi-directional
multi-hop inference network to iteratively extract and integrate rich sentiment
and act clues in a bi-directional manner. We also employ contrastive learning
and dual learning to explicitly model the correlations of sentiment and act
labels. Our experiments on two widely-used datasets show that BMIM outperforms
state-of-the-art baselines by at least 2.6% on F1 score in DAR and 1.4% on F1
score in DSC. Additionally, Our proposed model not only improves the
performance but also enhances the interpretability of the joint sentiment and
act prediction task.Comment: Accepted by NLPCC 202
ScalAna: Automating Scaling Loss Detection with Graph Analysis
Scaling a parallel program to modern supercomputers is challenging due to
inter-process communication, Amdahl's law, and resource contention. Performance
analysis tools for finding such scaling bottlenecks either base on profiling or
tracing. Profiling incurs low overheads but does not capture detailed
dependencies needed for root-cause analysis. Tracing collects all information
at prohibitive overheads. In this work, we design ScalAna that uses static
analysis techniques to achieve the best of both worlds - it enables the
analyzability of traces at a cost similar to profiling. ScalAna first leverages
static compiler techniques to build a Program Structure Graph, which records
the main computation and communication patterns as well as the program's
control structures. At runtime, we adopt lightweight techniques to collect
performance data according to the graph structure and generate a Program
Performance Graph. With this graph, we propose a novel approach, called
backtracking root cause detection, which can automatically and efficiently
detect the root cause of scaling loss. We evaluate ScalAna with real
applications. Results show that our approach can effectively locate the root
cause of scaling loss for real applications and incurs 1.73% overhead on
average for up to 2,048 processes. We achieve up to 11.11% performance
improvement by fixing the root causes detected by ScalAna on 2,048 processes.Comment: conferenc
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
SCALANA: Automating Scaling Loss Detection with Graph Analysis
Scaling a parallel program to modern supercomputers is challenging due to inter-process communication, Amdahl's law, and resource contention. Performance analysis tools for finding such scaling bottlenecks either base on profiling or tracing. Profiling incurs low overheads but does not capture detailed dependencies needed for root-cause analysis. Tracing collects all information at prohibitive overheads. In this work, we design SCALANA that uses static analysis techniques to achieve the best of both worlds - it enables the analyzability of traces at a cost similar to profiling. SCALANA first leverages static compiler techniques to build a Program Structure Graph, which records the main computation and communication patterns as well as the program's control structures. At runtime, we adopt lightweight techniques to collect performance data according to the graph structure and generate a Program Performance Graph. With this graph, we propose a novel approach, called backtracking root cause detection, which can automatically and efficiently detect the root cause of scaling loss. We evaluate SCALANA with real applications. Results show that our approach can effectively locate the root cause of scaling loss for real applications and incurs 1.73parcent overhead on average for up to 2,048 processes. We achieve up to 11.11parcent performance improvement by fixing the root causes detected by SCALANA on 2,048 processes. © 2020 IEE
Diversity and Characterization of Multicellular Magnetotactic Prokaryotes From Coral Reef Habitats of the Paracel Islands, South China Sea
International audienceWhile multicellular magnetotactic prokaryotes (MMPs) are ubiquitous in marine environments, the diversity of MMPs in sediments of coral reef ecosystems has rarely been reported. In this study, we made an investigation on the diversity and characteristics of MMPs in sediments at 11 stations in coral reef habitats of the Paracel Islands. The results showed that MMPs were present at nine stations, with spherical mulberry-like MMPs (s-MMPs) found at all stations and ellipsoidal pineapple-like MMPs (e-MMPs) found at seven stations. The maximum abundance of MMPs was 6 ind./cm 3. Phylogenetic analysis revealed the presence of one e-MMP species and five s-MMP species including two species of a new genus. The results indicate that coral reef habitats of the Paracel Islands have a high diversity of MMPs that bio-mineralize multiple intracellular chains of iron crystals and play important role in iron cycling in such oligotrophic environment. These observations provide new perspective of the diversity of MMPs in general and expand knowledge of the occurrence of MMPs in coral reef habitats
Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2021
The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a suite of database resources to support worldwide research activities in both academia and industry. With the explosive growth of multiomics data, CNCB-NGDC is continually expanding, updating and enriching its core database resources through big data deposition, integration and translation. In the past year, considerable efforts have been devoted to 2019nCoVR, a newly established resource providing a global landscape of SARS-CoV-2 genomic sequences, variants, and haplotypes, as well as Aging Atlas, BrainBase, GTDB (Glycosyltransferases Database), LncExpDB, and TransCirc (Translation potential for circular RNAs). Meanwhile, a series of resources have been updated and improved, including BioProject, BioSample, GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas) as well as several biodiversity and plant resources. Particularly, BIG Search, a scalable, one-stop, cross-database search engine, has been significantly updated by providing easy access to a large number of internal and external biological resources from CNCB-NGDC, our partners, EBI and NCBI. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn