An Efficient Source Model Selection Framework in Model Databases

With the explosive increase of big data, training a Machine Learning (ML) model becomes a computation-intensive workload, which would take days or even weeks. Thus, reusing an already trained model has received attention, which is called transfer learning. Transfer learning avoids training a new model from scratch by transferring knowledge from a source task to a target task. Existing transfer learning methods mostly focus on how to improve the performance of the target task through a specific source model, and assume that the source model is given. Although many source models are available, it is difficult for data scientists to select the best source model for the target task manually. Hence, how to efficiently select a suitable source model in a model database for model reuse is an interesting but unsolved problem. In this paper, we propose SMS, an effective, efficient, and flexible source model selection framework. SMS is effective even when the source and target datasets have significantly different data labels, and is flexible to support source models with any type of structure, and is efficient to avoid any training process. For each source model, SMS first vectorizes the samples in the target dataset into soft labels by directly applying this model to the target dataset, then uses Gaussian distributions to fit for clusters of soft labels, and finally measures the distinguishing ability of the source model using Gaussian mixture-based metric. Moreover, we present an improved SMS (I-SMS), which decreases the output number of the source model. I-SMS can significantly reduce the selection time while retaining the selection performance of SMS. Extensive experiments on a range of practical model reuse workloads demonstrate the effectiveness and efficiency of SMS

SparDL: Distributed Deep Learning Training with Efficient Sparse Communication

Top-k sparsification has recently been widely used to reduce the communication volume in distributed deep learning. However, due to the Sparse Gradient Accumulation (SGA) dilemma, the performance of top-k sparsification still has limitations. Recently, a few methods have been put forward to handle the SGA dilemma. Regrettably, even the state-of-the-art method suffers from several drawbacks, e.g., it relies on an inefficient communication algorithm and requires extra transmission steps. Motivated by the limitations of existing methods, we propose a novel efficient sparse communication framework, called SparDL. Specifically, SparDL uses the Spar-Reduce-Scatter algorithm, which is based on an efficient Reduce-Scatter model, to handle the SGA dilemma without additional communication operations. Besides, to further reduce the latency cost and improve the efficiency of SparDL, we propose the Spar-All-Gather algorithm. Moreover, we propose the global residual collection algorithm to ensure fast convergence of model training. Finally, extensive experiments are conducted to validate the superiority of SparDL

Large Language Models are reasoners with Self-Verification

When a large language model (LLM) performs complex reasoning by chain of thought (CoT), it can be highly sensitive to individual mistakes. We have had to train verifiers to address this issue. As we all know, after human inferring a conclusion, they often check it by re-verifying it, which can avoid some mistakes. We propose a new method called self-verification that uses the conclusion of the CoT as a condition to build a new sample and asks the LLM to re-predict the original conditions which be masked. We calculate an explainable verification score based on the accuracy. This method can improve the accuracy of multiple arithmetics and logical reasoning datasets when using few-shot learning. we have demonstrated that LLMs can conduct explainable self-verification of their own conclusions and achieve competitive reasoning performance. Extensive experimentals have demonstrated that our method can help multiple large language models with self-verification can avoid interference from incorrect CoT. Code is available at \url{https://github.com/WENGSYX/Self-Verification

Genome-Wide Expression Analysis in Down Syndrome: Insight into Immunodeficiency

Down syndrome (DS) is caused by triplication of Human chromosome 21 (Hsa21) and associated with an array of deleterious phenotypes, including mental retardation, heart defects and immunodeficiency. Genome-wide expression patterns of uncultured peripheral blood cells are useful to understanding of DS-associated immune dysfunction. We used a Human Exon microarray to characterize gene expression in uncultured peripheral blood cells derived from DS individuals and age-matched controls from two age groups: neonate (N) and child (C). A total of 174 transcript clusters (gene-level) with eight located on Hsa21 in N group and 383 transcript clusters including 56 on Hsa21 in C group were significantly dysregulated in DS individuals. Microarray data were validated by quantitative polymerase chain reaction. Functional analysis revealed that the dysregulated genes in DS were significantly enriched in two and six KEGG pathways in N and C group, respectively. These pathways included leukocyte trans-endothelial migration, B cell receptor signaling pathway and primary immunodeficiency, etc., which causally implicated dysfunctional immunity in DS. Our results provided a comprehensive picture of gene expression patterns in DS at the two developmental stages and pointed towards candidate genes and molecular pathways potentially associated with the immune dysfunction in DS

Identification of Dysregulated Complement Activation Pathways Driven by N-Glycosylation Alterations in T2D Patients

Diabetes has become a major public health concern worldwide, most of which are type 2 diabetes (T2D). The diagnosis of T2D is commonly based on plasma glucose levels, and there are no reliable clinical biomarkers available for early detection. Recent advances in proteome technologies offer new opportunity for the understanding of T2D; however, the underlying proteomic characteristics of T2D have not been thoroughly investigated yet. Here, using proteomic and glycoproteomic profiling, we provided a comprehensive landscape of molecular alterations in the fasting plasma of the 24 Chinese participants, including eight T2D patients, eight prediabetic (PDB) subjects, and eight healthy control (HC) individuals. Our analyses identified a diverse set of potential biomarkers that might enhance the efficiency and accuracy based on current existing biological indicators of (pre)diabetes. Through integrative omics analysis, we showed the capability of glycoproteomics as a complement to proteomics or metabolomics, to provide additional insights into the pathogenesis of (pre)diabetes. We have newly identified systemic site-specific N-glycosylation alterations underlying T2D patients in the complement activation pathways, including decreased levels of N-glycopeptides from C1s, MASP1, and CFP proteins, and increased levels of N-glycopeptides from C2, C4, C4BPA, C4BPB, and CFH. These alterations were not observed at proteomic levels, suggesting new opportunities for the diagnosis and treatment of this disease. Our results demonstrate a great potential role of glycoproteomics in understanding (pre)diabetes and present a new direction for diabetes research which deserves more attention

Causative agent distribution and antibiotic therapy assessment among adult patients with community acquired pneumonia in Chinese urban population

<p>Abstract</p> <p>Background</p> <p>Knowledge of predominant microbial patterns in community-acquired pneumonia (CAP) constitutes the basis for initial decisions about empirical antimicrobial treatment, so a prospective study was performed during 2003–2004 among CAP of adult Chinese urban populations.</p> <p>Methods</p> <p>Qualified patients were enrolled and screened for bacterial, atypical, and viral pathogens by sputum and/or blood culturing, and by antibody seroconversion test. Antibiotic treatment and patient outcome were also assessed.</p> <p>Results</p> <p>Non-viral pathogens were found in 324/610 (53.1%) patients among whom <it>M. pneumoniae </it>was the most prevalent (126/610, 20.7%). Atypical pathogens were identified in 62/195 (31.8%) patients carrying bacterial pathogens. Respiratory viruses were identified in 35 (19%) of 184 randomly selected patients with adenovirus being the most common (16/184, 8.7%). The nonsusceptibility of <it>S. pneumoniae </it>to penicillin and azithromycin was 22.2% (Resistance (R): 3.2%, Intermediate (I): 19.0%) and 79.4% (R: 79.4%, I: 0%), respectively. Of patients (312) from whom causative pathogens were identified and antibiotic treatments were recorded, clinical cure rate with β-lactam antibiotics alone and with combination of a β-lactam plus a macrolide or with fluoroquinolones was 63.7% (79/124) and 67%(126/188), respectively. For patients having mixed <it>M. pneumoniae </it>and/or <it>C. pneumoniae </it>infections, a better cure rate was observed with regimens that are active against atypical pathogens (e.g. a β-lactam plus a macrolide, or a fluoroquinolone) than with β-lactam alone (75.8% vs. 42.9%, <it>p </it>= 0.045).</p> <p>Conclusion</p> <p>In Chinese adult CAP patients, <it>M. pneumoniae </it>was the most prevalent with mixed infections containing atypical pathogens being frequently observed. With <it>S. pneumoniae</it>, the prevalence of macrolide resistance was high and penicillin resistance low compared with data reported in other regions.</p

Genome Sequence of the Versatile Fish Pathogen Edwardsiella tarda Provides Insights into its Adaptation to Broad Host Ranges and Intracellular Niches

BACKGROUND:Edwardsiella tarda is the etiologic agent of edwardsiellosis, a devastating fish disease prevailing in worldwide aquaculture industries. Here we describe the complete genome of E. tarda, EIB202, a highly virulent and multi-drug resistant isolate in China. METHODOLOGY/PRINCIPAL FINDINGS:E. tarda EIB202 possesses a single chromosome of 3,760,463 base pairs containing 3,486 predicted protein coding sequences, 8 ribosomal rRNA operons, and 95 tRNA genes, and a 43,703 bp conjugative plasmid harboring multi-drug resistant determinants and encoding type IV A secretion system components. We identified a full spectrum of genetic properties related to its genome plasticity such as repeated sequences, insertion sequences, phage-like proteins, integrases, recombinases and genomic islands. In addition, analysis also indicated that a substantial proportion of the E. tarda genome might be devoted to the growth and survival under diverse conditions including intracellular niches, with a large number of aerobic or anaerobic respiration-associated proteins, signal transduction proteins as well as proteins involved in various stress adaptations. A pool of genes for secretion systems, pili formation, nonfimbrial adhesions, invasions and hemagglutinins, chondroitinases, hemolysins, iron scavenging systems as well as the incomplete flagellar biogenesis might feature its surface structures and pathogenesis in a fish body. CONCLUSION/SIGNIFICANCE:Genomic analysis of the bacterium offered insights into the phylogeny, metabolism, drug-resistance, stress adaptation, and virulence characteristics of this versatile pathogen, which constitutes an important first step in understanding the pathogenesis of E. tarda to facilitate construction of a practical effective vaccine used for combating fish edwardsiellosis