Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality decomposition to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. The source codes are publicly available at https://github.com/rain305f/OSP.Comment: Accpected by CVPR 202

Simvastatin reduces atherogenesis and promotes the expression of hepatic genes associated with reverse cholesterol transport in apoE-knockout mice fed high-fat diet

<p>Abstract</p> <p>Background</p> <p>Statins are first-line pharmacotherapeutic agents for hypercholesterolemia treatment in humans. However the effects of statins on atherosclerosis in mouse models are very paradoxical. In this work, we wanted to evaluate the effects of simvastatin on serum cholesterol, atherogenesis, and the expression of several factors playing important roles in reverse cholesterol transport (RCT) in apoE-/- mice fed a high-fat diet.</p> <p>Results</p> <p>The atherosclerotic lesion formation displayed by oil red O staining positive area was reduced significantly by 35% or 47% in either aortic root section or aortic arch en face in simvastatin administrated apoE-/- mice compared to the control. Plasma analysis by enzymatic method or ELISA showed that high-density lipoprotein-cholesterol (HDL-C) and apolipoprotein A-I (apoA-I) contents were remarkably increased by treatment with simvastatin. And plasma lecithin-cholesterol acyltransferase (LCAT) activity was markedly increased by simvastatin treatment. Real-time PCR detection disclosed that the expression of several transporters involved in reverse cholesterol transport, including macrophage scavenger receptor class B type I, hepatic ATP-binding cassette (ABC) transporters ABCG5, and ABCB4 were induced by simvastatin treatment, the expression of hepatic ABCA1 and apoA-I, which play roles in the maturation of HDL-C, were also elevated in simvastatin treated groups.</p> <p>Conclusions</p> <p>We demonstrated the anti-atherogenesis effects of simvastatin in apoE-/- mice fed a high-fat diet. We confirmed here for the first time simvastatin increased the expression of hepatic ABCB4 and ABCG5, which involved in secretion of cholesterol and bile acids into the bile, besides upregulated ABCA1 and apoA-I. The elevated HDL-C level, increased LCAT activity and the stimulation of several transporters involved in RCT may all contribute to the anti-atherosclerotic effect of simvastatin.</p

L2L_2BN: Enhancing Batch Normalization by Equalizing the L2L_2 Norms of Features

In this paper, we show that the difference in $l_2$ norms of sample features can hinder batch normalization from obtaining more distinguished inter-class features and more compact intra-class features. To address this issue, we propose an intuitive but effective method to equalize the $l_2$ norms of sample features. Concretely, we $l_2$-normalize each sample feature before feeding them into batch normalization, and therefore the features are of the same magnitude. Since the proposed method combines the $l_2$ normalization and batch normalization, we name our method $L_2$BN. The $L_2$BN can strengthen the compactness of intra-class features and enlarge the discrepancy of inter-class features. The $L_2$BN is easy to implement and can exert its effect without any additional parameters or hyper-parameters. Therefore, it can be used as a basic normalization method for neural networks. We evaluate the effectiveness of $L_2$BN through extensive experiments with various models on image classification and acoustic scene classification tasks. The results demonstrate that the $L_2$BN can boost the generalization ability of various neural network models and achieve considerable performance improvements

scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data

Motivation: Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 30 enriched strategy in library construction, the most commonly used scRNA-seq protocol—10 Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Results: Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data.This research was supported in part by the Fundamental Research Funds for the Central Universities in China [Xiamen University: 20720170076 and 20720190106], and the National Natural Science Foundation of China [61802323, 31801268 and 61573296]

A colorimetric method for point mutation detection using high-fidelity DNA ligase

The present study reported proof-of-principle for a genotyping assay approach that can detect single nucleotide polymorphisms (SNPs) through the gold nanoparticle assembly and the ligase reaction. By incorporating the high-fidelity DNA ligase (Tth DNA ligase) into the allele-specific ligation-based gold nanoparticle assembly, this assay provided a convenient yet powerful colorimetric detection that enabled a straightforward single-base discrimination without the need of precise temperature control. Additionally, the ligase reaction can be performed at a relatively high temperature, which offers the benefit for mitigating the non-specific assembly of gold nanoparticles induced by interfering DNA strands. The assay could be implemented via three steps: a hybridization reaction that allowed two gold nanoparticle-tagged probes to hybrid with the target DNA strand, a ligase reaction that generates the ligation between perfectly matched probes while no ligation occurred between mismatched ones and a thermal treatment at a relatively high temperature that discriminate the ligation of probes. When the reaction mixture was heated to denature the formed duplex, the purple color of the perfect-match solution would not revert to red, while the mismatch gave a red color as the assembled gold nanoparticles disparted. The present approach has been demonstrated with the identification of a single-base mutation in codon 12 of a K-ras oncogene that is of significant value for colorectal cancers diagnosis, and the wild-type and mutant type were successfully scored. To our knowledge, this was the first report concerning SNP detection based on the ligase reaction and the gold nanoparticle assembly. Owing to its ease of operation and high specificity, it was expected that the proposed procedure might hold great promise in practical clinical diagnosis of gene-mutant diseases

Word n+2 preview effects in three-character chinese idioms and phrases

Prior research using the boundary paradigm suggests that Chinese readers only process word n+2 in the parafovea when word n+1 is a single character, high-frequency word. We attempted to replicate these findings (Experiment 1), and investigated whether greater n+2 preview effects are observed when word n+1 and n+2 form an idiom rather than a phrase (Experiment 2). Experiment 1 replicated prior findings, although additional analyses of word n+1 and n+2 as a single region revealed significant preview effects regardless of word n+1 frequency. In Experiment 2 there was a main effect of phrase type, such that idioms were read more quickly than phrases, and significant n+2 preview effects. There was no interaction between these variables, suggesting that idioms are not parafoveally processed to a greater extent than phrases. These results suggest that n+2 preview effects in Chinese occur under several circumstances. Factors influencing the observation of these effects are discussed

Looking into gait for perceiving emotions via bilateral posture and movement graph convolutional networks

Emotions can be perceived from a person's gait, i.e., their walking style. Existing methods on gait emotion recognition mainly leverage the posture information as input, but ignore the body movement, which contains complementary information for recognizing emotions evoked in the gait. In this paper, we propose a Bilateral Posture and Movement Graph Convolutional Network (BPM-GCN) that consists of two parallel streams, namely posture stream and movement stream, to recognize emotions from two views. The posture stream aims to explicitly analyse the emotional state of the person. Specifically, we design a novel regression constraint based on the hand-engineered features to distill the prior affective knowledge into the network and boost the representation learning. The movement stream is designed to describe the intensity of the emotion, which is an implicitly cue for recognizing emotions. To achieve this goal, we employ a higher-order velocity-acceleration pair to construct graphs, in which the informative movement features are utilized. Besides, we design a PM-Interacted feature fusion mechanism to adaptively integrate the features from the two streams. Therefore, the two streams collaboratively contribute to the performance from two complementary views. Extensive experiments on the largest benchmark dataset Emotion-Gait show that BPM-GCN performs favorably against the state-of-the-art approaches (with at least 4.59% performance improvement). The source code is released on https://github.com/exped1230/BPM-GCN