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

GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping

Constructing a 3D scene capable of accommodating open-ended language queries, is a pivotal pursuit, particularly within the domain of robotics. Such technology facilitates robots in executing object manipulations based on human language directives. To tackle this challenge, some research efforts have been dedicated to the development of language-embedded implicit fields. However, implicit fields (e.g. NeRF) encounter limitations due to the necessity of processing a large number of input views for reconstruction, coupled with their inherent inefficiencies in inference. Thus, we present the GaussianGrasper, which utilizes 3D Gaussian Splatting to explicitly represent the scene as a collection of Gaussian primitives. Our approach takes a limited set of RGB-D views and employs a tile-based splatting technique to create a feature field. In particular, we propose an Efficient Feature Distillation (EFD) module that employs contrastive learning to efficiently and accurately distill language embeddings derived from foundational models. With the reconstructed geometry of the Gaussian field, our method enables the pre-trained grasping model to generate collision-free grasp pose candidates. Furthermore, we propose a normal-guided grasp module to select the best grasp pose. Through comprehensive real-world experiments, we demonstrate that GaussianGrasper enables robots to accurately query and grasp objects with language instructions, providing a new solution for language-guided manipulation tasks. Data and codes can be available at https://github.com/MrSecant/GaussianGrasper

How do Chinese students collaborate in EFL group work?

Despite its origins in Western educational settings, communicative methodology, including the use of groups, has been largely accepted in both English-speaking and non-English-speaking English as a Foreign Language (EFL) contexts across the world. However, students who approach learning from a highly collectivist orientation may experience discomfort in Western-style group work situations that require a combination of both cooperative and individualist behaviour from participants. This study examines the collaborative behaviour of Chinese university students when they work in groups in English language lessons. The study shows that while Chinese students can collaborate successfully in groups, there are aspects of Chinese culture that may limit the effectiveness of group work in the language classroom in China.12 page(s

VDAC1 Negatively Regulates Floral Transition in Arabidopsis thaliana

Voltage-dependent anion channels (VDACs) are the most important proteins in mitochondria. They localize to the outer mitochondrial membrane and contribute to the metabolite transport between the mitochondria and cytoplasm, which aids plant growth regulation. Here, we report that Arabidopsis thaliana VDAC1 is involved in the floral transition, with the loss of AtVDAC1 function, resulting in an early-flowering phenotype. AtVDAC1 is expressed ubiquitously in Arabidopsis. To identify the flowering pathway integrators that may be responsible for AtVDAC1′s function during the floral transition, an RNA-seq analysis was performed. In total, 106 differentially expressed genes (DEGs) were identified between wild-type and atvdac1-5 mutant seedlings. However, none were involved in flowering-related pathways. In contrast, AtVDAC1 physically associated with FLOWERING LOCUS T. Thus, in the floral transition, AtVDAC1 may function partly through the FLOWERING LOCUS T protein

PINK1-mediated Drp1S616 phosphorylation modulates synaptic development and plasticity via promoting mitochondrial fission.

Dynamic change of mitochondrial morphology and distribution along neuronal branches are essential for neural circuitry formation and synaptic efficacy. However, the underlying mechanism remains elusive. We show here that Pink1 knockout (KO) mice display defective dendritic spine maturation, reduced axonal synaptic vesicles, abnormal synaptic connection, and attenuated long-term synaptic potentiation (LTP). Drp1 activation via S616 phosphorylation rescues deficits of spine maturation in Pink1 KO neurons. Notably, mice harboring a knockin (KI) phosphor-null Drp1S616A recapitulate spine immaturity and synaptic abnormality identified in Pink1 KO mice. Chemical LTP (cLTP) induces Drp1S616 phosphorylation in a PINK1-dependent manner. Moreover, phosphor-mimetic Drp1S616D restores reduced dendritic spine localization of mitochondria in Pink1 KO neurons. Together, this study provides the first in vivo evidence of functional regulation of Drp1 by phosphorylation and suggests that PINK1-Drp1S616 phosphorylation coupling is essential for convergence between mitochondrial dynamics and neural circuitry formation and refinement

Extrusion pump ABCC1 was first linked with nonsyndromic hearing loss in humans by stepwise genetic analysis

To determine the genetic etiology of deafness in a family (HN-SD01) with autosomal dominant nonsyndromic hearing loss (NSHL). Stepwise genetic analysis was performed on family HN-SD01, including hotspot variant screening, exome sequencing, virtual hearing loss gene panel, and genome-wide linkage analysis. Targeted region sequencing was used to screen ABCC1 in additional cases. Cochlear expression of Abcc1 was evaluated by messenger RNA (mRNA) and protein levels. Computational prediction, immunofluorescence, real-time quantitative polymerase chain reaction, and flow cytometry were conducted to uncover functional consequences of candidate variants. Stepwise genetic analysis identified a heterozygous missense variant, ABCC1:c.1769A>G (p.Asn590Ser), cosegregating with phenotype in HN-SD01. Screening of ABCC1 in an additional 217 cases identified candidate pathogenic variants c.692G>A (p.Gly231Asp) in a sporadic case and c.887A>T (p.Glu296Val) in a familial proband. Abcc1 expressed in stria vascularis and auditory nerve of mouse cochlea. Immunofluorescence showed p.Asn590Ser distributed in cytomembrane and cytoplasm, while wild type was shown only in cytomembrane. Besides, it generated unstable mRNA and decreased efflux capacity of ABCC1. Stepwise genetic analysis is efficient to analyze the genetic etiology of NSHL. Variants in ABCC1 are linked with NSHL and suggest an important role of extruding pumps in maintaining cochlea function