Face.evoLVe: A High-Performance Face Recognition Library

In this paper, we develop face.evoLVe -- a comprehensive library that collects and implements a wide range of popular deep learning-based methods for face recognition. First of all, face.evoLVe is composed of key components that cover the full process of face analytics, including face alignment, data processing, various backbones, losses, and alternatives with bags of tricks for improving performance. Later, face.evoLVe supports multi-GPU training on top of different deep learning platforms, such as PyTorch and PaddlePaddle, which facilitates researchers to work on both large-scale datasets with millions of images and low-shot counterparts with limited well-annotated data. More importantly, along with face.evoLVe, images before & after alignment in the common benchmark datasets are released with source codes and trained models provided. All these efforts lower the technical burdens in reproducing the existing methods for comparison, while users of our library could focus on developing advanced approaches more efficiently. Last but not least, face.evoLVe is well designed and vibrantly evolving, so that new face recognition approaches can be easily plugged into our framework. Note that we have used face.evoLVe to participate in a number of face recognition competitions and secured the first place. The version that supports PyTorch is publicly available at https://github.com/ZhaoJ9014/face.evoLVe.PyTorch and the PaddlePaddle version is available at https://github.com/ZhaoJ9014/face.evoLVe.PyTorch/tree/master/paddle. Face.evoLVe has been widely used for face analytics, receiving 2.4K stars and 622 forks.Comment: A short verson is accepted by NeuroComputing (https://www.sciencedirect.com/science/article/pii/S0925231222005057?via%3Dihub). Primary corresponding author is Dr. Jian Zha

TiC: Exploring Vision Transformer in Convolution

While models derived from Vision Transformers (ViTs) have been phonemically surging, pre-trained models cannot seamlessly adapt to arbitrary resolution images without altering the architecture and configuration, such as sampling the positional encoding, limiting their flexibility for various vision tasks. For instance, the Segment Anything Model (SAM) based on ViT-Huge requires all input images to be resized to 1024$\times$1024. To overcome this limitation, we propose the Multi-Head Self-Attention Convolution (MSA-Conv) that incorporates Self-Attention within generalized convolutions, including standard, dilated, and depthwise ones. Enabling transformers to handle images of varying sizes without retraining or rescaling, the use of MSA-Conv further reduces computational costs compared to global attention in ViT, which grows costly as image size increases. Later, we present the Vision Transformer in Convolution (TiC) as a proof of concept for image classification with MSA-Conv, where two capacity enhancing strategies, namely Multi-Directional Cyclic Shifted Mechanism and Inter-Pooling Mechanism, have been proposed, through establishing long-distance connections between tokens and enlarging the effective receptive field. Extensive experiments have been carried out to validate the overall effectiveness of TiC. Additionally, ablation studies confirm the performance improvement made by MSA-Conv and the two capacity enhancing strategies separately. Note that our proposal aims at studying an alternative to the global attention used in ViT, while MSA-Conv meets our goal by making TiC comparable to state-of-the-art on ImageNet-1K. Code will be released at https://github.com/zs670980918/MSA-Conv

On-Farm-Produced Organic Amendments on Maintaining and Enhancing Soil Fertility and Nitrogen Availability in Organic or Low Input Agriculture

Maintaining and enhancing soil fertility are key issues for sustainability in an agricultural system with organic or low input methods. On-farm–produced green manure as a source of soil organic matter (SOM) plays a critical role in long-term productivity. But producing green manure requires land and water; thus, increasing biodiversity, such as by intercropping with green manure crops, could be an approach to enhance the efficiency of renewable resources especially in developing countries. This article discusses soil fertility and its maintenance and enhancement with leguminous intercropping from four points of view: soil fertility and organic matter function, leguminous green manure, intercropping principles, and soil conservation. Important contributions of leguminous intercropping include SOM enhancement and fertility building, biological nitrogen (N) and other plant nutrition availability. Under a well-designed and managed system, competition between the target and intercropping crops can be reduced. The plant uptake efficiency of biologically fixed N is estimated to be double that of industrial N fertilizers. After N-rich plant residues are incorporated into soil, the carbon (C):nitrogen ratio of added straw decreases. Another high mitigation potential of legume intercropping lies in soil conservation by preventing soil and water erosion. Many opportunities exist to introduce legumes in short-term rotation, intercropping, living mulch, and cover crops in an organically managed farm system. Worldwide, long-term soil fertility enhancement remains a challenge due to the current world population and agricultural practices. Cropping system including legumes is a step in the right direction to meeting the needs of food security and sustainability

A Method to Detect AAC Audio Forgery

Advanced Audio Coding (AAC), a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. While AAC is also the default or standard audio format for many devices and AAC audio files may be presented as important digital evidences, the authentication of the audio files is highly needed but relatively missing. In this paper, we propose a scheme to expose tampered AAC audio streams that are encoded at the same encoding bit-rate. Specifically, we design a shift-recompression based method to retrieve the differential features between the re-encoded audio stream at each shifting and original audio stream, learning classifier is employed to recognize different patterns of differential features of the doctored forgery files and original (untouched) audio files. Experimental results show that our approach is very promising and effective to detect the forgery of the same encoding bit-rate on AAC audio streams. Our study also shows that shift recompression-based differential analysis is very effective for detection of the MP3 forgery at the same bit rate

NGF Inhibits M/KCNQ Currents and Selectively Alters Neuronal Excitability in Subsets of Sympathetic Neurons Depending on their M/KCNQ Current Background

M/KCNQ currents play a critical role in the determination of neuronal excitability. Many neurotransmitters and peptides modulate M/KCNQ current and neuronal excitability through their G protein–coupled receptors. Nerve growth factor (NGF) activates its receptor, a member of receptor tyrosine kinase (RTK) superfamily, and crucially modulates neuronal cell survival, proliferation, and differentiation. In this study, we studied the effect of NGF on the neuronal (rat superior cervical ganglion, SCG) M/KCNQ currents and excitability. As reported before, subpopulation SCG neurons with distinct firing properties could be classified into tonic, phasic-1, and phasic-2 neurons. NGF inhibited M/KCNQ currents by similar proportion in all three classes of SCG neurons but increased the excitability only significantly in tonic SCG neurons. The effect of NGF on excitability correlated with a smaller M-current density in tonic neurons. The present study indicates that NGF is an M/KCNQ channel modulator and the characteristic modulation of the neuronal excitability by NGF may have important physiological implications

Self-assembly of hydrofluorinated Janus graphene monolayer:a versatile route for designing novel Janus nanoscrolls

With remarkably interesting surface activities, two-dimensional Janus materials arouse intensive interests recently in many fields. We demonstrate by molecular dynamic simulations that hydrofluorinated Janus graphene (J-GN) can self-assemble into Janus nanoscroll (J-NS) at room temperature. The van der Waals (vdW) interaction and the coupling of C-H/π/C-F interaction and π/π interaction are proven to offer the continuous driving force of self-assembly of J-GN. The results show that J-GN can self-assemble into various J-NSs structures, including arcs, multi-wall J-NS and arm-chair-like J-NS by manipulating its original geometry (size and aspect ratio). Moreover, we also investigated self-assembly of hydrofluorinated J-GN and Fe nanowires (NWs), suggesting that Fe NW is a good alternative to activate J-GN to form J-NS. Differently, the strong vdW interaction between J-GN and Fe NW provides the main driving force of the self-assembly. Finally, we studied the hydrogen sorption over the formed J-NS with a considerable interlayer spacing, which reaches the US DOE target, indicating that J-NS is a promising candidate for hydrogen storage by controlling the temperature of system. Our theoretical results firstly provide a versatile route for designing novel J-NS from 2D Janus nanomaterials, which has a great potential application in the realm of hydrogen storage/separation

HDAC3 is crucial in shear- and VEGF-induced stem cell differentiation toward endothelial cells

Reendothelialization involves endothelial progenitor cell (EPC) homing, proliferation, and differentiation, which may be influenced by fluid shear stress and local flow pattern. This study aims to elucidate the role of laminar flow on embryonic stem (ES) cell differentiation and the underlying mechanism. We demonstrated that laminar flow enhanced ES cell–derived progenitor cell proliferation and differentiation into endothelial cells (ECs). Laminar flow stabilized and activated histone deacetylase 3 (HDAC3) through the Flk-1–PI3K–Akt pathway, which in turn deacetylated p53, leading to p21 activation. A similar signal pathway was detected in vascular endothelial growth factor–induced EC differentiation. HDAC3 and p21 were detected in blood vessels during embryogenesis. Local transfer of ES cell–derived EPC incorporated into injured femoral artery and reduced neointima formation in a mouse model. These data suggest that shear stress is a key regulator for stem cell differentiation into EC, especially in EPC differentiation, which can be used for vascular repair, and that the Flk-1–PI3K–Akt–HDAC3–p53–p21 pathway is crucial in such a process