Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently

Existing hand detection methods usually follow the pipeline of multiple stages with high computation cost, i.e., feature extraction, region proposal, bounding box regression, and additional layers for rotated region detection. In this paper, we propose a new Scale Invariant Fully Convolutional Network (SIFCN) trained in an end-to-end fashion to detect hands efficiently. Specifically, we merge the feature maps from high to low layers in an iterative way, which handles different scales of hands better with less time overhead comparing to concatenating them simply. Moreover, we develop the Complementary Weighted Fusion (CWF) block to make full use of the distinctive features among multiple layers to achieve scale invariance. To deal with rotated hand detection, we present the rotation map to get rid of complex rotation and derotation layers. Besides, we design the multi-scale loss scheme to accelerate the training process significantly by adding supervision to the intermediate layers of the network. Compared with the state-of-the-art methods, our algorithm shows comparable accuracy and runs a 4.23 times faster speed on the VIVA dataset and achieves better average precision on Oxford hand detection dataset at a speed of 62.5 fps.Comment: Accepted to AAAI201

Core-Periphery Principle Guided Redesign of Self-Attention in Transformers

Designing more efficient, reliable, and explainable neural network architectures is critical to studies that are based on artificial intelligence (AI) techniques. Previous studies, by post-hoc analysis, have found that the best-performing ANNs surprisingly resemble biological neural networks (BNN), which indicates that ANNs and BNNs may share some common principles to achieve optimal performance in either machine learning or cognitive/behavior tasks. Inspired by this phenomenon, we proactively instill organizational principles of BNNs to guide the redesign of ANNs. We leverage the Core-Periphery (CP) organization, which is widely found in human brain networks, to guide the information communication mechanism in the self-attention of vision transformer (ViT) and name this novel framework as CP-ViT. In CP-ViT, the attention operation between nodes is defined by a sparse graph with a Core-Periphery structure (CP graph), where the core nodes are redesigned and reorganized to play an integrative role and serve as a center for other periphery nodes to exchange information. We evaluated the proposed CP-ViT on multiple public datasets, including medical image datasets (INbreast) and natural image datasets. Interestingly, by incorporating the BNN-derived principle (CP structure) into the redesign of ViT, our CP-ViT outperforms other state-of-the-art ANNs. In general, our work advances the state of the art in three aspects: 1) This work provides novel insights for brain-inspired AI: we can utilize the principles found in BNNs to guide and improve our ANN architecture design; 2) We show that there exist sweet spots of CP graphs that lead to CP-ViTs with significantly improved performance; and 3) The core nodes in CP-ViT correspond to task-related meaningful and important image patches, which can significantly enhance the interpretability of the trained deep model.Comment: Core-periphery, functional brain networks, Vi

Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification

With the popularity of deep neural networks (DNNs), model interpretability is becoming a critical concern. Many approaches have been developed to tackle the problem through post-hoc analysis, such as explaining how predictions are made or understanding the meaning of neurons in middle layers. Nevertheless, these methods can only discover the patterns or rules that naturally exist in models. In this work, rather than relying on post-hoc schemes, we proactively instill knowledge to alter the representation of human-understandable concepts in hidden layers. Specifically, we use a hierarchical tree of semantic concepts to store the knowledge, which is leveraged to regularize the representations of image data instances while training deep models. The axes of the latent space are aligned with the semantic concepts, where the hierarchical relations between concepts are also preserved. Experiments on real-world image datasets show that our method improves model interpretability, showing better disentanglement of semantic concepts, without negatively affecting model classification performance

Graph pangenome captures missing heritability and empowers tomato breeding

Missing heritability in genome-wide association studies defines a major problem in genetic analyses of complex biological traits(1,2). The solution to this problem is to identify all causal genetic variants and to measure their individual contributions(3,4). Here we report a graph pangenome of tomato constructed by precisely cataloguing more than 19 million variants from 838 genomes, including 32 new reference-level genome assemblies. This graph pangenome was used forgenome-wide association study analyses and heritability estimation of 20,323 gene-expression and metabolite traits. The average estimated trait heritability is 0.41 compared with 0.33 when using the single linear reference genome. This 24% increase in estimated heritability is largely due to resolving incomplete linkage disequilibrium through the inclusion of additional causal structural variants identified using the graph pangenome. Moreover, by resolving allelic and locus heterogeneity, structural variants improve the power to identify genetic factors underlying agronomically important traits leading to, for example, the identification of two new genes potentially contributing to soluble solid content. The newly identified structural variants will facilitate genetic improvement of tomato through both marker-assisted selection and genomic selection. Our study advances the understanding of the heritability of complex traits and demonstrates the power of the graph pangenome in crop breeding

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Study on the Thermal Stabilizing Process of Layered Double Hydroxides in PVC Resin

Poly(vinyl chloride) (PVC) is widely used in various fields and requires the use of thermal stabilizers to enhance its thermal stability during processing because of its poor thermal stability. Layered double hydroxides (LDHs) are widely considered to be one kind of highly efficient and environmentally friendly PVC thermal stabilizer. To investigate the thermal stabilizing process of layered double hydroxides (LDHs) in PVC resin, PVC and MgAl-LDHs powders with different interlayer anions (CO32−, Cl−, and NO3−) were physically mixed and aged at 180 °C. The structure of LDHs at different aging times was studied using XRD, SEM, and FT-IR. The results show that the thermal stabilizing process of LDHs on PVC mainly has three stages. In the first stage, the layers of LDHs undergo a reaction with HCl, which is released during the thermal decomposition of PVC. Subsequently, the ion exchange process occurs between Cl− and interlayer CO32−, resulting in the formation of MgAl-Cl-LDHs. Finally, the layers of MgAl-Cl-LDHs react with HCl slowly. During the thermal stabilizing process of MgAl-Cl-LDHs, the peak intensity of XRD reduces slightly, and no new XRD peak emerges. It indicates that only the first step happens for MgAl-Cl-LDHs. The TG-DTA analysis of LDHs indicates that the interaction of LDHs with different interlayer anions has the following order: NO3− 32− −, according to the early coloring in the thermal aging test of PVC composites. The results of the thermal aging tests suggest that LDHs with a weak interaction between interlayer anions and layers can enhance the early stability of PVC significantly. Furthermore, the thermal aging test demonstrates that LDHs with high HCl absorption capacities exhibit superior long-term stabilizing effects on PVC resin. This finding provides a valuable hint for designing an LDHs/PVC resin with a novel structure and excellent thermal stability

Oral intake of titanium dioxide nanoparticles affect the course and prognosis of ulcerative colitis in mice: involvement of the ROS-TXNIP-NLRP3 inflammasome pathway

Abstract Background Titanium dioxide (TiO2), no matter in nanoscale or micron sizes, has been widely used in food industry as additives for decades. Given the potential impact of TiO2 on the gastrointestinal epithelial and parenchymal cells, including goblet cells, the public consumers may suffer the risk of diseases caused by its widespread dissemination in food products. We therefore set out to investigate the impact of TiO2 NPs on the course and prognosis of ulcerative colitis by oral gavaging TiO2 NPs at the doses levels of 0, 30, 100, and 300 mg/kg during the induction (7 days, from day 1 to day 7) and recovery (10 days, from day 8 to day 17) phases of colitis in mice. Results The ulcerative colitis (UC) disease model was established by administrating of 2.5% dextran sulfate sodium (DSS) solution. Our results show that TiO2 NPs significantly enhanced the severity of DSS-induced colitis, decreased the body weight, increased the disease activity index (DAI) and colonic mucosa damage index (CMDI) scores, shortened the colonic length, increased the inflammatory infiltration in the colon. The most significant changes occurred in the low dose (30 mg/kg) group of TiO2 NPs exposure during the development phase of UC and the high dose (300 mg/kg) group of TiO2 NPs during UC self-healing phase. Increased reactive oxygen species (ROS) level and upregulation of anti-oxidant enzymes including total superoxide dismutase (T-SOD), glutathione peroxidase (GSH-PX) and catalase (CAT), demonstrate that the TiO2 NP exposure has triggered oxidative stress in mice. Moreover, the upregulation of caspase-1 mRNA and increased expression of thioredoxin interacting protein (TXNIP) further demonstrate the involvement of the ROS-TXNIP-NLR family pyrin domain containing 3 (NLRP3) inflammasome pathway in aggravating the development of UC. Conclusion Oral intake of TiO2 NPs could affect the course of acute colitis in exacerbating the development of UC, prolonging the UC course and inhibiting UC recovery. Graphical Abstrac