Gender Recognition from Unconstrained and Articulated Human Body

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition

Gr\"obner-Shirshov bases and linear bases for free multi-operated algebras over algebras with applications to differential Rota-Baxter algebras and integro-differential algebras

Quite much recent studies has been attracted to the operated algebra since it unifies various notions such as the differential algebra and the Rota-Baxter algebra. An $\Omega$-operated algebra is a an (associative) algebra equipped with a set $\Omega$ of linear operators which might satisfy certain operator identities such as the Leibniz rule. A free $\Omega$-operated algebra $B$ can be generated on an algebra $A$ similar to a free algebra generated on a set. If $A$ has a Gr\"{o}bner-Shirshov basis $G$ and if the linear operators $\Omega$ satisfy a set $\Phi$ of operator identities, it is natural to ask when the union $G\cup \Phi$ is a Gr\"{o}bner-Shirshov basis of $B$. A previous work answers this question affirmatively under a mild condition, and thereby obtains a canonical linear basis of $B$. In this paper, we answer this question in the general case of multiple linear operators. As applications we get operated Gr\"{o}bner-Shirshov bases for free differential Rota-Baxter algebras and free integro-differential algebras over algebras as well as their linear bases. One of the key technical difficulties is to introduce new monomial orders for the case of two operators, which might be of independent interest.Comment: 27 page

Improved cellulolytic efficacy in Penicilium decumbens via heterologous expression of Hypocrea jecorina endoglucanase II

Hypocrea jecorina endoglucanase II (Hjegl2) was heterologously expressed in Penicillium decumbens (yielding strain Pd::Hjegl2). After induction in cellulose containing media, strain Pd::Hjeg2 displayed increased carboxymethylcellulase activity (CMCase, 5.77 IU/ml, representing a 21% increase) and cellulose degradation determined with a filter paper assay (FPA, 0.40 IU/ml, 67% increase), as compared to the parent strain. In media supplemented with glucose (2%), Pd::Hjegl2, displayed 51.2-fold and 3-fold higher CMCase and FPA activities, respectively, as compared to the parent strain. No changes in the expression levels of the four main native cellulase genes of P. decumbens (Pdegl1, Pdegl2, Pdcbh1, and Pdcbh2) were noted between the transformant and wild-type strains. These data support the idea that Hjegl2 cleaves both internal and terminal glycosidic residues, in a relatively random and processive manner. In situ polyacrylamide gelactivity staining of extracts derived from wild-type and Pd::Hjegl2 revealed two additional active fractions in the latter strain; one with a molecular mass ~50-65 KDa and another ~80-116 kDa

Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles

Video Anomaly Detection (VAD) is an important topic in computer vision. Motivated by the recent advances in self-supervised learning, this paper addresses VAD by solving an intuitive yet challenging pretext task, i.e., spatio-temporal jigsaw puzzles, which is cast as a multi-label fine-grained classification problem. Our method exhibits several advantages over existing works: 1) the spatio-temporal jigsaw puzzles are decoupled in terms of spatial and temporal dimensions, responsible for capturing highly discriminative appearance and motion features, respectively; 2) full permutations are used to provide abundant jigsaw puzzles covering various difficulty levels, allowing the network to distinguish subtle spatio-temporal differences between normal and abnormal events; and 3) the pretext task is tackled in an end-to-end manner without relying on any pre-trained models. Our method outperforms state-of-the-art counterparts on three public benchmarks. Especially on ShanghaiTech Campus, the result is superior to reconstruction and prediction-based methods by a large margin.Comment: Accepted by ECCV'2022; Code is available at https://github.com/gdwang08/Jigsaw-VA

A Wave Energy Extraction System in Experimental Flume

Ocean wave energy is a high energy density and renewable resource. High power conversion rate is an advantage of linear generators to be the competitive candidates for ocean wave energy extraction system. In this paper, the feasibility of a wave energy extraction system by linear generator has been verified in an experimental flume. Besides, the analytical equations of heaving buoy oscillating in vertical direction are proposed, and the analytical equations are proved conveniently. What is more, the active power output of linear generator of wave energy extraction system in experimental flume is presented. The theoretical analysis and experimental results play a significant role for future wave energy extraction system progress in real ocean waves

Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection

Anomaly detection (AD), aiming to find samples that deviate from the training distribution, is essential in safety-critical applications. Though recent self-supervised learning based attempts achieve promising results by creating virtual outliers, their training objectives are less faithful to AD which requires a concentrated inlier distribution as well as a dispersive outlier distribution. In this paper, we propose Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation (UniCon-HA), taking into account both the requirements above. Specifically, we explicitly encourage the concentration of inliers and the dispersion of virtual outliers via supervised and unsupervised contrastive losses, respectively. Considering that standard contrastive data augmentation for generating positive views may induce outliers, we additionally introduce a soft mechanism to re-weight each augmented inlier according to its deviation from the inlier distribution, to ensure a purified concentration. Moreover, to prompt a higher concentration, inspired by curriculum learning, we adopt an easy-to-hard hierarchical augmentation strategy and perform contrastive aggregation at different depths of the network based on the strengths of data augmentation. Our method is evaluated under three AD settings including unlabeled one-class, unlabeled multi-class, and labeled multi-class, demonstrating its consistent superiority over other competitors.Comment: Accepted by ICCV'202

Corrigendum: Collection and Curation of Transcriptional Regulatory Interactions in Aspergillus nidulans and Neurospora crassa Reveal Structural and Evolutionary Features of the Regulatory Networks

Transcriptional regulation has important roles in various biological processes (e.g., development and metabolism) in filamentous fungi. However, regulatory interactions between transcription factors (TFs) and their target genes in these species have only been described in different forms by primary scientific literature, which limits the integrated analysis of these data. Here, we extensively curated the reported transcriptional regulatory interactions in Aspergillus nidulans and Neurospora crassa. For each interaction, the identifiers of involved proteins or genes were unified, and the types of supporting experiments were recorded. Then, transcriptional regulatory networks were reconstructed from the interactions supported by classical low-throughput experiments. Analysis of the networks revealed the presence of hub targets regulated by multiple TFs and network motifs of other structures (e.g., regulatory loops). Comparison of the regulatory interactions between the two species identified 33 conserved interactions supported by classical experiments in both species, most of which are involved in the regulation of metabolic genes. We anticipate the curated data would serve as a catalog for the studies of transcriptional regulation in filamentous fungi