Gait recognition under carrying condition : a static dynamic fusion method

When an individual carries an object, such as a briefcase, conventional gait recognition algorithms based on average silhouette/Gait Energy Image (GEI) do not always perform well as the object carried may have the potential of being mistakenly regarded as a part of the human body. To solve such a problem, in this paper, instead of directly applying GEI to represent the gait information, we propose a novel dynamic feature template for classification. Based on this extracted dynamic information and some static feature templates (i.e., head part and trunk part), we cast gait recognition on the large USF (University of South Florida) database by adopting a static/dynamic fusion strategy. For the experiments involving carrying condition covariate, significant improvements are achieved when compared with other classic algorithms

Scalable Multiple Patterning Layout Decomposition Implemented by a Distribution Evolutionary Algorithm

As the feature size of semiconductor technology shrinks to 10 nm and beyond, the multiple patterning lithography (MPL) attracts more attention from the industry. In this paper, we model the layout decomposition of MPL as a generalized graph coloring problem, which is addressed by a distribution evolutionary algorithm based on a population of probabilistic model (DEA-PPM). DEA-PPM can strike a balance between decomposition results and running time, being scalable for varied settings of mask number and lithography resolution. Due to its robustness of decomposition results, this could be an alternative technique for multiple patterning layout decomposition in next-generation technology nodes

A Distribution Evolutionary Algorithm for Graph Coloring

Graph Coloring Problem (GCP) is a classic combinatorial optimization problem that has a wide application in theoretical research and engineering. To address complicated GCPs efficiently, a distribution evolutionary algorithm based on population of probability models (DEA-PPM) is proposed. Based on a novel representation of probability model, DEA-PPM employs a Gaussian orthogonal search strategy to explore the probability space, by which global exploration can be realized using a small population. With assistance of local exploitation on a small solution population, DEA-PPM strikes a good balance between exploration and exploitation. Numerical results demonstrate that DEA-PPM performs well on selected complicated GCPs, which contributes to its competitiveness to the state-of-the-art metaheuristics

Experimental study on secondary bearing mechanism of weakly cemented broken rock mass

In order to study the secondary bearing mechanism of weakly cemented broken surrounding rock, the surface of granite, limestone and mudstone broken rock samples were poured by cement slurry, and the weakly cemented rock mass was formed by static pressure infiltration method, and then an uniaxial loading test was carried out. The results show that the weakly cemented broken rock mass has a certain bearing capacity, but the bearing capacity is low, and the dispersion is high. The secondary bearing capacity of weakly cemented rock mass is affected by factors such as broken rock strength, rock particle size and rock gradation. The larger the rock particle size and strength are, the higher the secondary bearing capacity of the weakly cemented rock mass is. The average bearing capacity of the mudstone weak cementation specimen is 18.77 kN, and the residual bearing capacity is 1.46 kN, and a dispersion coefficient is 0.34. The average bearing capacity of granite is 343.65 kN, and the residual carrying capacity is 25.81 kN, and a dispersion coefficient is 0.11. The average bearing capacity of limestone is 367.22 kN, and the residual carrying capacity is 22.78 kN, and a dispersion coefficient is 0.3. After a certain grading, the average residual secondary bearing capacity of the weakly cemented rock mass is obviously improved, and the dispersion coefficient of peak bearing capacity is reduced. The grading scheme 1 has an average peak carrying capacity of 330.06 kN, a residual carrying capacity of 34.56 kN, and a dispersion coefficient is 0.07. The averaging scheme 2 has an average peak carrying capacity of 297.8 kN, a residual carrying capacity of 29.86 kN, and a dispersion coefficient is 0.14. The cementation regeneration mechanism of the broken rock mass mainly includes the cement-bonding effect of the cement slurry inside and on the broken rock mass. Under the loaded condition, the internal load-bearing network of the broken rock mass is the main mechanism for the secondary load of the broken rock mass, and the stability of the force-chain network is affected by the constraint. After the loss of the confinement, the force chain network fails, and the residual secondary bearing mechanism of the weakly cemented broken rock mass is transformed into the friction between the broken rock masses in the residual core rock pillar

Two dimensional vertex-decorated Lieb lattice with exact mobility edges and robust flat bands

The mobility edge (ME) that marks the energy separating extended and localized states is a central concept in understanding the metal-insulator transition induced by disordered or quasiperiodic potentials. MEs have been extensively studied in three dimensional disorder systems and one-dimensional quasiperiodic systems. However, the studies of MEs in two dimensional (2D) systems are rare. Here we propose a class of 2D vertex-decorated Lieb lattice models with quasiperiodic potentials only acting on the vertices of a (extended) Lieb lattice. By mapping these models to the 2D Aubry-Andr\'{e} model, we obtain exact expressions of MEs and the localization lengths of localized states, and further demonstrate that the flat bands remain unaffected by the quasiperiodic potentials. Finally, we propose a highly feasible scheme to experimentally realize our model in a quantum dot array. Our results open the door to studying and realizing exact MEs and robust flat bands in 2D systems

Hyperbolic Face Anti-Spoofing

Learning generalized face anti-spoofing (FAS) models against presentation attacks is essential for the security of face recognition systems. Previous FAS methods usually encourage models to extract discriminative features, of which the distances within the same class (bonafide or attack) are pushed close while those between bonafide and attack are pulled away. However, these methods are designed based on Euclidean distance, which lacks generalization ability for unseen attack detection due to poor hierarchy embedding ability. According to the evidence that different spoofing attacks are intrinsically hierarchical, we propose to learn richer hierarchical and discriminative spoofing cues in hyperbolic space. Specifically, for unimodal FAS learning, the feature embeddings are projected into the Poincar\'e ball, and then the hyperbolic binary logistic regression layer is cascaded for classification. To further improve generalization, we conduct hyperbolic contrastive learning for the bonafide only while relaxing the constraints on diverse spoofing attacks. To alleviate the vanishing gradient problem in hyperbolic space, a new feature clipping method is proposed to enhance the training stability of hyperbolic models. Besides, we further design a multimodal FAS framework with Euclidean multimodal feature decomposition and hyperbolic multimodal feature fusion & classification. Extensive experiments on three benchmark datasets (i.e., WMCA, PADISI-Face, and SiW-M) with diverse attack types demonstrate that the proposed method can bring significant improvement compared to the Euclidean baselines on unseen attack detection. In addition, the proposed framework is also generalized well on four benchmark datasets (i.e., MSU-MFSD, IDIAP REPLAY-ATTACK, CASIA-FASD, and OULU-NPU) with a limited number of attack types

Dietary Supplementation of Astaxanthin Improved the Growth Performance, Antioxidant Ability and Immune Response of Juvenile Largemouth Bass (Micropterus salmoides) Fed High-Fat Diet

High-fat diet (HFD) usually induces oxidative stress and astaxanthin is regarded as an excellent anti-oxidant. An 8-week feeding trial was conducted to investigate the effects of dietary astaxanthin supplementation on growth performance, lipid metabolism, antioxidant ability, and immune response of juvenile largemouth bass (Micropterus salmoides) fed HFD. Four diets were formulated: the control diet (10.87% lipid, C), high-fat diet (18.08% lipid, HF), and HF diet supplemented with 75 and 150 mg kg−1 astaxanthin (HFA1 and HFA2, respectively). Dietary supplementation of astaxanthin improved the growth of fish fed HFD, also decreased hepatosomatic index and intraperitoneal fat ratio of fish fed HFD, while having no effect on body fat. Malondialdehyde content and superoxide dismutase activity were increased in fish fed HFD, astaxanthin supplementation in HFD decreased the oxidative stress of fish. The supplementation of astaxanthin in HFD also reduced the mRNA levels of Caspase 3, Caspase 9, BAD, and IL15. These results suggested that dietary astaxanthin supplementation in HFD improved the growth performance, antioxidant ability and immune response of largemouth bass.publishedVersio

S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens

Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition system by presenting spoofed faces. State-of-the-art FAS techniques predominantly rely on deep learning models but their cross-domain generalization capabilities are often hindered by the domain shift problem, which arises due to different distributions between training and testing data. In this study, we develop a generalized FAS method under the Efficient Parameter Transfer Learning (EPTL) paradigm, where we adapt the pre-trained Vision Transformer models for the FAS task. During training, the adapter modules are inserted into the pre-trained ViT model, and the adapters are updated while other pre-trained parameters remain fixed. We find the limitations of previous vanilla adapters in that they are based on linear layers, which lack a spoofing-aware inductive bias and thus restrict the cross-domain generalization. To address this limitation and achieve cross-domain generalized FAS, we propose a novel Statistical Adapter (S-Adapter) that gathers local discriminative and statistical information from localized token histograms. To further improve the generalization of the statistical tokens, we propose a novel Token Style Regularization (TSR), which aims to reduce domain style variance by regularizing Gram matrices extracted from tokens across different domains. Our experimental results demonstrate that our proposed S-Adapter and TSR provide significant benefits in both zero-shot and few-shot cross-domain testing, outperforming state-of-the-art methods on several benchmark tests. We will release the source code upon acceptance