Face Recognition from Sequential Sparse 3D Data via Deep Registration

Previous works have shown that face recognition with high accurate 3D data is more reliable and insensitive to pose and illumination variations. Recently, low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and DoE based structured light systems enable us to access 3D data easily, e.g., via a mobile phone. However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly. In this paper, we aim at achieving high-performance face recognition for devices equipped with such modules which is very meaningful in practice as such devices will be very popular. We propose a framework to perform face recognition by fusing a sequence of low-quality 3D data. As 3D data are sparse and noisy which can not be well handled by conventional methods like the ICP algorithm, we design a PointNet-like Deep Registration Network(DRNet) which works with ordered 3D point coordinates while preserving the ability of mining local structures via convolution. Meanwhile we develop a novel loss function to optimize our DRNet based on the quaternion expression which obviously outperforms other widely used functions. For face recognition, we design a deep convolutional network which takes the fused 3D depth-map as input based on AMSoftmax model. Experiments show that our DRNet can achieve rotation error 0.95{\deg} and translation error 0.28mm for registration. The face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001 97.5% on Bosphorus dataset which is comparable with state-of-the-art high-quality data based recognition performance.Comment: To be appeared in ICB201

AMPose: Alternatively Mixed Global-Local Attention Model for 3D Human Pose Estimation

The graph convolutional networks (GCNs) have been applied to model the physically connected and non-local relations among human joints for 3D human pose estimation (HPE). In addition, the purely Transformer-based models recently show promising results in video-based 3D HPE. However, the single-frame method still needs to model the physically connected relations among joints because the feature representations transformed only by global relations via the Transformer neglect information on the human skeleton. To deal with this problem, we propose a novel method in which the Transformer encoder and GCN blocks are alternately stacked, namely AMPose, to combine the global and physically connected relations among joints towards HPE. In the AMPose, the Transformer encoder is applied to connect each joint with all the other joints, while GCNs are applied to capture information on physically connected relations. The effectiveness of our proposed method is evaluated on the Human3.6M dataset. Our model also shows better generalization ability by testing on the MPI-INF-3DHP dataset. Code can be retrieved at https://github.com/erikervalid/AMPose.Comment: ICASSP 2023 Accepted Pape

New Insight into the Anti-liver Fibrosis Effect of Multitargeted Tyrosine Kinase Inhibitors: From Molecular Target to Clinical Trials

Tyrosine kinases (TKs) is a family of tyrosine protein kinases with important functions in the regulation of a broad variety of physiological cell processes. Overactivity of TK disturbs cellular homeostasis and has been linked to the development of certain diseases, including various fibrotic diseases. In regard to liver fibrosis, several TKs, such as vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), fibroblast growth factor receptor (FGFR) and epidermal growth factor receptor (EGFR) kinases, have been identified as central mediators in collagen production and potential targets for anti-liver fibrosis therapies. Given the essential role of TKs during liver fibrogenesis, multitargeted inhibitors of aberrant TK activity, including sorafenib, erlotinib, imatinib, sunitinib, nilotinib, brivanib and vatalanib, have been shown to have potential for treating liver fibrosis. Beneficial effects are observed by researchers of this field using these multitargeted TK inhibitors in preclinical animal models and in patients with liver fibrosis. The present review will briefly summarize the anti-liver fibrosis effects of multitargeted TK inhibitors and molecular mechanisms

Structural properties and Raman spectroscopy of lipid Langmuir monolayers at the air-water interface

Spectra of octadecylamine (ODA) Langmuir monolayers and egg phosphatidylcholine (PC)/ODA-mixed monolayers at the air-water interface have been acquired. The organization of the monolayers has been characterized by surface pressure-area isotherms. Application of polarized optical microscopy provides further insight in the domain structures and interactions of the film components. Surface-enhanced Raman scattering (SERS) data indicate that enhancement in Raman spectra can be obtained by strong interaction between headgroups of the surfactants and silver particles in subphase. By mixing ODA with phospholipid molecules and spreading the mixture at the air-water interface, we acquired vibrational information of phospholipid molecules with surfactant-aided SERS effect.Comment: 8 pages, 9 figure

Adaptive Practicing Design to Facilitate Self-Regulated Learning

Online higher education provides exceptional flexibility in learning but demands high self-regulated learning skills. The deficiency of self-regulated learning skills in many students highlights the need for support. This study introduces a confidence-based adaptive practicing system as an intelligent assessment and tutoring solution to enhance self-regulated learning in STEM disciplines. Unlike conventional intelligent tutoring systems that depend entirely on machine control, confidence-based adaptive practicing integrates learner confidence and control options into the AI-based adaptive mechanism to improve learning autonomy and model efficiency, establishing an AI-learner shared control approach. Based on Vygotsky’s zone of proximal development (ZPD) concept, an innovative knowledge-tracing framework and model called ZPD-KT was designed and implemented in the confidence-based adaptive practicing system. To evaluate the effectiveness of the ZPD-KT model, a simulation of confidence-based adaptive practicing was conducted. Findings showed that ZPD-KT significantly improves the accuracy of knowledge tracing compared to the traditional Bayesian knowledge-tracing model. Also, interviews with experts in the field underlined the potential of the confidence-based adaptive practicing system in facilitating self-regulated learning and the interpretability of the ZPD-KT model. This study also sheds light on a new way of keeping humans apprised of adaptive learning implementation

Removing Learning Barriers in Self-paced Online STEM Education

Self-paced online learning provides great flexibility for learning, yet it brings some inherent learning barriers because of the nature of this educational paradigm. This review paper suggests some corresponding strategies to address these barriers in order to create a more supportive self-paced online learning environment. These strategies include a) increasing students’ self-awareness of learning, b) identifying struggling students, and c) facilitating mastery learning.Focusing on Science, Technology, Engineering, and Mathematics (STEM) disciplines’ delivery of self-paced online learning, this paper reviewed the role of formative assessment for learning. It is proposed that systematically designing and embedding adaptive practicing in STEM courses would be an effective learning design solution to implement these strategies. By examining the goals and context of adaptive practicing requested in this study, the feature requirements are depicted for such an adaptive practicing model. The models and techniques that can be used for adaptive assessment were then reviewed. Based on the review results, this paper argues that a reinforcement learning-based adaptive practicing model would be the best option to meet those feature requirements. Finally, we point out a research gap in this field and suggest a future research direction for ourselves and other researchers

Electromagnetic treatment enhancing performance of metal materials: A review

Due to the limitations of traditional metal processing methods, including high energy consumption, slow process dynamics and possible introduction of defects, electromagnetic treatment has been highlighted as a more environment-friendly and energy-efficient alternative, which can efficiently and selectively enhance material properties. This article summarizes the latest advances in electromagnetic treatment of metal materials to enhance material performance and outlooks the directions in electromagnetic treatment technology. It delves into electromagnetic field applications in manipulating the microstructure and properties. First, it explores the potential mechanisms of interaction between electromagnetic fields and metal materials, including thermal and athermal effects. Afterwards, it provides a structured overview of how electromagnetic fields affect multiple aspects of metal microstructure, such as grain size, phase composition, dislocation density and defect repair. The article also summarizes the influences of electromagnetic treatment on the physical and mechanical properties of metals, such as tensile strength, fatigue resistance, wear performance, corrosion resistance, impact toughness and the modification of residual stress state. Finally, it sheds new insights into great potentials of electromagnetic treatment technology in optimizing metal materials for a variety of industrial applications and points out challenges and future research directions