PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer

Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications (e.g., remote healthcare and affective computing). Recent deep learning approaches focus on mining subtle rPPG clues using convolutional neural networks with limited spatio-temporal receptive fields, which neglect the long-range spatio-temporal perception and interaction for rPPG modeling. In this paper, we propose the PhysFormer, an end-to-end video transformer based architecture, to adaptively aggregate both local and global spatio-temporal features for rPPG representation enhancement. As key modules in PhysFormer, the temporal difference transformers first enhance the quasi-periodic rPPG features with temporal difference guided global attention, and then refine the local spatio-temporal representation against interference. Furthermore, we also propose the label distribution learning and a curriculum learning inspired dynamic constraint in frequency domain, which provide elaborate supervisions for PhysFormer and alleviate overfitting. Comprehensive experiments are performed on four benchmark datasets to show our superior performance on both intra- and cross-dataset testings. One highlight is that, unlike most transformer networks needed pretraining from large-scale datasets, the proposed PhysFormer can be easily trained from scratch on rPPG datasets, which makes it promising as a novel transformer baseline for the rPPG community. The codes will be released at https://github.com/ZitongYu/PhysFormer.Comment: Accepted by CVPR202

Effects of Xinwei granule on expression levels of cyclin D1 and its upstream genes in gastric intraepithelial neoplasia tissues

Purpose: To explore the effects of Xinwei granule (XWG) on low-grade gastric intraepithelial neoplasia (LGIN) and the underlying mechanisms. Methods: To establish LGIN model, Wistar rats were treated with N-methyl-N'-nitrosoguanidine for 3 months. LGIN model rats were randomly grouped into five groups (n = 15), viz, negative control (NC), normal saline (NS) group, Xinwei granule (XWG) group, Weifuchun tablet (WFCT) group, and vatacoenayme tablet (VT) group. Normal rats (n = 17) served as negative control. Histological evaluation of gastric mucosa was undertaken using hematoxylin and eosin staining. Quantitative realtime polymerase chain reaction (qRT-PCR), western blot, and immunohistochemical assays were performed to determine mRNA expressions, protein expression, and the distribution of cyclin D1, kruppel-like factor 4 (KLF4), and p21-WAF1-CIP1, respectively. Results: Compared with LGIN group, the body weight of the rats increased in XWG, WFCT, and VT groups. The pathological characteristics of LGIN group were alleviated by XWG, WFCT and VT treatments. The positive expression of cyclin D1 was enhanced in LGIN group, but reduced in XWG, WFCT and VT groups. The expression levels of KLF4 and p21-WAF1-CIP1, upstream regulators of cyclin D1 reduced in LGIN groups. However, administration of XWG, WFCT and VT strengthened the expressions of KLF4 and p21-WAF1-CIP1. More importantly, the protective effects of XWG against LGIN were superior to those of WFCT and VT. Conclusion: Xinwei granules alleviate LGIN in vivo by inhibiting cyclin D1 expression and enhancing KLF4 and p21-WAF1-CIP1 expression

Deep Learning for Face Anti-Spoofing: A Survey

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, traditional FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

Stretchable elastic synaptic transistors for neurologically integrated soft engineering systems

Artificial synaptic devices that can be stretched similar to those appearing in soft-bodied animals, such as earthworms, could be seamlessly integrated onto soft machines toward enabled neurological functions. Here, we report a stretchable synaptic transistor fully based on elastomeric electronic materials, which exhibits a full set of synaptic characteristics. These characteristics retained even the rubbery synapse that is stretched by 50%. By implementing stretchable synaptic transistor with mechanoreceptor in an array format, we developed a deformable sensory skin, where the mechanoreceptors interface the external stimulations and generate presynaptic pulses and then the synaptic transistors render postsynaptic potentials. Furthermore, we demonstrated a soft adaptive neurorobot that is able to perform adaptive locomotion based on robotic memory in a programmable manner upon physically tapping the skin. Our rubbery synaptic transistor and neurologically integrated devices pave the way toward enabled neurological functions in soft machines and other applications

Study of Peeling of Single Crystal Silicon by Intense Pulsed Ion Beam

The surface peeling process induced by intense pulsed ion beam (IPIB) irradiation was studied. Single crystal silicon specimens were treated by IPIB with accelerating voltage of 350 kV current density of 130 A/cm2. It is observed that under smaller numbers of IPIB shots, the surface may undergo obvious melting and evaporation..

Rapid Determination of Th(IV) in Thorium Dioxide Solution by Arsenazo III

ThO2 can produce many nuclides after accelerator bombardment, such as Actinium-225, Radium-223, Molybdenum-99, Iodine-131, Strontium-90, etc. There are many methods for measuring heavy elements such as thorium, but most of them are complicated to operate. Spectrophotometry has the advantages of simple sample preparation and low operation difficulty, and can be used as a quantitative analysis method for thorium and other heavy elements. ThO2 was dissolved with nitric acid and hydrogen fluoride, and the concentration of Th(IV) in ThO2 solution was quantitatively analyzed with Arsenazo III as the chromogenic agent. Under the environment of 2 mol/L hydrochloric acid solution and 0.1 g/L Arsenazo III solution, the coordination ratio of Arsenazo III and Th(IV) is 5 : 2 under the experimental conditions. The maximum absorption wavelength before and after color development is 534.0 nm and 663.2 nm, respectively, and the contrast is 129.2 nm. The method in 0.2-5.0 mg/L is in accordance with Lambert-Beer’s law, the standard curve equation y＝0.06641x+0.00176, R2＝0.99981. The molar absorption coefficient of 1.64×104. Method detection limit is 0.31 μg/L, and quantitative limit is 0.97 μg/L. The method is stable within 4 h. The recovery is 97.33%-104.03%, and the largest standard deviation is 0.32%. The method was compared with GB/T 12690.12-2003, the process was simplified and the results were basically consistent