A Survey of Face Recognition

Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023

Objective identification and forecast method of PM2.5 pollution based on medium- and long-term ensemble forecasts in Beijing-Tianjin-Hebei region and its surrounding areas

Accurate long-term forecasts of PM2.5 pollution are essential to mitigating health risks and formulating pollutant control strategies for decision-makers in China. In this study, an objective identification and forecast method for PM2.5 pollution (OIF-PM2.5) is developed based on medium- and long-term ensemble forecasts of PM2.5 in Beijing-Tianjin-Hebei region and its surrounding areas. The results show that the observed PM2.5 pollution ratio increases with the aggravating PM2.5 pollution. For example, the ratio of meteorological stations with heavy pollution is 4.4 times that of light pollution and 3.9 times that of moderate pollution. In addition, the correlation coefficients between observations and forecasts are above 0.60 for all forecast leading times. Statistical results show that the average accuracy for forecasts with the leading times of 1–3 days, 4–7 days, and 8–15 days are 74.1%, 81.3%, and 72.9% respectively, indicating that the OIF-PM2.5 method has a high reliability in forecasts with the leading times of 1–15 days. The OIF-PM2.5 method is further applied in a severe PM2.5 pollution episode in the December of 2021, and the average forecast precision in forecasts with the leading times of 6–8 days reaches as high as 100%, showing a certain reference value for PM2.5 forecasts

Time scales of epidemic spread and risk perception on adaptive networks

Incorporating dynamic contact networks and delayed awareness into a contagion model with memory, we study the spreading patterns of infectious diseases in connected populations. It is found that the spread of an infectious disease is not only related to the past exposures of an individual to the infected but also to the time scales of risk perception reflected in the social network adaptation. The epidemic threshold $p_{c}$ is found to decrease with the rise of the time scale parameter s and the memory length T, they satisfy the equation $p_{c} =\frac{1}{T}+ \frac{\omega T}{a^s(1-e^{-\omega T^2/a^s})}$. Both the lifetime of the epidemic and the topological property of the evolved network are considered. The standard deviation $\sigma_{d}$ of the degree distribution increases with the rise of the absorbing time $t_{c}$, a power-law relation $\sigma_{d}=mt_{c}^\gamma$ is found

Mechanical transistors for logic-with-memory computing

As a potential revolutionary topic in future information processing, mechanical computing has gained tremendous attention for replacing or supplementing conventional electronics vulnerable to power outages, security attacks, and harsh environments. Despite its potential for constructing intelligent matter towards nonclassical computing systems beyond the von Neumann architecture, most works on mechanical computing demonstrated that the ad hoc design of simple logic gates cannot fully realize a universal mechanical processing framework involving interconnected arithmetic logic components and memory. However, such a logic-with-memory computing architecture is critical for complex and persistent state-dependent computations such as sequential logic. Here we propose a mechanical transistor (M-Transistor), abstracting omnipresent temperatures as the input-output mechanical bits, which consists of a metamaterial thermal channel as the gate terminal driving a nonlinear bistable soft actuator to selectively connect the output terminal to two other variable thermal sources. This M-Transistor is an elementary unit to modularly form various combinational and sequential circuits, such as complex logic gates, registers (volatile memory), and long-term memories (non-volatile memory) with much fewer units than the electronic counterparts. Moreover, they can establish a universal processing core comprising an arithmetic circuit and a register in a compact, reprogrammable network involving periodic read, write, memory, and logic operations of the mechanical bits. Our work contributes to realizing a non-electric universal mechanical computing architecture that combines multidisciplinary engineering with structural mechanics, materials science, thermal engineering, physical intelligence, and computational science.Comment: 25 pages, 4 figures, Articl

Annual report 1984-1985

BACKGROUND: HOTAIR, a newly discovered long intergenic noncoding RNA (lincRNA), has been reported to be aberrantly expressed in many types of cancers. This meta-analysis summarizes its potential role as a biomarker in malignancy. METHODS: A quantitative meta-analysis was performed through a systematic search in Pubmed, Medline and Web of Science for eligible papers on the prognostic impact of HOTAIR in cancer from inception to Feb. 28, 2014. Pooled hazard ratios (HRs) with 95% confidence interval (95% CI) were calculated to summarize the effect. RESULTS: Nineteen studies were included in the study, with a total of 2033 patients. A significant association was observed between high HOTAIR expression and poor overall survival (OS) in patients with cancer (pooled HR 2.22, 95% CI: 1.68-2.93). Place of residence (Asian or Western countries), type of cancer (digestive or non-digestive disease), sample size (more or less than 100), and paper quality (score more or less than 85%) did not alter the significant predictive value of HOTAIR in OS from various kinds of cancer but preoperative status did. By combining HRs from Cox multivariate analyses, we found that HOTAIR expression was an independent prognostic factor for cancer patients (pooled HR 2.26, 95% CI: 1.62-3.15). Subgroup analysis showed that HOTAIR abundance was an independent prognostic factor for cancer metastasis (HR 3.90, 95% CI: 2.25-6.74). For esophageal carcinoma, high HOTAIR expression was significantly associated with TNM stage (III/IV vs. I/II: OR 6.90, 95% CI: 2.81-16.9) without heterogeneity. In gastric cancer, HOTAIR expression was found to be significantly associated with lymph node metastases (present vs. absent: OR 4.47, 95% CI: 1.88-10.63) and vessel invasion (positive vs. negative: OR 2.88, 95% CI: 1.38-6.04) without obvious heterogeneity. CONCLUSIONS: HOTAIR abundance may serve as a novel predictive factor for poor prognosis in different types of cancers in both Asian and Western countries

Discovery and Characterization of a High-Affinity Small Peptide Ligand, H1, Targeting FGFR2IIIc for Skin Wound Healing

Background/Aims: How to aid recovery from severe skin injuries, such as burns, chronic or radiation ulcers, and trauma, is a critical clinical problem. Current treatment methods remain limited, and the discovery of ideal wound-healing therapeutics has been a focus of research. Functional recombinant proteins such as basic fibroblast growth factor (bFGF) and epidermal growth factor (EGF) have been developed for skin repair, however, some disadvantages in their use remain. This study reports the discovery of a novel small peptide targeting fibroblast growth factor receptor 2 IIIc (FGFR2IIIc) as a potential candidate for skin wound healing. Methods: A phage-displayed peptide library was used for biopanning FGFR2IIIc-targeting small peptides. The selected small peptides binding to FGFR2IIIc were qualitatively evaluated by an enzyme-linked immunosorbent assay. Their biological function was detected by a cell proliferation assay. Among them, an optimized small peptide named H1 was selected for further study. The affinity of the H1 peptide and FGFR2IIIc was determined by an isothermal titration calorimetry device. The ability of theH1 peptide to promote skin wound repair was investigated using an endothelial cell tube formation assay and wound healing scratch assay in vitro. Subsequently, the H1 peptide was assessed using a rat skin full-thickness wound model and chorioallantoic membrane (CAM) assays in vivo. To explore its molecular mechanisms, RNA-Seq, quantitative real-time PCR, and western blot assays were performed. Computer molecular simulations were also conducted to analyze the binding model. Results: We identified a novel FGFR2IIIc-targeting small peptide, called H1, with 7 amino acid residues using phage display. H1 had high binding affinity with FGFR2IIIc. The H1 peptide promoted the proliferation and motility of fibroblasts and vascular endothelial cells in vitro. In addition, the H1 peptide enhanced angiogenesis in the chick chorioallantoic membrane and accelerated wound healing in a rat full-thickness wound model in vivo. The H1 peptide activated both the PI3K-AKT and MAPK-ERK1/2 pathways and simultaneously increased the secretion of vascular endothelial growth factor. Computer analysis demonstrated that the model of H1 peptide binding to FGFR2IIIc was similar to that of FGF2 and FGFR2IIIc. Conclusion: The H1 peptide has a high affinity for FGFR2IIIc and shows potential as a wound healing agent. As a substitute for bFGF, it could be developed into a novel therapeutic candidate for skin wound repair in the future

Molecular definition of group 1 innate lymphoid cells in the mouse uterus

Determining the function of uterine lymphocytes is challenging because of the rapidly changing nature of the organ in response to sex hormones and, during pregnancy, to the invading fetal trophoblast cells. Here we provide the first genome-wide transcriptome atlas of mouse uterine group 1 innate lymphoid cells (g1 ILCs) at mid-gestation. The composition of g1 ILCs fluctuates throughout reproductive life, with Eomes-veCD49a+ ILC1s dominating before puberty and specifically expanding in second pregnancies, when the expression of CXCR6, a marker of memory cells, is upregulated. Tissue-resident Eomes+CD49a+ NK cells (trNK), which resemble human uterine NK cells, are most abundant during early pregnancy, and showcase gene signatures of responsiveness to TGF-β, connections with trophoblast, epithelial, endothelial and smooth muscle cells, leucocytes, as well as extracellular matrix. Unexpectedly, trNK cells express genes involved in anaerobic glycolysis, lipid metabolism, iron transport, protein ubiquitination, and recognition of microbial molecular patterns. Conventional NK cells expand late in gestation and may engage in crosstalk with trNK cells involving IL-18 and IFN-γ. These results identify trNK cells as the cellular hub of uterine g1 ILCs at mid-gestation and mark CXCR6+ ILC1s as potential memory cells of pregnancy.This work was funded by a Wellcome Trust Investigator Award 200841/Z/16/Z, the Centre for Trophoblast Research (CTR), and the Cambridge NIHR BRC Cell Phenotyping Hub to FC, the Associazione Italiana Ricerca per la Ricerca sul Cancro (AIRC) - Special Project 5x1000 no. 9962, AIRC IG 2017 Id.19920 and AIRC 2014 Id. 15283 to LM, and Ministero della Salute RF-2013, GR-2013-02356568 to PV. IF was funded by a CTR PhD fellowship

UMHexagonS search algorithm for fast motion estimation

Conference Name:2011 3rd International Conference on Computer Research and Development, ICCRD 2011. Conference Address: Shanghai, China. Time:March 11, 2011 - March 15, 2011.In video coding, motion estimation is the most time consuming part due to its high computational complexity. Based on the high temporal and spatial correlation of motion vector (MV), a new fast motion estimation algorithm of UMHexagonS (UMH) has been proposed to reduce computational complexity by using relatively few search points without degrading image quality, in which the modified patterns with new uneven cross, multi-hexagon-grid and hexagon are applied to. The proposed algorithm alleviates the computational burden and maintains the quality of video. Compared with the original UMH algorithm, the proposed algorithm has a better performance, and it reduces the number of search points by 32% at least and preserves similar average peak signal-to-noise ratio (PSNR) value at the same time. ? 2011 IEEE

Structures, properties, and challenges of emerging 2D materials in bioelectronics and biosensors

Bioelectronics are powerful tools for monitoring and stimulating biological and biochemical processes, with applications ranging from neural interface simulation to biosensing. The increasing demand for bioelectronics has greatly promoted the development of new nanomaterials as detection platforms. Recently, owing to their ultrathin structures and excellent physicochemical properties, emerging two-dimensional (2D) materials have become one of the most researched areas in the fields of bioelectronics and biosensors. In this timely review, the physicochemical structures of the most representative emerging 2D materials and the design of their nanostructures for engineering high-performance bioelectronic and biosensing devices are presented. We focus on the structural optimization of emerging 2D material-based composites to achieve better regulation for enhancing the performance of bioelectronics. Subsequently, the recent developments of emerging 2D materials in bioelectronics, such as neural interface simulation, biomolecular/biomarker detection, and skin sensors are discussed thoroughly. Finally, we provide conclusive views on the current challenges and future perspectives on utilizing emerging 2D materials and their composites for bioelectronics and biosensors. This review will offer important guidance in designing and applying emerging 2D materials in bioelectronics, thus further promoting their prospects in a wide biomedical field

The Additive Values of the Classification of Higher Serum Uric Acid Levels as a Diagnostic Criteria for Metabolic-Associated Fatty Liver Disease

Serum uric acid (SUA) is regarded as an independent risk factor for nonalcoholic fatty liver disease (NAFLD). However, the role of SUA in the new diagnosis flowchart of metabolic-associated fatty liver disease (MAFLD) remains unclear. A cross-sectional study enrolled consecutive individuals with ultrasonography and magnetic resonance imaging–based proton density fat fraction (MRI-PDFF) measurements in the First Affiliated Hospital of Sun Yat-sen University from January 2015 to December 2021. All patients were divided into four groups according to their baseline SUA levels and sex. Of the 3537 ultrasound-diagnosed and 1017 MRI-PDFF-diagnosed MAFLD patients included, the prevalence of severe steatosis determined with ultrasound or MRI-PDFF increased across the serum SUA quartiles. The SUA cutoffs were identified as ≥478 µmol/L and ≥423.5 µmol/L for severe steatosis in male and female MAFLD, respectively. Furthermore, using these cutoff values, patients with higher SUA levels in the NAFLD–non-MAFLD group had higher liver fat contents than those without (16.0% vs. 9.7%, p < 0.001). The lean/normal-weight NAFLD–non-MAFLD patients with higher SUA levels are still at high risk of severe steatosis. This study supports the rationale for SUA being established as another risk factor for metabolic dysfunctions in lean/normal-weight MAFLD