FACE RECOGNITION AND VERIFICATION IN UNCONSTRAINED ENVIRIONMENTS

Face recognition has been a long standing problem in computer vision. General face recognition is challenging because of large appearance variability due to factors including pose, ambient lighting, expression, size of the face, age, and distance from the camera, etc. There are very accurate techniques to perform face recognition in controlled environments, especially when large numbers of samples are available for each face (individual). However, face identification under uncontrolled( unconstrained) environments or with limited training data is still an unsolved problem. There are two face recognition tasks: face identification (who is who in a probe face set, given a gallery face set) and face verification (same or not, given two faces). In this work, we study both face identification and verification in unconstrained environments. Firstly, we propose a face verification framework that combines Partial Least Squares (PLS) and the One-Shot similarity model[1]. The idea is to describe a face with a large feature set combining shape, texture and color information. PLS regression is applied to perform multi-channel feature weighting on this large feature set. Finally the PLS regression is used to compute the similarity score of an image pair by One-Shot learning (using a fixed negative set). Secondly, we study face identification with image sets, where the gallery and probe are sets of face images of an individual. We model a face set by its covariance matrix (COV) which is a natural 2nd-order statistic of a sample set.By exploring an efficient metric for the SPD matrices, i.e., Log-Euclidean Distance (LED), we derive a kernel function that explicitly maps the covariance matrix from the Riemannian manifold to Euclidean space. Then, discriminative learning is performed on the COV manifold: the learning aims to maximize the between-class COV distance and minimize the within-class COV distance. Sparse representation and dictionary learning have been widely used in face recognition, especially when large numbers of samples are available for each face (individual). Sparse coding is promising since it provides a more stable and discriminative face representation. In the last part of our work, we explore sparse coding and dictionary learning for face verification application. More specifically, in one approach, we apply sparse representations to face verification in two ways via a fix reference set as dictionary. In the other approach, we propose a dictionary learning framework with explicit pairwise constraints, which unifies the discriminative dictionary learning for pair matching (face verification) and classification (face recognition) problems

Flame and acoustic waves interactions and flame control

In this PhD project, the investigation of the stability of a laminar diffusion flame and the interaction of the flame with acoustic waves inside an acoustically excited cylindrical tube is presented. Interesting phenomena have been observed by studying both the infrasound and sound effect on the flame structure and dynamics.When a cylindrical tube burner is acoustically excited at one end, a standing wave will be produced along the tube burner. By applying a programming controlled signal from a signal generator, the loudspeaker generates acoustic waves with different frequencies and intensities to excite the flame, which can make the flame relatively stable or unstable, even blow out. Different methods in both frequency domain and time domain have been applied to analyze the flame stability affected by acoustic waves. Both infrasound and sound are tested in this research. Infrasound is the acoustic wave with a frequency too low to be heard by human ear covering sounds beneath the lowest limits of human hearing (20Hz) down to 0.001Hz. It is found that infrasound is able to take over buoyancy-driven flame flickering and make the flame flicker at the same frequency as the forcing infrasound. For some infrasound, half excited frequency has been detected clearly in the power spectrum of CH* chemiluminescence signals acquired by a photomultiplier. On the other hand, some higher frequency acoustic wave can have observable effect on flame flickering but the buoyancy-driven flickering is still the dominant oscillating mode; some other higher frequency acoustic wave can make the flame very stable, such as the acoustic wave at 140Hz. Image processing technique has shown that the influence of acoustic waves on the laminar diffusion flame varies spatially. It is also observed that a diffusion flame may oscillate at different frequency spatially. Taking the flame without acoustic excitation as an example, the inner most area of the flame oscillates at the typical flickering frequency, but the most outer areas of the flame oscillate at the second-harmonic of the typical flickering frequency. Finally, some control strategies are developed for the laboratory tube burner based on the gained physical insights in this research.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Spatio-Temporal Calibration for Omni-Directional Vehicle-Mounted

We present a solution to the problem of spatio-temporal calibration for event cameras mounted on an onmi-directional vehicle. Different from traditional methods that typically determine the camera's pose with respect to the vehicle's body frame using alignment of trajectories, our approach leverages the kinematic correlation of two sets of linear velocity estimates from event data and wheel odometers, respectively. The overall calibration task consists of estimating the underlying temporal offset between the two heterogeneous sensors, and furthermore, recovering the extrinsic rotation that defines the linear relationship between the two sets of velocity estimates. The first sub-problem is formulated as an optimization one, which looks for the optimal temporal offset that maximizes a correlation measurement invariant to arbitrary linear transformation. Once the temporal offset is compensated, the extrinsic rotation can be worked out with an iterative closed-form solver that incrementally registers associated linear velocity estimates. The proposed algorithm is proved effective on both synthetic data and real data, outperforming traditional methods based on alignment of trajectories

Progress in the diagnosis of lymph node metastasis in rectal cancer: a review

Historically, the chief focus of lymph node metastasis research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen a rapid accumulation of massive omics and imaging data catalyzed by the rapid development of advanced technologies. This rapid increase in data has driven improvements in the accuracy of diagnosis of lymph node metastasis, and its analysis further demands new methods and the opportunity to provide novel insights for basic research. In fact, the combination of omics data, imaging data, clinical medicine, and diagnostic methods has led to notable advances in our basic understanding and transformation of lymph node metastases in rectal cancer. Higher levels of integration will require a concerted effort among data scientists and clinicians. Herein, we review the current state and future challenges to advance the diagnosis of lymph node metastases in rectal cancer

Visualizing symmetry-breaking electronic orders in epitaxial Kagome magnet FeSn films

Kagome lattice hosts a plethora of quantum states arising from the interplay of topology, spin-orbit coupling, and electron correlations. Here, we report symmetry-breaking electronic orders tunable by an applied magnetic field in a model Kagome magnet FeSn consisting of alternating stacks of two-dimensional Fe3Sn Kagome and Sn2 honeycomb layers. On the Fe3Sn layer terminated FeSn thin films epitaxially grown on SrTiO3(111) substrates, we observe trimerization of the Kagome lattice using scanning tunneling microscopy/spectroscopy, breaking its six-fold rotational symmetry while preserving the transitional symmetry. Such a trimerized Kagome lattice shows an energy-dependent contrast reversal in dI/dV maps, which is significantly enhanced by bound states induced by Sn vacancy defects. This trimerized Kagome lattice also exhibits stripe modulations that are energy-dependent and tunable by an applied in-plane magnetic field, indicating symmetry-breaking nematicity from the entangled magnetic and charge degrees of freedom in antiferromagnet FeSn

Data for High-Throughput Estimation of Specific Activities of Enzyme/Mutants in Cell Lysates Through Immunoturbidimetric Assay of Proteins

Data in this article are associated with the research article “Highthroughput estimation of specific activities of enzyme/mutants in cell lysates through immunoturbidimetric assay of proteins” (Yang et al., 2017) [1]. This article provided data on how to develop an immunoturbidimetric assay (ITA) of enzyme/mutants as proteins in cell lysates in high-throughput (HTP) mode together with HTP assay of their activities to derive their specific activities in cell lysates for comparison, with Pseudomonas aeruginosa arylsulfatase (PAAS) and Bacillus fastidious uricase (BFU) plus their mutants as models. Data were made publicly available for further analyses

In situ earthworm breeding in orchards significantly improves the growth, quality and yield of papaya (Carica papaya L.)

ABSTRACT The aim of this study was to compare the effects of four fertilizer applications-control (C), chemical fertilizer (F), compost (O), and in situ earthworm breeding (E)-on the growth, quality and yield of papaya (Carica papaya L.). In this study, 5 g plant −1 urea (CH 4 N 2 O, %N = 46.3%) and 100 g plant −1 microelement fertilizer was applied to each treatment. The fertilizer applications of these four treatments are different from each other. The results showed that the E treatment had the highest growth parameters over the whole growth period. At 127 days after transplantation, the order of plant heights from greatest to smallest was E > F > O > C, and the stem diameters were E > F > O > C, with significant differences between all treatments. Soluble-solid, sugar, vitamin C, and protein content significantly increased in the E treatment. In addition, the total acid and the electrical conductivity of the fruit significantly decreased in the E treatment. Fruit firmness clearly increased in the O treatment, and decreased in the F treatment. The fresh individual fruit weights, fruit numbers, and total yields were greatly improved in the F and E treatments, and the total yield of the E treatment was higher than that in the F treatment. In conclusion, the in situ earthworm breeding treatment performed better than conventional compost and chemical fertilizer treatments. Furthermore, in situ earthworm breeding may be a potential organic fertilizer application in orchards because it not only improves the fruit quality and yield but also reduces the amount of organic wastes from agriculture as a result of the activities of earthworms

比利时

peer reviewedQuinoa is an excellent source of nutritional and bioactive components. Protein is considered a key nutritional advantage of quinoa grain, and many studies have highlighted the nutritional and physicochemical properties of quinoa protein. In addition, quinoa protein is a good precursor of bioactive peptides. This review focused on the biological properties of quinoa protein hydrolysate and peptides, and gave a summary of the preparation and functional test of quinoa protein hydrolysate and peptides. A combination of milling fractionation and solvent extraction is recommended for the efficient production of quinoa protein. The biological functionalities of quinoa protein hydrolysate, including antidiabetic, antihypertensive, anti-inflammatory, antioxidant activities, and so on, have been extensively investigated based on in vitro studies and limited animal models. Additionally, bioinformatics analysis, including proteolysis simulation, virtual screening, and molecular docking, provides an alternative or assistive approach for exploring the potential bioactivity of quinoa protein and peptides. Nevertheless, further research is required for industrial production of bioactive quinoa peptides, verification of health benefits in humans, and mechanism interpretation of observed effects

Forebrain overexpression of CaMKII abolishes cingulate long term depression and reduces mechanical allodynia and thermal hyperalgesia

Activity-dependent synaptic plasticity is known to be important in learning and memory, persistent pain and drug addiction. Glutamate NMDA receptor activation stimulates several protein kinases, which then trigger biochemical cascades that lead to modifications in synaptic efficacy. Genetic and pharmacological techniques have been used to show a role for Ca(2+)/calmodulin-dependent kinase II (CaMKII) in synaptic plasticity and memory formation. However, it is not known if increasing CaMKII activity in forebrain areas affects behavioral responses to tissue injury. Using genetic and pharmacological techniques, we were able to temporally and spatially restrict the over expression of CaMKII in forebrain areas. Here we show that genetic overexpression of CaMKII in the mouse forebrain selectively inhibits tissue injury-induced behavioral sensitization, including allodynia and hyperalgesia, while behavioral responses to acute noxious stimuli remain intact. CaMKII overexpression also inhibited synaptic depression induced by a prolonged repetitive stimulation in the ACC, suggesting an important role for CaMKII in the regulation of cingulate neurons. Our results suggest that neuronal CaMKII activity in the forebrain plays a role in persistent pain