On the Exponential Stability and Periodic Solutions of Delayed Cellular Neural Networks

AbstractA set of criteria is presented for the global exponential stability and the existence of periodic solutions of delayed cellular neural networks (DCNNs) by constructing suitable Lyapunov functionals, introducing many parameters and combining with the elementary inequality technique. These criteria have important leading significance in the design and applications of globally stable DCNNs and periodic oscillatory DCNNs. In addition, earlier results are extended and improved; other results are contained. Two examples are given to illustrate the theory

VGGFace2: A dataset for recognising faces across pose and age

In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians). The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise. We describe how the dataset was collected, in particular the automated and manual filtering stages to ensure a high accuracy for the images of each identity. To assess face recognition performance using the new dataset, we train ResNet-50 (with and without Squeeze-and-Excitation blocks) Convolutional Neural Networks on VGGFace2, on MS- Celeb-1M, and on their union, and show that training on VGGFace2 leads to improved recognition performance over pose and age. Finally, using the models trained on these datasets, we demonstrate state-of-the-art performance on all the IARPA Janus face recognition benchmarks, e.g. IJB-A, IJB-B and IJB-C, exceeding the previous state-of-the-art by a large margin. Datasets and models are publicly available.Comment: This paper has been accepted by IEEE Conference on Automatic Face and Gesture Recognition (F&G), 2018. (Oral

Anisotropic Rabi model

We define the anisotropic Rabi model as the generalization of the spin-boson Rabi model: The Hamiltonian system breaks the parity symmetry; the rotating and counter-rotating interactions are governed by two different coupling constants; a further parameter introduces a phase factor in the counter-rotating terms. The exact energy spectrum and eigenstates of the generalized model is worked out. The solution is obtained as an elaboration of a recent proposed method for the isotropic limit of the model. In this way, we provide a long sought solution of a cascade of models with immediate relevance in different physical fields, including i) quantum optics: two-level atom in single mode cross electric and magnetic fields; ii) solid state physics: electrons in semiconductors with Rashba and Dresselhaus spin-orbit coupling; iii) mesoscopic physics: Josephson junctions flux-qubit quantum circuits.Comment: 5 pages+ 6 pages supplementary, 7 figures, accepted by Phys. Rev.

Template Adaptation for Face Verification and Identification

Face recognition performance evaluation has traditionally focused on one-to-one verification, popularized by the Labeled Faces in the Wild dataset for imagery and the YouTubeFaces dataset for videos. In contrast, the newly released IJB-A face recognition dataset unifies evaluation of one-to-many face identification with one-to-one face verification over templates, or sets of imagery and videos for a subject. In this paper, we study the problem of template adaptation, a form of transfer learning to the set of media in a template. Extensive performance evaluations on IJB-A show a surprising result, that perhaps the simplest method of template adaptation, combining deep convolutional network features with template specific linear SVMs, outperforms the state-of-the-art by a wide margin. We study the effects of template size, negative set construction and classifier fusion on performance, then compare template adaptation to convolutional networks with metric learning, 2D and 3D alignment. Our unexpected conclusion is that these other methods, when combined with template adaptation, all achieve nearly the same top performance on IJB-A for template-based face verification and identification

Some Topics on Similarity Metric Learning

The success of many computer vision problems and machine learning algorithms critically depends on the quality of the chosen distance metrics or similarity functions. Due to the fact that the real-data at hand is inherently task- and data-dependent, learning an appropriate distance metric or similarity function from data for each specific task is usually superior to the default Euclidean distance or cosine similarity. This thesis mainly focuses on developing new metric and similarity learning models for three tasks: unconstrained face verification, person re-identification and kNN classification. Unconstrained face verification is a binary matching problem, the target of which is to predict whether two images/videos are from the same person or not. Concurrently, person re-identification handles pedestrian matching and ranking across non-overlapping camera views. Both vision problems are very challenging because of the large transformation differences in images or videos caused by pose, expression, occlusion, problematic lighting and viewpoint. To address the above concerns, two novel methods are proposed. Firstly, we introduce a new dimensionality reduction method called Intra-PCA by considering the robustness to large transformation differences. We show that Intra-PCA significantly outperforms the classic dimensionality reduction methods (e.g. PCA and LDA). Secondly, we propose a novel regularization framework called Sub-SML to learn distance metrics and similarity functions for unconstrained face verifica- tion and person re-identification. The main novelty of our formulation is to incorporate both the robustness of Intra-PCA to large transformation variations and the discriminative power of metric and similarity learning, a property that most existing methods do not hold. Working with the task of kNN classification which relies a distance metric to identify the nearest neighbors, we revisit some popular existing methods for metric learning and develop a general formulation called DMLp for learning a distance metric from data. To obtain the optimal solution, a gradient-based optimization algorithm is proposed which only needs the computation of the largest eigenvector of a matrix per iteration. Although there is a large number of studies devoted to metric/similarity learning based on different objective functions, few studies address the generalization analysis of such methods. We describe a novel approch for generalization analysis of metric/similarity learning which can deal with general matrix regularization terms including the Frobenius norm, sparse L1-norm, mixed (2, 1)-norm and trace-norm. The novel models developed in this thesis are evaluated on four challenging databases: the Labeled Faces in the Wild dataset for unconstrained face verification in still images; the YouTube Faces database for video-based face verification in the wild; the Viewpoint Invariant Pedestrian Recognition database for person re-identification; the UCI datasets for kNN classification. Experimental results show that the proposed methods yield competitive or state-of-the-art performance

GraMMaR: Ground-aware Motion Model for 3D Human Motion Reconstruction

Demystifying complex human-ground interactions is essential for accurate and realistic 3D human motion reconstruction from RGB videos, as it ensures consistency between the humans and the ground plane. Prior methods have modeled human-ground interactions either implicitly or in a sparse manner, often resulting in unrealistic and incorrect motions when faced with noise and uncertainty. In contrast, our approach explicitly represents these interactions in a dense and continuous manner. To this end, we propose a novel Ground-aware Motion Model for 3D Human Motion Reconstruction, named GraMMaR, which jointly learns the distribution of transitions in both pose and interaction between every joint and ground plane at each time step of a motion sequence. It is trained to explicitly promote consistency between the motion and distance change towards the ground. After training, we establish a joint optimization strategy that utilizes GraMMaR as a dual-prior, regularizing the optimization towards the space of plausible ground-aware motions. This leads to realistic and coherent motion reconstruction, irrespective of the assumed or learned ground plane. Through extensive evaluation on the AMASS and AIST++ datasets, our model demonstrates good generalization and discriminating abilities in challenging cases including complex and ambiguous human-ground interactions. The code will be available at https://github.com/xymsh/GraMMaR.Comment: Accepted to ACM Multimedia 2023. The code will be available at https://github.com/xymsh/GraMMa

Cryopreservation of human failed-matured oocytes followed by in vitro maturation: vitrification is superior to the slow freezing method

<p>Abstract</p> <p>Background</p> <p>Oocyte cryopreservation is an important method used in a number of human fertility circumstances. Here, we compared the survival, <it>in vitro </it>maturation, fertilization, and early embryonic development rates of frozen-thawed human immature oocytes using two different cryopreservation methods.</p> <p>Methods</p> <p>A total of 454 failed-matured oocytes [germinal vesicle (GV) and metaphase I (MI) stages] were collected from 135 patients (mean age 33.84 +/- 5.0 y) who underwent intracytoplasmic sperm injection (ICSI) cycles between February 2009 and December 2009 and randomly divided into a slow freezing group [1.5 mol/L-1, 2-propanediol (PROH) + 0.2 mol/l sucrose] and vitrification group [20% PROH + 20% ethylene glycol (EG) + 0.5 mol/l sucrose].</p> <p>Results</p> <p>The vitrification protocol yielded a better survival rate than the slow freezing protocol at each maturation stage assessed. Regardless of the maturation stage (GV + MI), the slow freezing protocol had a significantly lower survival rate than the vitrification protocol (p < 0.001). In addition, a significant difference was found in the survival rates between GV and MI oocytes regardless of the protocol used (90.1 vs. 64.7%, respectively; p < 0.01). We also found that the maturation rates of GV and MI oocytes from the slow freezing and vitrification groups were 16.7 vs. 24.4% and 50.8 vs. 55.4%, respectively. Regardless of the protocol used, the GV oocytes had significantly lower viability than MI oocytes after 36 h of <it>in vitro </it>maturation (21.2 vs. 54.0%, respectively; p < 0.01). In addition, the GV and MI oocytes from the slow freezing group had a markedly lower maturation rate than those from the vitrification group (33.6 vs. 43.1%, respectively), but no statistical difference was found between the two groups (P > 0.05). For the GV-matured oocytes, no fertilized eggs were obtained in the slow-freezing group, while a 19.0% (4/21) fertilization rate was observed in the vitrification group. For the MI-matured oocytes, fertilization rates for the slow freezing and vitrified groups were 36% and 61.1%, respectively, but no significant difference was found between the two groups (PIn the Methods section in the MS, all procedures were compliant with ethical guidelines, i.e. approved by the Ethical Committee of our university and Informed Consent signed by each patient. > 0.05). In the GV vitrification group, no embryo formed; however, in the MI slow freezing group, 12 oocytes were fertilized, but only two achieved cleavage and were subsequently blocked at the 2-cell stage. In the MI vitrification group, a total of 22 embryos were obtained, five of which developed to the blastocyst stage.</p> <p>Conclusions</p> <p>Vitrification is superior to the slow freezing method in terms of the survival and developmental rates for the cryopreservation of human failed-matured oocytes. In addition, GV oocytes appeared to be more resistant than MI oocytes to the low temperature and cryoprotectant used during cryopreservation.</p