Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos

Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of curating diverse large-scale video datasets. This paper addresses both of those challenges, through an image to video feature-level domain adaptation approach, to learn discriminative video frame representations. The framework utilizes large-scale unlabeled video data to reduce the gap between different domains while transferring discriminative knowledge from large-scale labeled still images. Given a face recognition network that is pretrained in the image domain, the adaptation is achieved by (i) distilling knowledge from the network to a video adaptation network through feature matching, (ii) performing feature restoration through synthetic data augmentation and (iii) learning a domain-invariant feature through a domain adversarial discriminator. We further improve performance through a discriminator-guided feature fusion that boosts high-quality frames while eliminating those degraded by video domain-specific factors. Experiments on the YouTube Faces and IJB-A datasets demonstrate that each module contributes to our feature-level domain adaptation framework and substantially improves video face recognition performance to achieve state-of-the-art accuracy. We demonstrate qualitatively that the network learns to suppress diverse artifacts in videos such as pose, illumination or occlusion without being explicitly trained for them.Comment: accepted for publication at International Conference on Computer Vision (ICCV) 201

Boundary two-parameter eight-state supersymmetric fermion model and Bethe ansatz solution

The recently introduced two-parameter eight-state $U_q[gl(3|1)]$ supersymmetric fermion model is extended to include boundary terms. Nine classes of boundary conditions are constructed, all of which are shown to be integrable via the graded boundary quantum inverse scattering method. The boundary systems are solved by using the coordinate Bethe ansatz and the Bethe ansatz equations are given for all nine cases.Comment: 11 pages, RevTex; some typos correcte

银杏达莫对急性脑梗死患者血清补体C3、IL-6和IL-8的影响

Objective:To explore the effect of yinxingdamo on serum Complement C3、interleukin-6,8 in Patients with Acute Cerebral Infarction(ACI).Methods:136 cases of ACI were equally and randomly divided into two groups,one is the control group,the other group is having both routine treatment and yinxingdamo treatment for 4 weeks. Both the patients of two groups were respectively observed the serum Complement C3、interleukin-6,8 before and after treatment.Meantime,another 40 cases of normal subjects are as reference.Results:Before the treatment,the serum Complement C3、interleukin-6,8 levels of patients with ACI were obviously higher than the reference group(P<0.05),but after treatment,the levels of the treatment group were obviously lower than before.The difference was statistically significant (P<0.05).Conclusion:The treatment of Yinxingdamo injection can lower the level of the serum Complement C3、interleukin-6,8 in Patients with ACI,causing a significant effect of anti-inflammatory.目的  探讨银杏达莫对急性脑梗死(ACI)患者血清补体C3、白细胞介素-6（IL-6）及白细胞介素-8（IL-8）水平变化的影响。方法 将ACI患者136例随机分为常规治疗对照组68例和银杏达莫治疗+常规治疗治疗组68例，分别治疗前及治疗4周后测定血清补体C3、IL-6及IL-8。同期另选40例健康体检者作参考组。结果  ACI患者治疗前血清补体C3、IL-6及IL-8水平均明显高于参考组(P<0.05)。治疗组经过治疗后血清补体C3、IL-6及IL-8水平均明显低于治疗前，并且更低于对照组，差异具有统计学意义(P<0.05)；与参考组比较无显著性差异(P<0.05)。对照组患者治疗前后血清补体C3、IL-6及IL-8无显著性差异(P<0.05)。结论  ACI患者应用银杏达莫治疗使补体C3、IL-6及IL-8显著降低，具有明显抗炎作用效果。 

Entanglement distribution maximization over one-side Gaussian noisy channel

The optimization of entanglement evolution for two-mode Gaussian pure states under one-side Gaussian map is studied. Even there isn't complete information about the one-side Gaussian noisy channel, one can still maximize the entanglement distribution by testing the channel with only two specific states

Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids

Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices will greatly improve the efficiency of density functional theory (DFT) calculations. In this work, we proposed a general analytic Hamiltonian representation in an E(3) equivariant framework, which can fit the ab initio Hamiltonian of molecules and solids by a complete data-driven method and are equivariant under rotation, space inversion, and time reversal operations. Our model reached state-of-the-art precision in the benchmark test and accurately predicted the electronic Hamiltonian matrices and related properties of various periodic and aperiodic systems, showing high transferability and generalization ability. This framework provides a general transferable model that can be used to accelerate the electronic structure calculations on different large systems with the same network weights trained on small structures.Comment: 33 pages, 6 figure