Deep Learning for Person Reidentification Using Support Vector Machines

© 2017 Mengyu Xu et al. Due to the variations of viewpoint, pose, and illumination, a given individual may appear considerably different across different camera views. Tracking individuals across camera networks with no overlapping fields is still a challenging problem. Previous works mainly focus on feature representation and metric learning individually which tend to have a suboptimal solution. To address this issue, in this work, we propose a novel framework to do the feature representation learning and metric learning jointly. Different from previous works, we represent the pairs of pedestrian images as new resized input and use linear Support Vector Machine to replace softmax activation function for similarity learning. Particularly, dropout and data augmentation techniques are also employed in this model to prevent the network from overfitting. Extensive experiments on two publically available datasets VIPeR and CUHK01 demonstrate the effectiveness of our proposed approach

Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-Identification

In visible-infrared video person re-identification (re-ID), extracting features not affected by complex scenes (such as modality, camera views, pedestrian pose, background, etc.) changes, and mining and utilizing motion information are the keys to solving cross-modal pedestrian identity matching. To this end, the paper proposes a new visible-infrared video person re-ID method from a novel perspective, i.e., adversarial self-attack defense and spatial-temporal relation mining. In this work, the changes of views, posture, background and modal discrepancy are considered as the main factors that cause the perturbations of person identity features. Such interference information contained in the training samples is used as an adversarial perturbation. It performs adversarial attacks on the re-ID model during the training to make the model more robust to these unfavorable factors. The attack from the adversarial perturbation is introduced by activating the interference information contained in the input samples without generating adversarial samples, and it can be thus called adversarial self-attack. This design allows adversarial attack and defense to be integrated into one framework. This paper further proposes a spatial-temporal information-guided feature representation network to use the information in video sequences. The network cannot only extract the information contained in the video-frame sequences but also use the relation of the local information in space to guide the network to extract more robust features. The proposed method exhibits compelling performance on large-scale cross-modality video datasets. The source code of the proposed method will be released at https://github.com/lhf12278/xxx.Comment: 11 pages,8 figure

Can Philanthropy be Taught?

In recent years, colleges and universities have begun investing significant resources into an innovative pedagogy known as experiential philanthropy. The pedagogy is considered to be a form of service-learning. It is defined as a learning approach that provides students with opportunities to study social problems and nonprofit organizations and then make decisions about investing funds in them. Experiential philanthropy is intended to integrate academic learning with community engagement by teaching students not only about the practice of philanthropy but also how to evaluate philanthropic responses to social issues. Despite this intent, there has been scant evidence demonstrating that this type of pedagogic instruction has quantifiable impacts on students\u27 learning or their personal development. Therefore, this study explores learning and development outcomes associated with experiential philanthropy, and examines the efficacy of experiential philanthropy as a pedagogic strategy within higher education. Essentially, we seek to answer the question: Can philanthropy be taught

Study on the Time-lag Failure of Sandstone With Different Degrees of Unloading Damage

The unloading effect of rock mass excavation is an inevitable practice, and it’s often characterized by a relatively large-scale engineering hazard with a noticeable time lag.A set of unloading triaxial tests were conducted on a sandstone rock to establish the deformation law and the threshold time. Based on the renormalization group theory, the unloading sandstone model was developed by considering the interaction between particles. Similarly, a logistic model was used to predict the unloading damage of sandstone. The unloading time lag damage of sandstone rock was predicted by using the damage threshold. The research shows that: (1) The higher the degree of unloading, the shorter the time-lag failure. (2) The damage range of critical values was optimized. (3) The error between the predicted value and the experimental value of the time threshold was almost less than 5 %, the prediction result was found to be good, and the employed logistic evolution model was reasonable. The findings of this research provide a prediction method and precise information about the mechanism of unloading time lag deformation. Therefore, it can be used as a reference for excavation-support design of underground structures

Performance and Analysis of the Alchemical Transfer Method for Binding Free Energy Predictions of Diverse Ligands

The Alchemical Transfer Method (ATM) is herein validated against the relative binding free energies of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and the AToM-OpenMM software to compute the relative binding free energies (RBFE) of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical relative binding free energy methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and post-corrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 relative binding free energy calculations for eight protein targets and found that ATM achieves accuracy comparable to existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM is applicable as a production tool for relative binding free energy (RBFE) predictions across a wide range of perturbation types within a unified, open-source framework