Preliminary study of 10Be/7Be in rainwater from Xi'an by Accelerator Mass Spectrometry

The 10Be/7Be ratio is a sensitive tracer for the study of atmospheric transport, particularly with regard to stratosphere-troposphere exchange. Measurements with high accuracy and efficiency are crucial to 7Be and 10Be tracer studies. This article describes sample preparation procedures and analytical benchmarks for 7Be and 10Be measurements at the Xian Accelerator Mass Spectrometry (Xian-AMS) laboratory for the study of rainwater samples. We describe a sample preparation procedure to fabricate beryllium oxide (BeO) AMS targets that includes co-precipitation, anion exchange column separation and purification. We then provide details for the AMS measurement of 7Be and 10Be following the sequence BeO- -> Be2+ -> Be4+ in the Xian- AMS. The 10Be/7Be ratio of rainwater collected in Xian is shown to be about 1.3 at the time of rainfall. The virtue of the method described here is that both 7Be and 10Be are measured in the same sample, and is suitable for routine analysis of large numbers of rainwater samples by AMS

Support Neighbor Loss for Person Re-Identification

Person re-identification (re-ID) has recently been tremendously boosted due to the advancement of deep convolutional neural networks (CNN). The majority of deep re-ID methods focus on designing new CNN architectures, while less attention is paid on investigating the loss functions. Verification loss and identification loss are two types of losses widely used to train various deep re-ID models, both of which however have limitations. Verification loss guides the networks to generate feature embeddings of which the intra-class variance is decreased while the inter-class ones is enlarged. However, training networks with verification loss tends to be of slow convergence and unstable performance when the number of training samples is large. On the other hand, identification loss has good separating and scalable property. But its neglect to explicitly reduce the intra-class variance limits its performance on re-ID, because the same person may have significant appearance disparity across different camera views. To avoid the limitations of the two types of losses, we propose a new loss, called support neighbor (SN) loss. Rather than being derived from data sample pairs or triplets, SN loss is calculated based on the positive and negative support neighbor sets of each anchor sample, which contain more valuable contextual information and neighborhood structure that are beneficial for more stable performance. To ensure scalability and separability, a softmax-like function is formulated to push apart the positive and negative support sets. To reduce intra-class variance, the distance between the anchor's nearest positive neighbor and furthest positive sample is penalized. Integrating SN loss on top of Resnet50, superior re-ID results to the state-of-the-art ones are obtained on several widely used datasets.Comment: Accepted by ACM Multimedia (ACM MM) 201

Learning Robust and Discriminative Subspace With Low-Rank Constraints

IEEE Transactions on Neural Networks and Learning SystemsThe article of record as published may be found at http://dx.doi.org/10.1109/tnnls.2015.2464090In this paper, we aim at learning robust and discriminative subspaces from noisy data. Subspace learning is widely used in extracting discriminative features for classifica- tion. However, when data are contaminated with severe noise, the performance of most existing subspace learning methods would be limited. Recent advances in low-rank modeling provide effective solutions for removing noise or outliers contained in sample sets, which motivates us to take advantage of low-rank constraints in order to exploit robust and discriminative subspace for classification. In particular, we present a discriminative subspace learning method called the supervised regularization- based robust subspace (SRRS) approach, by incorporating the low-rank constraint. SRRS seeks low-rank representations from the noisy data, and learns a discriminative subspace from the recovered clean data jointly. A supervised regularization function is designed to make use of the class label information, and therefore to enhance the discriminability of subspace. Our approach is formulated as a constrained rank-minimization problem. We design an inexact augmented Lagrange multiplier optimization algorithm to solve it. Unlike the existing sparse representation and low-rank learning methods, our approach learns a low-dimensional subspace from recovered data, and explicitly incorporates the supervised information. Our approach and some baselines are evaluated on the COIL-100, ALOI, Extended YaleB, FERET, AR, and KinFace databases. The exper- imental results demonstrate the effectiveness of our approach, especially when the data contain considerable noise or variations.Funded by Naval Postgraduate SchoolNational Science Foundation Computer and Network SystemsONR Young InvestigatorOffice of Naval ResearchU.S. Army Research Office Young Investigato

Real photons produced from photoproduction in pppp collisions

We calculate the production of real photons originating from the photoproduction in relativistic $pp$ collisions. The Weizs$\ddot{\mathrm{a}}$cker-Williams approximation in the photoproduction is considered. Numerical results agree with the experimental data from Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC). We find that the modification of the photoproduction is more prominent in large transverse momentum region.Comment: 2 figure

Production of large transverse momentum dileptons and photons in pppp, dAdA and AAAA collisions by photoproduction processes

The production of large $P_{T}$ dileptons and photons originating from photoproduction processes in $pp$, $dA$ and $AA$ collisions is calculated. We find that the contribution of dileptons and photons produced by photoproduction processes is not prominent at RHIC energies. However, the numerical results indicate that the modification of photoproduction processes becomes evident in the large $P_{T}$ region for $pp$, $dA$ and $AA$ collisions at LHC energies.Comment: 10 figure