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

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