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Person re-identification (re-ID) models trained on one domain often fail to generalize well to another. In our attempt, we present a "learning via translation" framework. In the baseline, we translate the labeled images from source to target domain in an unsupervised manner. We then train re-ID models with the translated images by supervised methods. Yet, being an essential part of this framework, unsupervised image-image translation suffers from the information loss of source-domain labels during translation.Our motivation is two-fold. First, for each image, the discriminative cues contained in its ID label should be maintained after translation. Second, given the fact that two domains have entirely different persons, a translated image should be dissimilar to any of the target IDs. To this end, we propose to preserve two types of unsupervised similarities, 1) self-similarity of an image before and after translation, and 2) domain-dissimilarity of a translated source image and a target image. Both constraints are implemented in the similarity preserving generative adversarial network (SPGAN) which consists of an Siamese network and a Cy-cleGAN. Through domain adaptation experiment, we show that images generated by SPGAN are more suitable for domain adaptation and yield consistent and competitive re-ID accuracy on two large-scale datasets.

This paper will discuss the design and construction of BESIII [1], which is designed to study physics in the τ-charm energy region utilizing the new high luminosity BEPCII double ring e + ecollider [2]. The expected performance will be given based on Monte Carlo simulations and results of cosmic ray and beam tests. In BESIII, tracking and momentum measurements for charged particles are made by a cylindrical multilayer drift chamber in a 1 T superconducting solenoid. Charged particles are identified with a time-of-flight system based on plastic scintillators in conjunction with dE/dx (energy loss per unit pathlength) measurements in the drift chamber. Energies of electromagnetic showers are measured by a CsI(Tl) crystal calorimeter located inside the solenoid magnet. Muons are identified by arrays of resistive plate chambers in the steel magnetic flux return. The level 1 trigger system, Data Acquisition system and the event filter system based on networked computers will also be described.

A measurement of electron antineutrino oscillation by the Daya Bay Reactor Neutrino Experiment is described in detail. Six 2.9-GWth nuclear power reactors of the Daya Bay and Ling Ao nuclear power facilities served as intense sources of ν e 's. Comparison of theν e rate and energy spectrum measured by antineutrino detectors far from the nuclear reactors (∼1500-1950 m) relative to detectors near the reactors (∼350-600 m) allowed a precise measurement ofν e disappearance. More than 2.5 millionν e inverse beta-decay interactions were observed, based on the combination of 217 days of operation of six antineutrino detectors (December, 2011-July, 2012) with a subsequent 1013 days using the complete configuration of eight detectors (October, 2012-July, 2015. Theν e rate observed at the far detectors relative to the near detectors showed a significant deficit, R ¼ 0.949 AE 0.002ðstatÞAE 0.002ðsystÞ. The energy dependence ofν e disappearance showed the distinct variation predicted by neutrino oscillation. Analysis using an approximation for the three-flavor oscillation probability yielded the flavor-mixing angle sin 2 2θ 13 ¼ 0.0841 AE 0.0027ðstatÞ AE 0.0019ðsystÞ and the effective neutrino mass-squared difference of jΔm 2 ee j ¼ ð2.50 AE 0.06ðstatÞ AE 0.06ðsystÞÞ × 10 −3 eV 2 . Analysis using the exact three-flavor probability found Δm