Identification of Two categories of optically bright gamma-ray bursts

We present the results of a systematical analysis of the intrinsic optical afterglow light curves for a complete sample of gamma-ray bursts (GRBs) observed in the period from Feb. 1997 to Aug. 2005. These light curves are generally well-sampled, with at least four detections in the $R$ band. The redshifts of all the bursts in the sample are available. We derive the intrinsic $R$ band afterglow lightcurves (luminosity versus time within the cosmic proper rest frame) for these GRBs, and discover a fact that they essentially follow two universal tracks after 2 hours since the GRB triggers. The optical luminosities at 1 day show a clear bimodal distribution, peaking at 1.4*10^{46} ergs~s^{-1} for the luminous group and 5.3*10^{44} ergs~s^{-1} for the dim group. About 75% of the GRBs are in the luminous group, and the other 25% belong to the dim group. While the luminous group has a wide range of redshift distribution, the bursts in the dim group all appear at a redshift lower than 1.1.Comment: 10 pages, 2 figures, emulateapj style, accepted for publication by ApJ Letter

TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China

TiEV is an autonomous driving platform implemented by Tongji University of China. The vehicle is drive-by-wire and is fully powered by electricity. We devised the software system of TiEV from scratch, which is capable of driving the vehicle autonomously in urban paths as well as on fast express roads. We describe our whole system, especially novel modules of probabilistic perception fusion, incremental mapping, the 1st and the 2nd planning and the overall safety concern. TiEV finished 2016 and 2017 Intelligent Vehicle Future Challenge of China held at Changshu. We show our experiences on the development of autonomous vehicles and future trends

One for More: Selecting Generalizable Samples for Generalizable ReID Model

Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the performance on samples outside the batch. It will inevitably cause the model to over-fit the data in the dominant position (e.g., head data in imbalanced class, easy samples or noisy samples). %We call the sample that updates the model towards generalizing on more data a generalizable sample. The latest resampling methods address the issue by designing specific criterion to select specific samples that trains the model generalize more on certain type of data (e.g., hard samples, tail data), which is not adaptive to the inconsistent real world ReID data distributions. Therefore, instead of simply presuming on what samples are generalizable, this paper proposes a one-for-more training objective that directly takes the generalization ability of selected samples as a loss function and learn a sampler to automatically select generalizable samples. More importantly, our proposed one-for-more based sampler can be seamlessly integrated into the ReID training framework which is able to simultaneously train ReID models and the sampler in an end-to-end fashion. The experimental results show that our method can effectively improve the ReID model training and boost the performance of ReID models

Spectral and temporal analysis of the joint Swift/BAT-Fermi/GBM GRB sample

Using the gamma-ray bursts simultaneously detected by Swift/BAT and Fermi/GBM we performed a joint spectral and temporal analysis of the prompt emission data and confirm the rough correlation between the BAT-band photon index Gamma_BAT and the peak spectral energy Epeak. With the redshift known sub-sample, we derived the isotropic gamma-ray energy E_gamma,iso and also confirm the E_gamma,iso - Epeak,rest relation, with a larger scatter than the Amati sample but consistent with GBM team analyses. We also compare the T_90 values derived in the GBM band with those derived in the BAT band and find that for long GRBs the BAT T_90 is usually longer than the GBM T_90, while for short GRBs the trend reverses. This is consistent with the soft/hard nature of long/short GRBs and suggests the importance of an energy-dependent temporal analysis of GRBs.Comment: 18 pages, 9 figures, 3 tables, MNRAS accepted, spectral fits updated, conclusions unchange