Grasping asymmetric information in market impacts

The price impact for a single trade is estimated by the immediate response on an event time scale, i.e., the immediate change of midpoint prices before and after a trade. We work out the price impacts across a correlated financial market. We quantify the asymmetries of the distributions and of the market structures of cross-impacts, and find that the impacts across the market are asymmetric and non-random. Using spectral statistics and Shannon entropy, we visualize the asymmetric information in price impacts. Also, we introduce an entropy of impacts to estimate the randomness between stocks. We show that the useful information is encoded in the impacts corresponding to small entropy. The stocks with large number of trades are more likely to impact others, while the less traded stocks have higher probability to be impacted by others

Gluon contribution to open heavy-meson production in heavy-ion collisions

A sizable contribution to heavy quark production in high-energy hadronic and nuclear collisions comes from heavy quark-antiquark pair production from gluon splitting during the parton shower evolution. We investigate the effect of gluon-medium interaction on open heavy flavor spectra in ultra-relativistic heavy-ion collisions. The interaction of hard gluons and heavy quarks with the hot QCD medium is simulated by utilizing a Langevin transport model that simultaneously incorporates contributions from collisional and radiative processes. It is found that while the gluon splitting channel has quite an important contribution to the single $D$ meson production cross section, its influence on the final heavy meson nuclear modification turns out to be quite modest because the average lifetime of hard gluons is short before splitting into heavy quark-antiquark pairs during the evolution and propagation of the parton shower.Comment: 5 pages, 6 figure

Unsupervised Triplet Hashing for Fast Image Retrieval

Hashing has played a pivotal role in large-scale image retrieval. With the development of Convolutional Neural Network (CNN), hashing learning has shown great promise. But existing methods are mostly tuned for classification, which are not optimized for retrieval tasks, especially for instance-level retrieval. In this study, we propose a novel hashing method for large-scale image retrieval. Considering the difficulty in obtaining labeled datasets for image retrieval task in large scale, we propose a novel CNN-based unsupervised hashing method, namely Unsupervised Triplet Hashing (UTH). The unsupervised hashing network is designed under the following three principles: 1) more discriminative representations for image retrieval; 2) minimum quantization loss between the original real-valued feature descriptors and the learned hash codes; 3) maximum information entropy for the learned hash codes. Extensive experiments on CIFAR-10, MNIST and In-shop datasets have shown that UTH outperforms several state-of-the-art unsupervised hashing methods in terms of retrieval accuracy