Possible observables for the chiral electric separation effect in Cu + Au collisions

The quark-gluon plasma (QGP) generated in relativistic heavy-ion collisions could be locally parity-odd. In parity-odd QGP, the electric field may induce a chiral current which is called the chiral electric separation effect (CESE). We propose two possible observables for CESE in Cu + Au collisions: The first one is the correlation $\zeta_{\alpha\beta}=\langle \cos[2(\phi_\alpha+\phi_\beta-2\Psi_{\rm RP})]\rangle$; the second one is the charge-dependent event-plane angle $\Psi^{q}_2$ with $q=\pm$ being charge. Nonzero $\Delta\zeta=\zeta_{opp}-\zeta_{same}$ and $\Delta\Psi=\langle|\Psi_2^+-\Psi_2^-|\rangle$ may signal the CESE in Cu + Au collisions. Within a multiphase transport model, we study how the final state interaction affects these observables. We find that the correlation $\gamma_{\alpha\beta}=\langle\cos(\phi_{\alpha}+\phi_{\beta}-\Psi_{\rm RP})\rangle$ is sensitive to the out-of-plane charge separation caused by the chiral magnetic effect and to the in-plane charge separation caused by the in-plane electric field, but it is not sensitive to the CESE. On the other hand, $\Delta\zeta$ and $\Delta\Psi$ are sensitive to the CESE. Therefore, we suggest that the future experiments measure the above observables in Cu+Au collisions in order to disentangle different chiral and charge separation mechanisms.Comment: 6 pages, 6 figures, final published versio

Lattice Boltzmann simulation of separation phenomenon in a binary gaseous flow through a microchannel

Gas separation of a binary gaseous mixture is one of characteristic phenomena in the micro-scale flows that differ from the conventional size flows. In this work, the separation in a binary gas mixture flows through a microchannel is investigated by the lattice Boltzmann method with a diffuse-bounce-back (DBB) boundary condition. The separation degree and rate are measured in the He--Ar and Ne--Ar systems for different mole fractions, pressure ratios, and Knudsen numbers. The results show that the separation phenomenon in the He--Ar mixture is more obvious than that in the Ne--Ar mixture at the same mole fraction owing to the larger molecular mass ratio. In addition, the increase in the pressure ratio reduces the difference in the molecular velocities between the two species, and the separation phenomenon becomes weaker. However, the gas separation is enhanced with an increase in the Knudsen number. This is because the resulting rarefaction effect reduces the interactions between the gas molecules of the two species, and thus increases the difference in the molecular velocity.Comment: 19pages,15figure

DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval

This paper concerns a deep learning approach to relevance ranking in information retrieval (IR). Existing deep IR models such as DSSM and CDSSM directly apply neural networks to generate ranking scores, without explicit understandings of the relevance. According to the human judgement process, a relevance label is generated by the following three steps: 1) relevant locations are detected, 2) local relevances are determined, 3) local relevances are aggregated to output the relevance label. In this paper we propose a new deep learning architecture, namely DeepRank, to simulate the above human judgment process. Firstly, a detection strategy is designed to extract the relevant contexts. Then, a measure network is applied to determine the local relevances by utilizing a convolutional neural network (CNN) or two-dimensional gated recurrent units (2D-GRU). Finally, an aggregation network with sequential integration and term gating mechanism is used to produce a global relevance score. DeepRank well captures important IR characteristics, including exact/semantic matching signals, proximity heuristics, query term importance, and diverse relevance requirement. Experiments on both benchmark LETOR dataset and a large scale clickthrough data show that DeepRank can significantly outperform learning to ranking methods, and existing deep learning methods.Comment: Published as a conference paper at CIKM 2017, CIKM'17, November 6--10, 2017, Singapore TextNet (https://github.com/pl8787/textnet-release) PyTorch (https://github.com/pl8787/DeepRank_PyTorch

Locally Smoothed Neural Networks

Convolutional Neural Networks (CNN) and the locally connected layer are limited in capturing the importance and relations of different local receptive fields, which are often crucial for tasks such as face verification, visual question answering, and word sequence prediction. To tackle the issue, we propose a novel locally smoothed neural network (LSNN) in this paper. The main idea is to represent the weight matrix of the locally connected layer as the product of the kernel and the smoother, where the kernel is shared over different local receptive fields, and the smoother is for determining the importance and relations of different local receptive fields. Specifically, a multi-variate Gaussian function is utilized to generate the smoother, for modeling the location relations among different local receptive fields. Furthermore, the content information can also be leveraged by setting the mean and precision of the Gaussian function according to the content. Experiments on some variant of MNIST clearly show our advantages over CNN and locally connected layer.Comment: In Proceedings of 9th Asian Conference on Machine Learning (ACML2017

A Study of MatchPyramid Models on Ad-hoc Retrieval

Deep neural networks have been successfully applied to many text matching tasks, such as paraphrase identification, question answering, and machine translation. Although ad-hoc retrieval can also be formalized as a text matching task, few deep models have been tested on it. In this paper, we study a state-of-the-art deep matching model, namely MatchPyramid, on the ad-hoc retrieval task. The MatchPyramid model employs a convolutional neural network over the interactions between query and document to produce the matching score. We conducted extensive experiments to study the impact of different pooling sizes, interaction functions and kernel sizes on the retrieval performance. Finally, we show that the MatchPyramid models can significantly outperform several recently introduced deep matching models on the retrieval task, but still cannot compete with the traditional retrieval models, such as BM25 and language models.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieva

Optical Spectroscopy of Four Young Radio Sources

We report the optical spectroscopy of four young radio sources which are observed with the Lijiang 2.4m telescope. The Eddington ratios of these sources are similar with those of narrow-line Seyfert 1 galaxies (NLS1s). Their Fe {\sc ii} emission is strong while [O {\sc iii}] strength is weak. These results confirm the NLS1 features of young radio sources, except that the width of broad H$\beta$ of young radio sources is larger than that of NLS1s. We thus suggest that the young radio sources are the high black hole mass counterparts of steep-spectrum radio-loud NLS1s. In addition, the broad H$\beta$ component of \astrobj{4C 12.50} is the blue wing of the narrow component, but not from the broad line region.Comment: 11 pages, 5 Figures, 2 Tables, accepted by New Astronom

Filtration and Extraction of Quantum States from Classical Inputs

We propose using nonlinear Mach-Zehnder interferometer (NMZI) to efficiently prepare photonic quantum states from a classical input. We first analytically investigate the simple NMZI that can filtrate single photon state from weak coherent state by preferrentially blocking two-photon component. As a generalization, we show that the cascaded NMZI can deterministically extract arbitrary quantum state from a strong coherent state. Finally, we numerically demonstrate that the cascaded NMZI can be very efficient in both the input power and the level of cascade. The protocol of quantum state preparation with NMZI can be extended to various systems of bosonic modes.Comment: 5 pages, 3 figure

Chiral magnetic effect in isobaric collisions

We give a numerical simulation of the generation of the magnetic field and the charge-separation signal due to the chiral magnetic effect (CME) --- the induction of an electric current by the magnetic field in a parity-odd matter --- in the collisions of isobaric nuclei, $^{96}_{44}$Ru + $^{96}_{44}$Ru and $^{96}_{40}$Zr + $^{96}_{40}$Zr, at $\sqrt{s_{\rm NN}}=200$ GeV. We show that such collisions provide an ideal tool to disentangle the CME signal from the possible elliptic-flow driven background effects. We also discuss some other effects that can be tested by using the isobaric collisions.Comment: Proceedings of XXVIth International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (Quark Matter 2017

dbCoRC: a database of core transcriptional regulatory circuitries modeled by H3K27ac ChIP-seq signals.

Core transcription regulatory circuitry (CRC) is comprised of a small group of self-regulated transcription factors (TFs) and their interconnected regulatory loops. Studies from embryonic stem cells and other cellular models have revealed the elementary roles of CRCs in transcriptional control of cell identity and cellular fate. Systematic identification and subsequent archiving of CRCs across diverse cell types and tissues are needed to explore both cell/tissue type-specific and disease-associated transcriptional networks. Here, we present a comprehensive and interactive database (dbCoRC, http://dbcorc.cam-su.org) of CRC models which are computationally inferred from mapping of super-enhancer and prediction of TF binding sites. The current version of dbCoRC contains CRC models for 188 human and 50 murine cell lines/tissue samples. In companion with CRC models, this database also provides: (i) super enhancer, typical enhancer, and H3K27ac landscape for individual samples, (ii) putative binding sites of each core TF across the super-enhancer regions within CRC and (iii) expression of each core TF in normal or cancer cells/tissues. The dbCoRC will serve as a valuable resource for the scientific community to explore transcriptional control and regulatory circuitries in biological processes related to, but not limited to lineage specification, tissue homeostasis and tumorigenesis

Test the chiral magnetic effect with isobaric collisions

The quark-gluon matter produced in relativistic heavy-ion collisions may contain local domains in which P and CP symmetries are not preserved. When coupled with an external magnetic field, such P- and CP-odd domains will generate electric currents along the magnetic field --- a phenomenon called the chiral magnetic effect (CME). Recently, the STAR Collaboration at RHIC and the ALICE Collaboration at the LHC released data of charge-dependent azimuthal-angle correlators with features consistent with the CME expectation. However, the experimental observable is contaminated with significant background contributions from elliptic-flow-driven effects, which makes the interpretation of the data ambiguous. In this Letter, we show that the collisions of isobaric nuclei, $^{96}_{44}$Ru + $^{96}_{44}$Ru and $^{96}_{40}$Zr + $^{96}_{40}$Zr, provide an ideal tool to disentangle the CME signal from the background effects. Our simulation demonstrates that the two collision types at $\sqrt{s_{\rm NN}}=200$ GeV have more than $10\%$ difference in the CME signal and less than $2\%$ difference in the elliptic-flow-driven backgrounds for the centrality range of $20-60\%$.Comment: V1: 4 pages, 4 figure