arXiv subjects

Qiang Zhang

Publications and source records attributed to Qiang Zhang.

At least 523 records · Page 29Linked to original sources

arXiv2018

MedSim: A Novel Semantic Similarity Measure in Bio-medical Knowledge Graphs

We present MedSim, a novel semantic SIMilarity method based on public well-established bio-MEDical knowledge graphs (KGs) and large-scale corpus, to study the therapeutic substitution of antibiotics. Besides hierarchy and corpus of KGs, MedSim further interprets medicine characteristics by constructing multi-dimensional medicine-specific feature vectors. Dataset of 528 antibiotic pairs scored by doctors is applied for evaluation and MedSim has produced statistically significant improvement over other semantic similarity methods. Furthermore, some promising applications of MedSim in drug substitution and drug abuse prevention are presented in case study.

cs.CL↗

arXiv2018

Improving Medical Short Text Classification with Semantic Expansion Using Word-Cluster Embedding

Automatic text classification (TC) research can be used for real-world problems such as the classification of in-patient discharge summaries and medical text reports, which is beneficial to make medical documents more understandable to doctors. However, in electronic medical records (EMR), the texts containing sentences are shorter than that in general domain, which leads to the lack of semantic features and the ambiguity of semantic. To tackle this challenge, we propose to add word-cluster embedding to deep neural network for improving short text classification. Concretely, we first use hierarchical agglomerative clustering to cluster the word vectors in the semantic space. Then we calculate the cluster center vector which represents the implicit topic information of words in the cluster. Finally, we expand word vector with cluster center vector, and implement classifiers using CNN and LSTM respectively. To evaluate the performance of our proposed method, we conduct experiments on public data sets TREC and the medical short sentences data sets which is constructed and released by us. The experimental results demonstrate that our proposed method outperforms state-of-the-art baselines in short sentence classification on both medical domain and general domain.

cs.CL↗

arXiv2018

New dynamics of energy use and CO2 emissions in China

Global achievement of climate change mitigation will heavy reply on how much of CO2 emission has and will be released by China. After rapid growth of emissions during last decades, China CO2 emissions declined since 2014 that driven by decreased coal consumption, suggesting a possible peak of China coal consumption and CO2 emissions. Here, by combining a updated methodology and underlying data from different sources, we reported the soaring 5.5% (range: +2.5% to +8.5% for one sigma) increase of China CO2 emissions in 2018 compared to 2017, suggesting China CO2 is not yet to peak and leaving a big uncertain to whether China emission will continue to rise in the future. Although our best estimate of total emission (9.9Gt CO2 in 2018) is lower than international agencies in the same year, the results show robust on a record-high energy consumption and total CO2 emission in 2018. During 2014-2016, China energy intensity (energy consumption per unit of GDP) and total CO2 emissions has decreased driven by energy and economic structure optimization. However, the decrease in emissions is now offset by stimulates of heavy industry production under economic downturn that driving coal consumption (+5% in 2018), as well as the surging of natural gas consumption (+18% in 2018) due to the government led coal-to-gas energy transition to reduce local air pollutions. Timing policy and actions are urgent needed to address on these new drivers to turn down the total emission growth trend.

econ.GN↗

arXiv2018

Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series

Volatility is a quantity of measurement for the price movements of stocks or options which indicates the uncertainty within financial markets. As an indicator of the level of risk or the degree of variation, volatility is important to analyse the financial market, and it is taken into consideration in various decision-making processes in financial activities. On the other hand, recent advancement in deep learning techniques has shown strong capabilities in modelling sequential data, such as speech and natural language. In this paper, we empirically study the applicability of the latest deep structures with respect to the volatility modelling problem, through which we aim to provide an empirical guidance for the theoretical analysis of the marriage between deep learning techniques and financial applications in the future. We examine both the traditional approaches and the deep sequential models on the task of volatility prediction, including the most recent variants of convolutional and recurrent networks, such as the dilated architecture. Accordingly, experiments with real-world stock price datasets are performed on a set of 1314 daily stock series for 2018 days of transaction. The evaluation and comparison are based on the negative log likelihood (NLL) of real-world stock price time series. The result shows that the dilated neural models, including dilated CNN and Dilated RNN, produce most accurate estimation and prediction, outperforming various widely-used deterministic models in the GARCH family and several recently proposed stochastic models. In addition, the high flexibility and rich expressive power are validated in this study.

cs.LG↗

arXiv2018

Ni-based transition-metal trichalcogenide monolayer: a strongly correlated quadruple-layer graphene

We investigate the electronic physics of layered Ni-based trichalcogenide NiPX$_3$ (X=S, Se), a member of transition-metal trichalcogenides (TMTs) with the chemical formula, ABX$_3$. These Ni-based TMTs distinguish themselves from other TMTs as their low energy electronic physics can be effectively described by the two $e_g$ d-orbitals. The major band kinematics is characterized by the unusal long-range effective hopping between two third nearest-neighbor (TNN) Ni sites in the two-dimensional Ni honeycomb lattice so that the Ni lattice can be equivalently viewed as four weakly coupled honeycomb sublattices. Within each sublattice, the electronic physics is described by a strongly correlated two-orbital graphene-type model that results in an antiferromagnetic (AFM) ground state near half filling. We show that the low energy physics in a paramagnetic state is determined by the eight Dirac cones which locate at $K$, $K'$, $\frac{K}{2}$ and $\frac{K'}{2}$ points in the first Brillouin zone with a strong AFM fluctuation between two $K (K')$ and $\frac{K'}{2} (\frac{K}{2})$ Dirac cones and carrier doping can sufficiently suppress the long-range AFM order and allow other competing orders, such as superconductivity, to emerge. The material can be an ideal system to study many exotic phenomena emerged from strong electron-electron correlation, including a potential $d\pm id$ superconducting state at high temperature.

cond-mat.str-el↗

arXiv2018

Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics

Disorders of consciousness are a heterogeneous mixture of different diseases or injuries. Although some indicators and models have been proposed for prognostication, any single method when used alone carries a high risk of false prediction. This study aimed to develop a multidomain prognostic model that combines resting state functional MRI with three clinical characteristics to predict one year outcomes at the single-subject level. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 90% on three datasets from two medical centers. It was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics. To our knowledge, this is the first implementation reported of a multidomain prognostic model based on resting state functional MRI and clinical characteristics in chronic disorders of consciousness. We therefore suggest that this novel prognostic model is accurate, robust, and interpretable.

q-bio.NC↗

arXiv2018

Large scale quantum key distribution: challenges and solutions

Quantum key distribution (QKD) together with one time pad encoding can provide information-theoretical security for communication. Currently, though QKD has been widely deployed in many metropolitan fiber networks, its implementation in a large scale remains experimentally challenging. This letter provides a brief review on the experimental efforts towards the goal of global QKD, including the security of practical QKD with imperfect devices, QKD metropolitan and backbone networks over optical fiber and satellite-based QKD over free space.

quant-ph↗

arXiv2018

Testing Local Realism into the Past without Detection and Locality Loopholes

Inspired by the recent remarkable progress in the experimental test of local realism, we report here such a test that achieves an efficiency greater than (78%)^2 for entangled photon pairs separated by 183 m. Further utilizing the randomness in cosmic photons from pairs of stars on the opposite sides of the sky for the measurement setting choices, we not only close the locality and detection loopholes simultaneously, but also test the null hypothesis against local hidden variable mechanisms for events that took place 11 years ago (13 orders of magnitude longer than previous experiments). After considering the bias in measurement setting choices, we obtain an upper bound on the p value of 7.87 * 10^-4, which clearly indicates the rejection with high confidence of potential local hidden variable models. One may further push the time constraint on local hidden variable mechanisms deep into the cosmic history by taking advantage of the randomness in photon emissions from quasars with large aperture telescopes.

quant-ph↗

arXiv2018

Single Crystalline Silver Films for Plasmonics: From Monolayer to Optically Thick Film

Epitaxial growth of single crystalline noble metals on dielectric substrates has received tremendous attention recently due to their technological potentials as low loss plasmonic materials. Currently there are two different growth approaches, each with its strengths and weaknesses. One adopts a sophisticated molecular beam epitaxial procedure to grow atomically smooth epitaxial Ag films. However, the procedure is rather slow and becomes impractical to grow films with thickness > 50 nm. Another approach adopts a growth process using rapid e-beam deposition which is capable of growing single crystalline Ag films in the thick regime (> 300 nm). However, the rapid growth procedure makes it difficult to control film thickness precisely, i.e., the method is not applicable to growing thin epitaxial films. Here we report a universal approach to grow atomically smooth epitaxial Ag films with precise thickness control from a few monolayers to the optically thick regime, overcoming the limitations of the two aforementioned methods. In addition, we develop an in-situ growth of aluminum oxide as the capping layer which exhibits excellent properties protecting the epitaxial Ag films. The performance of the epitaxial Ag films as a function of the film thickness is investigated by directly measuring the propagation length of the surface plasmon polaritons (SPPs) as well as their device performance to support a waveguide plasmonic nanolaser in infrared incorporating an InGaAsP quantum well as the gain media.

cond-mat.mtrl-sci↗

arXiv2018

Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications. In this paper, a novel deep learning-based method for this task is proposed, by learning a non-linear end-to-end mapping between the noisy and clean HSIs with a combined spatial-spectral deep convolutional neural network (HSID-CNN). Both the spatial and spectral information are simultaneously assigned to the proposed network. In addition, multi-scale feature extraction and multi-level feature representation are respectively employed to capture both the multi-scale spatial-spectral feature and fuse the feature representations with different levels for the final restoration. The simulated and real-data experiments demonstrate that the proposed HSID-CNN outperforms many of the mainstream methods in both the quantitative evaluation indexes, visual effects, and HSI classification accuracy.

cs.CV↗

arXiv2018

Device independent quantum random number generation

Randomness is critical for many information processing applications, including numerical modeling and cryptography. Device-independent quantum random number generation (DIQRNG) based on the loophole free violation of Bell inequality produces unpredictable genuine randomness without any device assumption and is therefore an ultimate goal in the field of quantum information science. However, due to formidable technical challenges, there were very few reported experimental studies of DIQRNG, which were vulnerable to the adversaries. Here we present a fully functional DIQRNG against the most general quantum adversaries. We construct a robust experimental platform that realizes Bell inequality violation with entangled photons with detection and locality loopholes closed simultaneously. This platform enables a continuous recording of a large volume of data sufficient for security analysis against the general quantum side information and without assuming independent and identical distribution. Lastly, by developing a large Toeplitz matrix (137.90 Gb $\times$ 62.469 Mb) hashing technique, we demonstrate that this DIQRNG generates $6.2469\times 10^7$ quantum-certified random bits in 96 hours (or 181 bits/s) with uniformity within $10^{-5}$. We anticipate this DIQRNG may have profound impact on the research of quantum randomness and information-secured applications.

quant-ph↗

arXiv2018

i-Josephson Junction as Topological Superconductor

We show that the time reversal symmetry inevitably breaks in a superconducting Josephson junction formed by two superconductors with different pairing symmetries dubbed as i-Josephson junction. While the leading conventional Josephson coupling vanishes in such an i-Josephson junction, the second order coupling from tunneling always generates chiral superconductivity orders with broken time reversal symmetry. Josephson frequency in the i-junction is doubled, namely $ω= 4eV /h$. The result provides a way to engineer topological superconductivity such as the d + id' -wave superconducting state characterized by a nonzero Chern number.

cond-mat.mes-hall↗

arXiv2018

Experimental test of measurement dependent local Bell inequality with human free will

A Bell test can rule out local realistic models, and has potential applications in communications and information tasks. For example, a Bell inequality violation can certify the presence of intrinsic randomness in measurement outcomes, which then can be used to generate unconditional randomness. A Bell test requires, however, measurements that are chosen independently of other physical variables in the test, as would be the case if the measurement settings were themselves unconditionally random. This situation seems to create a "bootstrapping problem" that was recently addressed in The BIG Bell Test, a collection of Bell tests and related tests using human setting choices. Here we report in detail our experimental methods and results within the BIG Bell Test. We perform a experimental test of a special type of Bell inequality - the measurement dependent local inequality. With this inequality, even a small amount of measurement independence makes it possible to disprove local realistic models. The experiment uses human-generated random numbers to select the measurement settings in real time, and implements the measurement setting with space-like separation from the distant measurement. The experimental result shows a Bell inequality violation that cannot be explained by local hidden variable models with independence parameter (as defined in [Putz et al. Phys. Rev. Lett. 113, 190402 (2014).] ) l > 0.10 +/- 0.05. This result quantifies the degree to which a hidden variable model would need to constrain human choices, if it is to reproduce the experimental results.

quant-ph↗

arXiv2018

STM and DFT studies of CO2 adsorption on Cu(100)-O surface

We characterized CO2 adsorption and diffusion on the missing row reconstructed Cu(100)-O surface using a combination of scanning tunneling microscopy (STM) and density functional theory (DFT) calculations with dispersion. We deposited CO2 molecules in situ at 5K, which allowed us to unambiguously identify individual CO2 molecules and their adsorption sites. Based on a comparison of experimental and DFT-generated STM images, we find that the CO2 molecules sit in between the O atoms in the missing row reconstructed Cu(100)-O surface. The CO2 molecules are easily perturbed by the STM tip under typical imaging conditions, suggesting that the molecules are weakly bound to the surface. The calculated adsorption energy, vibrational modes, and diffusion barriers of the CO2 molecules also indicate weak adsorption, in qualitative agreement with the experiments. A comparison of tunneling spectroscopy and DFT-calculated density of states shows that the primary change near the Fermi level is associated with changes to the surface states with negligible contribution from the CO2 molecular states.

cond-mat.mes-hall↗

arXiv2018

Active Object Reconstruction Using a Guided View Planner

Inspired by the recent advance of image-based object reconstruction using deep learning, we present an active reconstruction model using a guided view planner. We aim to reconstruct a 3D model using images observed from a planned sequence of informative and discriminative views. But where are such informative and discriminative views around an object? To address this we propose a unified model for view planning and object reconstruction, which is utilized to learn a guided information acquisition model and to aggregate information from a sequence of images for reconstruction. Experiments show that our model (1) increases our reconstruction accuracy with an increasing number of views (2) and generally predicts a more informative sequence of views for object reconstruction compared to other alternative methods.

cs.CV↗

arXiv2018

Image Correction via Deep Reciprocating HDR Transformation

Image correction aims to adjust an input image into a visually pleasing one. Existing approaches are proposed mainly from the perspective of image pixel manipulation. They are not effective to recover the details in the under/over exposed regions. In this paper, we revisit the image formation procedure and notice that the missing details in these regions exist in the corresponding high dynamic range (HDR) data. These details are well perceived by the human eyes but diminished in the low dynamic range (LDR) domain because of the tone mapping process. Therefore, we formulate the image correction task as an HDR transformation process and propose a novel approach called Deep Reciprocating HDR Transformation (DRHT). Given an input LDR image, we first reconstruct the missing details in the HDR domain. We then perform tone mapping on the predicted HDR data to generate the output LDR image with the recovered details. To this end, we propose a united framework consisting of two CNNs for HDR reconstruction and tone mapping. They are integrated end-to-end for joint training and prediction. Experiments on the standard benchmarks demonstrate that the proposed method performs favorably against state-of-the-art image correction methods.

cs.CV↗

arXiv2018

Integrating quantum key distribution with classical communications in backbone fiber network

Quantum key distribution (QKD) provides information-theoretic security based on the laws of quantum mechanics. The desire to reduce costs and increase robustness in real-world applications has motivated the study of coexistence between QKD and intense classical data traffic in a single fiber. Previous works on coexistence in metropolitan areas have used wavelength-division multiplexing, however, coexistence in backbone fiber networks remains a great experimental challenge, as Tbps data of up to 20 dBm optical power is transferred, and much more noise is generated for QKD. Here we present for the first time, to the best of our knowledge, the integration of QKD with a commercial backbone network of 3.6 Tbps classical data at 21 dBm launch power over 66 km fiber. With 20 GHz pass-band filtering and large effective core area fibers, real-time secure key rates can reach 4.5 kbps and 5.1 kbps for co-propagation and counter-propagation at the maximum launch power, respectively. This demonstrates feasibility and represents an important step towards building a quantum network that coexists with the current backbone fiber infrastructure of classical communications.

quant-ph↗

arXiv2018

A Low-latency Pipeline for GRB Light Curve and Spectrum using Fermi/GBM Near Real-time Data

Rapid response and short time latency are very important for Time Domain Astronomy, such as the observations of Gamma-ray Bursts (GRBs) and electromagnetic (EM) counterparts of gravitational waves (GWs). Based on the near real-time Fermi/GBM data, we developed a low-latency pipeline to automatically calculate the temporal and spectral properties of GRBs. With this pipeline, some important parameters can be obtained, such as T90 and fluence, within ~20 minutes after the GRB trigger. For ~90% GRBs, T90 and fluence are consistent with the GBM catalog results within 2 sigma errors. This pipeline has been used by the Gamma-ray Bursts Polarimeter (POLAR) and the Insight Hard X-ray Modulation Telescope (Insight-HXMT) to follow up the bursts of interest. For GRB 170817A, the first EM counterpart of GW events detected by Fermi/GBM and INTEGRAL/SPI-ACS, the pipeline gave T90 and spectral information in 21 minutes after the GBM trigger, providing important information for POLAR and Insight-HXMT observations.

astro-ph.IM↗