arXiv subjects

Shuo Zhang

Publications and source records attributed to Shuo Zhang.

At least 307 records · Page 17Linked to original sources

arXiv2019

Two Efficient Beamformers for Secure Precise Jamming and Communication with Phase Alignment

To achieve a better effect of interference on eavesdropper with an enhanced security, a secure precise jamming (PJ) and communication (SPJC) is proposed and its basic idea is to force the transmit energy of artificial noise (AN) and confidential message into the neighborhoods of Eve and Bob by using random subcarrier selection (RSS), directional modulation, and beamforming under phase alignment (PA) constraint (PAC). Here, we propose two high-performance beamforming schemes: minimum transmit power (Min-TP) and minimum regularized transmit power (Min-RTP) to achieve SPJC under PAC and orthogonal constraint (OC), where OC means that AN and CM are projected onto the null-spaces of the desired and eavesdropping channels, respectively. Simulation results show that the proposed Min-TP and Min-RTP methods perform much better than existing equal amplitude (EA) method in terms of both bit-error-rate (BER) and secrecy rate (SR) at medium and high signal-to-noise ratio regions. The SR performance difference between the proposed two methods becomes trivial as the number of transmit antennas approaches large-scale. More importantly, we also find the fact that all three schemes including EA, Min-TP, and Min-RTP can form two main peaks of AN and CM around Eve and Bob, respectively. This achieves both PJ and secure precise wireless transmission (SPWT), called SPJC.

eess.SP↗

arXiv2019

Recommending Related Tables

Tables are an extremely powerful visual and interactive tool for structuring and manipulating data, making spreadsheet programs one of the most popular computer applications. In this paper we introduce and address the task of recommending related tables: given an input table, identifying and returning a ranked list of relevant tables. One of the many possible application scenarios for this task is to provide users of a spreadsheet program proactively with recommendations for related structured content on the Web. At its core, the related table recommendation task boils down to computing the similarity between a pair of tables. We develop a theoretically sound framework for performing table matching. Our approach hinges on the idea of representing table elements in multiple semantic spaces, and then combining element-level similarities using a discriminative learning model. Using a purpose-built test collection from Wikipedia tables, we demonstrate that the proposed approach delivers state-of-the-art performance.

cs.IR↗

arXiv2019

Data mining Mandarin tone contour shapes

In spontaneous speech, Mandarin tones that belong to the same tone category may exhibit many different contour shapes. We explore the use of data mining and NLP techniques for understanding the variability of tones in a large corpus of Mandarin newscast speech. First, we adapt a graph-based approach to characterize the clusters (fuzzy types) of tone contour shapes observed in each tone n-gram category. Second, we show correlations between these realized contour shape types and a bag of automatically extracted linguistic features. We discuss the implications of the current study within the context of phonological and information theory.

cs.CL↗

arXiv2019

Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval

Tables contain valuable knowledge in a structured form. We employ neural language modeling approaches to embed tabular data into vector spaces. Specifically, we consider different table elements, such caption, column headings, and cells, for training word and entity embeddings. These embeddings are then utilized in three particular table-related tasks, row population, column population, and table retrieval, by incorporating them into existing retrieval models as additional semantic similarity signals. Evaluation results show that table embeddings can significantly improve upon the performance of state-of-the-art baselines.

cs.IR↗

arXiv2019

Optimal Power Allocation for Secure Directional Modulation Networks with a Full-duplex UAV User

This paper make an investigation of a secure unmanned aerial vehicle (UAV)-aided communication network based on directional modulation(DM), in which one ground base station (Alice), one legitimate full-duplex (FD) user (Bob) and one illegal receiver (Eve) are involved. In this network, Alice acts as a control center to transmit confidential message and artificial noise (AN). The UAV user, moving along a linear flight trajectory, is intended to receive the useful information from Alice. At the same time, it also sends AN signals to further interference Eve's channel. Aiming at maximizing secrecy rate during the UAV flight process, a joint optimization problem is formulated corresponding to power allocation (PA) factors, beamforming vector, AN projection matrices. For simplicity, maximum ratio transmission, null-space projection and the leakage-based method are applied to form the transmit beamforming vector, AN projection matrix at Alice, and AN projection vector at Bob, respectively. Following this, the optimization problem reduces into a bivariate optimization programme with two PA factors. We put forward an alternating iterative algorithm to optimize the two PA factors. Simulation results demonstrate that the proposed strategy for FD mode achieves a higher SR than the half-duplex (HD) mode, and outperforms the FD mode with fixed PA strategy.

cs.IT↗

arXiv2019

Metallic-Line Stars Identified from Low Resolution Spectra of LAMOST DR5

LAMOST DR5 released more than 200,000 low resolution spectra of early-type stars with S/N>50. Searching for metallic-line (Am) stars in such a large database and study of their statistical properties are presented in this paper. Six machine learning algorithms were experimented with using known Am spectra, and both the empirical criteria method(Hou et al. 2015) and the MKCLASS package(Gray et al. 2016) were also investigated. Comparing their performance, the random forest (RF) algorithm won, not only because RF has high successful rate but also it can derives and ranks features. Then the RF was applied to the early type stars of DR5, and 15,269 Am candidates were picked out. Manual identification was conducted based on the spectral features derived from the RF algorithm and verified by experts. After manual identification, 9,372 Am stars and 1,131 Ap candidates were compiled into a catalog. Statistical studies were conducted including temperature distribution, space distribution, and infrared photometry. The spectral types of Am stars are mainly between F0 and A4 with a peak around A7, which is similar to previous works. With the Gaia distances, we calculated the vertical height Z from the Galactic plane for each Am star. The distribution of Z suggests that the incidence rate of Am stars shows a descending gradient with increasing jZj. On the other hand, Am stars do not show a noteworthy pattern in the infrared band. As wavelength gets longer, the infrared excess of Am stars decreases, until little or no excess in W1 and W2 bands.

astro-ph.SR↗

arXiv2019

A Deep Chandra View of A Candidate Parsec-Scale Jet from the Galactic Center Super-massive Black Hole

We have investigated the linear X-ray filament, G359.944-0.052, previously identified as a likely X-ray counterpart of a parsec-scale jet from the Galactic Center super-massive black hole (SMBH), Sagittarius A* (Sgr A*), using a total of ~5.6 Ms ultra-deep Chandra observations taken from September 1999 to July 2017. This unprecedented dataset enables us to examine flux and spectral variations that might be related to intrinsic properties of the weakly accreting SMBH. We find no flux or spectral variation in G359.944-0.052 after the G2 periapsis passage around early 2014, however, a moderate flux increase of ~2 sigma significance might be associated with the periapsis passage of G1 in early 2001. The filament exhibits an unusually hard spectrum (photon-index < 1) in its portion closest to Sgr A* (i.e., near-side) and a significant spectral softening in the more distant portion, which can be interpreted as synchrotron cooling of the relativistic electrons moving along the jet path. In particular, the hard spectrum of the near-side suggests a piling up of quasi-monoenergetic electrons caused by rapid radiative cooling. The spectral and temporal properties of G359.944-0.052 strengthen the case of it being the X-ray counterpart of a jet launched by Sgr A*.

astro-ph.HE↗

arXiv2019

Effect of Rotational Speed on the Stability of Two Rotating Side-by-side Circular Cylinders at Low Reynolds Number

Flow around two rotating side-by-side circular cylinders of equal diameter D is numerically studied at the Reynolds number Re=40-200 and various rotation rate theta_i. The incoming flow is assumed to be two-dimensional laminar flow. The governing equations are the incompressible Navier-Stokes equations and solved by the finite volume method (FVM). The ratio of the center-to-center spacing to the cylinder diameter is T/D=2. The objective of the present work is to investigate the effect of rotational speed and Reynolds number on the stability of the flow. The simulation results are compared with the experimental data and a good agreement is achieved. The stability of the flow is analyzed by using the energy gradient theory, which produces the energy gradient function K to identify the region where the flow is the most prone to be destabilized and the degree of the destabilization. Numerical results reveal that K is the most significant at the separated shear layers of the cylinder pair. With Re increases, the length of the wake is shorter and the vortex shedding generally exhibits a symmetrical distribution for theta_i <theta_crit. It is also shown that the unsteady vortex shedding can be suppressed by rotating the cylinders in the counter-rotating mode.

physics.flu-dyn↗

arXiv2019

Investigating the origin of the faint non-thermal emission of the Arches cluster using the 2015-2016 NuSTAR and XMM-Newton X-ray observations

Recent NuSTAR and XMM-Newton observations of the molecular cloud around the Arches stellar cluster demonstrate a dramatic change both in morphology and intensity of its non-thermal X-ray emission, similar to that observed in many molecular clouds of the Central Molecular Zone at the Galactic Center. These variations trace the propagation of illuminating fronts, presumably induced by past flaring activities of Sgr A$^{\star}$. In this paper we present results of a long NuSTAR observation of the Arches complex in 2016, taken a year after the previous XMM+NuSTAR observations which revealed a strong decline in the cloud emission. The 2016 NuSTAR observation shows that both the non-thermal continuum emission and the Fe K$_α$ 6.4~keV line flux are consistent with the level measured in 2015. No significant variation has been detected in both spectral shape and Fe K$_α$ equivalent width EW$_{\rm 6.4\ keV}$, which may be interpreted as the intensity of the Arches non-thermal emission reaching its stationary level. At the same time, the measured 2016 non-thermal flux is not formally in disagreement with the declining trend observed in 2007-2015. Thus, we cannot assess whether the non-thermal emission has reached a stationary level in 2016, and new observations, separated by a longer time period, are needed to draw stringent conclusions. Detailed spectral analysis of three bright clumps of the Arches molecular cloud performed for the first time showed different EW$_{\rm 6.4\ keV}$ and absorption. This is a strong hint that the X-ray emission from the molecular cloud is a mix of two components with different origins.

astro-ph.HE↗

arXiv2019

Efforts estimation of doctors annotating medical image

Accurate annotation of medical image is the crucial step for image AI clinical application. However, annotating medical image will incur a great deal of annotation effort and expense due to its high complexity and needing experienced doctors. To alleviate annotation cost, some active learning methods are proposed. But such methods just cut the number of annotation candidates and do not study how many efforts the doctor will exactly take, which is not enough since even annotating a small amount of medical data will take a lot of time for the doctor. In this paper, we propose a new criterion to evaluate efforts of doctors annotating medical image. First, by coming active learning and U-shape network, we employ a suggestive annotation strategy to choose the most effective annotation candidates. Then we exploit a fine annotation platform to alleviate annotating efforts on each candidate and first utilize a new criterion to quantitatively calculate the efforts taken by doctors. In our work, we take MR brain tissue segmentation as an example to evaluate the proposed method. Extensive experiments on the well-known IBSR18 dataset and MRBrainS18 Challenge dataset show that, using proposed strategy, state-of-the-art segmentation performance can be achieved by using only 60% annotation candidates and annotation efforts can be alleviated by at least 44%, 44%, 47% on CSF, GM, WM separately.

cs.LG↗

arXiv2018

Complementary-multiphase quantum search for all numbers of target items

Grover's algorithm achieves a quadratic speedup over classical algorithms, but it is considered necessary to know the value of $λ$ exactly [Phys. Rev. Lett. 95, 150501 (2005); Phys. Rev. Lett. 113, 210501 (2014)], where $λ$ is the fraction of target items in the database. In this paper, we find out that the Grover algorithm can actually apply to the case where one can identify the range that $λ$ belongs to from a given series of disjoint ranges. However, Grover's algorithm still cannot maintain high success probability when there exist multiple target items. For this problem, we proposed a complementary-multiphase quantum search algorithm, %with general iterations, in which multiple phases complement each other so that the overall high success probability can be maintained. Compared to the existing algorithms, in the case defined above, for the first time our algorithm achieves the following three goals simultaneously: (1) the success probability can be no less than any given value between 0 and 1, (2) the algorithm is applicable to the entire range of $λ$, and (3) the number of iterations is almost the same as that of Grover's algorithm. Especially compared to the optimal fixed-point algorithm [Phys. Rev. Lett. 113, 210501 (2014)], our algorithm uses fewer iterations to achieve success probability greater than 82.71\%, e.g., when the minimum success probability is required to be 99.25\%, the number of iterations can be reduced by 50\%.

quant-ph↗

arXiv2018

Alternating Iterative Secure Structure between Beamforming and Power Allocation for UAV-aided Directional Modulation Networks

In unmanned aerial vehicle (UAV) networks, directional modulation (DM) is adopted to improve the secrecy rate (SR) performance. Alice, a ground base station, behaves as a control center, and Bob is a UAV of flying along a linear flight trajectory who optimizes its SR performance by dynamically adjusting its beamforming vectors and power allocation (PA) strategy. Per fixed time interval during the Bob's flight process, the transmit beamforming vectors for useful messages and AN projection are given by the rule of maximizing signal-to-leakage-and-noise ratio (Max-SLNR) and maximizing AN-and-leakage-to-noise ratio (ANLNR), and the optimal PA strategy is based on maximizing SR (Max-SR). More importantly, an alternating iterative structure (AIS) between beamforming and PA is proposed to further improve the SR performance. Simulation results show that the proposed AIS converges rapidly, and can achieve substantial SR gains over Max-SLNR plus Max-ANLNR with fixed PA such as PA factor $β=$ 0.5, and 0.9. In particular, in the case of small-scale antenna array, the SR performance gain achieved by the proposed AIS is more attractive. Additionally, as the number of antennas tends to be large-scale, the average SR performance of the proposed AIS approaches an SR ceil.

cs.IT↗

arXiv2018

Two Practical Random-Subcarrier-Selection Methods for Secure Precise Wireless Transmission

In secure precise directional modulation (DM) networks, two practical random-subcarrier-selection (RSS) methods are proposed to transmit confidential message to the desired user per orthogonal frequency division multiplexing (OFDM) symbol with only single receive power peak formed by constructing random subcarrier set and performing a randomization procedure. This scheme completely addresses the crucial problem facing secure precise wireless transmission (SPWT), how to achieve the SPWT per OFDM symbol while the traditional SPWT holds only in statistically average sense. Several necessary conditions for SPWT per OFDM is derived and proposed: randomly distributed, sparse, and distinct subcarrier distance between two pair of adjacent antennas. Two random subcarrier sets (RSSs), quadratic subcarrier set (QSS) and prime subcarrier set (PSS), are constructed, where the former means the subcarrier index associated with any antennas is the square of antenna index, and the latter implies that the subcarrier indices over all antennas are prime numbers. Subsequently, following those conditions for SPWT per OFDM, a random factor is defined, and a randomization procedure (RP) is proposed. Its detailed process includes the following steps: integer mod, ordering, blocking, and block interleaving where BI is repeated until the random factor is greater than the predefined threshold. This yields a single main receive energy peak (SMREP) at the desired position with other positions, outside the SMREP, harvesting only weak receive energy seriously corrupted by AN.

cs.IT↗

arXiv2018

A Multi-channel Network with Image Retrieval for Accurate Brain Tissue Segmentation

Magnetic Resonance Imaging (MRI) is widely used in the pathological and functional studies of the brain, such as epilepsy, tumor diagnosis, etc. Automated accurate brain tissue segmentation like cerebro-spinal fluid (CSF), gray matter (GM), white matter (WM) is the basis of these studies and many researchers are seeking it to the best. Based on the truth that multi-channel segmentation network with its own ground truth achieves up to average dice ratio 0.98, we propose a novel method that we add a fourth channel with the ground truth of the most similar image's obtained by CBIR from the database. The results show that the method improves the segmentation performance, as measured by average dice ratio, by approximately 0.01 in the MRBrainS18 database. In addition, our method is concise and robust, which can be used to any network architecture that needs not be modified a lot.

cs.CV↗

arXiv2018

DASN:Data-Aware Skilled Network for Accurate MR Brain Tissue Segmentation

Accurate segmentation of MR brain tissue is a crucial step for diagnosis, surgical planning, and treatment of brain abnormalities. Automatic and reliable segmenta-tion methods are required to assist doctor. Over the last few years, deep learning especially deep convolutional neural networks (CNNs) have emerged as one of the most prominent approaches for image recognition problems in various do-mains. But the improvement of deep networks always needs inspiration, which is rare for the ordinary. Until now,there have been reasonable MR brain tissue segmentation methods,all of which can achieve promising performance. These different methods have their own characteristic and are distinctive for data sets. In other words, different models performance vary widely on the same data sets and each model has what it is skilled in. It is on the basis of this, we propose a judgement to distinguish data sets that different models are good at. With our method, the segmentation accuracy can be improved easily based on the existing models, neither without increasing training data nor improving the network. We validate our method on the widely used IBSR 18 dataset and obtain average dice ratio of 88.06%,while it is 85.82% and 86.92% when only using separate one model respectively.

cs.CV↗

arXiv2018

NuSTAR Detection of a Hard X-ray Source in the Supernova Remnant - Molecular Cloud Interaction Site of IC 443

We report on a broadband study of a complex X-ray source (1SAX J0618.0+2227) associated with the interaction site of the supernova remnant (SNR) IC 443 and ambient molecular cloud (MC) using NuSTAR, XMM_Newton, and Chandra observations. Its X-ray spectrum is composed of both thermal and non-thermal components. The thermal component can be equally well represented by either a thin plasma model with kT=0.19 keV or a blackbody model with kT=0.11 keV. The non-thermal component can be fit with either a power-law with Gamma~1.7 or a cutoff power-law with Gamma~1.5 and a cutoff energy at E_cut~18 keV. Using the newly obtained NuSTAR dataset, we test three possible scenarios for isolated X-ray sources in the SNR-MC interaction site: 1) pulsar wind nebula (PWN); 2) SNR ejecta fragment; 3) shocked molecular clump. We conclude that this source is most likely composed of a SNR ejecta (or a PWN) and surrounding shocked molecular clumps. The nature of this hard X-ray source in the SNR-MC interaction site of IC 443 may shed light on unidentified X-ray sources with hard X-ray spectra in rich environments for star forming regions, such as the Galactic center.

astro-ph.HE↗

arXiv2018

SmartTable: A Spreadsheet Program with Intelligent Assistance

We introduce SmartTable, an online spreadsheet application that is equipped with intelligent assistance capabilities. With a focus on relational tables, describing entities along with their attributes, we offer assistance in two flavors: (i) for populating the table with additional entities (rows) and (ii) for extending it with additional entity attributes (columns). We provide details of our implementation, which is also released as open source. The application is available at http://smarttable.cc.

cs.IR↗

arXiv2018

On-the-fly Table Generation

Many information needs revolve around entities, which would be better answered by summarizing results in a tabular format, rather than presenting them as a ranked list. Unlike previous work, which is limited to retrieving existing tables, we aim to answer queries by automatically compiling a table in response to a query. We introduce and address the task of on-the-fly table generation: given a query, generate a relational table that contains relevant entities (as rows) along with their key properties (as columns). This problem is decomposed into three specific subtasks: (i) core column entity ranking, (ii) schema determination, and (iii) value lookup. We employ a feature-based approach for entity ranking and schema determination, combining deep semantic features with task-specific signals. We further show that these two subtasks are not independent of each other and can assist each other in an iterative manner. For value lookup, we combine information from existing tables and a knowledge base. Using two sets of entity-oriented queries, we evaluate our approach both on the component level and on the end-to-end table generation task.

cs.IR↗