arXiv subjects

Chao Zhang

Publications and source records attributed to Chao Zhang.

At least 1,153 records · Page 64Linked to original sources

arXiv2015

Priori-Information Aided Iterative Hard Threshold: A Low-Complexity High-Accuracy Compressive Sensing Based Channel Estimation for TDS-OFDM

This paper develops a low-complexity channel estimation (CE) scheme based on compressive sensing (CS) for time-domain synchronous (TDS) orthogonal frequency-division multiplexing (OFDM) to overcome the performance loss under doubly selective fading channels. Specifically, an overlap-add method of the time-domain training sequence is first proposed to obtain the coarse estimates of the channel length, path delays and path gains of the wireless channel, by exploiting the channel's temporal correlation to improve the robustness of the coarse CE under the severe fading channel with long delay spread. We then propose the priori-information aided (PA) iterative hard threshold (IHT) algorithm, which utilizes the priori information of the acquired coarse estimate for the wireless channel and therefore is capable of obtaining an accurate channel estimate of the doubly selective fading channel. Compared with the classical IHT algorithm whose convergence requires the $l_2$ norm of the measurement matrix being less than 1, the proposed PA-IHT algorithm exploits the priori information acquired to remove such a limitation as well as to reduce the number of required iterations. Compared with the existing CS based CE method for TDS-OFDM, the proposed PA-IHT algorithm significantly reduces the computational complexity of CE as well as enhances the CE accuracy. Simulation results demonstrate that, without sacrificing spectral efficiency and changing the current TDS-OFDM signal structure, the proposed scheme performs better than the existing CE schemes for TDS-OFDM in various scenarios, especially under severely doubly selective fading channels.

cs.IT↗

arXiv2015

Nonlinear Image Formation by Optical Superlattices

The angular spectrum theory is applied to the nonlinear harmonic generation process in optical superlattices. Several explicit and analytical structure functions are deduced to design optical superlattices for various purposes. Employing this method, nonlinear image formation is achieved during the second-harmonic generation process in a properly designed optical superlattice. This method is universal for both nonlinear beam shaping and nonlinear arbitrary image formation. The theory has been experimentally validated in two-dimensional optical superlattice of LiTaO3 crystals and the results agree well with the theoretical prediction. This work not only extends the application of optical superlattices, but also opens a new area for imaging technologies.

physics.optics↗

arXiv2015

Correlation between intercalated magnetic layers and superconductivity in pressurized EuFe2(As0.81P0.19)2

We report comprehensive high pressure studies on correlation between intercalated magnetic layers and superconductivity in EuFe2(As0.81P0.19)2 single crystal through in-situ high pressure resistance, specific heat, X-ray diffraction and X-ray absorption measurements. We find that an unconfirmed magnetic order of the intercalated layers coexists with superconductivity in a narrow pressure range 0-0.5GPa, and then it converts to a ferromagnetic (FM) order at pressure above 0.5 GPa, where its superconductivity is absent. The obtained temperature-pressure phase diagram clearly demonstrates that the unconfirmed magnetic order can emerge from the superconducting state. In stark contrast, the superconductivity cannot develop from the FM state that is evolved from the unconfirmed magnetic state. High pressure X-ray absorption (XAS) measurements reveal that the pressure-induced enhancement of Eu's mean valence plays an important role in suppressing the superconductivity and tuning the transition from the unconfirmed magnetic state to a FM state. The unusual interplay among valence state of Eu ions, magnetism and superconductivity under pressure may shed new light on understanding the role of the intercalated magnetic layers in Fe-based superconductors.

cond-mat.supr-con↗

arXiv2015

Metal-Enhanced Near-Infrared Fluorescence by Micropatterned Gold Nanocages

In metal-enhanced fluorescence (MEF), the localized surface plasmon resonances of metallic nanostructures amplify the absorption of excitation light and assist in radiating the consequent fluorescence of nearby molecules to the far-field. This effect is at the base of various technologies that have strong impact on fields such as optics, medical diagnostics and biotechnology. Among possible emission bands, those in the near-infrared (NIR) are particularly intriguing and widely used in proteomics and genomics due to its noninvasive character for biomolecules, living cells, and tissues, which greatly motivates the development of effective, and eventually multifunctional NIR-MEF platforms. Here we demonstrate NIR-MEF substrates based on Au nanocages micropatterned with a tight spatial control. The dependence of the fluorescence enhancement on the distance between the nanocage and the radiating dipoles is investigated experimentally and modeled by taking into account the local electric field enhancement and the modified radiation and absorption rates of the emitting molecules. At a distance around 80 nm, a maximum enhancement up to 2-7 times with respect to the emission from pristine dyes (in the region 660 nm-740 nm) is estimated for films and electrospun nanofibers. Due to their chemical stability, finely tunable plasmon resonances, and large light absorption cross sections, Au nanocages are ideal NIR-MEF agents. When these properties are integrated with the hollow interior and controllable surface porosity, it is feasible to develop a nanoscale system for targeted drug delivery with the diagnostic information encoded in the fluorophore.

cond-mat.mtrl-sci↗

arXiv2015

Mechanical Strain Effects on Black Phosphorus Nanoresonators

We perform classical molecular dynamics to investigate the effects of mechanical strain on single-layer black phosphorus nanoresonators at different temperatures. We find that the resonant frequency is highly anisotropic in black phosphorus due to its intrinsic puckered configuration, and that the quality factor in the armchair direction is higher than in the zigzag direction at room temperature. The quality factors are also found to be intrinsically larger than graphene and MoS2 nanoresonators. The quality factors can be increased by more than a factor of two by applying tensile strain, with uniaxial strain in the armchair direction being most effective. However, there is an upper bound for the quality factor increase due to nonlinear effects at large strains, after which the quality factor decreases. The tension induced nonlinear effect is stronger along the zigzag direction, resulting in a smaller maximum strain for quality factor enhancement.

cond-mat.mtrl-sci↗

arXiv2015

Measurement of Radiation Damage of Water-based Liquid Scintillator and Liquid Scintillator

Liquid scintillating phantoms have been proposed as a means to perform real-time 3D dosimetry for proton therapy treatment plan verification. We have studied what effect radiation damage to the scintillator will have upon this application. We have performed measurements of the degradation of the light yield and optical attenuation length of liquid scintillator and water-based liquid scintillator after irradiation by 201 MeV proton beams that deposited doses of approximately 52 Gy, 300 Gy, and 800 Gy in the scintillator. Liquid scintillator and water-based liquid scintillator (composed of $5\%$ scintillating phase) exhibit light yield reductions of $1.74 \pm 0.55 \%$ and $1.31 \pm 0.59 \%$ after $\approx$ 800 Gy of proton dose, respectively. Whilst some increased optical attenuation was observed in the irradiated samples, the measured reduction to the light yield is also due to damage to the scintillation light production. Based on our results and conservative estimates of the expected dose in a clinical context, a scintillating phantom used for proton therapy treatment plan verification would exhibit a systematic light yield reduction of approximately $0.1\%$ after a year of operation.

physics.med-ph↗

arXiv2015

Breakdown of Three-dimensional Dirac Semimetal State in pressurized Cd3As2

We report the first observation of a pressure-induced breakdown of the 3D-DSM state in Cd3As2, evidenced by a series of in-situ high-pressure synchrotron X-ray diffraction (XRD) and single crystal transport measurements. We find that Cd3As2 undergoes a structural phase transition from a metallic tetragonal (T) phase in space group I41/acd to a semiconducting monoclinic (M) phase in space group P21/c at critical pressure 2.57 GPa, above this pressure, an activation energy gap appears, accompanied by distinct switches in Hall resistivity slope and electron mobility. These changes of crystal symmetry and corresponding transport properties manifest the breakdown of the 3D-DSM state in pressurized Cd3As2.

cond-mat.supr-con↗

arXiv2015

Pressure-induced quantum phase transitions in topological insulator YbB6

Topological insulators (TIs) containing 4f electrons have recently attracted intensive interests due to the possible interplay of their non-trivial topological properties and strong electronic correlations. YbB6 and SmB6 are the prototypical systems with such unusual properties, which may be tuned by external pressure to give rise to new emergent phenomena. Here, we report the first observation, through in-situ high pressure resistance, Hall, X-ray diffraction and X-ray absorption measurements, of two pressure-induced quantum phase transitions (QPTs) in YbB6. Our data revealthat the two insulating phases are separated by a metallic phase due to the pressure-driven valence change of Yb f-orbitals. In combination with previous studies, our results suggest that the two insulating states may be topologically different in nature and originate from the d-p and d-f hybridization, respectively. The tunable topological properties of YbB6 revealed in this study may shed light on the intriguing correlation between the topology and the 4f electrons from the perspective of pressure dependent studies.

cond-mat.str-el↗

arXiv2015

Neutrino Oscillation Studies with Reactors

Nuclear reactors are one of the most intense, pure, controllable, cost-effective, and well-understood sources of neutrinos. Reactors have played a major role in the study of neutrino oscillations, a phenomenon that indicates that neutrinos have mass and that neutrino flavors are quantum mechanical mixtures. Over the past several decades reactors were used in the discovery of neutrinos, were crucial in solving the solar neutrino puzzle, and allowed the determination of the smallest mixing angle $θ_{13}$. In the near future, reactors will help to determine the neutrino mass hierarchy and to solve the puzzling issue of sterile neutrinos.

hep-ex↗

arXiv2015

Unsupervised Feature Learning for Dense Correspondences across Scenes

We propose a fast, accurate matching method for estimating dense pixel correspondences across scenes. It is a challenging problem to estimate dense pixel correspondences between images depicting different scenes or instances of the same object category. While most such matching methods rely on hand-crafted features such as SIFT, we learn features from a large amount of unlabeled image patches using unsupervised learning. Pixel-layer features are obtained by encoding over the dictionary, followed by spatial pooling to obtain patch-layer features. The learned features are then seamlessly embedded into a multi-layer match- ing framework. We experimentally demonstrate that the learned features, together with our matching model, outperforms state-of-the-art methods such as the SIFT flow, coherency sensitive hashing and the recent deformable spatial pyramid matching methods both in terms of accuracy and computation efficiency. Furthermore, we evaluate the performance of a few different dictionary learning and feature encoding methods in the proposed pixel correspondences estimation framework, and analyse the impact of dictionary learning and feature encoding with respect to the final matching performance.

cs.CV↗

arXiv2015

Dynamic Power Splitting Policies for AF Relay Networks with Wireless Energy Harvesting

Wireless energy harvesting (WEH) provides an exciting way to supply energy for relay nodes to forward information for the source-destination pairs. In this paper, we investigate the problem on how the relay node dynamically adjusts the power splitting ratio of information transmission (IT) and energy harvesting (EH) in order to achieve the optimal outage performance. According to the knowledge of channel state information (CSI) at the relay, optimal dynamic power splitting policy with full CSI and partial CSI are both provided. Finally, through simulations, the proposed power splitting policies can improve the outage performances and the policy with full CSI achieves the best performance. It is also shown that the policy with partial CSI can approach the policy with full CSI closely and incurs far less system overhead.

cs.IT↗

arXiv2015

Superconductivity emerging from suppressed large magnetoresistant state in WTe2

The recent discovery of large and non-saturating magnetoresistance (LMR) in WTe2 provides a unique playground to find new phenomena and significant perspective for potential applications. Here we report the first observation of superconductivity near the proximity of suppressed LMR state in pressurized WTe2 through high-pressure synchrotron X-ray diffraction, electrical resistance, magnetoresistance, and ac magnetic susceptibility measurements. It is found that the positive magnetoresistance effect can be turned off at a critical pressure of 10.5 GPa without crystal structure change and superconductivity emerges simultaneously. The maximum superconducting transition temperature can be reached to 6.5 K at ~15 GPa and it decreases down to 2.6 K at ~25 GPa. In-situ high pressure Hall coefficient measurements at 10 K demonstrate that elevating pressure decreases hole carrier's population but increases electron carrier's population. Significantly, at the critical pressure, we observed a sign change in the Hall coefficient, indicating a possible Lifshitz-type quantum phase transition in WTe2.

cond-mat.supr-con↗

arXiv2015

Recent Results From The Daya Bay Experiment

The Daya Bay reactor neutrino experiment has observed the disappearance of electron antineutrinos from nuclear reactors at $\sim$kilometer baselines. The relative measurement of the $\barν_e$ rate and spectrum between near and far detectors allows for a precision measurement of the oscillation parameters $\sin^22θ_{13}$ and $|Δ{m}^2_{ee}|$. Two new antineutrino detectors (ADs) were installed in summer 2012, bringing the experiment to the final 8-AD configuration. With 621 days of data, Daya Bay has measured $\sin^22θ_{13} = 0.084 \pm 0.005$ and $|Δ{m}^2_{ee}| = 2.44^{+0.10}_{-0.11} \times 10^{-3}$ eV$^2$. This is the most precise measurement of $\sin^22θ_{13}$ to date and the most precise measurement of $|Δ{m}^2_{ee}|$ in this channel. Several other analyses are also performed, including an independent measurement of $\sin^22θ_{13}$ using $\barν_e$ samples tagged by neutron capture on hydrogen, a search for light sterile neutrinos, and a measurement of the absolute reactor antineutrino flux.

hep-ex↗

arXiv2015

On algebras of strongly derived unbounded type

Let $A$ be a finite-dimensional algebra over an algebraically closed field. We prove $A$ is a strongly derived unbounded algebra if and only if there exists an integer $m$, such that $C_m(\proj A)$, the category of all minimal projective complexes with degree concentrated in $[0, m]$, is of strongly unbounded type, which is also equivalent to the statement the repetitive algebra $\hat{A}$ is of strongly unbounded representation type. As a corollary, we can establish the dichotomy on the representation type of $C_m(\proj A)$, the homotopy category $K^b(\proj A)$ and the repetitive algebra $\hat{A}$.

math.RT↗

arXiv2014

Relations among Some Low Rank Subspace Recovery Models

Recovering intrinsic low dimensional subspaces from data distributed on them is a key preprocessing step to many applications. In recent years, there has been a lot of work that models subspace recovery as low rank minimization problems. We find that some representative models, such as Robust Principal Component Analysis (R-PCA), Robust Low Rank Representation (R-LRR), and Robust Latent Low Rank Representation (R-LatLRR), are actually deeply connected. More specifically, we discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations. Since R-PCA is the simplest, our discovery makes it the center of low rank subspace recovery models. Our work has two important implications. First, R-PCA has a solid theoretical foundation. Under certain conditions, we could find better solutions to these low rank models at overwhelming probabilities, although these models are non-convex. Second, we can obtain significantly faster algorithms for these models by solving R-PCA first. The computation cost can be further cut by applying low complexity randomized algorithms, e.g., our novel $\ell_{2,1}$ filtering algorithm, to R-PCA. Experiments verify the advantages of our algorithms over other state-of-the-art ones that are based on the alternating direction method.

cs.LG↗

arXiv2014

Connection between ambient-pressure and pressure-induced superconducting phases in alkaline iron selenide superconductors

A unique platform for investigating the correlation between the antiferromagnetic (AFM) and superconducting (SC) states in high temperature superconductors is created by the discovery of alkaline iron selenide superconductors which are composed of an AFM insulating phase and a SC phase separated spatially. Our previous studies showed that pressure can fully suppress the superconductivity of ambient-pressure superconducting phase (SC-I) and AFM order simultaneously, then induce another superconducting phase (SC-II) at higher pressure. Consequently, the connection between the two superconducting phases becomes an intriguing issue. In this study, on the basis of observing pressure-induced reemergence of superconductivity in Rb0.8Fe2-ySe2-xTex (x=0, 0.19 and 0.28) superconductors, we find that the superconductivity of the SC-I and SC-II phases as well as the AFM ordered state can be synchronously tuned by Te doping and disappear together at the doping level of x=0.4. We propose that the two superconducting phases are connected by the AFM phase, in other words, the state of long-ranged AFM order plays a role in giving rise to superconductivity of the SC-I phase, while the fluctuation state of the suppressed AFM phase drives the emergence of SC-II phase. These results comprehensively demonstrate the versatile roles of AFM states in stabilizing and developing superconductivity in the alkaline iron selenide superconductors.

cond-mat.supr-con↗

arXiv2014

Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning

In this paper, we study the generalization performance of regularized multi-task learning (RMTL) in a vector-valued framework, where MTL is considered as a learning process for vector-valued functions. We are mainly concerned with two theoretical questions: 1) under what conditions does RMTL perform better with a smaller task sample size than STL? 2) under what conditions is RMTL generalizable and can guarantee the consistency of each task during simultaneous learning? In particular, we investigate two types of task-group relatedness: the observed discrepancy-dependence measure (ODDM) and the empirical discrepancy-dependence measure (EDDM), both of which detect the dependence between two groups of multiple related tasks (MRTs). We then introduce the Cartesian product-based uniform entropy number (CPUEN) to measure the complexities of vector-valued function classes. By applying the specific deviation and the symmetrization inequalities to the vector-valued framework, we obtain the generalization bound for RMTL, which is the upper bound of the joint probability of the event that there is at least one task with a large empirical discrepancy between the expected and empirical risks. Finally, we present a sufficient condition to guarantee the consistency of each task in the simultaneous learning process, and we discuss how task relatedness affects the generalization performance of RMTL. Our theoretical findings answer the aforementioned two questions.

cs.LG↗

arXiv2014

Simultaneous Wireless Information and Power Transfer with Co-Channel Interference

Simultaneous wireless information and power transfer (SWIPT) is an appealing solution to balance the energy distribution in wireless networks and improve the energy-efficiency of the entire network. In this paper, we study the optimal policies for SWIPT over the flat-fading channel subject to the co-channel interference. The SWIPT receiver using dynamic power splitting (DPS) is expected to achieve the maximum information rate given an average harvested energy constraint. In the case that channel state information at the transmitter (CSIT) is not available, the optimal power splitting is derived. For the case with CSIT, the joint optimal policy of the transmit power and the power splitting are also provided. Through simulations, we show the optimal rate-energy (R-E) trade-offs of our proposed policies. The case with CSIT achieves better R-E trade-off than the case with no CSIT.

cs.IT↗