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This paper describes a prototype two-step ADC with adjustable resolution in 40 nm CMOS technology. It uses a front-end successive-approximation-register ADC and a back-end time-domain ADC. Digital statistical calibration overcomes errors from front-end mismatch as well as inter-stage and back-end nonlinearity by analyzing specific statistical properties. In the 12-bit mode at 20 MS/s, the maximum SNDR is 59 dB before calibration and 68 dB after calibration, using 6.2 fJ per conversion-step, excluding the power dissipation required by the calibration. [ABSTRACT FROM AUTHOR]

In real video surveillance scenes, the extracted face regions generally have low-resolution (LR) and are sensitive to pose and illumination variations; these flaws undoubtedly degrade the subsequent recognition task. To overcome these challenges, we propose an approach named multilayer locality-constrained structural orthogonal Procrustes regression (MLCSOPR). The proposed MLCSOPR not only learns the pose-robust discriminative representation features to reduce the resolution gap between the LR image space and the high-resolution (HR) one but also strengthens the consistency between the LR and HR image space. In particular, several contributions are made in this paper: (i) Inspired by the orthogonal Procrustes problem (OPP), a matrix approximation is exploited to find an optimal correction between two data matrices. (ii) The nuclear norm constraint is applied to the reconstruction error to maintain the structural property. (iii) Based on the abovementioned learned resolution-robust representation features, a linear regression-based classification strategy is adopted to recognize the LR input face images. Experiments on commonly used face databases have shown the effectiveness of the proposed method on cross-resolution face matching with pose variations. [ABSTRACT FROM AUTHOR]

In this work, travel destinations and business locations are taken as venues. Discovering a venue by a photograph is very important for visual context-aware applications. Unfortunately, few efforts paid attention to complicated real images such as venue photographs generated by users. Our goal is fine-grained venue discovery from heterogeneous social multimodal data. To this end, we propose a novel deep learning model, category-based deep canonical correlation analysis. Given a photograph as input, this model performs: 1) exact venue search (find the venue where the photograph was taken) and 2) group venue search (find relevant venues that have the same category as the photograph), by the cross-modal correlation between the input photograph and textual description of venues. In this model, data in different modalities are projected to a same space via deep networks. Pairwise correlation (between different modality data from the same venue) for exact venue search and category-based correlation (between different modality data from different venues with the same category) for group venue search are jointly optimized. Because a photograph cannot fully reflect rich text description of a venue, the number of photographs per venue in the training phase is increased to capture more aspects of a venue. We build a new venue-aware multimodal data set by integrating Wikipedia featured articles and Foursquare venue photographs. Experimental results on this data set confirm the feasibility of the proposed method. Moreover, the evaluation over another publicly available data set confirms that the proposed method outperforms state of the arts for cross-modal retrieval between image and text. [ABSTRACT FROM AUTHOR]

Abstract We propose two sparsity-aware normalized subband adaptive filter (NSAF) algorithms by using the gradient descent method to minimize a combination of the original NSAF cost function and the l 1 -norm penalty function on the filter coefficients. This l 1 -norm penalty exploits the sparsity of a system in the coefficients update formulation, thus improving the performance when identifying sparse systems. Compared with prior work, the proposed algorithms have lower computational complexity with comparable performance. We study and devise statistical models for these sparsity-aware NSAF algorithms in the mean square sense involving their transient and steady-state behaviors. This study relies on the vectorization argument and the paraunitary assumption imposed on the analysis filter banks, and thus does not restrict the input signal to being Gaussian or having another distribution. In addition, we propose to adjust adaptively the intensity parameter of the sparsity attraction term. Finally, simulation results in sparse system identification demonstrate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]

In all presented analyses of the normalized subband adaptive filter (NSAF) algorithm, there is a common assumption that the length of the adaptive filter is equal to that of the unknown system. In many practices, however, the adaptive filter usually works in an under-modeling situation. Namely, the length of the adaptive filter is less than that of the unknown system. Therefore, for this case, the existing analysis results for the NSAF algorithm are not applicable. In this paper, we analyze the performance of the deficient length NSAF algorithm based on some reasonable assumptions and approximations. More precisely, the expressions that characterize the transient-state and steady-state mean-square behavior of the algorithm are presented. Simulation results in various scenarios support our theoretical expressions. In addition, based on our analyses, a variable step size NSAF algorithm suitable for the under-modeling case is developed, which improves the performance. [ABSTRACT FROM AUTHOR]

In order to improve the filter׳s performance when identifying sparse system, this paper develops two sparse-aware algorithms by incorporating the l 0 -norm constraint of the weight vector into the conventional normalized subband adaptive filter (NSAF) algorithm. The first algorithm is obtained from the principle of the minimum perturbation; and the second one is based on the gradient descent principle. The resulting algorithms have almost the same convergence and steady-state performance while the latter saves computational complexity. What׳s more, the performance of both algorithms is analyzed by resorting to some assumptions commonly used in the analyses of adaptive algorithms. Simulation results in the context of sparse system identification not only demonstrate the effectiveness of the proposed algorithms, but also verify the theoretical analyses. [ABSTRACT FROM AUTHOR]

In the presence of impulsive noises, the normalized least mean M-estimate (NLMM) algorithm has behaved better robustness and convergence than the normalized least mean square (NLMS) algorithm. In order to further solve the trade-off of the NLMM algorithm between convergence rate and steady-state misadjustment, we design a combined step-size (CSS) scheme that combines large and small step-sizes through an adaptive mixing factor, and the resulting CSS-NLMM algorithm obtains fast convergence and low steady-state misadjustment simultaneously. Importantly, the proposed CSS scheme can be straightforwardly extended to other robust NLMS algorithms. Moreover, the performance analysis of the CSS-NLMM algorithm is provided. Simulation results in impulsive noises have supported the effectiveness of the proposed CSS-NLMM algorithm and its performance analysis. [ABSTRACT FROM AUTHOR]

Extensive research has been lavished on the effects of spin-orbit couplings (SOCs) in attractively interacting Fermi systems in both neutral cold-atom systems and condensed-matter systems. Recently, it was suggested that a SOC drives a class of BCS to Bose-Einstein condensate (BEC) crossover that is different from the conventional one without a SOC. Here, we explore what are the most relevant physical quantities to describe such a BCS to BEC crossover and their experimental detections. We extend the concepts of the coherence length and "Cooper-pair size" in the absence of SOC to Fermi systems with SOC. We investigate the dependence of chemical potential, coherence length, and Cooper-pair size on the SOC strength and the scattering length at three dimensions (3D) (the bound-state energy at 2D) for three attractively interacting Fermi gases with 3D Rashba, 3D Weyl, and 2D Rashba SOC, respectively. We show that only the coherence length can be used to characterize this BCS to BEC crossover. Furthermore, it is the only length which can be directly measured by radio-frequency dissociation spectra type of experiments. We stress crucial differences among the coherence length, Cooper-pair size, and the two-body bound-state size. Our results provide the fundamental and global picture of the BCS to BEC crossover and its experimental detections in various cold-atom and condensed-matter systems. [ABSTRACT FROM AUTHOR]

We investigate the D+s→K+K-π+ decay theoretically with the final state interactions, which is based on the chiral unitary approach and takes into account the external and internal W-emission mechanisms at the quark level. Only considering three resonances contributions, the f0(980) in S-wave, the ̅K*(892)0 and ϕ(1020) in P-wave, one can make a good description of the recent experimental data from the BESIII Collaboration, where the contribution from the S-wave is found to be small. Besides, we also make a calculation of the corresponding branching fractions, which are consistent with the results of the BESIII Collaboration and Particle Data Group. [ABSTRACT FROM AUTHOR]

In the present work, we investigate several theoretical models of the pion vector form factor and aim at getting the best fit for the two-pion cross sections to reduce the uncertainties of the calculation of two-pion contribution to the muon anomalous magnetic moment. Combined with a polynomial description to the pion vector form factor, we obtain the best fit from the Gounaris-Sakurai (or Kühn-Santamaria) model for the experimental data up to 1 GeV. As a by-product of the fitting results, the branching ratio of ω→ππ can be extracted as Br(ω→ππ)=(1.52±0.06)%, which is consistent with the one of the Particle Data Group. With the best fit of the data, we obtain the muon anomalous magnetic moment from two-pion contribution as aHVP,LOμ(π+π-≤1  GeV)=(497.76±3.15)×10-10. Our results are consistent with the other works. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a recent numerical study conducted by researchers at the National Renewable Energy Laboratory on a point absorber wave energy conversion (WEC) system using a Reynolds-averaged Navier–Stokes (RANS)-based Computational Fluid Dynamics (CFD) method. The device we studied was a two-body floating-point absorber (FPA) that operates predominantly in heave and generates energy from the relative motion between the two bodies. We performed a series of numerical simulation to analyze the hydrodynamic response and the power absorption performance of the system in regular waves. Overall, it was successful to use the RANS method to model the complex hydrodynamics interaction of the FPA system. We demonstrated the significance of the nonlinear effects, including viscous damping and wave overtopping. The study showed that the nonlinear effects could significantly decrease the power output and the motion of the FPA system, particularly in larger waves. [Copyright &y& Elsevier]

The random matrix theory (RMT) can be used to classify both topological phases of matter and quantum chaos. We develop a systematic and transformative RMT to classify the quantum chaos in the colored Sachdev-Ye-Kitaev (SYK) model first introduced by Gross and Rosenhaus. Here we focus on the two-colored case and the four-colored case with a balanced number of Majorana fermions N. By identifying the maximal symmetries, the independent parity conservation sectors, the minimum (irreducible) Hilbert space, and especially the relevant antiunitary and unitary operators, we show that the color degrees of freedom lead to novel quantum chaotic behaviors. When N is odd, different symmetry operators need to be constructed to make the classifications complete. The two-colored case only shows the threefold Wigner-Dyson way, and the four-colored case shows the tenfold generalized Wigner-Dyson way which may also have nontrivial edge exponents. We also study two- and four-colored hybrid SYK models, which display many salient quantum chaotic features hidden in the corresponding pure SYK models. These features motivate us to develop a systematic RMT to study the energy level statistics of two or four uncorrelated random matrix ensembles. The exact diagonalizations are performed to study both the bulk energy level statistics and the edge exponents and to find excellent agreement with our exact maximal symmetry classifications. Our complete and systematic methods can be easily extended to study the generic imbalanced cases. They may be transferred to the classifications of colored tensor models, quantum chromodynamics with pairings across different colors, quantum black holes, and interacting symmetry protected (or enriched) topological phases. [ABSTRACT FROM AUTHOR]

Four standard quantum optics models, that is, the Rabi, Dicke, Jaynes-Cummings, and Tavis-Cummings models, were proposed by physicists many decades ago. Despite their relative simple forms and many previous theoretical works, their physics at a finite N, especially inside the superradiant regime, remain unknown. In this work, by using the strong-coupling expansion and exact diagonalization (ED), we study the Z2-U(1) Dicke model with independent rotating-wave coupling g and counterrotating-wave coupling g' at a finite N. This model includes the four standard quantum optics models as its various special limits. We show that in the superradiant phase, the system's energy levels are grouped into doublets with even and odd parity. Any anisotropy β=g'/g1 leads to the oscillation of parities in both the ground and excited doublets as the atom-photon coupling strength increases. The oscillations will be pushed to the infinite coupling strength in the isotropic Z2 limit β=1. We find nearly perfect agreement between the strong-coupling expansion and the ED in the superradiant regime when β is not too small. We also compute the photon correlation functions, squeezing spectrum, and number correlation functions that can be measured by various standard optical techniques. [ABSTRACT FROM AUTHOR]

Details the ability of China to maintain the stability of the Renminbi in 1999. Factors affecting the economic condition of China; Inflation rate in the country; Stability of the country's economy and political situation.

The article discusses performance of the Chinese caprolactam industry in 2012. Various indicators discussed includes increased capacity, distribution and demand. Statistics that lists production, supply, demand and capacity of caprolactam are also presented. Various new companies that entered in the market includes Luxi Chemical Group Co. Ltd., Xuyang Jiaohua Group and Sinopec Baling Petrochemical Co. Ltd.

This paper presents a new least mean square (LMS) algorithm with defining hyperbolic secant cost function. Then, the detailed analysis is conducted to reveal the behavior of the proposed algorithm. The transient analysis is conducted based on a Taylor expansion approach and energy conservation (EC) relation expression. Following the results of transient state analysis of excess means square-error (EMSE), we achieve the steady-state EMSE and mean-square deviation (MSD) expressions of the proposed algorithm. Moreover, the sufficient condition for the stability convergence is given. Simulation results validate the theoretical analyses and demonstrate the enhanced performance of the proposed algorithm as compared with conventional LMS algorithm in the presence of white Gaussian noise (WGN) and uniform noise. [ABSTRACT FROM AUTHOR]

Abstract Current learning based face hallucination approaches mainly focus on how to design a reasonable objective function, such as using different assumptions and incorporating different regularization terms, but do not give a reasonable way of selecting the model parameters. In this paper, we propose to exploit the facial structure prior to learn a parameter map based on differential evolution. Specifically, we claim that different position patches have different parameter settings because of their different statistical properties, and patches from the same position of different face images should have similar parameter settings. As a result, we first learn a parameter map for each training sample by leveraging an evolutionary algorithm based on differential evolution, and then fuse these learned parameter maps to an optimal parameter map for testing via mean-pooling strategy. Finally, we use the predicted parameter map to guide the co-occurrence relationship modeling in different regions of the input low-resolution (LR) face image. Experimental results demonstrate that, even without seeing the ground truth, results of proposed parameter map learning method are comparable to or better than those traditional unified parameter setting methods and some recently proposed deep learning methods. [ABSTRACT FROM AUTHOR]

Abstract A bias-compensated constrained least mean square (BC-CLMS) adaptive filter algorithm for noisy input is proposed. To derive the proposed algorithm, we present a novel cost function whose gradient vector is unbiased. Thereby, the proposed algorithm can mitigate the effect of input noise and obtain an unbiased estimation. Then, the detail performance analysis of the proposed algorithm is also provided. Finally, simulations are carried out to illustrate the advantage of the proposed algorithm. In addition, the correctness of performance analysis is also verified by simulations. [ABSTRACT FROM AUTHOR]

This paper presents a family of robust set-membership normalized subband adaptive filtering (RSM-NSAF) algorithms for acoustic echo cancellation (AEC). By using a new robust set-membership error bound, the RSM-NSAF algorithm obtains improved robustness against impulsive noises and decreased steady-state misalignment relative to the conventional set-membership NSAF (SM-NSAF) algorithm. To exploit the sparsity of the impulse response, the L0 norm constraint robust set-membership NSAF ( L0 -RSM-NSAF), robust set-membership improved proportionate NSAF (RSM-IPNSAF), and L0 norm constraint robust set-membership improved proportionate NSAF ( L0 -RSM-IPNSAF) algorithms are derived by minimizing a differentiable cost function that utilizes the Riemannian distance between the updated and previous weight vectors as well as the L0 norm of the weighted updated weight vector. Simulations in AEC application confirm the improvements of the proposed algorithms in performance. [ABSTRACT FROM PUBLISHER]