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Price inflation has outbalanced the income of residents and buyers in major post-industrial city-regions, and real estate has become an important driver of these inequalities. In a context of a resilient inflation of home values during the last two decades in the greater Paris Region, it is critical to examine housing price dynamics to get a better understanding of socioeconomic segregation. This paper aims at presenting spatial analysis of the dynamics of segregation pertaining to inflation, analyzing price and sellers and buyers data. Using interpolation techniques and multivariate analysis, the paper presents a spatial analysis of property-level data from the Paris Chamber of Notaries (1996-2012) in a GIS (159,000 transactions in suburban areas, single family homes only). Multivariate analysis capture price change and local trajectories of occupational status, i.e. changes in balance between inward and outward flows of sellers and buyers. We adopt a method that fits the fragmented spatial patterns of suburbanization. To do so, we remove the spatial bias by means of a regular 1-km spatial grid, interpolating the variables within it, using a time-distance matrix. The main results are threefold. We document the spatial patterns of professionalization (a rise of executives, intermediate occupation and employees) to describe the main trends of inward mobility in property ownership in suburbs, offsetting the outward mobility of retired persons. Second, neighborhood trajectories are related the diverging patterns of appreciation, between local contexts of accumulation with a growth of residential prices, and suburbs with declining trends. The maturity of suburbanization yields a diversified structure of segregation between the social groups, that do not simply oppose executives vs. blue collar suburbs. A follow-up research agenda is finally outlined. [ABSTRACT FROM AUTHOR]

In order to design the optimal size of carbon fiber collapsible hinge for space engineering application, the modal and folding analysis of carbon fiber collapsible hinge is carried out in this paper. Natural frequencies and mode shapes are investigated through finite element model. The result is also verified by modal experiments. The maximum stress during folding process of the hinge is obtained by quasi-static simulation analysis. In order to increase the stiffness and stability of this collapsible structure, the fundamental frequency and maximum stress are optimized with respect to the hinge size. A co-simulation optimal design method is proposed in this paper. The optimal hinge size is obtained through non-dominated sorting genetic algorithm based on radial basis function neural network surrogate model. The fundamental frequency is increased by 12%, while the maximum stress is decreased by 13% after optimal design. [ABSTRACT FROM AUTHOR]

Wind and temperature data are important parameters in aircraft performance studies. The lack of accurate measurements of these parameters forces researchers to rely on numerical weather prediction models, which are often filtered for a larger area with decreased local accuracy. Aircraft, however, also transmit information related to weather conditions, in response to interrogation by air traffic controller surveillance radars. Although not intended for this purpose, aircraft surveillance data contains information that can be used for weather models. This paper presents a method that can be used to reconstruct a weather field from surveillance data that can be received with a simple 1090 MHz receiver. Throughout the paper, we answer two main research questions: how to accurately infer wind and temperature from aircraft surveillance data, and how to reconstruct a real-time weather grid efficiently. We consider aircraft as moving sensors that measure wind and temperature conditions indirectly at different locations and flight levels. To address the first question, aircraft barometric altitude, ground velocity, and airspeed are decoded from down-linked surveillance data. Then, temperature and wind observations are computed based on aeronautical speed conversion equations. To address the second question, we propose a novel Meteo-Particle (MP) model for constructing the wind and temperature fields. Short-term local prediction is also possible by employing a predictor layer. Using an unseen observation test dataset, we are able to validate that the mean absolute errors of inferred wind and temperature using MP model are 67% and 26% less than using the interpolated model based on GFS reanalysis data. [ABSTRACT FROM AUTHOR]

Traffic congestion in freeway network is a prevalent transportation problem and leads to a strong degradation of freeway network facilities. This paper proposes a new coordinated optimization approach for freeway network, aiming to relieve the traffic congestion in freeway network equipped with the coordinated traffic controllers. The challenge is how to solve the problem between the traffic control goals of each path and those of the network. In this paper, the bi-level programming theory is used to address this problem. More exactly, in the path level, the optimal control model is designed to relieve congestion in each path of the test network. Furthermore, in the network level, the optimal model considers the traffic equilibrium and cost optimization. The efficiency of the proposed approach is tested on a well-known traffic network calibrated by the field test data of freeway network. The results show that the proposed approach has positive effects in balancing traffic load and improving the service level of freeway networks. [ABSTRACT FROM AUTHOR]

Interpolation and approximation methods are used across many fields. Standard interpolation and approximation methods rely on "ordering" that actually means tessellation in d-dimensional space in general, like sorting, triangulation, generating of tetrahedral meshes etc. Tessellation algorithms are quite complex in d-dimensional case. On the other hand, interpolation and approximation can be made using meshfree (meshless) techniques using Radial Basis Function (RBF). The RBF interpolation and approximation methods lead generally to a solution of linear system of equations. However, a similar approach can be taken for a reconstruction of a surface of scanned objects, etc. In this case this leads to a linear system of homogeneous equations, when a different approach has to be taken. In this paper we describe novel approaches based on RBFs for data interpolation and approximation generally in d-dimensional space. We will show properties and differences of "global" and "Compactly Supported RBF (CSRBF)", run-time and memory complexities. As the RBF interpolation and approximation naturally offer smoothness, we will analyze such properties as well as approaches how to decrease computational expenses. The proposed meshless interpolation and approximation will be demonstrated on different problems, e.g. in painting removal, restoration of corrupted images with high percentage of corrupted pixels, digital terrain interpolation and approximation for GIS applications and methods for decreasing computational complexity. [ABSTRACT FROM AUTHOR]

The repeated replacement method (RRM) is a Lagrangian meshfree method which we have previously applied to the Euler equations for compressible fluid flow. In this paper we present new enhancements to RRM, and we apply the enhanced method to both linear and nonlinear elasticity. We compare the results of ten test problems to those of analytic solvers, to demonstrate that RRM can successfully simulate these elastic systems without many of the requirements of traditional numerical methods such as numerical derivatives, equation system solvers, or Riemann solvers. We also show the relationship between error and computational effort for RRM on these systems, and compare RRM to other methods to highlight its strengths and weaknesses. And to further explain the two elastic equations used in the paper, we demonstrate the mathematical procedure used to create Riemann and Sedov-Taylor solvers for them, and detail the numerical techniques needed to embody those solvers in code. [ABSTRACT FROM AUTHOR]

Network modeling in electromagnetics is an effective technique in treating scattering problems by canonical and complex structures. Geometries constituted of angular regions (wedges) together with planar layers can now be approached with the Generalized Wiener-Hopf Technique supported by network representation in spectral domain. Even if the network representations in spectral planes are of great importance by themselves, the aim of this paper is to present a theoretical base and a general procedure for the formulation of complex scattering problems using network representation for the Generalized Wiener Hopf Technique starting basically from the wave equation. In particular while the spectral network representations are relatively well known for planar layers, the network modelling for an angular region requires a new theory that will be developed in this paper. With this theory we complete the formulation of a network methodology whose effectiveness is demonstrated by the application to a complex scattering problem with practical solutions given in terms of GTD/UTD diffraction coefficients and total far fields for engineering applications. The methodology can be applied to other physics fields. [ABSTRACT FROM AUTHOR]

Temporal hierarchies have been widely used during the past few years as they are capable to provide more accurate coherent forecasts at different planning horizons. However, they still display some limitations, being mainly subject to the forecasting methods used for generating the base forecasts and the particularities of the examined series. This paper deals with such limitations by considering three different strategies: (i) combining forecasts of multiple methods, (ii) applying bias adjustments and (iii) selectively implementing temporal hierarchies to avoid seasonal shrinkage. The proposed strategies can be applied either separately or simultaneously, being complements to the method considered for reconciling the base forecasts and completely independent from each other. Their effect is evaluated using the monthly series of the M and M3 competitions. The results are very promising, displaying lots of potential for improving the performance of temporal hierarchies, both in terms of accuracy and bias. [ABSTRACT FROM AUTHOR]

SPEC CPU is one of the most common benchmark suites used in computer architecture research. CPU2017 has recently been released to replace CPU2006. In this paper we present a detailed evaluation of the memory hierarchy performance for both the CPU2006 and single-threaded CPU2017 benchmarks. The experiments were executed on an Intel Xeon Skylake-SP, which is the first Intel processor to implement a mostly non-inclusive last-level cache (LLC). We present a classification of the benchmarks according to their memory pressure and analyze the performance impact of different LLC sizes. We also test all the hardware prefetchers showing they improve performance in most of the benchmarks. After comprehensive experimentation, we can highlight the following conclusions: i) almost half of SPEC CPU benchmarks have very low miss ratios in the second and third level caches, even with small LLC sizes and without hardware prefetching, ii) overall, the SPEC CPU2017 benchmarks demand even less memory hierarchy resources than the SPEC CPU2006 ones, iii) hardware prefetching is very effective in reducing LLC misses for most benchmarks, even with the smallest LLC size, and iv) from the memory hierarchy standpoint the methodologies commonly used to select benchmarks or simulation points do not guarantee representative workloads. [ABSTRACT FROM AUTHOR]

To deal with the conflicts between left-turn and through traffic streams and increase the discharge capacity, this paper addresses the pre-signal which is implemented at a signalized intersection. Such an intersection with pre-signal is termed as a tandem intersection. For the tandem intersection, phase swap sorting strategy is deemed as the most effective phasing scheme in view of some exclusive merits, such as easier compliance of drivers, and shorter sorting area. However, a major limitation of the phase swap sorting strategy is not considered in previous studies: if one or more vehicle is left at the sorting area after the signal light turns to red, the capacity of the approach would be dramatically dropped. Besides, previous signal control studies deal with a fixed timing plan that is not adaptive with the fluctuation of traffic flows. Therefore, to cope with these two gaps, this paper firstly takes an in-depth analysis of the traffic flow operations at the tandem intersection. Secondly, three groups of loop detectors are placed to obtain the real-time vehicle information for adaptive signalization. The lane selection behavior in the sorting area is considered to set the green time for intersection signals. With the objective of minimizing the vehicle delay, the signal control parameters are then optimized based on a dynamic programming method. Finally, numerical experiments show that average vehicle delay and maximum queue length can be reduced under all scenarios. [ABSTRACT FROM AUTHOR]

For the critical engine parts such as the crankshaft, the fatigue limit load is one of the most important parameters involved the design and manufacture stage. In previous engineering applications, this parameter has always been obtained by experiment, which is expensive and time-consuming. This paper, based on the theory of critical distance (TCD), first analyzes the stress distribution of a crankshaft under its limit load. In this way, the length of the critical distance can be obtained. Then a certain load is applied to a new crankshaft made of the same material and the effective stress is calculated based on the critical distance above. Finally, the fatigue limit load of the new crankshaft can be obtained by comparing the effective stress and the fatigue limit of the material. Comparison between the prediction and the corresponding experimental data shows that the traditional TCD may result in bigger errors on some occasions, while the modified TCD proposed in this paper can provide a more satisfactory result in terms of the fatigue limit for a quick engineering prediction. [ABSTRACT FROM AUTHOR]

The DEM generalization is the basis of multi-dimensional observation, the basis of expressing and analyzing the terrain. DEM is also the core of building the Multi-Scale Geographic Database. Thus, many researchers have studied both the theory and the method of DEM generalization. This paper proposed a new method of generalizing terrain, which extracts feature points based on the tree model construction which considering the nested relationship of watershed characteristics. The paper used the 5 m resolution DEM of the Jiuyuan gully watersheds in the Loess Plateau as the original data and extracted the feature points in every single watershed to reconstruct the DEM. The paper has achieved generalization from 1:10000 DEM to 1:50000 DEM by computing the best threshold. The best threshold is 0.06. In the last part of the paper, the height accuracy of the generalized DEM is analyzed by comparing it with some other classic methods, such as aggregation, resample, and VIP based on the original 1:50000 DEM. The outcome shows that the method performed well. The method can choose the best threshold according to the target generalization scale to decide the density of the feature points in the watershed. Meanwhile, this method can reserve the skeleton of the terrain, which can meet the needs of different levels of generalization. Additionally, through overlapped contour contrast, elevation statistical parameters and slope and aspect analysis, we found out that the W8D algorithm performed well and effectively in terrain representation. [ABSTRACT FROM AUTHOR]

Hydroxymethylcytosine (5hmC) methylation is a well-known epigenetic mark that is involved in gene regulation and may impact genome stability. To investigate a possible role of 5hmC in cancer development and progression, one must be able to detect and quantify its level first. In this paper, we address the issue of 5hmC detection at a single base resolution, starting with consideration of the well-established 5hmC measure Δβ and, in particular, with an analysis of its properties, both analytically and empirically. Then we propose several alternative hydroxymethylation measures and compare their properties with those of Δβ. In the absence of a gold standard, the (pairwise) resemblance of those 5hmC measures to Δβ is characterized by means of a similarity analysis and relative accuracy analysis. All results are illustrated on matched healthy and cancer tissue data sets as derived by means of bisulfite (BS) and oxidative bisulfite converting (oxBS) procedures. [ABSTRACT FROM AUTHOR]

Abstract Given a finite group G , we say that G has property P n if for every prime integer p , G has at most n − 1 irreducible characters whose degrees are multiples of p. In this paper, we classify all finite groups that have property P 4. We show that the groups satisfying property P 4 are exactly the finite groups with at most three nonlinear irreducible characters, one solvable group of order 168, SL 2 (3) , A 5 , S 5 , PSL 2 (7) and A 6. [ABSTRACT FROM AUTHOR]

Computer-aided polyp detection in gastric gastroscopy has been the subject of research over the past few decades. However, despite significant advances, automatic polyp detection in real time is still an unsolved problem. In this paper, we report on a convolutional neural network (CNN) for polyp detection that is constructed based on Single Shot MultiBox Detector (SSD) architecture and which we call SSD for Gastric Polyps (SSD-GPNet). To take full advantages of feature maps’ information from the feature pyramid and to acquire higher accuracy, we re-use information that is abandoned by Max-Pooling layers. In other words, we reuse the lost data from the pooling layers and concatenate that data as extra feature maps to contribute to classification and detection. Meanwhile, in the feature pyramid, we concatenate feature maps of the lower layers and feature maps that are deconvolved from upper layers to make explicit relationships between layers and to effectively increase the number of channels. The results show that our enhanced SSD for gastric polyp detection can realize real-time polyp detection with 50 frames per second (FPS) and can improve the mean average precision (mAP) from 88.5% to 90.4%, with only a little loss in time-performance. And the further experiment shows that SSD-GPNet has excellent performance in improving polyp detection recalls over 10% (p = 0.00053), especially in small polyp detection. This can help endoscopic physicians more easily find missed polyps and decrease the gastric polyp miss rate. It may be applicable in daily clinical practice to reduce the burden on physicians. [ABSTRACT FROM AUTHOR]

The 3D Tune-In Toolkit (3DTI Toolkit) is an open-source standard C++ library which includes a binaural spatialiser. This paper presents the technical details of this renderer, outlining its architecture and describing the processes implemented in each of its components. In order to put this description into context, the basic concepts behind binaural spatialisation are reviewed through a chronology of research milestones in the field in the last 40 years. The 3DTI Toolkit renders the anechoic signal path by convolving sound sources with Head Related Impulse Responses (HRIRs), obtained by interpolating those extracted from a set that can be loaded from any file in a standard audio format. Interaural time differences are managed separately, in order to be able to customise the rendering according the head size of the listener, and to reduce comb-filtering when interpolating between different HRIRs. In addition, geometrical and frequency-dependent corrections for simulating near-field sources are included. Reverberation is computed separately using a virtual loudspeakers Ambisonic approach and convolution with Binaural Room Impulse Responses (BRIRs). In all these processes, special care has been put in avoiding audible artefacts produced by changes in gains and audio filters due to the movements of sources and of the listener. The 3DTI Toolkit performance, as well as some other relevant metrics such as non-linear distortion, are assessed and presented, followed by a comparison between the features offered by the 3DTI Toolkit and those found in other currently available open- and closed-source binaural renderers. [ABSTRACT FROM AUTHOR]

A new Interpolation based Reversible Data Hiding (IRDH) scheme is reported in this paper. For different applications of an IRDH scheme to the digital image, video, multimedia, big-data and biological data, the embedding capacity requirement usually varies. Disregarding this important consideration, existing IRDH schemes do not offer a better embedding rate-distortion performance for varying size payloads. To attain this varying capacity requirement with our proposed adaptive embedding, we formulate a capacity control parameter and propose to utilize it to determine a minimum set of embeddable bits in a pixel. Additionally, we use a logical (or bit-wise) correlation between the embeddable pixel and estimated versions of an embedded pixel. Thereby, while a higher range between an upper and lower limit of the embedding capacity is maintained, a given capacity requirement within that limit is also attained with a better-embedded image quality. Computational modeling of all new processes of the scheme is presented, and performance of the scheme is evaluated with a set of popular test-images. Experimental results of our proposed scheme compared to the prominent IRDH schemes have recorded a significantly better-embedding rate-distortion performance. [ABSTRACT FROM AUTHOR]

Current efforts to assess human health response to chemicals based on high-throughput in vitro assay data on intra-cellular changes have been hindered for some illnesses by lack of information on higher-level extracellular, inter-organ, and organism-level interactions. However, a dose-response function (DRF), informed by various levels of information including apical health response, can represent a template for convergent top-down, bottom-up analysis. In this paper, a general DRF for chronic chemical and other health stressors and mixtures is derived based on a general first-order model previously derived and demonstrated for illness progression. The derivation accounts for essential autocorrelation among initiating event magnitudes along a toxicological mode of action, typical of complex processes in general, and reveals the inverse relationship between the minimum illness-inducing dose, and the illness severity per unit dose (both variable across a population). The resulting emergent DRF is theoretically scale-inclusive and amenable to low-dose extrapolation. The two-parameter single-toxicant version can be monotonic or sigmoidal, and is demonstrated preferable to traditional models (multistage, lognormal, generalized linear) for the published cancer and non-cancer datasets analyzed: chloroform (induced liver necrosis in female mice); bromate (induced dysplastic focia in male inbred rats); and 2-acetylaminofluorene (induced liver neoplasms and bladder carcinomas in 20,328 female mice). Common- and dissimilar-mode mixture models are demonstrated versus orthogonal data on toluene/benzene mixtures (mortality in Japanese medaka, Oryzias latipes, following embryonic exposure). Findings support previous empirical demonstration, and also reveal how a chemical with a typical monotonically-increasing DRF can display a J-shaped DRF when a second, antagonistic common-mode chemical is present. Overall, the general DRF derived here based on an autocorrelated first-order model appears to provide both a strong theoretical/biological basis for, as well as an accurate statistical description of, a diverse, albeit small, sample of observed dose-response data. The further generalizability of this conclusion can be tested in future analyses comparing with traditional modeling approaches across a broader range of datasets. [ABSTRACT FROM AUTHOR]

In this paper, a novel unequal error protection (UEP) technique is proposed for video streaming over multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Based on the concept of hierarchical quadrature amplitude modulation (HQAM) UEP and multi-antenna UEP, the proposed technique combines the relative protection levels (PLs) of constellation symbols and the differentiated PLs of the transmit antennas. In the proposed technique, standard square quadrature amplitude modulation (QAM) constellations are used instead of HQAM so that the QAM mapper at the transmitter side and the soft decision calculation at the receiver side remain unchanged, but the UEP benefit of HQAM is retained. The superior performance of the proposed technique is explained by the improved connections between data with various priorities and data paths with various PLs. The assumed video compression method is H.264/AVC, which is known to be commercially successful. The IEEE802.16m system is adopted as a data transmission system. With the aid of realistic simulations in strict accordance with the standards of IEEE802.16m systems and H.264/AVC video compression systems, the proposed technique HQAM-multi-antenna UEP is shown to improve the video quality significantly for a given average bit error rate when compared with previous techniques. [ABSTRACT FROM AUTHOR]

Codebook-based feature encodings are a standard framework for image recognition issues. A codebook is usually constructed by clusterings, such as the k-means and the Gaussian Mixture Model (GMM). A codebook size is an important factor to decide the trade-off between recognition performance and computational complexity and a traditional framework has the disadvantage to image recognition issues when a large codebook; the number of unique clusters becomes smaller than a designated codebook size because some clusters converge to close positions. This paper focusses on the disadvantage from a perspective of the distribution of prior probabilities and presents a clustering framework including two objectives that are alternated to the k-means and the GMM. Our approach is first evaluated with synthetic clustering datasets to analyze a difference to traditional clustering. In the experiment section, although our approach alternated to the k-means generates similar results to the k-means results, our approach is able to finely tune clusters for our objective. Our approach alternated to the GMM significantly improves our objective and constructs intuitively appropriate clusters, especially for huge and complicatedly distributed samples. In the experiment on image recognition issues, two state-of-the-art encodings, the Fisher Vector (FV) using the GMM and the Vector of Locally Aggregated Descriptors (VLAD) using the k-means, are evaluated with two publicly available image datasets, the Birds and the Butterflies. For the results of the VLAD with our approach, the recognition performances tend to be worse compared to the original VLAD results. On the other hand, the FV using our approach is able to improve the performance, especially in a larger codebook size. [ABSTRACT FROM AUTHOR]

We describe in this paper the experimental procedure, the data treatment and the quantification of the black body correction: an experimental approach to compensate for scattering and systematic biases in quantitative neutron imaging based on experimental data. The correction algorithm is based on two steps; estimation of the scattering component and correction using an enhanced normalization formula. The method incorporates correction terms into the image normalization procedure, which usually only includes open beam and dark current images (open beam correction). Our aim is to show its efficiency and reproducibility: we detail the data treatment procedures and quantitatively investigate the effect of the correction. Its implementation is included within the open source CT reconstruction software MuhRec. The performance of the proposed algorithm is demonstrated using simulated and experimental CT datasets acquired at the ICON and NEUTRA beamlines at the Paul Scherrer Institut. [ABSTRACT FROM AUTHOR]

In this paper, a computationally efficient algorithm for Bayesian joint change point detection (CPD) in multiple time series is presented. The data generation model includes a number of change configurations (CC), each affecting a unique subset of the time series, which introduces correlation between the positions of change points (CPs) in the monitored time series. The inference objective is to identify joint changes and the associated CC. The algorithm consists of two stages: First, a univariate CPD algorithm is applied separately to each of the involved time series. The outcomes of this step are maximum a posteriori (MAP) detected CPs and posterior distributions of CPs conditioned on the MAP CPs. These outcomes are used in combination to approximate the posterior for the CCs. In the second algorithm stage, dynamic programming is used to find the maxima of this approximate CC posterior. The algorithm is applied to synthetic data, and it is shown to be both significantly faster and more accurate compared to a previously proposed algorithm designed to solve similar problems. Also, the initial algorithm is extended with steps from the maximization–maximization algorithm, which allows the hyperparameters of the data generation model to be estimated jointly with the CCs, and we show that these estimates coincide with estimates obtained from a Markov chain Monte Carlo algorithm. [ABSTRACT FROM AUTHOR]

Analytical CT reconstruction is popular in practice because of its computational efficiency, but it suffers from low reconstruction quality when an insufficient number of projections are used. To address this issue, this paper presents a new analytical method of backprojection Wiener deconvolution (BPWD). BPWD executes backprojection first, and then applies a Wiener deconvolution to the whole backprojected image. The Wiener filter is derived from a ramp filter, enabling the proposed approach to perform reconstruction and denoising simultaneously. The use of a filter after backprojection does not differentiate between real sampled projections and interpolated ones, introducing reconstruction errors. Therefore a weighted ramp filter was applied to increase the contribution of real sampled projections in the reconstruction, thus improving reconstruction quality. Experiments on synthetic data and real phase-contrast x-ray images showed that the proposed approach yields better reconstruction quality compared to the classical filtered backprojection (FBP) method, with comparable reconstruction speed. [ABSTRACT FROM AUTHOR]

As a supply chain solution integrator, fourth party logistics (4PL) has become an important focus for improving the operational efficiency of the logistics industry in recent days. This paper addresses the mechanism design problem of the 4PL for selecting a third party logistics (3PL) provider who involves loss-averse behavior to form a longer-term strategic partnership in multi-attribute reverse auctions. Due to fluctuating costs of energy or labor and unintentional delivery failures like traffic jam or technology malfunctions, we consider two incomplete attributes, namely cost uncertainty and delivery risk. Integrating the loss-averse behavior of 3PLs, based on the prospect theory, the bid decision model is constructed to obtain 3PLs’ bidding strategies. The corresponding efficient and optimal scoring auctions that consist of cost-sharing contract and contingent penalty are developed to maximize the ex ante expected profit of the system or the 4PL depending on whether the 4PL is willing to cooperate or not. Theoretical analysis verified by numerical examples illustrates the advantage of the proposed mechanisms. Impacts of model parameters on the 4PL’s decision are also investigated and managerial insights are presented. [ABSTRACT FROM AUTHOR]

In this paper, a new quantum sliding mode control, for improving the performance of the two-level quantum sliding mode control systems with bounded uncertainties, is introduced. The presented quantum sliding surface is based on the error which occurs between the predetermined sliding mode and the system state. The control objective is to derive the system state to reach the sliding mode domain and then maintain its motion on it. For this purpose, we use the sliding mode control method and periodic projective measurements. A theorem for facilitating the presented method is proved. The simulated example shows that both the reaching time to the sliding mode and the control amplitude are significantly decreased, which demonstrate the effectiveness and validity of the presented method. [ABSTRACT FROM AUTHOR]

Economic cycles may lead to changes in corporate bond credit ratings. This paper utilizes the Markov model to describe transition probability matrixes of economic states for the separation of economic cycles. We develop a new model, which we term the Markov rating shopping dual reputation model, incorporating two reputation effects. This model is well suited to analyze the conditions of the dual rating incentive regulation and the constraint regulation for preventing rating inflation in collusion among credit rating agencies. Then, we apply the Markov regime switching-vector auto-regression (MS-VAR) to estimate the transition probability matrixes of America, England, Japan and China. Based on the numerical analysis and the simulations, the results show that a dual rating regulation can prevent the collusion of inflated ratings, as well as increased rating fees with the separation of economic cycles; additionally, when separating the economic cycles, a constraint regulation is more effective at reducing the risk of rating inflation in collusion and regulatory cost. [ABSTRACT FROM AUTHOR]

Sparse spectral clustering (SSC) has become one of the most popular clustering approaches in recent years. However, its high computational complexity prevents its application to large-scale datasets such as hyperspectral images (HSIs). In this paper, we propose two efficient approximate sparse spectral clustering methods for HSIs clustering in which clustering performance is improved by utilizing local information among the data. Firstly, we construct a smaller representative dataset on which sparse spectral clustering is performed. Then the labels of ground object are extending to whole dataset based on the local information according to two extending strategies. The first one is that the local interpolation is utilized to improve the extension of the clustering result. The other one is that the label extension is turned to a problem of subspace embedding, and is fulfilled by locally linear embedding (LLE). Several experiments on HSIs demonstrated that the proposed algorithms are effective for HSIs clustering. [ABSTRACT FROM AUTHOR]

This paper considers the DCSK system under jamming environment. We consider both fast and slow switching pulse jamming (PJ). We derive approximations of the system bit error rates (BERs) in analytic expressions containing only well-known special functions, from which we show that for both jamming cases increasing the spreading factor can enhance the BER. In addition, we reveal that under the fast switching PJ, the BER does not depend on the jamming duty cycle ρ when ρ ≤ 0.5, however when ρ > 0.5, increasing its value degrades the BER. Moreover, we find out that under the slow switching PJ, increasing ρ may either enhance or degrade the BER. [ABSTRACT FROM AUTHOR]

This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study. [ABSTRACT FROM AUTHOR]

Space operations will be the main space missions in the future. This paper focuses on the precise operations for non-cooperative target, and researches of coordinated parameter identification (CPI) which allows the motion of multi-joints. The contents of this paper are organized: (1) Summarize the inertial parameters identification techniques which have been conducted now, and the technique based on momentum conservation is selected for reliability and realizability; (2) Elaborate the basic principles and primary algorithm of coordinated parameter identification, and analyze some special problems in calculation (3) Numerical simulation of coordinated identification technique by an case study on non-cooperative target of spacecraft mounting dual-arm with six joints is done. The results show that the coordinated parameter identification technique could get all the inertial parameters of the target in 3D by one-time identification, and does not need special configuration or driven joints, moreover the results are highly precise and save much more time than traditional ones. [ABSTRACT FROM AUTHOR]

A system for real-time ultrasound (US) elastography will advance interventions for the diagnosis and treatment of cancer by advancing methods such as thermal monitoring of tissue ablation. A multi-stream graphics processing unit (GPU) based accelerated normalized cross-correlation (NCC) elastography, with a maximum frame rate of 78 frames per second, is presented in this paper. A study of NCC window size is undertaken to determine the effect on frame rate and the quality of output elastography images. This paper also presents a novel system for Online Tracked Ultrasound Elastography (O-TRuE), which extends prior work on an offline method. By tracking the US probe with an electromagnetic (EM) tracker, the system selects in-plane radio frequency (RF) data frames for generating high quality elastograms. A novel method for evaluating the quality of an elastography output stream is presented, suggesting that O-TRuE generates more stable elastograms than generated by untracked, free-hand palpation. Since EM tracking cannot be used in all systems, an integration of real-time elastography and the da Vinci Surgical System is presented and evaluated for elastography stream quality based on our metric. The da Vinci surgical robot is outfitted with a laparoscopic US probe, and palpation motions are autonomously generated by customized software. It is found that a stable output stream can be achieved, which is affected by both the frequency and amplitude of palpation. The GPU framework is validated using data from in-vivo pig liver ablation; the generated elastography images identify the ablated region, outlined more clearly than in the corresponding B-mode US images. [ABSTRACT FROM AUTHOR]

This paper investigated the seismic retrofitting of an existing cable-stayed bridge through the use of a seismic isolation system. The bridge is situated in a high seismic zone. During the Saguenay earthquake 1988, one of the anchorage plates of the bridge supports failed. Herein, several configurations of seismic isolation system were considered to identify an appropriate solution for the seismic retrofitting of the bridge in both the longitudinal and transverse directions. A three-dimensional model of the bridge was created, and its seismic behavior studied through nonlinear dynamic time-history analysis. The comparative performance study among the five retrofitting configurations showed that the partial seismic isolation of the bridge led to an enhancement of the seismic response of the bridge in one direction only. However, the overall seismic response of the cable-stayed bridge substantially improved in the longitudinal and transverse directions in cases where the isolation systems were utilized between the supports and the deck-tower connection of the bridge. [ABSTRACT FROM AUTHOR]

In the Artificial Bee Colony (ABC) algorithm, the employed bee and the onlooker bee phase involve updating the candidate solutions by changing a value in one dimension, dubbed one-dimension update process. For some problems which the number of dimensions is very high, the one-dimension update process can cause the solution quality and convergence speed drop. This paper proposes a new algorithm, using reinforcement learning for solution updating in ABC algorithm, called R-ABC. After updating a solution by an employed bee, the new solution results in positive or negative reinforcement applied to the solution dimensions in the onlooker bee phase. Positive reinforcement is given when the candidate solution from the employed bee phase provides a better fitness value. The more often a dimension provides a better fitness value when changed, the higher the value of update becomes in the onlooker bee phase. Conversely, negative reinforcement is given when the candidate solution does not provide a better fitness value. The performance of the proposed algorithm is assessed on eight basic numerical benchmark functions in four categories with 100, 500, 700, and 900 dimensions, seven CEC2005’s shifted functions with 100, 500, 700, and 900 dimensions, and six CEC2014’s hybrid functions with 100 dimensions. The results show that the proposed algorithm provides solutions which are significantly better than all other algorithms for all tested dimensions on basic benchmark functions. The number of solutions provided by the R-ABC algorithm which are significantly better than those of other algorithms increases when the number of dimensions increases on the CEC2005’s shifted functions. The R-ABC algorithm is at least comparable to the state-of-the-art ABC variants on the CEC2014’s hybrid functions. [ABSTRACT FROM AUTHOR]

Motion capture allows accurate recording of human motion, with applications in many fields, including entertainment, medicine, sports science and human computer interaction. A common difficulty with this technology is the occurrence of missing data, due to occlusions, or recording conditions. Various models have been proposed to estimate missing data. Some are based on interpolation, low-rank properties or inter-correlations. Others involve dataset matching or skeleton constraints. While the latter have the advantage of promoting a realistic motion estimation, they require prior knowledge of skeleton constraints, or the availability of a prerecorded dataset. In this article, we propose a probabilistic averaging method of several recovery models (referred to as Probabilistic Model Averaging (PMA) in this paper), based on the likelihoods of the distances between body points. This method has the advantage of being automatic, while allowing an efficient gap data recovery. To support and validate the proposed method, we use a set of four individual recovery models, based on linear/nonlinear regression in local coordinate systems. Finally, we propose two heuristic algorithms to enforce skeleton constraints in the reconstructed motion, which can be used on any individual recovery model. For validation purposes, random gaps were introduced into motion-capture sequences, and the effects of factors such as the number of simultaneous gaps, gap length and sequence duration were analyzed. Results show that the proposed probabilistic averaging method yields better recovery than (i) each of the four individual models and (ii) two recent state-of-the-art models, regardless of gap length, sequence duration and number of simultaneous gaps. Moreover, both of our heuristic skeleton-constraint algorithms significantly improve the recovery for 7 out of 8 tested motion-capture sequences (p < 0.05), for 10 simultaneous gaps of 5 seconds. The code is available for free download at: . [ABSTRACT FROM AUTHOR]

An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective. [ABSTRACT FROM AUTHOR]

In this paper, we propose a theory for linear time fractional PDEs on L 2 ( R d ) , with two time parameters. The order of the time derivatives under consideration is less than 1. We study well-posedness, regularizing effects and dissipative properties. Regarding regularizing effects, we describe quite precisely the equations that have this effect or not. We highlight that, in purely fractional settings, the regularizing effect acts always only up to finite order; unlike to the standard case. Also, we investigate the properties of the three time variables solution operator generated by these PDEs. [ABSTRACT FROM AUTHOR]

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations. [ABSTRACT FROM AUTHOR]

The spectrum of a first order sentence is the set of all α such that G ( n , n − α ) does not obey zero–one law with respect to this sentence. In this paper, we prove that the minimal number of quantifier alternations of a first order sentence with infinite spectrum equals 3. We have also proved that the spectrum of a first-order sentence with quantifier depth 4 has no limit points except possibly the points 1 ∕ 2 and 3 ∕ 5 . [ABSTRACT FROM AUTHOR]

In the present paper we propose an improved class of estimators in the presence of measurement error and non-response under stratified random sampling for estimating the finite population mean. The theoretical and numerical studies reveal that the proposed class of estimators performs better than other existing estimators. [ABSTRACT FROM AUTHOR]

The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system’s modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model. [ABSTRACT FROM AUTHOR]

This paper presents a morphing model of vector geographical data based on Fourier transformation. This model involves three main steps. They are conversion from vector data to Fourier series, generation of intermediate function by combination of the two Fourier series concerning a large scale and a small scale, and reverse conversion from combination function to vector data. By mirror processing, the model can also be used for morphing of linear features. Experimental results show that this method is sensitive to scale variations and it can be used for vector map features’ continuous scale transformation. The efficiency of this model is linearly related to the point number of shape boundary and the interceptive value n of Fourier expansion. The effect of morphing by Fourier transformation is plausible and the efficiency of the algorithm is acceptable. [ABSTRACT FROM AUTHOR]

The sampling of scale-free networks in Molecular Biology is usually achieved by growing networks from a seed using recursive algorithms with elementary moves which include the addition and deletion of nodes and bonds. These algorithms include the Barabási-Albert algorithm. Later algorithms, such as the Duplication-Divergence algorithm, the Solé algorithm and the iSite algorithm, were inspired by biological processes underlying the evolution of protein networks, and the networks they produce differ essentially from networks grown by the Barabási-Albert algorithm. In this paper the mean field analysis of these algorithms is reconsidered, and extended to variant and modified implementations of the algorithms. The degree sequences of scale-free networks decay according to a powerlaw distribution, namely P(k) ∼ k− γ, where γ is a scaling exponent. We derive mean field expressions for γ, and test these by numerical simulations. Generally, good agreement is obtained. We also found that some algorithms do not produce scale-free networks (for example some variant Barabási-Albert and Solé networks). [ABSTRACT FROM AUTHOR]

The choice of an optimal feature detector-descriptor combination for image matching often depends on the application and the image type. In this paper, we propose the Log-Polar Magnitude feature descriptor—a rotation, scale, and illumination invariant descriptor that achieves comparable performance to SIFT on a large variety of image registration problems but with much shorter feature vectors. The descriptor is based on the Log-Polar Transform followed by a Fourier Transform and selection of the magnitude spectrum components. Selecting different frequency components allows optimizing for image patterns specific for a particular application. In addition, by relying only on coordinates of the found features and (optionally) feature sizes our descriptor is completely detector independent. We propose 48- or 56-long feature vectors that potentially can be shortened even further depending on the application. Shorter feature vectors result in better memory usage and faster matching. This combined with the fact that the descriptor does not require a time-consuming feature orientation estimation (the rotation invariance is achieved solely by using the magnitude spectrum of the Log-Polar Transform) makes it particularly attractive to applications with limited hardware capacity. Evaluation is performed on the standard Oxford dataset and two different microscopy datasets; one with fluorescence and one with transmission electron microscopy images. Our method performs better than SURF and comparable to SIFT on the Oxford dataset, and better than SIFT on both microscopy datasets indicating that it is particularly useful in applications with microscopy images. [ABSTRACT FROM AUTHOR]

The model of population growth is revised in this paper. A new model is proposed based on the concept of fractional differentiation that uses the generalized Mittag-Leffler function as kernel of differentiation. The new model includes the choice of sexuality. The existence of unique solution is investigated and numerical solution is provided. [ABSTRACT FROM AUTHOR]

Craniofacial registration is used to establish the point-to-point correspondence in a unified coordinate system among human craniofacial models. It is the foundation of craniofacial reconstruction and other craniofacial statistical analysis research. In this paper, a non-rigid 3D craniofacial registration method using thin-plate spline transform and cylindrical surface projection is proposed. First, the gradient descent optimization is utilized to improve a cylindrical surface fitting (CSF) for the reference craniofacial model. Second, the thin-plate spline transform (TPST) is applied to deform a target craniofacial model to the reference model. Finally, the cylindrical surface projection (CSP) is used to derive the point correspondence between the reference and deformed target models. To accelerate the procedure, the iterative closest point ICP algorithm is used to obtain a rough correspondence, which can provide a possible intersection area of the CSP. Finally, the inverse TPST is used to map the obtained corresponding points from the deformed target craniofacial model to the original model, and it can be realized directly by the correspondence between the original target model and the deformed target model. Three types of registration, namely, reflexive, involutive and transitive registration, are carried out to verify the effectiveness of the proposed craniofacial registration algorithm. Comparison with the methods in the literature shows that the proposed method is more accurate. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the ability of various site-condition proxies (SCPs) to reduce ground-motion aleatory variability and evaluate how SCPs capture nonlinearity site effects. The SCPs used here are time-averaged shear-wave velocity in the top 30 m ( V ), the topographical slope (slope), the fundamental resonance frequency ( f ) and the depth beyond which V exceeds 800 m/s ( H ). We considered first the performance of each SCP taken alone and then the combined performance of the 6 SCP pairs [ V - f ], [ V - H ], [ f -slope], [ H -slope], [ V -slope] and [ f - H ]. This analysis is performed using a neural network approach including a random effect applied on a KiK-net subset for derivation of ground-motion prediction equations setting the relationship between various ground-motion parameters such as peak ground acceleration, peak ground velocity and pseudo-spectral acceleration PSA ( T), and M , R , focal depth and SCPs. While the choice of SCP is found to have almost no impact on the median ground-motion prediction, it does impact the level of aleatory uncertainty. V is found to perform the best of single proxies at short periods ( T < 0.6 s), while f and H perform better at longer periods; considering SCP pairs leads to significant improvements, with particular emphasis on [ V - H ] and [ f -slope] pairs. The results also indicate significant nonlinearity on the site terms for soft sites and that the most relevant loading parameter for characterising nonlinear site response is the 'stiff' spectral ordinate at the considered period. [ABSTRACT FROM AUTHOR]

This paper focuses on the impact of rainfall on the temporal and spatial distribution of taxi passengers. The main objective is to provide guidance for taxi scheduling on rainy days. To this end, we take the occupied and empty states of taxis as units of analysis. By matching a taxi's GPS data to its taximeter data, we can obtain the taxi's operational time and the taxi driver's income from every unit of analysis. The ratio of taxi operation time to taxi drivers' income is used to measure the quality of taxi passengers. The research results show that the spatio-temporal evolution of urban taxi service demand differs based on rainfall conditions and hours of operation. During non-rush hours, taxi demand in peripheral areas is significantly reduced under increasing precipitation conditions, whereas during rush hours, the demand for highly profitable taxi services steadily increases. Thus, as an intelligent response for taxi operations and dispatching, taxi services should guide cruising taxis to high-demand regions to increase their service time and ride opportunities. [ABSTRACT FROM AUTHOR]

Shortwave solar radiation is an important component of the surface energy balance and provides the principal source of energy for terrestrial ecosystems. This paper presents a machine learning approach in the form of a random forest (RF) model for estimating daily downward solar radiation flux at the land surface over complex terrain using MODIS (MODerate Resolution Imaging Spectroradiometer) remote sensing data. The model-building technique makes use of a unique network of 16 solar flux measurements in the semi-arid Reynolds Creek Experimental Watershed and Critical Zone Observatory, in southwest Idaho, USA. Based on a composite RF model built on daily observations from all 16 sites in the watershed, the model simulation of downward solar radiation matches well with the observation data (r2 = 0.96). To evaluate model performance, RF models were built from 12 of 16 sites selected at random and validated against the observations at the remaining four sites. Overall root mean square errors (RMSE), bias, and mean absolute error (MAE) are small (range: 37.17 W/m2-81.27 W/m2, -48.31 W/m2-15.67 W/m2, and 26.56 W/m2-63.77 W/m2, respectively). When extrapolated to the entire watershed, spatiotemporal patterns of solar flux are largely consistent with expected trends in this watershed. We also explored significant predictors of downward solar flux in order to reveal important properties and processes controlling downward solar radiation. Based on the composite RF model built on all 16 sites, the three most important predictors to estimate downward solar radiation include the black sky albedo (BSA) near infrared band (0.858 μm), BSA visible band (0.3–0.7 μm), and clear day coverage. This study has important implications for improving the ability to derive downward solar radiation through a fusion of multiple remote sensing datasets and can potentially capture spatiotemporally varying trends in solar radiation that is useful for land surface hydrologic and terrestrial ecosystem modeling. [ABSTRACT FROM AUTHOR]

Generating a reliable computer-simulated synthetic population is necessary for knowledge processing and decision-making analysis in agent-based systems in order to measure, interpret and describe each target area and the human activity patterns within it. In this paper, both synthetic reconstruction (SR) and combinatorial optimisation (CO) techniques are discussed for generating a reliable synthetic population for a certain geographic region (in Australia) using aggregated- and disaggregated-level information available for such an area. A CO algorithm using the quadratic function of population estimators is presented in this paper in order to generate a synthetic population while considering a two-fold nested structure for the individuals and households within the target areas. The baseline population in this study is generated from the confidentialised unit record files (CURFs) and 2006 Australian census tables. The dynamics of the created population is then projected over five years using a dynamic micro-simulation model for individual- and household-level demographic transitions. This projection is then compared with the 2011 Australian census. A prediction interval is provided for the population estimates obtained by the bootstrapping method, by which the variability structure of a predictor can be replicated in a bootstrap distribution. [ABSTRACT FROM AUTHOR]