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Recent advance of Intelligent Reflecting Surface (IRS) introduces a new dimension for secure communications by reconfiguring the transmission environments. In this paper, we devise a secure transmission scheme for multi-user MISO systems by leveraging multiple collaborative IRSs. Specifically, to guarantee the worst-case achievable secrecy rate among multiple legitimate users, we formulate a max-min problem that can be solved by an alternating optimization method to decouple it into multiple sub-problems. Based on semidefinite relaxation and successive convex approximation, each sub-problem can be further converted into convex problem and easily solved. Extensive experimental results demonstrate that our proposed scheme can adapt to complex scenarios for multiple users and achieve significant gain in terms of achievable secrecy rate. Compared to the traditional single IRS scheme, the proposed scheme can achieve better performance at the range of 2.4-6.4 bps/Hz with the increase in the number of reflecting elements in the multi-user scenarios. We also evaluate the gap between the secrecy rate for our proposed scheme under continuous phase shift/amplitude control and discrete phase shift/amplitude control, and our results show that the secrecy rate obtained from discrete approximation method converges to that achieved from the proposed scheme when increasing the discretization granularity.

When predicting crop yield using both functional and multivariate predictors, the prediction performances benefit from the inclusion of the interactions between the two sets of predictors. We assume the interaction depends on a nonparametric, single-index structure of the multivariate predictor and reduce each functional predictor’s dimension using functional principal component analysis (FPCA). Allowing the number of FPCA scores to diverge to infinity, we consider a sequence of semiparametric working models with a diverging number of predictors, which are FPCA scores with estimation errors. We show that the parametric component of the model is root-n consistent and asymptotically normal, the overall prediction error is dominated by the estimation of the nonparametric interaction function, and justify a CV-based procedure to select the tuning parameters.

Physiological studies have shown that there are some differences in speech and facial activities between depressive and healthy individuals. Based on this fact, we propose a novel Spatio-Temporal Attention (STA) network and a Multimodal Attention Feature Fusion (MAFF) strategy to obtain the multimodal representation of depression cues for predicting the individual depression level. Specifically, we firstly divide the speech amplitude spectrum/video into fixed-length segments and input these segments into the STA network, which not only integrates the spatial and temporal information through attention mechanism, but also emphasizes the audio/video frames related to depression detection. The audio/video segment-level feature is obtained from the output of the last full connection layer of the STA network. Secondly, this paper employs the eigen evolution pooling method to summarize the changes of each dimension of the audio/video segment-level features to aggregate them into the audio/video level feature. Thirdly, the multimodal representation with modal complementary information is generated using the MAFF and inputs into the support vector regression predictor for estimating depression severity. Experimental results on the AVEC2013 and AVEC2014 depression databases illustrate the effectiveness of our method.

Face recognition is one of the useful tasks and can be used for many applications as security systems, it is necessary to find effective and low complexity facial classifier methods. In this paper, we proposed a new one-dimensional CNN deep convolutional neural network (1D-DCNN) classifier was combined with linear discriminative analysis (LDA) techniques to produce a new face recognition methodology. The contribution of this paper is generated of one-dimensional face feature set by LDA from original image database to training of 1D-DCNN classifier, that it contributed in the improvement of facial recognition performance. The model has been tested on MCUT dataset consisted of 3755 images for 276 classes. The results of the implementation of face recognition were accuracy of 100%, precision of 100%, recall of 100%, and F-measure of 100%.

Recently, Borello and Jamous have investigated some lower bounds on the dimension and minimum distance for dihedral codes, in analogy with the theory of BCH codes. In this paper, we extend some of their results to split metacyclic codes, that is, codes over semidirect products of cyclic groups.

This paper introduces a spatial panel quantile model with unobserved heterogeneity. The proposed model is capable of capturing high-dimensional cross-sectional dependence and allows heterogeneous regression coefficients. For estimating model parameters, a new estimation procedure is proposed. When both the time and cross-sectional dimensions of the panel go to infinity, the uniform consistency and the asymptotic normality of the estimated parameters are established. In order to determine the dimension of the interactive fixed effects, we propose a new information criterion. It is shown that the criterion asymptotically selects the true dimension. Monte Carlo simulations document the satisfactory performance of the proposed method. Finally, the method is applied to study the quantile co-movement structure of the U.S. stock market by taking into account the input–output linkages as firms are connected through the input–output production network.

This article presents the design and implementation of PCube, a phase-based parallel packet decoder for concurrent transmissions of LoRa nodes. The key enabling technology behind PCube is a novel air-channel phase measurement technique that is able to extract phase differences of air-channels between LoRa nodes and multiple antennas of a gateway. PCube leverages the reception diversities of multiple receiving antennas of a gateway and scales the concurrent transmissions of a large number of LoRa nodes, even exceeding the number of receiving antennas at a gateway. As a phase-based parallel decoder, PCube provides a new dimension to resolve collisions and supports more concurrent transmissions by complementing time and frequency-based parallel decoders. PCube is implemented and evaluated with synchronized software defined radios and off-the-shelf LoRa nodes in both indoors and outdoors. Results demonstrate that PCube can substantially outperform state-of-the-art works in terms of aggregated throughput by 4.9× and the number of concurrent nodes by up to 5×. More importantly, PCube scales well with the number of receiving antennas of a gateway, which is promising to break the barrier of concurrent transmissions.

A simple necessary and sufficient condition ensuring that a real matrix of dimension 3 is similar to a Metzler matrix is exhibited. When this condition is satisfied, a construction of the transfer matrix is given. This construction is used to design an interval observer for a family of continuous-time systems. An example is provided with interval observer design for the so-called love dynamics in the case of limit cycles.

Traditional Deep Neural Network (DNN) security is mostly related to the well-known adversarial input example attack.Recently, another dimension of adversarial attack, namely, attack on DNN weight parameters, has been shown to be very powerful. Asa representative one, the Bit-Flip based adversarial weight Attack (BFA) injects an extremely small amount of faults into weight parameters to hijack the executing DNN function. Prior works of BFA focus on un-targeted attacks that can hack all inputs into a random output class by flipping a very small number of weight bits stored in computer memory. This paper proposes the first work oftargetedBFA based (T-BFA) adversarial weight attack on DNNs, which can intentionally mislead selected inputs to a target output class. The objective is achieved by identifying the weight bits that are highly associated with classification of a targeted output through a class-dependent weight bit searching algorithm. Our proposed T-BFA performance is successfully demonstrated on multiple DNN architectures for image classification tasks. For example, by merely flipping 27 out of 88 million weight bits of ResNet-18, our T-BFA can misclassify all the images from Hen class into Goose class (i.e., 100% attack success rate) in ImageNet dataset, while maintaining 59.35% validation accuracy.

Given m d-dimensional responsors and n d-dimensional predictors, sparse regression finds at most k predictors for each responsor for linear approximation, 1≤ k ≤ d-1. The key problem in sparse regression is subset selection, which usually suffers from high computational cost. In recent years, many improved approximate methods of subset selection have been published. However, less attention has been paid to the nonapproximate method of subset selection, which is very necessary for many questions in data analysis. Here, we consider sparse regression from the view of correlation and propose the formula of conditional uncorrelation. Then, an efficient nonapproximate method of subset selection is proposed in which we do not need to calculate any coefficients in the regression equation for candidate predictors. By the proposed method, the computational complexity is reduced from O([1/6]k³+(m+1)k²+mkd) to O([1/6]k³+[1/2](m+1)k²) for each candidate subset in sparse regression. Because the dimension d is generally the number of observations or experiments and large enough, the proposed method can greatly improve the efficiency of nonapproximate subset selection. We also apply the proposed method in real scenarios of dental age assessment and sparse coding to validate the efficiency of the proposed method.

Finite-dimensional observer-based controller design for PDEs is a challenging problem. Recently, such controllers were introduced for the 1D heat equation, under the assumption that one of the observation or control operators is bounded. This paper suggests a constructive method for such controllers for 1D parabolic PDEs with both (observation and control) operators being unbounded. We consider the Kuramoto-Sivashinsky equation (KSE) under either boundary or in-domain point measurement and boundary actuation. We employ a modal decomposition approach via dynamic extension, using eigenfunctions of a Sturm-Liouville operator. The controller dimension is defined by the number of unstable modes, whereas the observer dimension $N$ may be larger than this number. We suggest a direct Lyapunov approach to the full-order closed-loop system, which results in an LMI whose elements and dimension depend on $N$. The value of $N$ and the decay rate are obtained from the LMI. We extend our approach to internal stabilization with guaranteed $L^2$-gain and input-to-state stabilization. We prove two crucial properties of the derived LMIs. First, We prove that the LMIs are always feasible provided $N$ and the $L^2$ or ISS gains are large enough, thereby obtaining guarantees for our approach. Moreover, for the case of stabilization, we show that feasibility of the LMI for some $N$ implies its feasibility for $N+1$ (i.e., enlarging $N$ in the LMI cannot deteriorate the resulting decay rate of the closed-loop system). Numerical examples demonstrate the efficiency of the method.

Contactless fault diagnosis is one of the most important technique for fault identification of equipment. Based on the idea of contactless fault diagnosis, this paper presents a sound-based diagnosis method for railway point machines (RPMs). First, the sound signals are preprocessed using empirical mode decomposition (EMD). Entropy, time-domain and frequency-domain statistical parameters of the first 15 intrinsic mode functions (IMFs) are then extracted. Second, a two-stage feature selection strategy blending Filter method and Wrapper method is proposed, which can significantly reduce the dimension of features and select the optimal features. The superiority and effectiveness of the proposed feature selection strategy are verified by comparing with other feature selection methods. Third, a weighted majority voting (WMV)-based ensemble classifier optimized using particle swarm optimization (PSO) is developed and compared with single classifiers. And the ensemble patterns are discussed to select the most optimal ensemble pattern. The average diagnosis accuracies of 10 repeated trails of reverse-normal and normal-reverse switching processes reach 99% and 99.93%, respectively, which indicates the effectiveness and feasibility of the proposed method.

Switch-based adaptive dynamic programming (ADP) is an optimal control problem in which a cost must be minimized by switching among a family of dynamical modes. When the system dimension increases, the solution to switch-based ADP is made prohibitive by the exponentially increasing structure of the value function approximator and by the exponentially increasing modes. This technical correspondence proposes a distributed computational method for solving switch-based ADP. The method relies on partitioning the system into agents, each one dealing with a lower dimensional state and a few local modes. Each agent aims to minimize a local version of the global cost while avoiding that its local switching strategy has conflicts with the switching strategies of the neighboring agents. A heuristic algorithm based on the consensus dynamics and Nash equilibrium is proposed to avoid such conflicts. The effectiveness of the proposed method is verified via traffic and building test cases.

Time-varying formation (TVF) and trajectory tracking $H_{∞}$ control problem of multiagent systems (MASs) subject to communication delays and external disturbances under the directed communication topology is studied. This article's objective is for all agents to attain the desired TVF and track the pregiven formation center trajectory simultaneously. First, a distributed TVF and trajectory tracking control protocol employing neighborhood interaction information is developed in the presence of communication delays. Second, since the Laplacian matrix of a graph can be decomposed into the product of two specific matrices, the TVF and trajectory tracking $H_{∞}$ control problem is converted into the lower dimension asymptotic stability problem of a closed-loop system by applying an appropriate variable conversion. Third, a Lyapunov-Krasovskii functional is constructed to analyze the stability of MASs. Sufficient conditions are obtained in the form of linear matrix inequalities (LMIs) to ensure the completion of the TVF and formation center trajectory tracking of MASs. In the meantime, the maximum allowable communication delay can be calculated by the LMIs. Finally, the results of numerical simulations are presented to verify the validity of the approach this article proposes.

Statistical solutions have recently been introduced as an alternative solution framework for hyperbolic systems of conservation laws. In this work, we derive a novel a posteriori error estimate in the Wasserstein distance between dissipative statistical solutions and numerical approximations obtained from the Runge-Kutta Discontinuous Galerkin method in one spatial dimension, which rely on so-called regularized empirical measures. The error estimator can be split into deterministic parts which correspond to spatio-temporal approximation errors and a stochastic part which reflects the stochastic error. We provide numerical experiments which examine the scaling properties of the residuals and verify their splitting., Baden-Württemberg Stiftung, Projekt DEAL, Deutsche Forschungsgemeinschaft

Isolation Forest (or iForest) is a well-known technique for anomaly detection. It is, however, a bulky approach that assumes the luxury of large storage space and is also ineffective with dynamic streaming data so common nowadays in varied application domains. In this work, we present the Preprocessed Isolation Forest (PiForest) approach for anomaly detection that works well in resource constrained environments and is also effective on streaming data. PiForest is largely based on the iForest algorithm and to effectively handle the streaming data includes a pre-processing stage. In the pre-processing stage, Principal Component Analysis (PCA) is first harnessed to significantly reduce the dimension and bulk of the data. Subsequently, the streaming characteristic of the data is handled through a sliding window mechanism that creates sequential blocks of data for systematic processing. PiForest is able to identify anomalies as effectively as iForest and other state-of-the-art anomaly detection techniques but has substantially low storage and prediction complexity. We conduct empirical evaluation of the proposed approach with standard data sets and show that it performs comparably with standard techniques in terms of Area Under the Receiver Operating Characteristic Curve (AUC-ROC) and is able to work with high-dimensional, streaming data. Subsequently, we do a real-world hardware implementation of PiForest and demonstrate that the approach is realistic and practicable in resource-constrained environments.

For systems of linear differential equations whose dimension can be lowered, we estimate the growth of meromorphic vector solutions. As an essentially new feature, we can mention the fact that no additional restrictions are imposed on the order of growth of the coefficients of the system.

Fault diagnosis which identifies the root of the abnormal status is of great importance to eliminate faults in the complex chemical processes. Many data-driven fault diagnosis models ignore different faults which occurred with varied frequencies in chemical plants, and they need a complete retraining process with the arrival of new fault modes. In this paper, a novel incremental imbalance modified convolutional neural network (II-CNN) is proposed to solve the aforementioned issues. The proposed method employs an imbalance modified method to extract the valuable information from the imbalance data, and generate new samples. After that, a local hyperplane-based dynamic Relief is designed to reduce the dimension of the chemical data and simplify the complex learning process. Finally, for the arrival of new fault modes, the proposed method is prompted in an incremental hierarchical way. Unlike the traditional models that are trained on static data, the proposed method inherits the existing knowledge and updates itself to include new coming fault classes. The proposed method is utilized in a simulated process and a real industrial process. Experimental results illustrate that the proposed method is better than the existing methods and has significant robustness and reliability in chemical fault diagnosis.

The fusion of tracks with different state spaces is referred to as heterogeneous track-to-track fusion (HT2TF). In this paper, we present novel approaches for HT2TF using Covariance Intersection (CI). The underlying idea is to augment the low dimensional track. We investigate whether the augmentation approaches proposed for mode mixing in the Interacting Multiple Model (IMM) algorithm can also be employed for the CI. As the augmentation influences the CI optimization, we compare different optimization variants. Finally, we evaluate the presented approaches for collective perception.

This paper investigates the algorithmic dimension spectra of lines in the Euclidean plane. Given any line L with slope a and vertical intercept b, the dimension spectrum sp ( L ) is the set of all effective Hausdorff dimensions of individual points on L. We draw on Kolmogorov complexity and geometrical arguments to show that if the effective Hausdorff dimension dim ( a , b ) is equal to the effective packing dimension Dim ( a , b ), then sp ( L ) contains a unit interval. We also show that, if the dimension dim ( a , b ) is at least one, then sp ( L ) is infinite. Together with previous work, this implies that the dimension spectrum of any line is infinite.

Deep convolutional neural networks have achieved remarkable progress in recent years. However, the large volume of intermediate results generated during inference poses a significant challenge to the accelerator design for resource-constraint FPGA. Due to the limited on-chip storage, partial results of intermediate layers are frequently transferred back and forth between on-chip memory and off-chip DRAM, leading to a non-negligible increase in latency and energy consumption. In this paper, we propose block convolution, a hardware-friendly, simple, yet efficient convolution operation that can completely avoid the off-chip transfer of intermediate feature maps at run-time. The fundamental idea of block convolution is to eliminate the dependency of feature map tiles in the spatial dimension when spatial tiling is used, which is realized by splitting a feature map into independent blocks so that convolution can be performed separately on individual blocks. We conduct extensive experiments to demonstrate the efficacy of the proposed block convolution on both the algorithm side and the hardware side. Specifically, we evaluate block convolution on 1) VGG-16, ResNet-18, ResNet-50, and MobileNet-V1 for ImageNet classification task; 2) SSD, FPN for COCO object detection task, and 3) VDSR for Set5 single image super-resolution task. Experimental results demonstrate that comparable or higher accuracy can be achieved with block convolution. We also showcase two CNN accelerators via algorithm/hardware co-design based on block convolution on memory-limited FPGAs, and evaluation shows that both accelerators substantially outperform the baseline without off-chip transfer of intermediate feature maps.

Approximate Nearest Neighbor(ANN) search is the core problem in many large-scale machine learning and computer vision applications such as multimodal retrieval. Hashing is becoming increasingly popular, since it can provide efficient similarity search and compact data representations suitable for handling such large-scale ANN search problems. Most hashing algorithms concentrate on learning more effective projection functions. However, the accuracy loss in the quantization step has been ignored and barely studied. In this paper, we analyse the importance of various projected dimensions, distribute them into several groups and quantize them with two types of values which can both better preserve the neighborhood structure among data. One is Variable Integer-based Quantization (VIQ) that quantizes each projected dimension with integer values. The other is Variable Codebook-based Quantization (VCQ) that quantizes each projected dimension with corresponding codebook values. We conduct experiments on five common public data sets containing up to one million vectors. The results show that the proposed VCQ and VIQ algorithms can both achieve much higher accuracy than state-of-the-art quantization methods. Furthermore, although VCQ performs better than VIQ, ANN search with VIQ provides much higher search efficiency.

Large-scale antenna array techniques are key enablers for modern wireless communication systems. Channel state information (CSI) is indispensable for large-scale multi-antenna systems, but is challenging to obtain. To tackle this issue, in this paper we propose a unified precoding and pilot design frame-work, that allows minimal and precoding-sensitive modified CSI (mCSI) to be collected. This results in a significant reduction in the CSI overheads and complexity compared to classical physical CSI (pCSI) estimation. Based on this unified framework, we further propose an intelligent pilot (IP) approach that senses and selects the mCSI to be collected. The IP approach utilizes a compressive sensing formulation to attach sensing and selection of significant mCSI to precoding optimization. We apply the above techniques to the multi-user frequency division duplexing (FDD) downlink as an example. Our study shows that the advantages of the IP approach are three-fold. First, in contrast to the pCSI, precoding-sensitive information is only captured, which reduces the training and feedback overheads. Second, the precoders are optimized directly based on the mCSI, which avoids recovering the pCSI of high-dimension. Third, since the mCSI of reduced dimension is utilized, the scale of the problem to optimize the precoder is also reduced and thus it is much easier to solve.

We show that the projection of a product of two or three Eisenstein series of level one onto the cuspidal subspace is not an eigenform unless the dimension of the cuspidal subspace is one.

Nonparametric Bayesian estimation subject to overidentified moment equations is a challenge because the support of the posterior is a manifold of lower dimension than the number of model parameters. The manifold therefore has Lebesgue measure zero thus inhibiting the use of the most commonly used Bayesian estimation method: MCMC (Markov Chain Monte Carlo). This study proposes an effective MCMC algorithm and algorithms for estimating scale and the normalizing constant. The algorithms are illustrated with two illustrative applications.

The high-precision parameters in distribution networks are dif-ficult to obtain, which brings difficulties to the model-based methods and analysis. With the widespread deployment of high-precision measurement units, data-driven methods have greater advantages in practice. In addition, with massive integra-tion of distributed renewable generation and fast electric vehicle chargers, the fluctuations of net load increase significantly. The data-driven power flow calculation method based on the linear model becomes difficult to obtain accurate results for the low nonlinear adaptability. To improve the data-driven power flow calculation accuracy under high penetration of renewable distri-bution generation, this paper proposed an approach with high adaptability to the nonlinearity of power flow. Based on the thought of Koopman operator theory, the nonlinear relationship in power flow calculation is converted into a linear mapping in a higher dimension state space, which can significantly improve the calculation accuracy. Case studies on different IEEE cases have demonstrated that the proposed method can realize higher accu-racy in power flow calculation with large-scale power fluctuations, compared to the existing data-driven method, in both mesh and radial networks. Finally, measurement data of a practical 10kV distribution network has been further used to verify the effec-tiveness of the proposed method in practical applications.

Based on the maximum entropy method with fractional exponential moments, a new method is proposed for deriving the probability density function of the limit state function for reliability calculation. Since numerical evaluation of fractional exponential moments is of critical significance, a novel transformed mixed-degree cubature rule is developed. First, the integral related to fractional exponential moment evaluation is transformed over the standard normal space, where the cubature rule can be employed for the numerical evaluation. Then, the spherical and radial rules with different degrees are combined to formulate a novel mixed-degree cubature rule, where the sample size only increases linearly with the dimension. To further enhance and improve the precision, a rotational transformation is performed over the samples produced by the mixed-degree cubature rule to obtain a transformed cubature rule, in which a proper angle needs to be specified. A two-step strategy is then put forward to specify the appropriate angle in the transformed mixed-degree cubature rule, where the standard deviations of input variables and KullbackLeibler divergence are used to formulate the objective functions. The proposed approach is verified via a set of numerical examples involving different types of problems.

Optimal sensor and actuator selection is a central challenge in high-dimensional estimation and control. Nearly all subsequent control decisions are affected by these sensor/actuator locations, and optimal placement amounts to an intractable brute-force search among the combinatorial possibilities. In this work, we exploit balanced model reduction and greedy optimization to efficiently determine sensor and actuator selections that optimize observability and controllability. In particular, we determine locations that optimize scalar measures of observability and controllability via greedy matrix QR pivoting on the dominant modes of the direct and adjoint balancing transformations. Pivoting runtime scales linearly with the state dimension, making this method tractable for high-dimensional systems. The results are demonstrated on the linearized Ginzburg-Landau system, for which our algorithm approximates known optimal placements computed using costly gradient descent methods., Comment: 8 pages, 6 figures

Made available in DSpace on 2021-06-25T11:03:26Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 In this work we propose a steady-state modeling of DC-DC converters driven by multiple pulse width modulation (PWM) signals. The proposed DC-DC converter modeling uses a switched state-space framework and the switching signal discontinuities is represented by a Fourier series to obtain the average model. The advantage of the proposed modeling is that it is not dependent on the DC-DC converter topology and the dimension of the state-space model is not increased with the number of storage devices. Additionally, the duty-cycle and phase displacements among the driving PWM signals can be arbitrary and systematically modeled. The effectiveness of the proposed modeling is supported by simulation and experimental results. Engenharia Eltrica, CEFET/RJ, 74351 Rio de Janeiro, Brazil, 20271-110 (e-mail: rafael.magossi@cefet-rj.br) IFSP, 119519 Sao Paulo, Brazil, 01109-010 (e-mail: leandro.elias@usp.br) Mathematics, UNESP, 28108 Ilha Solteira, SP, Brazil, 15385-000 (e-mail: flavio.faria@unesp.br) of Electrical Engeneering, University of So Paulo - USP, So Carlos, SP, Brazil, 13566-590 (e-mail: rquadros@sc.usp.br) Engenharia Eletrica, Universidade de Sao Paulo, 28133 Sao Paulo, Brazil, 05508-900 (e-mail: vilma@sc.usp.br)

This article presents a data-driven method for computing reachable sets where active learning (AL) is used to reduce the computational burden. Set-based methods used to estimate reachable sets typically do not scale well with the state-space dimension, or rely heavily on the existence of a model. If such a model is not available, it is simple to generate state trajectory data by numerically simulating black-box oracles of systems (whose dynamics are unknown) from sampled initial conditions. Using these data samples, the estimation of reachable sets can be posed as a classification problem, wherein AL can intelligently select samples that are most informative and least similar to previously labeled samples. By exploiting submodularity, the actively learned samples can be selected efficiently, with bounded suboptimality. Our proposed framework is illustrated by estimating the domains of attractions of model predictive controllers (MPCs) and reinforcement learners. We also consider a scenario where there are two oracles that differ with respect to evaluation costs and labeling accuracy. We propose a framework to reduce the dependency of the expensive oracle in labeling samples using disagreement-based AL (DBAL). The potential of the DBAL algorithm is demonstrated on a solver selection problem for real-time MPC.

The accurate forecast of the number of effective telegram frames (NETF) received by the balise transmission module (BTM) at different speeds is the key to assessing its speed adaptability. However, the NETF index has a complex correlation with multiple BTM transmission parameters and exhibits highly nonlinear variations in the speed dimension, which poses a challenge to the NETF series prediction. Thus, a deep-adaptive-attention-based model is proposed in this study, which combines two enhanced attention mechanisms into the long short-term memory encoder-decoder network to thoroughly explore the spatial-speed relationship between the historical multivariate series and the future NETF series. Specifically, the proposed model consists of an encoder with a dual-phase spatial-attention mechanism and a decoder with an adaptive speed-attention mechanism, which can jointly capture the stable spatial correlation and relevant long-term speed-dependent information of the input multivariate series across all speed steps for the robust and accurate prediction of the NETF series. Experimental results on real-world BTM datasets demonstrated that the proposed model can predict the NETF series with the lowest local predicted error and highest overall prediction accuracy, compared with other state-of-the-art attention-based methods.

The generalized Riccati equation mapping method (GREMM) is used in this paper to obtain different types of soliton solutions for nonlinear Schrodinger equation with higher dimension that existed in the regimes of anomalous dispersion. Later, we use the q-homotopy analysis method combined with the Laplace transform (q-HATM) to obtain approximate solutions of the bright and dark optical solitons. The q-HATM illustrates the solutions as a rapid convergent series. In addition, to show the physical behavior of the solutions obtained by the proposed techniques, the graphical representation has been provided with some parameter values. The findings demonstrate that the proposed techniques are useful, efficient and reliable mathematical method for the extraction of soliton solutions.

Recognizing and organizing different series in an MRI examination is important both for clinical review and research, but it is poorly addressed by the current generation of picture archiving and communication systems (PACSs) and post-processing workstations. In this paper, we study the problem of using deep convolutional neural networks for automatic classification of abdominal MRI series to one of many series types. Our contributions are three-fold. First, we created a large abdominal MRI dataset containing 3717 MRI series including 188,665 individual images, derived from liver examinations. 30 different series types are represented in this dataset. The dataset was annotated by consensus readings from two radiologists. Both the MRIs and the annotations were made publicly available. Second, we proposed a 3D pyramid pooling network, which can elegantly handle abdominal MRI series with varied sizes of each dimension, and achieved state-of-the-art classification performance. Third, we performed the first ever comparison between the algorithm and the radiologists on an additional dataset and had several meaningful findings.

Existing Orthogonal Frequency-Division Multiplexing (OFDM) variants based on cyclic prefix (CP) allow for efficient time synchronization, but suffer from lower power efficiency compared to zero-padded (ZP)-OFDM. Because of its power efficiency, ZP-OFDM is considered as an appealing solution for the emerging low-power wireless systems. However, in the absence of CP, time synchronization in ZP-OFDM is a challenging task. In this paper, the non-data-aided (NDA) maximum-likelihood (ML) time synchronization for ZP-OFDM is analytically derived. We show that the optimal NDA-ML synchronization algorithm offers a high lock-in probability and can be efficiently implemented using Monte Carlo sampling (MCS) technique in combination with golden-section search method in one dimension. To obtain the optimal NDA-ML time synchronization algorithm, we first derive a closed-form expression for the joint probability density function (PDF) of the received ZP-OFDM samples in frequency-selective fading channels. The derived expression is valid for doubly-selective fading channels with mobile users as well. The performance of the proposed synchronization algorithm is evaluated under various practical settings through simulation experiments. It is shown that the proposed optimal NDA-ML synchronization algorithm and its MCS implementation substantially outperforms existing NDA time synchronization algorithms in terms of lock-in probability and mean-squared error (MSE).

For a random sample of n iid p-dimensional vectors, each partitioned into b sub-vectors of dimensions pi, i=1,…,b, tests for zero correlation of sub-vectors are presented when pi ≫ n and the distribution need not be normal. The test statistics are composed of U-statistics based estimators of the Frobenius norm measuring the distance between the null and alternative hypotheses. Asymptotic distributions of the tests are provided for n,pi → ∞, with their finite-sample performance demonstrated through simulations. Some related tests are discussed. A real data application is also given.

Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood. It constructs an approximate posterior distribution by finding parameters for which the simulated data are close to the observations in terms of summary statistics. These statistics are defined beforehand and might induce a loss of information, which has been shown to deteriorate the quality of the approximation. To overcome this problem, Wasserstein-ABC has been recently proposed, and compares the datasets via the Wasserstein distance between their empirical distributions, but does not scale well to the dimension or the number of samples. We propose a new ABC technique, called Sliced-Wasserstein ABC and based on the Sliced-Wasserstein distance, which has better computational and statistical properties. We derive two theoretical results showing the asymptotical consistency of our approach, and we illustrate its advantages on synthetic data and an image denoising task., Comment: Accepted at ICASSP 2020 (publication and oral presentation)

Unlike the real line, the real space, in dimension d ≥ 2, is not canonically ordered. As a consequence, extending to a multivariate context fundamental univariate statistical tools such as quantiles, signs, and ranks is anything but obvious. Tentative definitions have been proposed in the literature but do not enjoy the basic properties (e.g., distribution-freeness of ranks, their independence with respect to the order statistic, their independence with respect to signs) they are expected to satisfy. Based on measure transportation ideas, new concepts of distribution and quantile functions, ranks, and signs have been proposed recently that, unlike previous attempts, do satisfy these properties. These ranks, signs, and quantiles have been used, quite successfully, in several inference problems and have triggered, in a short span of time, a number of applications: fully distribution-free testing for multiple-output regression, MANOVA, and VAR models; R-estimation for VARMA parameters; distribution-free testing for vector independence; multiple-output quantile regression; nonlinear independent component analysis; and so on.

We derive conditions for structural controllability of temporal networks that change topology and edge weights with time. The existing results for structural controllability of directed networks assume that all the edge weights are chosen independently of each other. The undirected case is challenging due to the constraints on the edge weights. We show that even with this additional restriction, the structural controllability results for the directed case are applicable to the undirected case. We further address two important issues; the first is optimizing the number of driver nodes to ensure the structural controllability of the temporal network. The second is to characterize the maximum reachable subspace when there are constraints on the number of driver nodes. Using the max-flow min-cut theorem, we show that the dimension of the reachable subspace is a submodular function of a set of driver nodes. Hence, we propose greedy algorithms with approximation guarantees to solve the above NP-hard problems. The results of the two case studies illustrate that the proposed greedy algorithm efficiently computes the optimum driver node set for both directed and undirected temporal networks

Car-following models have been widely applied and made remarkable achievements in traffic engineering. However, the traffic micro-simulation accuracy of car-following models in a platoon level, especially during traffic oscillations, still needs to be enhanced. Rather than using traditional individual car-following models, we proposed a new trajectory generation approach to generate platoon level trajectories given the first leading vehicle's trajectory. In this article, we discussed the temporal and spatial error propagation issue for the traditional approach by a car following block diagram representation. Based on the analysis, we pointed out that error comes from the training method and the model structure. In order to fix that, we adopt two improvements on the basis of the traditional LSTM-based car-following model. We utilized a scheduled sampling technique during the training process to solve the error propagation in the temporal dimension. Furthermore, we developed a unidirectional interconnected LSTM model structure to extract trajectories features from the perspective of the platoon. As indicated by the systematic empirical experiments, the proposed novel structure could efficiently reduce the temporal-spatial error propagation. Compared with the traditional LSTM-based car-following model, the proposed model has almost 40% less error. The findings will benefit the design and analysis of micro-simulation for platoon-level car-following models.

In order to effectively recognize and identify liquor quality by nuclear magnetic resonance (NMR) spectrum data, we propose a hybrid data processing and recognition algorithm. In this algorithm Kernel principal component analysis (KPCA) was used to remove the correlation and reduce dimension of the NMR spectrum, and then convolutional neural network (CNN) was used for classification and identification of the processed spectrum. The compared experiment result show that the proposed KPCA+CNN method can obtain higher accuracy than CNN and PCA+CNN methods based on the NMR spectrum. The KPCA+CNN algorithm has good application prospect and reference value.

This paper studies the Integrated Fault-Tolerant Control (IFTC) and Fault Estimation (FE) problem using Polynomial Fuzzy Model (PFM) based on the Sum Of Squares (SOS) approach. The integrated approach not only considers the bi-directional interaction between fault diagnosis and control units but also ensures an optimal response. The transient management in the fault occurrence situation and the alleviation of bi-directional interaction between FE and Fault-Tolerant Control (FTC) are the motivational challenges. However, the integrated approach increases the complexity and problem's dimension. In this situation, PFM can reduce the problem's dimensions and increase modeling accuracy. The additive actuator faults, input disturbance, and structured uncertainty are considered to bring the problem closer to practical applications. A polynomial fuzzy observer with unknown input is designed to estimate the time-varying faults that is used to remove the effects of faults on the control input. Thus, the proposed approach benefits from PFM in FE and FTC synthesis to reduce the design dimensions, have a more precise model, and make the synthesis less conservative. Moreover, the PFM is more robust against intense model uncertainties and faults. To evaluate the proposed approach, the FTC of an inverted-pendulum system is simulated. The fault-tolerant and fault estimation performances of the proposed approach are compared with those of the Linear Matrix Inequality (LMI) approach. The simulated results show that the proposed SOS approach outperforms the LMI approach.

In this paper, weighted extra-weak and weak type inequalities have been characterized for the one-sided Hardy–Littlewood maximal function on the plane. We have addressed conditions on pair of weights for which the dyadic one-sided maximal function on higher dimension is locally integrable. In the process, we characterize weights for which the one-sided Hardy-Littlewood maximal function satisfies restricted weak type inequalities on the plane, thus extending the result of Kerman and Torchinsky to the one-sided Hardy-Littlewood maximal function.

The key to hyperspectral anomaly detection is to effectively distinguish anomalies from the background, especially in the case that background is complex and anomalies are weak. Hyperspectral imagery (HSI) as an image-spectrum merging cube data can be intrinsically represented as a third-order tensor that integrates spectral information and spatial information. In this article, a prior-based tensor approximation (PTA) is proposed for hyperspectral anomaly detection, in which HSI is decomposed into a background tensor and an anomaly tensor. In the background tensor, a low-rank prior is incorporated into spectral dimension by truncated nuclear norm regularization, and a piecewise-smooth prior on spatial dimension can be embedded by a linear total variation-norm regularization. For anomaly tensor, it is unfolded along spectral dimension coupled with spatial group sparse prior that can be represented by the l2,1-norm regularization. In the designed method, all the priors are integrated into a unified convex framework, and the anomalies can be finally determined by the anomaly tensor. Experimental results validated on several real hyperspectral data sets demonstrate that the proposed algorithm outperforms some state-of-the-art anomaly detection methods.

In this work, we present linear exact repair schemes for one or two erasures in decreasing monomial-Cartesian codes DM-CC, a family of codes which provides a framework for polar codes. In the case of two erasures, the positions of the erasures should satisfy a certain restriction. We present families of augmented Reed-Muller (ARM) and augmented Cartesian codes (ACar) which are families of evaluation codes obtained by strategically adding vectors to Reed-Muller and Cartesian codes, respectively. We develop repair schemes for one or two erasures for these families of augmented codes. Unlike the repair scheme for two erasures of DM-CC, the repair scheme for two erasures for the augmented codes has no restrictions on the positions of the erasures. When the dimension and base field are fixed, we give examples where ARM and ACar codes provide a lower bandwidth (resp., bitwidth) in comparison with Reed-Solomon (resp., Hermitian) codes. When the length and base field are fixed, we give examples where ACar codes provide a lower bandwidth in comparison with ARM. Finally, we analyze the asymptotic behavior when the augmented codes achieve the maximum rate.

For the landing legs with single air chamber in the buffer structure of the reusable landing vehicle, the geometric topological models and the dynamic model associated with the hard points of the landing legs are established. The geometric constraint relationship in the design of the landing legs is also obtained. The whole vehicle dropping test is conducted, and the test results agree well with that of the simulation model, which validates the dynamic model. Based on the verified model, the effect of hard point positions on the performance of the landing system is analyzed. The multidisciplinary collaborative optimization algorithm and archive-based micro genetic algorithm (CO-AMGA) are used to optimize the design parameters that contain the hard points and the damper. Compared with artificial iteration, the maximum landing impact acceleration response of the vehicle and the buffer struct maximum force are reduced by 30.70% and 14.51% respectively, and the maximum length of retractable pillar decreases by 8.54% while the design margin increases by 69.11%. The proposed optimization method is efficient and can greatly facilitate the design of landing legs.

Individually fabrication forged parts and then joining them together through Laser Melting Deposition (LMD) is a viable way for manufacturing large components. For investigating the effect of the grain morphology of LMD joint, two 80mm-thick Ti6Al4V plates are successfully manufactured using three different scanning speeds (10, 15, and 20 mm/s). It is essential for understanding the thermal behaviour of melt pool during LMD to improve process quality. This study focuses on the energy density of heat source and the direction of heat flux, analyzing the effect of thermal behavior on the grain morphology and dimension of deposition area, equiaxed crystal zone (EQZ) and the substrate. The macrostructure is evaluated in the different thermal condition and scanning speeds. An extremely fine equiaxed crystal was observed near the joint boundary with a high temperature gradient and cooling rate. The curve epitaxial growth of fine columnar crystal rather than along straight lines is induced by the direction of heat conduction near the joint boundary. However, the orientation angle of epitaxial growth of the coarse columnar crystal is the same as previous deposition layer at the center of deposition area. Given the effect of high heat accumulation and low temperature gradient during LMD, the dimension of columnar crystal is coarsen significantly at the center of deposition area.

This article constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients as well as cross-sectional heteroscedasticity. The panel considered in this article features a large cross-sectional dimension N but short time series T. Due to the short T, traditional methods have difficulty in disentangling the heterogeneous parameters from the shocks, which contaminates the estimates of the heterogeneous parameters. To tackle this problem, I assume that there is an underlying distribution of heterogeneous parameters, model this distribution nonparametrically allowing for correlation between heterogeneous parameters and initial conditions as well as individual-specific regressors, and then estimate this distribution by combining information from the whole panel. Theoretically, I prove that in cross-sectional homoscedastic cases, both the estimated common parameters and the estimated distribution of the heterogeneous parameters achieve posterior consistency, and that the density forecasts asymptotically converge to the oracle forecast. Methodologically, I develop a simulation-based posterior sampling algorithm specifically addressing the nonparametric density estimation of unobserved heterogeneous parameters. Monte Carlo simulations and an empirical application to young firm dynamics demonstrate improvements in density forecasts relative to alternative approaches.

Detecting the number of signals (NOS) is a fundamental problem in array processing. However, most existing methods have a computational complexity of $O(M^3)$ , where M is the number of sensors. In this paper we propose a fast detection algorithm with a complexity of $O(DM^2)$ , where D is the number of signals. Different from the conventional approaches that depend on the eigenvalues of the sample covariance matrix (SCM), the proposed method is based on the Ritz values --- the eigenvalues of the projected SCM in Krylov subspace. We first present a scheme to construct the Krylov subspace via the Lanczos tridiagonalization. Then we prove that the first D Ritz values are equal to the $D$ signal eigenvalues. Furthermore, if the Krylov subspace has a dimension greater than D, the minimum Ritz value (MRV) converges to the noise power. Therefore, we can detect the NOS by comparing the MRV with a threshold determined by the noise power. Because the proposed algorithm does not perform eigenvalue decomposition, it has a simple implementation structure. Moreover, by using the Sylvester's law of inertia, the Ritz values do not need to be computed either. Simulation results show that the proposed algorithm outperforms the existing methods under the conditions of small sample size and low signal-to-noise ratio.

SUMMARY Background Cinematic Rendering (CR) is a recently introduced post-processing 3D-visualization imaging tool. The aim of this study was to assess its clinical value in the preoperative planning of deep inferior epigastric artery perforator (DIEP) or muscle-sparing transverse rectus abdominis myocutaneous (MS-TRAM) flaps, and to compare it to maximum intensity projection (MIP) images. The study presents the first application of CR for perforator mapping prior to autologous breast reconstruction. Methods Two senior surgeons independently analyzed CR and MIP images based on computed tomography angiography (CTA) datasets of 20 patients in terms of vascular pedicle characteristics, the possibility to harvest a DIEP or MS-TRAM flap, and the side of the flap harvest. We calculated inter- and intra-observer agreement in order to examine the accordance of both image techniques. Results We observed a good inter- and intra-observer agreement concerning the type of flap and the side of the flap harvest. However, the agreement on the pedicle characteristics varies depending on the considered variable. Both investigators identified a significantly higher number of perforators with MIP compared to CR (observer one p Conclusion The current study serves as an explorative study, showing first experiences with CR in abdominal-based autologous breast reconstruction. In addition to MIP images, CR might improve the surgeon's understanding of the individual's anatomy. Future studies are required to compare CR with other 3D visualization tools and its possible effects on operative parameters.