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Originated from the complex network theory, the Lagrangian flow network (LFN) has motivated various tools to analyze fluid flows' transport and mixing behaviors. These tools include the so-called finite time escape rate (FTER), the transition matrix, and also the finite time entropy. This paper proposes novel Eulerian algorithms to compute these LFN quantities in two dimensions based on a partial differential equation (PDE) formulation for the underlying flow map. Compared to the typical Lagrangian computations based on ray tracing, our numerical approaches are computationally efficient. Moreover, for velocity fields obtained numerically by computational fluid dynamic (CFD) solvers, our approach does not require further interpolation of the velocity field but can use the discrete velocity data directly. Finally, we will apply our proposed algorithms to several test examples, including a real-life dataset, to demonstrate the performance of the method. [ABSTRACT FROM AUTHOR]

• Method (modBIP) presented to pre-train random coefficients of extreme learning machines (ELM). • modBIP can work with any activation function and is more accurate than the BIP method. • ELM/modBIP is highly accurate and accuracy is insensitive to initial random coefficients. In extreme learning machines (ELM) the hidden-layer coefficients are randomly set and fixed, while the output-layer coefficients of the neural network are computed by a least squares method. The randomly-assigned coefficients in ELM are known to influence its performance and accuracy significantly. In this paper we present a modified batch intrinsic plasticity (modBIP) method for pre-training the random coefficients in the ELM neural networks. The current method is devised based on the same principle as the batch intrinsic plasticity (BIP) method, namely, by enhancing the information transmission in every node of the neural network. It differs from BIP in two prominent aspects. First, modBIP does not involve the activation function in its algorithm, and it can be applied with any activation function in the neural network. In contrast, BIP employs the inverse of the activation function in its construction, and requires the activation function to be invertible (or monotonic). The modBIP method can work with the often-used non-monotonic activation functions (e.g. Gaussian, swish, Gaussian error linear unit, and radial-basis type functions), with which BIP breaks down. Second, modBIP generates target samples on random intervals with a minimum size, which leads to highly accurate computation results when combined with ELM. The combined ELM/modBIP method is markedly more accurate than ELM/BIP in numerical simulations. Ample numerical experiments are presented with shallow and deep neural networks for function approximation and boundary/initial value problems with partial differential equations. They demonstrate that the combined ELM/modBIP method produces highly accurate simulation results, and that its accuracy is insensitive to the random-coefficient initializations in the neural network. This is in sharp contrast with the ELM results without pre-training of the random coefficients. [ABSTRACT FROM AUTHOR]

This paper proposes a novel technique that aims at detecting the effect of damage on structural frequency signals as "bad" outliers. To this end, a procedure is developed based on the Variational Mode Decomposition (VMD), Minimum Covariance Determinant (MCD), and Recurrent Neural Network (RNN) with Bi-directional Long-Short Term Memory (BiLSTM) cells. The VMD is first used in a pre-processing stage to denoise the signals and remove the seasonal patterns in them. Then, the proposed method seeks to learn the rules behind calculation of the Mahalanobis distances of the points from their distribution, using the parameters obtained from the MCD algorithm, through training an RNN on signals obtained from the inferior state of the structure (healthy state). It will be shown that, since the rule behind the effect of damage on the Mahalanobis distances has not been learnt by the trained RNN, the prediction errors of these values will increase significantly as soon as damage occurs using the data obtained from the posterior state of the structure (including damage). The performance of the proposed method is first tested on a numerical example and further validated through solving an experimental example of the Z24 bridge. Moreover, the proposed method is compared against a PCA-based method. The results demonstrate the superiority of the proposed method in long-term condition monitoring of civil infrastructures. The proposed method is an output-only condition monitoring method that requires only a couple of lowest structural natural frequency signals measured over a long-term monitoring of the structure. Therefore, it is recommended for cases when the measurements from the EOV are not available. Also the proposed method can be used along with other output-only or input-out methods to either improve or confirm the validity of their results. • A new condition monitoring method of the structures under EOV is proposed. • VMD is used to denoise the frequency signals while removing the seasonal pattern. • An RNN is trained to learn the rule behind calculation of the Mahalanobis distances. • The method is tested on both numerical and experimental examples. • The prediction error of Mahalanobis distance increases as damage occurs. [ABSTRACT FROM AUTHOR]

Variable selection for multi-sample problems is of great interest in statistics. Existing methods for addressing this problem have some limits or disadvantages. In this paper, we propose distance-based variable importance measures to deal with these problems, which are inspired by the Multi-response permutation procedure (MRPP), Energy distance (ED) and Distance components (DISCO) analysis. The proposed variable importance assessments can effectively measure the importance of an individual dimension by quantifying its influence on the differences between multivariate distributions across treatment groups. An importance-measure-based backward selection (IM-BWS) algorithm is developed that can be used in variable selection for multi-sample problems to discover important variables. We propose a modified MRPP based on the IM-BWS procedure for improving the power performance of the original MRPP. Our proposed methods are model-free, work for high-dimensional data, and can capture important variables under different models. Both simulations and real data applications demonstrate that our proposed method enjoys good properties and has advantages over other existing methods. [ABSTRACT FROM AUTHOR]

• Proposing a model for blind image deconvolution which adopts the L 0 -norm and total variation to regularize the latent image and blur kernel, respectively. • Proposing an automatic algorithm which can simultaneously estimate the intermediate sharp image, blur kernel and regularization parameters. • Proposing a model for non-blind image deconvolution which takes the mixture of L 0 -norm of image gradients and total variation for regularization. • Proposing an algorithm which is easy to be extended to solve the non-blind image deconvolution problem. This paper proposes a blind image deconvolution method which consists of two sequential phases, i.e., blur kernel estimation and image restoration. In the first phase, we adopt the L 0 -norm of image gradients and total variation (TV) to regularize the latent image and blur kernel, respectively. Then we design an alternating optimization algorithm which jointly incorporates the estimation of intermediately restored image, blur kernel and regularization parameters into account. In the second phase, we propose to take the mixture of L 0 -norm of image gradients and TV to regularize the latent image, and design an efficient non-blind deconvolution algorithm to achieve the restored image. Experimental results on both a benchmark image dataset and real-world blurred images show that the proposed method can effectively restore image details while suppress noise and ringing artifacts, the result is of high quality which is competitive with some state of the art methods. [ABSTRACT FROM AUTHOR]

• Interface-capturing is based on a second-order unstructured finite-volume method. • A central interpolation technique is applied for cell-faces approximations. • The numerical method is quite suitable to work within the signed distance function. • 2D and 3D benchmarks validate the capabilities and performance of the algorithm. • The interface accurately returns to its original shape for the single vortex problem. In this paper, we introduce a second-order unstructured finite-volume method developed to solve a conservative level-set equation in two- and three-dimensional geometries. The characteristic function for the flow is defined at the center of each cell while the face-normal velocities are calculated at the mid-points of the corresponding cell faces. An interpolation method independent of cell shape and based on a central scheme is applied for the approximations at the cell faces. The capabilities and performance of the proposed scheme are validated using several 2D and 3D tests. Results show that the present numerical method is quite suitable for working within the smooth framework resulting from the signed distance function. Moreover, this method yields very accurate results in interface-capturing problems such as the single vortex deformation. [ABSTRACT FROM AUTHOR]

• A new algorithm incorporates model reduction in the spatial and temporal domains. • We study parabolic problems with heterogeneous coefficients and rough initial data. • We derive convergence analysis that weakly depends on the heterogeneous coefficients. • The convergence is rigorously studied, which greatly improves the current result. • Extensive numerical tests are performed to show the fast convergence of our algorithm. We propose in this paper the Wavelet-based Edge Multiscale Parareal (WEMP) Algorithm to solve parabolic equations with heterogeneous coefficients efficiently. This algorithm combines the advantages of multiscale methods that can deal with heterogeneity in the spatial domain effectively, and the strength of parareal algorithms for speeding up time evolution problems when sufficient processors are available. We derive the convergence rate of this algorithm in terms of the mesh size in the spatial domain, the level parameter used in the multiscale method, the coarse-scale time step and the fine-scale time step. Extensive numerical tests are presented to demonstrate the performance of our algorithm, which verify our theoretical results perfectly. [ABSTRACT FROM AUTHOR]

• Hybrid flux algorithm to find fluxes across cell faces in same phase and different phase. • Resolves the profiles where both smooth and discontinuous solutions coexist. • Automatically adjust the weight between MUSCL and THINC schemes using a slope-weight parameter. This paper presents a single-pressure-field two-fluid model with finite-volume discretization to solve the equations of motion of compressible multiphase flows. To capture the discontinuities caused by shock waves and fluid interfaces, we propose a generalized discontinuity sharpening technique that combines the conventional monotonic upstream scheme for conservation law (MUSCL) and tangent of hyperbola interface capturing (THINC) schemes. In addition, a slope ratio-weighted parameter, ζ , is used to control the proportion of values reconstructed by MUSCL and THINC, and we show that the present method can retain sharp interfaces when the value of the parameter β in the THINC scheme is set ranging from 1.6 to 3.0. Fluxes across various interfaces are evaluated using a hybrid AUSMD-type flux algorithm, where the mass flux and pressure induced on the cell faces are calculated using an approximate Riemann solver. The accuracy and robustness of the proposed method are validated by solving a series of one- and two-dimensional single-phase flows. Furthermore, complex wave patterns arising from two-dimensional shock bubble/water-column interactions are examined, which indicate that compared with the existing schemes applied to two-fluid modeling, the proposed scheme significantly sharpens the interfaces and captures more details of the flow features. Finally, simulations of a three-dimensional example of the liquid jet crossflow are conducted. The proposed scheme shows more details of the fluid interface, including the interfacial instabilities on the windward side of the liquid jet and droplet formation due to the breakup phenomenon in the downstream of the crossflow, than the existing schemes. [ABSTRACT FROM AUTHOR]

• A neural network enhanced Boltzmann model is proposed. • The mechanical and neural models are unified into a differentiable architecture and the neural- ODE-type training strategy is constructed. • A general numerical scheme is designed to solve the universal Boltzmann equation. • Numerical experiments of homogeneous and inhomogeneous systems are provided to validate the current method. One of the biggest challenges for simulating the Boltzmann equation is the evaluation of fivefold collision integral. Given the recent successes of deep learning and the availability of efficient tools, it is an obvious idea to try to substitute the calculation of the collision operator by the evaluation of a neural network. However, it is unlcear whether this preserves key properties of the Boltzmann equation, such as conservation, invariances, the H-theorem, and fluid-dynamic limits. In this paper, we present an approach that guarantees the conservation properties and the correct fluid dynamic limit at leading order. The concept originates from a recently developed scientific machine learning strategy which has been named "universal differential equations". It proposes a hybridization that fuses the deep physical insights from classical Boltzmann modeling and the desirable computational efficiency from neural network surrogates. The construction of the method and the training strategy are demonstrated in detail. We conduct an asymptotic analysis and illustrate the multi-scale applicability of the method. The numerical algorithm for solving the neural network-enhanced Boltzmann equation is presented as well. Several numerical test cases are investigated. The results of numerical experiments show that the time-series modeling strategy enjoys the training efficiency on this supervised learning task. [ABSTRACT FROM AUTHOR]

• First dynamical low-rank algorithm that is mass, momentum, and energy conservative. • Can be combined with an explicit integrator that maintains conservation. • Conserves the underlying continuity equations in addition to the invariants. • Low-rank breaks the curse of dimensionality for high-dimensional kinetic equations. The primary challenge in solving kinetic equations, such as the Vlasov equation, is the high-dimensional phase space. In this context, dynamical low-rank approximations have emerged as a promising way to reduce the high computational cost imposed by such problems. However, a major disadvantage of this approach is that the physical structure of the underlying problem is not preserved. In this paper, we propose a dynamical low-rank algorithm that conserves mass, momentum, and energy as well as the corresponding continuity equations. We also show how this approach can be combined with a conservative time and space discretization. [ABSTRACT FROM AUTHOR]

The maintenance and monitoring of skin health have been an issue of concern for a long time. While many technologies for daily skin monitoring have been proposed, these methods only have the capability to inspect superficial skin layers. Ultrasonic elastography has been used for measuring the stiffness of deeper soft tissue, however, the current technologies are not compatible with the mobile environment. In this paper, we present a solution for quantitative ultrasonic elastography compatible with mobile environments utilizing ubiquitous sound sources, such as voices of the users or music played by a smartphone for remotely palpating the deep skin layer. In addition, instead of using an array ultrasound, the proposed technology only requires a single-element transducer to perform repetitive ultrasonic transmissions and receptions of the tracking echo signals. The proposed algorithm could successfully quantify the stiffness of tissue phantoms and skin regions in human subjects. The performance of the proposed elastography was comparable with that of a commercial system. Owing to the reduction in hardware requirements, single-beam tracking elastography could be integrated into current mobile devices. [ABSTRACT FROM AUTHOR]

Structural repairs alter the physical properties of a structure, changing its responses, both in terms of its normal condition and of its different damage states. This difference in responses manifests itself as a shift between the pre- and post-repair data distributions, which can be problematic for conventional data-driven approaches to structural health monitoring (SHM), and limits their effectiveness in industrial applications. This limitation occurs typically because approaches assume that the data distribution is the same in training as appears in testing; with an algorithm failing to generalise when this assumption is not true; that is, pre-repair labels no longer apply to the post-repair data. Transfer learning, in the form of domain adaptation, proposes a solution to this issue, by mapping the pre- and post-repair data distributions onto a shared latent space where their distributions are approximately equal, allowing pre-repair label knowledge to be used to classify the post-repair data. This paper demonstrates the applicability of domain adaptation as a method for overcoming the problem of repair on a dataset from a Gnat trainer aircraft. In addition, a novel modification to an existing domain adaptation technique – joint distribution adaptation – is proposed, which seeks to improve the semi-supervised learning phase of the algorithm by considering a metric-informed procedure. The metric-informed joint distribution adaptation algorithm is benchmarked against, and shown to outperform, both conventional data-based approaches and other domain adaptation techniques. • Proposes, for the first time, the application of domain adaptation to overcome the problem of repair in data-based structural health monitoring. • A novel modification to joint domain adaptation, named metric-informed joint domain adaptation , improves initial pseudo-label estimates for the target dataset. • Demonstrates existing and proposed domain adaptation techniques on a repair problem for a Gnat trainer aircraft wing. [ABSTRACT FROM AUTHOR]

Current approaches in the automotive domain to predict booming noise essentially target extreme loading conditions. This is useful when mechanical strength is of concern, but not representative of the actual vehicle usage. Usage is however important when addressing acoustic annoyance. One issue in this respect is the lack of databases representative of the diversity of client usages and, therefore, of the excitation forces applied to the vehicle in real usage conditions. This paper introduces a possible answer to this problem. First, it proposes a measurement protocol to estimate the excitation forces in real usage responsible of booming noise. Second, it provides an array of algorithms to analyze the large amount of data collected during the measurement step. In particular, an ad hoc Independent Component Analysis algorithm is introduced to extract excitation components specific to well-defined operating condition regions, thus providing insights in the excitation behaviour, as well as data reduction. Next, the excitation components are modelled as a function of the vehicle operating conditions with a Radial Basis Function network. A meta-model of the excitations in real usage conditions is thus obtained, composed of two levels: the analysis level and the modelling level. The proposed methodology is developed in the Bayesian framework. In addition to its advantages linked to the trivial introduction of prior knowledge, the Bayesian framework is found particularly useful for propagating uncertainties throughout the successive steps of the approach. [ABSTRACT FROM AUTHOR]

Model checking (MC) for Halpern and Shoham's interval temporal logic HS has been recently shown to be decidable. An intriguing open question is its exact complexity for full HS : it is at least EXPSPACE -hard, and the only known upper bound, which exploits an abstract representation of Kripke structure paths (descriptor), is non-elementary. In this paper, we provide a uniform framework to MC for full HS and meaningful fragments of it, with a specific type of descriptor for each fragment. Then, we devise a general MC alternating algorithm, parameterized by the descriptor's type, which has a polynomially bounded number of alternations and whose running time is bounded by the length of minimal representatives of descriptors (certificates). We analyze its complexity and give tight bounds on the length of certificates. For two types of descriptor, we obtain exponential upper and lower bounds; for the other ones, we provide non-elementary lower bounds. [ABSTRACT FROM AUTHOR]

A Simple Temporal Network (STN) consists of time points modeling temporal events and constraints modeling the minimal and maximal temporal distance between them. A Simple Temporal Network with Decisions (STND) extends an STN to model temporal plans with decisions. STNDs label time points and constraints by conjunctions of literals saying for which scenarios (i.e., complete truth value assignments to the propositions) they are relevant. In this paper, we deal with the use of STNDs for modeling and synthesizing execution strategies. We propose an incremental hybrid SAT-based consistency checking algorithm for STNDs that is faster than the one previously proposed and allows for the synthesis of all consistent scenarios and related early execution schedules (offline temporal planning). We carry out an experimental evaluation with Kappa, a tool that we developed for STNDs. We also show that any STND can be easily translated into a disjunctive temporal network and vice versa. [ABSTRACT FROM AUTHOR]

Quasi-subfield polynomials were introduced by Huang et al. together with a new algorithm to solve the Elliptic Curve Discrete Logarithm Problem (ECDLP) over finite fields of small characteristic. In this paper we provide both new quasi-subfield polynomial families and a new theorem limiting their existence. Our results do not allow to derive any speedup for the new ECDLP algorithm compared to previous approaches. [ABSTRACT FROM AUTHOR]

• The minimum MCMSA problem is proposed and investigated in EODCNs. • Various proportions of secure services are considered in the proposed problem. • An ILP and a heuristic algorithm are developed for the DST-MCMSA scheme. • We compare the performance of the proposed methods with other existing methods. Multicast provisioning is important for supporting applications such as data backup and cloud computing in elastic optical data center networks (EODCNs). As the traffic demand from these applications is quickly increasing, it is important to reduce the resource usage and implement cost-effective multicasting. By mapping multiple traffic flows into a single light-tree, the light-tree-based multicast service aggregation can reduce the number of required transceivers while accommodating many multicast requests. However, it may increase the spectrum usage, and it is not always feasible because some subscription-based or secure multicast services in the network should be sent only to authorized users. In this paper, we focus on the problem of routing, modulation level selection, spectrum, and transceiver assignment for multicast services with different security levels. We study potential performance gains resulting from applying a distributed sub-tree scheme to multi-class multicast service aggregation, realizing reduced overall cost. By formulating the mathematical models using an integer linear program (ILP), we obtain results for a small-scale network. We also propose a heuristic algorithm for large-scale networks, and results show that our proposed algorithm can be beneficial in terms of both spectrum and transceiver utilization in EODCNs. [ABSTRACT FROM AUTHOR]

• Spectrum fragmentation in SDM-EON causes non-aligned slots or non-adjacent slots to fragment bandwidth. • The fragmentation problem increases blocking probability due to scattering of frequency slots. • We propose three novel fragmentation-aware algorithms. • The algorithms improve spectrum utilization and reduce blocking probability under multi-core fiber. • We reduce fragmentation by considering both core classification and block cost function in SDM-EONs. Space Division Multiplexing Elastic Optical Networks (SDM-EONs) has been presented to provide flexibility and high speed transmission to increase network capacity and optimal use of network resources. Dynamic establishment and release of connections causes fragmentation in EONs, thus increasing blocking probability as a result of increased fragmentation. In this paper, we present three novel algorithms that focus on how to allocate resources in SDM-EONs under Multi-Core Fiber (MCF) in order to reduce blocking probability by reducing bandwidth fragmentation. The Core Classification Aware of Fragmentation (CCAF) is a method based on the core classification mechanism that uses the concept of demand splitting and a cost function to determine the appropriate spectral space for a new request. The Fragmentation Aware Spectrum and Core Assignment (FASCA-without priority) is a routing and spectrum allocation method that uses a cost function to specify the appropriate spectral space to improve spectrum efficiency and reduce fragmentation. The Fragmentation Aware Spectrum and Core Assignment (FASCA-with priority) is a routing and spectrum allocation method that considers priority of requests to improve spectrum efficiency and reduce fragmentation. Simulation results show that the proposed CCAF algorithm can achieve better performance compared with the FASCA-without priority and FASCA-with priority in terms of blocking probability and spectrum utilization. Moreover, FASCA-without priority can provide better performance than FASCA-with priority. [ABSTRACT FROM AUTHOR]

Enhancers are regulatory elements involved in gene expression.It is a part of DNA, which can enhance the transcription rate of gene. However, the identification of enhancer by biological experimental methods is time-consuming and expensive. Therefore, there is an urgent need for more efficient methods to identify them.In this study, we propose a new feature extraction method RKPK, which combines three feature methods and uses the recursive feature elimination algorithm for feature selection, and apply deep neural network as classifier to construct the iEnhancer-RD calculation method for enhancer identification. It is a two-layer classification architecture in which the first layer(layer I) identifies enhancers from a set of DNA sequences, and the second layer(layer II) divides the identified enhancers into two subgroups, namely strong and weak enhancers. Independent dataset test indicates that the proposed method is significantly better than most existing methods, and attains the accuracy of 78.8% and 70.5% in the two layers, respectively. Our iEnhancer-RD architecture is implemented in Python and is available at https://github.com/YangHuan639/iEnhancer-RD. [Display omitted] • In this paper, we propose iEnhancer-RD, a method to identify enhancers and their strength. • We propose new feature methods KPCV and RKPK, and compare and analyze these two representations. • Deep neural network (DNN) is used as our classififier. • Optimize several feature methods and the parameters of the constructed deep neural network. • Compared with other work, this method has signifificant improvement in most performance indicators. [ABSTRACT FROM AUTHOR]

• A sign language recognition framework based on global-local attention, including: • A Multiple Group Random Segment Sampling algorithm to extract the key frame clips; • A global Attention-Model based ResC3D network with an improved timing conversion layer; • A local SLR algorithm based on Mask RCNN hand detection network. Video-level sign language recognition is still a challenging task due to the influence of sign language-independent factors and timing requirements. This paper constructs a sign language recognition framework based on global-local feature description, and proposes a three-dimensional residual global network model with attention layer and a local network model based on target detection. The global feature description is based on the whole video behavior for time series modeling. The improved timing conversion layer is used to explore the timing information of different periods and learn the video representations of different timings. In the local module the hand is located through the target detection network to highlight its key role in the whole sign language behavior, which strengthens the category differences, and compensates the global network. Experiments on two well-known Chinese sign language datasets (SLR_Dataset and DEVSIGN_D) show that the proposed method can obtain higher recognition accuracy (respectively 89.2%, 91%) and better generalization performance. [ABSTRACT FROM AUTHOR]

• The modified artificial fish swarm algorithm is proposed to optimize the network. • Elliptical strain sensing model of fiber Bragg grating sensor is established. • The exponential attenuation model between sensor and the signal source is constructed. • Attenuation factors of the three adaptive step size are introduced into the algorithm. • The performance of the modified algorithm is better than PSO and GA. The effectiveness of sensor networks depends largely on the coverage provided by sensor deployment schemes. In order to improve the coverage rate of sensor network, it is necessary to optimize the sensor layout. In this paper, an optimized layout method of fiber Bragg grating (FBG) sensor network based on modified artificial fish swarm algorithm (MAFSA) is proposed. Firstly, the elliptical sensing model of FBG is established. According to the transfer characteristics of FBG, the exponential attenuation model between the sensor node and signal source is constructed. Secondly, taking the network coverage rate as the objective function, the movement of nodes is analogized to the behavior of artificial fish such as swarming, following and preying. In the process of status updating for artificial fish, aiming at the defects of the standard artificial fish swarm algorithm (AFSA) in optimization, three adaptive step methods are proposed. The convergence accuracy and speed of the algorithm are improved. Finally, the performance of the MAFSA is evaluated with particle swarm optimization algorithm and genetic algorithm. The results indicate that the MASFA has stronger search performance and can make the sensor node layout more reasonable, which is suitable for solving the problem of the sensor layout optimization. [ABSTRACT FROM AUTHOR]

• Behavioral reaction-time task elicits electrocorticographic broadband gamma response. • Functional connectivity analysis reveals cortical response trajectories. • Subthreshold broadband gamma activity predicts cortical onset time. • Onset times accumulated across trajectories explain behavioral reaction-time variance. [Display omitted] Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Using a previously described technique that can identify the onset of population-level cortical activity and a novel functional connectivity algorithm described herein, we show that the cumulative latency difference of population-level neural activity across the task-related cortical network can explain up to 41% of the trial-by-trial variance in reaction time. Furthermore, we show that reaction time variance may primarily be due to the latencies in specific brain regions and demonstrate that behavioral latency variance is accumulated across the whole task-related cortical network. Our results suggest that population-level neural activity monotonically increases prior to movement execution, and that trial-by-trial changes in that increase are, in part, accounted for by inhibitory activity indexed by low-frequency oscillations. This pre-movement neural activity explains 19% of the measured variance in neural latencies in our data. Thus, our study provides a mechanistic explanation for a sizable fraction of behavioral reaction time when the subject's task is the same from trial to trial. [ABSTRACT FROM AUTHOR]

Lorentz invariant structure-preserving algorithms possess reference-independent secular stability, which is vital for simulating relativistic multi-scale dynamical processes. The splitting method has been widely used to construct structure-preserving algorithms, but without exquisite considerations, it can easily break the Lorentz invariance of continuous systems. In this paper, we introduce a Lorentz invariant splitting technique to construct high order explicit Lorentz invariant volume-preserving algorithms (LIVPAs) for simulating charged particle dynamics. Using this method, long-term stable explicit LIVPAs with different orders are developed and their performances of Lorentz invariance and long-term stability are analyzed in detail. [ABSTRACT FROM AUTHOR]

The ball number of a link L , denoted by b a l l (L) , is the minimum number of solid balls (not necessarily of the same size) needed to realize a necklace representing L. In this paper, we show that b a l l (L) ≤ 5 c r (L) where c r (L) denotes the crossing number of a nontrivial nonsplittable link L. To this end, we use the connection of the Lorentz geometry with the ball packings. The well-known Koebe–Andreev–Thurston circle packing Theorem is also an important brick for the proof. Our approach yields an algorithm to construct explicitly the desired necklace representation of L in R 3. [ABSTRACT FROM AUTHOR]

A variant of adaptive arithmetic coding is proposed, adding cryptographic features to this classical compression method. The idea is to perform the updates of the frequency tables for characters of the underlying alphabet selectively, according to some randomly chosen secret key K. We give empirical evidence that with reasonably chosen parameters, the compression performance is not hurt, and discuss also aspects of how to improve the security of the system being used as an encryption method. To keep the paper self-contained, we add a short description of the arithmetic coding algorithm that is necessary to understand the details of the new suggested method. [ABSTRACT FROM AUTHOR]

Alahmadi et al. (2017) [2] introduced the notion of twisted centralizer codes, C F q (A , γ) , defined as C F q (A , γ) = { X ∈ F q n × n : A X = γ X A } , for A ∈ F q n × n , and γ ∈ F q. Moreover, Alahmadi et al. (2017) [3] also investigated the dimension of such codes and obtained upper and lower bounds for the dimension, and the exact value of the dimension only for cyclic or diagonalizable matrices A. Generalizing and sharpening Alahmadi et al.'s results, in this paper, we determine the exact value of the dimension as well as provide an algorithm to construct an explicit basis of the codes for any given matrix A. [ABSTRACT FROM AUTHOR]

Let E ⊂ R 2 be a finite set, and let f : E → 0 , ∞). In this paper, we address the algorithmic aspects of nonnegative C 2 interpolation in the plane. Specifically, we provide an efficient algorithm to compute a nonnegative C 2 (R 2) extension of f with norm within a universal constant factor of the least possible. We also provide an efficient algorithm to approximate the trace norm. [ABSTRACT FROM AUTHOR]

Passive optical network (PON) with online polling-based dynamic bandwidth allocation (DBA) algorithms effectively reduces the granting and back-off delays as well as the overall end-to-end delay of the PON with offline polling-based DBA schemes. However, in the online polling-based DBA schemes, distribution of excess bandwidths from the lightly loaded optical network units (ONUs) to the highly loaded ONUs is not possible. In both the conventional online and offline polling-based DBA schemes, the optical line terminal (OLT) assigns the time-slots to the ONUs for upstream data transmissions based on the first-come-first-serve principle. That is why the highly loaded ONUs those remain behind the lightly loaded ONUs should be waited until the end of the upstream data transmissions of all the preceding ONUs. Due to this longer waiting time before getting the chance to transmit the upstream data increases the possibility of buffer overflow of the highly loaded ONUs. In this paper, a new cluster-based PON architecture with a dynamic upstream data transmission sequence (DUDTS) algorithm is introduced. In the cluster-based PON with DUDTS scheme, all the active ONUs of a PON system are divided into several clusters. Then the data transmission sequence of the ONUs in each cluster is dynamically changed to give higher priority to the highly loaded ONUs for assigning the earlier time-slots than the lightly loaded ONUs. The DUDTS scheme also considers the traffic incoming statuses in the ONUs' buffer, distributes the excess bandwidth from the lightly loaded ONUs to the highly loaded ONUs within a cluster, and adds the unutilized bandwidth of a cluster to the ONUs of the following cluster. Numerical simulations are performed to evaluate and compare the performances among the proposed cluster-based PON with DUDTS scheme and the existing dynamic hybrid slot-size bandwidth allocation (DHSSBA) algorithm to demonstrate the effectiveness of the proposed scheme. The cluster-based PON with DUDTS scheme provides better results in terms of the grant to request ratio, end-to-end delay, packet loss probability, and fairness compared to the existing DHSSBA scheme. [ABSTRACT FROM AUTHOR]

• A new method to mine driving patterns from drivers' behavioral signals was proposed. • The most useful combination of segmentation and clustering methods was obtained. • Our extracted driving patterns were descriptive in the maneuver level. • Our proposed methodology could help effective online prediction of driving behavior. Understanding drivers' behavioral characteristics is critical for the design of decision-making modules in autonomous vehicles (AVs) and advanced driver assistance systems (ADASs). Current relevant studies are mainly based on supervised learning methods which involve extensive human efforts in model development. This paper proposed a framework for automatic descriptive driving pattern extraction from driving sequence data using unsupervised algorithms. Based on the Bayesian multivariate linear regression model, two unsupervised algorithms were utilized to segment driving sequences into fragments. Three extended latent Dirichlet allocation models were applied to cluster the fragments into multiple descriptive driving patterns. The collected driving data from a naturalistic driving experiment was applied to examine the effectiveness of our proposed framework. Results show that the unsupervised segmentation algorithms could help effectively detect the switch characteristics between two continuous driving maneuvers along time, and the clustered patterns could effectively describe the characteristics of each driving maneuver. The proposed unsupervised framework provides an effective and efficient data mining solution to facilitating deep and comprehensive understanding on drivers' behavioral characteristics, which will benefit the development of AVs and ADASs. [ABSTRACT FROM AUTHOR]

Expectiles are the solution to an asymmetric least squares minimization problem for univariate data. They resemble the quantiles, and just like them, expectiles are indexed by a level α in the unit interval. In the present paper, we introduce and discuss the main properties of the (multivariate) expectile regions, a nested family of sets, whose instance with level 0 < α ≤ 1 ∕ 2 is built up by all points whose univariate projections lie between the expectiles of levels α and 1 − α of the projected dataset. Such level is interpreted as the degree of centrality of a point with respect to a multivariate distribution and therefore serves as a depth function. We propose here algorithms for determining all the extreme points of the bivariate expectile regions as well as for computing the depth of a point in the plane. We also study the convergence of the sample expectile regions to the population ones and the uniform consistency of the sample expectile depth. Finally, we present some real data examples for which the Bivariate Expectile Plot (BExPlot) is introduced. [ABSTRACT FROM AUTHOR]

The Heckman selection model is perhaps the most popular econometric model in the analysis of data with sample selection. The analyses of this model are based on the normality assumption for the error terms, however, in some applications, the distribution of the error term departs significantly from normality, for instance, in the presence of heavy tails and/or atypical observation. In this paper, we explore the Heckman selection-t model where the random errors follow a bivariate Student's-t distribution. We develop an analytically tractable and efficient EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters, with standard errors as a by-product. The algorithm has closed-form expressions at the E-step, that rely on formulas for the mean and variance of the truncated Student's-t distributions. Simulation studies show the vulnerability of the Heckman selection-normal model, as well as the robustness aspects of the Heckman selection-t model. Two real examples are analyzed, illustrating the usefulness of the proposed methods. The proposed algorithms and methods are implemented in the new R package HeckmanEM. [ABSTRACT FROM AUTHOR]

• We develop a detection algorithm and analyze results for different occluded sizes. • We selectively drop objects' features to enrich the occluded scenes. • We learn the implicit mapping to recover occlude noise to real feature. • We verify the effect of GC-FRCN and apply it to the in-filed Rice Panicles Counting. Detecting the object with external occlusion has always been a hot topic in computer version, while its accuracy is always limited due to the loss of original object information and increase of new occlusion noise. In this paper, we propose a occluded object detection algorithm named GC-FRCN (Generative feature completing Faster RCNN), which consists of the OSGM (Occlusion Sample Generation Module) and OSIM (Occlusion Sample Inpainting Module). Specifically, the OSGM mines and discards the feature points with high category response on the feature map to enhance the richness of occlusion scenes in the training data set. OSIM learns an implicit mapping relationship from occluded feature map to real feature map adversarially, which aims at improving feature quality by repair the noisy object feature. Extensive experiments and ablation studies have been conducted on four different datasets. All the experiments demonstrate the GC-FRCN can effectively detect objects with local external occlusion and has good robustness for occlusion at different scales. [ABSTRACT FROM AUTHOR]

The axial vibration of the liquid-filled pipes can be represented with a four-equation fluid–structure interaction (FSI) model. When distributed friction was neglected, this model could be solved with an exact solution without computational grid, while the only weakness was the high time cost. A fast meshless solution (FMS) developed earlier used the time-line interpolation rather than the recursive algorithm, to speed the calculation. For the purpose of practical applications, the FMS for supports at pipe end and middle position is proposed, and series connection is also studied in this paper. The support is described by the complex constraint including elastic, damping and inertial effects. A numerical case of the double-pipe water hammer is employed to validate the series connection and end supports. The FMS for the middle support is validated by an existing experiment, where the support can be described by the dry friction. Both numerical and experimental cases indicate the effectiveness and the efficiency of the proposed solution method. • The fast meshless solution for the double pipe is proposed. • Complex constraints at pipe supports are incorporated and solved. • Calculation efficiency is improved significantly and high accuracy is achieved. [ABSTRACT FROM AUTHOR]

• We present the SSLE approach for Bayesian model inversion based on SSE and SLE. • Posterior quantities are obtained analytically from the SSLE coefficients. • The original SSE algorithm is enhanced with an active learning enrichment scheme. • This method generalizes and drastically improves the efficiency of the SLE approach. • We showcase the method on three problems of different complexity and dimensionality. In this paper we propose a new sampling-free approach to solve Bayesian model inversion problems that is an extension of the previously proposed spectral likelihood expansions (SLE) method. Our approach, called stochastic spectral likelihood embedding (SSLE), uses the recently presented stochastic spectral embedding (SSE) method for local spectral expansion refinement to approximate the likelihood function at the core of Bayesian inversion problems. We show that, similar to SLE, this approach results in analytical expressions for key statistics of the Bayesian posterior distribution, such as evidence, posterior moments and posterior marginals , by direct post-processing of the expansion coefficients. Because SSLE and SSE rely on the direct approximation of the likelihood function, they are in a way independent of the computational/mathematical complexity of the forward model. We further enhance the efficiency of SSLE by introducing a likelihood specific adaptive sample enrichment scheme. To showcase the performance of the proposed SSLE, we solve three problems that exhibit different kinds of complexity in the likelihood function: multimodality, high posterior concentration and high nominal dimensionality. We demonstrate how SSLE significantly improves on SLE, and present it as a promising alternative to existing inversion frameworks. [ABSTRACT FROM AUTHOR]