Search

This article is dedicated to investigating the impulsive-based almost surely synchronization issue of neural network systems (NSSs) with quality-of-service constraints. First, the communication network considered suffers from random double deception attacks, which are modeled as a nonlinear function and a desynchronizing impulse sequence, respectively. Meanwhile, the impulsive instants and impulsive gains are randomly and only their expectations are available. Second, by taking two different types of random deception attacks into consideration, a novel mathematical model for vulnerable NSSs is constructed. Then, almost surely synchronization criteria are established by using Borel-Cantelli lemma. Furthermore, based on the derived strong and weak sufficient conditions, the almost surely synchronization of NSSs is achieved. Finally, the section of numerical example is shown to illustrate the effectiveness of the proposed method.

In this brief, stabilization of Boolean networks (BNs) by flipping a subset of nodes is considered, here we call such action state-flipped control. The state-flipped control implies that the logical variables of certain nodes are flipped from 1 to 0 or 0 to 1 as time flows. Under state-flipped control on certain nodes, a state-flipped-transition matrix is defined to describe the impact on the state transition space. Weak stabilization is first defined and then some criteria are presented to judge the same. An algorithm is proposed to find a stabilizing kernel such that BNs can achieve weak stabilization to the desired state with in-degree more than 0. By defining a reachable set, another approach is proposed to verify weak stabilization, and an algorithm is given to obtain a flip sequence steering an initial state to a given target state. Subsequently, the issue of finding flip sequences to steer BNs from weak stabilization to global stabilization is addressed. In addition, a model-free reinforcement algorithm, namely the Q-learning (QL) algorithm, is developed to find flip sequences to achieve global stabilization. Finally, several numerical examples are given to illustrate the obtained theoretical results.

Nonoccurring behavior (NOB) studies have attracted the growing attention of scholars as a crucial part of behavioral science. As an effective method to discover both NOB and occurring behaviors (OB), negative sequential pattern (NSP) mining is successfully used in analyzing medical treatment and abnormal behavior patterns. At this time, NSP mining is still an active and challenging research domain. Most of the algorithms are inefficient in practice. Briefly, the key weaknesses of NSP mining are: 1) an inefficient positive sequential pattern (PSP) mining process, 2) a strict constraint of negative containment, and 3) the lack of an effective Negative Sequential Candidate (NSC) generation method. To address these weaknesses, we propose a highly efficient algorithm with improved techniques, named sc-NSP, to mine NSP efficiently. We first propose an improved PrefixSpan algorithm in the PSP mining process, which connects to a bitmap storage structure instead of the original structure. Second, sc-NSP loosens the frequency constraint and exploits the NSC generation method of positive and negative sequential patterns mining (PNSP) (a classic NSP mining method). Furthermore, a novel pruning strategy is designed to reduce the computational complexity of sc-NSP. Finally, sc-NSP obtains the support of NSC by using the most efficient bitwise-based calculation operation. Theoretical analyses show that sc-NSP performs particularly well on data sets with a large number of elements and items in sequence. Comparison and extensive experiments along with case studies on health data show that sc-NSP is 10 times more efficient than other state-of-the-art methods, and the number of NSPs obtained is 5 times greater than other methods.

Bu çalışma, Kahramanmaraş, Türkiye bölgesindeki kafes çiftliklerinde kültürlenen gökkuşağı alabalığındaki Vibrio anguillarum suşlarının fenotipik ve genotipik özelliklerinin izolasyonu ve incelenmesi ile ilgilidir . Bu amaç için, karaciğer, dalak, böbrek ve bağırsak örnekleri fenotipik özelliklerini ve biyokimyasal kimlik incelemek üzere Haziran 2014 farklı kafes çiftliklerinde yaklaşık 250-300 g ağırlığında alabalık farklı gökkuşağı alınmıştır V. anguillarum , Biolog Sistemi, gökkuşağı alabalığından alınan örneklerden elde edilen saf vibrio suşlarına uygulandı. 20 V. anguillarumu doğrulamak için spesifik bir primer ile polimeraz zincir reaksiyon tekniği kullanıldıfenotipik ve biyokimyasal özelliklere göre belirlenen suşlar. Daha sonra, bunların hepsinin V. anguillarum olduğu doğrulandı . Örneklerden elde edilen V. anguillarum suşlarının sekans analizi gerçekleştirildi ve daha sonra bunların karşılık gelen verileri yorumlandı. Bu çalışmanın sonucunda V. anguillarum bakterileri iki farklı alabalık kafesi çiftliğinde tespit edilmiştir. V. anguillarum'un tanımlanma süresi kültür yöntemi ile üç hafta sürerken , kısa sürede PCR tekniği ile yapıldı.

MotivationUsing DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content. Due to the data and process variations from experiment to experiment, the error variation and its effect on data recovery remain to be uncovered. To close the gap, we systematically investigate the storage channel, i.e., error characteristics in the storage process.ResultsWe first propose a new concept named sequence corruption to unify the error characteristics into the sequence level, easing the channel analysis. Then we derived the formulations of the data imperfection at the decoder including both sequence loss and sequence corruption, revealing the decoding demand and monitoring the data recovery. Furthermore, we extensively explored several data-dependent unevenness observed in the base error patterns and studied a few potential factors and their impacts on the data imperfection at the decoder both theoretically and experimentally. The results presented here introduce a more comprehensive channel model and offer a new angle towards the data recovery issue in DNA data storage by further elucidating the error characteristics of the storage process.Contactpoh.chuehloo@nus.edu.sg

Z-complementary pairs (ZCPs) have been widely used in different communication systems. In this paper, we first investigate the odd-periodic correlation property of ZCPs, and propose a new class of ZCPs, called ZOC-ZCPs with zero correlation zone (ZCZ) width \begin{document}$ Z $\end{document} and zero odd-period correlation zone (ZOCZ) width \begin{document}$ Z_{odd} = Z $\end{document} by horizontal concatenation of a certain combination of some known ZCPs. Particularly, based on any known Golay pair, we can generate a class of GCPs of more flexible length whose ZOCZ width is larger than a quarter of the sequence length.

In this paper, we propose a new class of optimal one-coincidence FHS (OC-FHS) sets with respect to the Peng-Fan bounds, including prime sequence sets and HMC sequence sets as special cases. Thereafter, through investigating their properties, we determine all of the FHS distances in the OC-FHS set. Finally, for a given positive integer, we also propose a new class of wide-gap one-coincidence FHS (WG-OC-FHS) sets where the FHS gap is larger than the given positive integer. Moreover, such a WG-OC-FHS set is optimal with respect to the WG-Lempel-Greenberger bound and the WG-Peng-Fan bounds simultaneously.

In this paper, for each of six families of three-valued \begin{document}$ m $\end{document} -sequence correlation, we construct an infinite family of five-weight codes from trace codes over the ring \begin{document}$ R = \mathbb{F}_2+u\mathbb{F}_2 $\end{document} , where \begin{document}$ u^2 = 0. $\end{document} The trace codes have the algebraic structure of abelian codes. Their Lee weight distribution is computed by using character sums. Their support structure is determined. An application to secret sharing schemes is given. The parameters of the binary image are \begin{document}$ [2^{m+1}(2^m-1),4m,2^{m}(2^m-2^r)] $\end{document} for some explicit \begin{document}$ r. $\end{document}

A Boolean function in \begin{document}$ n $\end{document} variables is 2-rotation symmetric if it is invariant under even powers of \begin{document}$ \rho(x_1, \ldots, x_n) = (x_2, \ldots, x_n, x_1) $\end{document} , but not under the first power (ordinary rotation symmetry); we call such a function a 2-function. A 2-function is called monomial rotation symmetric (MRS) if it is generated by applying powers of \begin{document}$ \rho^2 $\end{document} to a single monomial. If the quartic MRS 2-function in \begin{document}$ 2n $\end{document} variables has a monomial \begin{document}$ x_1 x_q x_r x_s $\end{document} , then we use the notation \begin{document}$ {2-}(1,q,r,s)_{2n} $\end{document} for the function. A detailed theory of equivalence of quartic MRS 2-functions in \begin{document}$ 2n $\end{document} variables was given in a \begin{document}$ 2020 $\end{document} paper by Cusick, Cheon and Dougan. This theory divides naturally into two classes, called \begin{document}$ mf1 $\end{document} and \begin{document}$ mf2 $\end{document} in the paper. After describing the equivalence classes, the second major problem is giving details of the linear recursions that the Hamming weights for any sequence of functions \begin{document}$ {2-}(1,q,r,s)_{2n} $\end{document} (with \begin{document}$ q say), \begin{document}$ n = s, s+1, \ldots $\end{document} can be shown to satisfy. This problem was solved for the \begin{document}$ mf1 $\end{document} case only in the \begin{document}$ 2020 $\end{document} paper. Using new ideas about "short" functions, Cusick and Cheon found formulas for the \begin{document}$ mf2 $\end{document} weights in a \begin{document}$ 2021 $\end{document} sequel to the \begin{document}$ 2020 $\end{document} paper. In this paper the actual recursions for the weights in the \begin{document}$ mf2 $\end{document} case are determined.

In this paper, we study the complexity of solving overdetermined generic systems of bilinear polynomials over a finite field \begin{document}$ \mathbb{F} $\end{document} . Given a generic bilinear sequence \begin{document}$ B\in \mathbb{F}[ \textbf{x}, \textbf{y}] $\end{document} , with respect to a partition of variables \begin{document}$ \textbf{x} $\end{document} , \begin{document}$ \textbf{y} $\end{document} , we show that, the solutions of the system \begin{document}$ B = \textbf{0} $\end{document} can be efficiently found on the \begin{document}$ \mathbb{F}[ \textbf{y}] $\end{document} -module generated by \begin{document}$ B $\end{document} . Following this observation, we define notions of regularity for overdetermined bilinear systems, and we conjecture that they are generic properties. Also, we propose three variations of Grobner basis algorithms, that only involve multiplication by monomials in the \begin{document}$ \textbf{y} $\end{document} -variables, namely, \begin{document}$ \textbf{y} $\end{document} -XL, based on the XL algorithm, \begin{document}$ \textbf{y} $\end{document} -MXL, based on the mutant XL algorithm, and \begin{document}$ \textbf{y} $\end{document} -HXL, based on a hybrid approach. We develop the necessary theoretical tools to estimate the complexity of the algorithms for such sequences. We present experimental evidence for testing our conjecture, verifying our results, and comparing the complexity of the various methods. Based on the experimental data, we can conclude that \begin{document}$ \textbf{y} $\end{document} -MXL outperforms F4 on bilinear systems.

This article considers the problem of cyclic flow shop (CFS) scheduling problems in robotic cells deploying several single and dual gripper robots. In this problem, different part types are successively processed on multiple machines with different pickup criteria including free pickup, pickup within time windows, and no-waiting times. The parts are transported between the machines by the robots. We propose a novel integer programming approach that determines the optimal sequence of parts simultaneously for the sequencing of the robots’ movements and the cyclic schedule, which also results in maximizing the throughput rate. The proposed mathematical model is validated on a number of randomly generated test instances. These problem instances are constructed by varying the number of machines, part types, robots, and gripper options. This allows a detailed analysis of the model including how it scales with increasing numbers of machines, part types, robots, and grippers. An experimental evaluation shows that our proposed model is very effective for a range of small medium-sized problems. In particular, we find that differing number of robots and gripper options directly affects the cycle time and in turn, the throughput rate. Overall, our integer programming model finds good feasible solutions for up to 20 machines and 10 part types, which is comparable to real-world industrial problems.

In this work, multi-variable derivative-free optimization algorithms for unconstrained optimization problems are developed. A novel procedure for approximating the gradient of multi-variable objective functions based on non-commutative maps is introduced. The procedure is based on the construction of an exploration sequence to specify where the objective function is evaluated and the definition of so-called gradient generating functions which are composed with the objective function such that the procedure mimics a gradient descent algorithm. Various theoretical properties of the proposed class of algorithms are investigated and numerical examples are presented.

This paper is concerned with the security issue in the state estimation problem for a networked control system (NCS). A new model of joint false data injection (FDI) attack is established wherein attacks are injected to both the remote estimator and the communication channels. Such a model is general that includes most existing FDI attack models as special cases. The joint FDI attacks are subjected to limited access and/or resource constraints, and this gives rise to a few attack scenarios to be examined one by one. Our objective is to establish the so-called insecurity conditions under which there exists an attack sequence capable of driving the estimation bias to infinity while bypassing the anomaly detector. By resorting to the generalized inverse theory, necessary and sufficient conditions are derived for the insecurity under different attack scenarios. Subsequently, easy-to-implement algorithms are proposed to generate attack sequences on insecure NCSs with respect to different attack scenarios. In particular, by using a matrix splitting technique, the constraint-induced sparsity of the attack vectors is dedicatedly investigated. Several numerical examples are

Real-world graphs exhibit many interesting properties that differentiate them from random graphs, which have been extensively studied for the past decades. For various proposed generative models, a majority of them build the graph by sequentially adding each node and the attached edges. However, the growth of many real-world graphs, such as social networks, is naturally modeled by the sequential insertion of edges. Unfortunately, to the best of our knowledge, no generative model has been proposed to reveal this process. We propose the first sequence-of-edges model, denoted as temporal preferential attachment (TPA). It relies on preferential attachment (PA), one of the most influential mechanisms to generate scale-free graphs, and takes time-decay effect and node fitness into consideration. Empirical analysis demonstrates that our model preserves several key properties of the real-world graphs, including both the properties observed from the snapshot graphs (e.g., power-law distribution) and temporal properties observed from the graph generation process (e.g., shrinking diameter). Meanwhile, our model is sufficiently general to accommodate several forms of time decay and fitness distributions. Then, we design two efficient algorithms that generate TPA graphs with billions of edges in several minutes.

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.

Thanks to the advancements in deep learning, chatbots are widely used in messaging applications. Undoubtedly, a chatbot is a new way of interaction between humans and machines. However, most of the chatbots act as a simple question answering system that responds with formulated answers. Traditional conversational chatbots usually adopt a retrieval-based model that requires a large amount of conversational data for retrieving various intents. Hence, training a chatbot model that uses low-resource conversational data to generate more diverse dialogues is desirable. We propose a method to build a task-oriented chatbot using a sentence generation model that generates sequences based on the generative adversarial network. The architecture of our model contains a generator that generates a diverse sentence and a discriminator that judges the sentences by comparing the generated and the ground-truth sentences. In the generator, we combine the attention model with the sequence-to-sequence model using hierarchical long short-term memory to extract sentence information. For the discriminator, our reward mechanism assigns low rewards for repeated sentences and high rewards for diverse sentences. Extensive experiments are presented to demonstrate the utility of our model that generates more diverse and information-rich sentences than those of the existing approaches.

In this paper, we present a new hybrid spectral-conjugate gradient (SCG) algorithm for finding approximate solutions to nonlinear monotone operator equations. The hybrid conjugate gradient parameter has the Polak–Ribiere–Polyak (PRP), Dai–Yuan (DY), Hestenes–Stiefel (HS) and Fletcher–Reeves (FR) as special cases. Moreover, the spectral parameter is selected such that the search direction has the descent property. Also, the search directions are bounded and the sequence of iterates generated by the new hybrid algorithm converge globally. Furthermore, numerical experiments were conducted on some benchmark nonlinear monotone operator equations to assess the efficiency of the proposed algorithm. Finally, the algorithm is shown to have the ability to recover disturbed signals.

Different data transmission times, processing times which are difficult to predict and node-dependent access times make MapReduce task scheduling rather complex. In this paper, we consider the problem of scheduling MapReduce tasks to heterogeneous geo-distributed data centers to minimize the total tardiness. A new architecture is constructed to analyze data in the considered scenario. We model distinct data transmission levels, inter- and intra- data centers and heterogeneity of nodes mathematically. An algorithm framework is proposed to schedule MapReduce tasks to heterogeneous nodes in geographically distributed data centers. The proposed algorithm is suitable for both Hadoop MRv1 and MRv2. In terms of the number of idle containers detected in each heartbeat, the same number of tasks are selected from a sorted job sequence. For the map and reduce phases, two measurements are developed with data locality and completion time, respectively, based on which the classical Hungarian algorithm is adopted to optimally assign selected tasks to corresponding idle containers. Components and parameters of the proposal are statistically calibrated over a large set of random instances. A comparison of the proposed algorithm to existing methods for similar problems is carried out. Experimental results demonstrate the proposal is effective for the considered problem.

Nearest neighbor (NN) is a simple machine learning algorithm, which is often used in classification problems. In this paper, a concept of modified nearest neighbor (MNN) is proposed to strengthen the optimization capability of artificial bee colony (ABC) algorithm. The new approach is called ABC based on modified nearest neighbor sequence (NNSABC). Firstly, MNN is used to construct solution sequences. Unlike the original roulette selection, NNSABC randomly chooses a solution from the corresponding nearest neighbor sequence to generate offspring. Then, two novel search strategies based on the modified nearest neighbor sequence are employed to build a strategy pool. In the optimization process, different search strategies are dynamically chosen from the strategy pool according to the current search status. In order to study the optimization capability of NNSABC, two benchmark sets including 22 classical problems and 28 CEC 2013 complex problems are tested. Experimental results show NNSABC obtains competitive performance when compared with twenty-three other ABCs and evolutionary algorithms.

Predicting treatment medicines is a key aspect of many intelligent healthcare systems. It's a very challenging task due to the following reasons: (1) heterogeneous nature of EHR data that typically include laboratory results, treatment medicines, disease conditions, and demographic details collected from disparate sources; (2) complex correlations among medical sequences, including inter-correlations between sequences and temporal intra-correlations within each sequence; (3) temporal diversity of these correlations, which is highly affected by changing disease progression. We proposes a dual adaptive sequential network, entitled DASNet, to dynamically predict treatment medicines for patients. Specifically, DASNet comprises the following three components. Decomposed Adaptive Long Short-Term Memory network (DA-LSTM) is designed to capture the intra- and inter-correlations in multiple heterogeneous temporal sequences. Then, we develop an Attentive Meta learning Network (AT-MetaNet), which produces location- and context-specific dynamic weight parameters for DA-LSTM, thus enabling it to model the time-varying multi-level correlations. Finally, we employ an ATtentive Fusion Network (AT-FuNet) to retrieve historical information and collectively fuse heterogeneous data representation embeddings to predict treatment medicines. The results of extensive experiments on the public MIMIC-III dataset covering 11 medical conditions demonstrate that the proposed end-to-end model can achieve the state-of-the-art prediction performance while providing clinically useful insights.

We introduce an object called a tree growing sequence (TGS) in an effort to generalize bijective correspondences between G -parking functions, spanning trees, and the set of monomials in the Tutte polynomial of a graph G . A tree growing sequence determines an algorithm which can be applied to a single function, or to the set P G , q of G -parking functions. When the latter is chosen, the algorithm uses splitting operations – inspired by the recursive definition of the Tutte polynomial – to iteratively break P G , q into disjoint subsets. This results in bijective maps τ and ρ from P G , q to the spanning trees of G and Tutte monomials, respectively. We compare the TGS algorithm to Dhar’s algorithm and the family described by Chebikin and Pylyavskyy in 2005. Finally, we compute a Tutte polynomial of a zonotopal tiling using analogous splitting operations.

In this article, we study the finite-time stabilization and the asymptotic stabilization with probability one of Markovian jump Boolean control networks (MJBCNs) by sampled-data state feedback controls (SDSFCs). Based on the semi-tensor product (STP), we introduce an augmented variable multiplied by the vector form of the switching signal and the state of MJBCN. We find that under SDSFC, the sequence of the states of the augmented variable at sampling instants satisfies the Markov property. Based on the convergences of the switching signal and the augmented variable, we obtain the sufficient and necessary criteria for the finite-time stabilization and the asymptotic stabilization of MJBCNs by SDSFCs, respectively. Moreover, for the two kinds of stabilization, the feedback matrices of SDSFCs are constructed, respectively. Finally, the obtained results are applied to an apoptosis network and a model of the lactose operon in the Escherichia Coli.

This paper addresses the approximate and exact controllability issues of switched infinite dimensional linear systems. Firstly, a controllability map that is related to switching sequence and a controllability Gramian operator are proposed, based on which two necessary and sufficient conditions are established respectively for approximate and exact controllability; Secondly, it shows that the obtained conditions can degenerate into the existing controllability criteria of infinite dimensional linear systems, switched finite dimensional linear systems and single finite dimensional linear system; Finally, the new results are applied to analyze the approximate controllability of switched linear parabolic systems and the exact controllability of switched linear wave systems. Two examples are given to illustrate the effectiveness of the proposed approaches.

Active learning is an effective approach for tasks with limited labeled data. It samples a small set of data to annotate actively and is widely applied in various AI tasks. It uses an iterative process, during which we utilize the current trained model to evaluate all unlabeled samples and annotate the best samples based on a specific query strategy to update the underlying model iteratively. Most existing active learning approaches rely on only the evaluation results generated by the current model and ignore the results from previous iterations. In this paper, we propose using more historical evaluation results which can provide additional information to help better select samples. First, we apply two kinds of heuristic features of the historical evaluation results, the weighted sum of and fluctuation of the historical evaluation sequence, to improve the effectiveness of sampling. Next, to use the information contained in the historical results more globally, we design a novel query strategy that learns how to select samples based on the historical sequences automatically. We also improve current state-of-the-art active learning methods by introducing historical evaluation results. Experimental results on two common NLP tasks including text classification and named entity recognition show that our methods significantly outperform current methods

Various crowdsourced logistics platforms are forming rapidly along with the booming mobile Internet. Motivated by modern crowdsourced truck logistics platforms, we introduce the online crowdsourced truck delivery (OCTD) problem and reformulate it as the online bipartite hyper-matching problem. We then explore the possibility of accommodating historical information to design efficient online algorithms to serve online orders. To the best of our knowledge, it is the first work on incorporating historical information to solve the online bipartite hyper-matching problem. Depending on whether orders can be partially served, we investigate two practical situations, i.e. separable and inseparable cases. For the inseparable case, we propose a randomized online algorithm, named Hyper-Matching , whose competitive ratio is a non-decreasing function of the amount of historical information. For the separable case, we modify Hyper-Matching to present another randomized online algorithm, named Separable-Hyper-Matching . It is worth noting that the competitive ratios of Hyper-Matching and Separable-Hyper-Matching either beat or match the current best online algorithms when no historical information is considered. We then present four computationally efficient heuristic algorithms, including a greedy variant and a batch-processing variant for each of the inseparable and separable cases. We perform a sequence of experiments using synthetic and real-world datasets, with an emphasis on the influence that historical information has on algorithm performance. The experiment results demonstrate the effectiveness of our algorithms and particularly the positive influence of historical information on our algorithms.

In this paper, a parallel and gradient-free strategy is presented for autonomous navigation of a quadrotor with a limited field of view (FOV). The FOV constraint is nonlinear and non-continuous in general and involves higher order dynamics of the vehicle. Planning problems with such constraints are difficult to solve, especially with the realtime requirement. The proposed method first constructs an environment map by integrating the measurement of a limited-FOV sensor in a parallel process. Based on the map, it then selects a feasible local goal from a global path and generates a constraint-free nominal control sequence. Finally, a stochastic optimal control problem is solved to satisfy the limited FOV constraint, the dynamic constraint, the dynamic constraint, and the collision-free conditions at the same time. The objective functions are non-linear and non-continuous and involves dynamic of MAV. A parallel gradient-free algorithm is used to address these problems. Both the sensing and planning problems are solved parallelly on a light-weight Graphics Processing Unit (GPU). The effectiveness of the proposed strategy is demonstrated through both simulation and real flight experiments.

Cooperative sweep coverage of multiagent systems (MASs) has found broad applications in various fields. This article proposes a scheme to address the sweep coverage problem of MASs within uncertain environments. In the proposed formulation, the coverage region is divided into multiple stripes, of which each has the workload completed by MASs in sequence. When the workload on the current stripe is completed, all the agents switch to the next together. The temporal dependence between the switching time computation and the sweep coverage operation is taken into account, and an online learning strategy is designed to handle environmental uncertainties and balance the workload among agents on the same stripe. Thereby, the distributed sweep coverage algorithm is developed to guarantee the complete sweep coverage, which consists of three operations, i.e., communication, workload partition, and sweeping. Theoretical analysis is afterward conducted to estimate the upper bound for the error between the actual and optimal coverage time. Finally, numerical simulations are carried out to substantiate the effectiveness and superiority of the proposed scheme.

Event sequences are central to the analysis of data in domains that range from biology and health, to logfile analysis and people's everyday behavior. Many visualization tools have been created for such data, but people are error-prone when asked to judge the similarity of event sequences with basic presentation methods. This article describes an experiment that investigates whether local and global alignment techniques improve people's performance when judging sequence similarity. Participants were divided into three groups (basic versus local versus global alignment), and each participant judged the similarity of 180 sets of pseudo-randomly generated sequences. Each set comprised a target, a correct choice and a wrong choice. After training, the global alignment group was more accurate than the local alignment group (98 versus 93 percent correct), with the basic group getting 95 percent correct. Participants' response times were primarily affected by the number of event types, the similarity of sequences (measured by the Levenshtein distance) and the edit types (nine combinations of deletion, insertion and substitution). In summary, global alignment is superior and people's performance could be further improved by choosing alignment parameters that explicitly penalize sequence mismatches.

This article studies the state estimation for probabilistic Boolean networks via observing output sequences. Detectability describes the ability of an observer to uniquely estimate system states. By defining the probability of an observed output sequence, a new concept called detectability measure is proposed. The detectability measure is defined as the limit of the sum of probabilities of all detectable output sequences when the length of output sequences goes to infinity, and it can be regarded as a quantitative assessment of state estimation. A stochastic state estimator is designed by defining a corresponding nondeterministic stochastic finite automaton, which combines the information of state estimation and probability of output sequences. The proposed concept of detectability measure further performs the quantitative analysis on detectability. Furthermore, by defining a Markov chain, the calculation of detectability measure is converted to the calculation of the sum of probabilities of certain specific states in Markov chain. Finally, numerical examples are given to illustrate the obtained theoretical results.

Let G be a finite group. For a positive integer d, let s d N ( G ) denote the smallest integer l such that every sequence S over G of length | S | ≥ l has a nonempty product-one subsequence T with | T | ≡ 0 (mod d). In this paper, we mainly study this invariant for dihedral groups D 2 n and metacyclic groups C p ⋉ s C q .

We define and analyze the notion of variational Wardrop equilibrium for nonatomic aggregative games with an infinity of players types. These equilibria are characterized through an infinite-dimensional variational inequality. We show, under monotonicity conditions, a convergence theorem enables to approximate such an equilibrium with arbitrary precision. To this end, we introduce a sequence of nonatomic games with a finite number of players types, which approximates the initial game. We show the existence of a symmetric Wardrop equilibrium in each of these games. We prove that those symmetric equilibria converge to an equilibrium of the infinite game, and that they can be computed as solutions of finite-dimensional variational inequalities. The model is illustrated through an example from smart grids: the description of a large population of electricity consumers by a parametric distribution gives a nonatomic game with an infinity of different players types, with actions subject to coupling constraints., arXiv admin note: substantial text overlap with arXiv:1806.06230

In this paper we study time-dependent scheduling problems where activities consume a resource with limited availability. Activity durations as well as resource consumptions are assumed to be time-dependent and the resource can be replenished between activities. Because of the interaction of time-dependent activity durations and resource consumptions, scheduling policies based on starting all activities as early as possible may fail due to unnecessarily high resource consumptions. We propose a dynamic discretization discovery algorithm that generates a partially time-expanded network during the search. We propose preloading techniques allowing to significantly reduce the computational effort if the approach is embedded in an iterative solution procedure that frequently evaluates activity sequences that start with the same activities. We evaluate our approaches on a case of routing a fleet of electric vehicles in which vehicles can recharge batteries during the route., 40 pages

Impedance models of modular multilevel convers (MMC) are required to analyze the stability of the MMC-HVDC system. In practical engineering, the positive-negative sequence separation algorithm is the basis of positive- and negative-sequence current control of MMC. Therefore, the influence of sequence separation on the impedance models and stability of MMC system needs to be further studied. This paper develops complete impedance models of the MMC with consideration of all control loops and positive-negative sequence separation, and comprehensively analyzes the impact of sequence separation on impedances and stability of the MMC-HVDC. It is found that the positive-negative sequence separation results in periodic fluctuations in middle and high frequency regions of AC impedance, and distinct fluctuations in middle frequency region of the DC impedance. Meanwhile, the difference in the parameters of the positive- and negative-sequence current controllers will greatly aggravate the fluctuations of both AC and DC impedances, and increase negative damping of the system. Moreover, the analysis reveals that the sequence separation is a key factor weakening the stability of the MMC-HVDC. Finally, the correctness of the impedance models and the theoretical analysis are verified by experimental results.

Zero-sequence circulating current (ZSCC) suppression and fault-tolerant control (FTC) are important guarantees for the safe and reliable operation of parallel T-type three-level rectifiers (PT23LRs). However, none of the existing literature can solve the FTC under the ZSCC suppression of the PT23LRs when the open switch fault (OSF) occurs in neutral-point (NP) switches. In order to solve these issues, a novel FTC method based on ZSCC suppression is proposed in this paper. Firstly, an improved finite-time controller is constructed to suppress the ZSCC by adjusting the difference of zero-sequence duty ratio between two rectifiers, completely. Then, based on the principle of area equivalence, the switching sequence is reconstructed by changing the state switching mode of fault phase in real time. As a result, the FTC under the OSF of NP switches is realized without affecting the suppression of ZSCC. Compared with the existing methods, the proposed method not only realizes the suppression of the ZSCC of the PT23LRs, but also completes the FTC under the OSF of NP switches, which guarantees the ZSCC suppression ability under the OSF of NP switches. Consequently, the power quality of AC side of the PT23LRs is improved significantly during the normal operation and the operation under the OSF of NP switches. Thus, the safe and reliable operation of the PT23LRs are guaranteed effectively. Finally, the feasibility and correctness of the proposed method is verified by experiment results.

This paper introduces the multiphase course timetabling problem and presents mathematical formulations and effective solution algorithms to solve it in a real case study. Consider a pool of lessons and a number of students who are required to take a subset of these lessons to graduate. Each lesson consists of a predetermined and consecutive sequence of phases with possibly different resource requirements and duration. Students who take a lesson must be present in certain phases of the lesson. Similarly, resources that are allocated to the lesson must be present in certain phases of the lesson depending on their roles. The objective is to maximize the weighted sum of student-lesson allocations subject to some capacity, availability and prerequisite constraints. The problem is formulated as an integer linear program called the session-index formulation. The formulation is then extended by introducing various side constraints that capture business requirements of a pilot training program. An enhanced branch-and-check algorithm is proposed to solve the problem more efficiently. Large instances of the problem are solved by utilizing an effective fix-and-optimize matheuristic. Results of a computational study on a set of real-world instances of the problem demonstrate the efficacy of the proposed exact and matheuristic algorithms. Due to the novelty and complexity of the problem, the efficacy of the proposed methodology to solve its real-world instances, and the implementation and realization of these contributions within a decision support system, this work was recognized as a semi-finalist of INFORMS Franz Edelman Award (2021).

Speed prediction is a crucial yet complicated task for intelligent transportation systems. The challenge derives from the complex spatiotemporal dependencies of traffic parameters. In the past few years, deep neural networks have achieved the best traffic speed prediction performance. However, most models depend on short-term input sequences to predict short/long-term traffic speed (e.g., predicting speed for the next hour using data from the past hour). These models fail to consider the daily and weekly periodic behavior of traffic. Another problem posed by neural networks is the lack of interpretability as they often operate as ``black boxes''. In this paper, an attention-based multi-encoder-decoder (Att-MED) model is proposed to predict traffic speed. The model uses convolutional-LSTMs to capture the spatiotemporal relationship of multiple input sequences, namely short-term, daily and weekly traffic patterns. The model also employs an LSTM to model the output predictions sequentially. Furthermore, attention mechanism is used to weigh the contribution of each traffic sequence towards the output predictions. The proposed network architecture, when trained end-to-end, results in a superior prediction accuracy compared to baseline models. In addition to contributing towards performance, the attention mechanism creates weight values, which when visualized, provide insights into the decision-making process of the neural network, and consequently produce explainable outputs. Att-MED's extracted attention weights highlight the contribution of daily and weekly periodic input towards speed prediction.

The characteristics of the short circuit current of renewable energy sources are quite different from those of traditional generators. When applied to tie lines and collector lines of renewable energy sources, the traditional directional elements may not operate correctly. A new method of fault direction identification suitable for inverter power sources and doubly fed power sources based on the fault features of renewable energy sources is proposed. The fault type is judged by sequence voltage. The fault direction is identified by comparing the amplitude between the sequence currents and the phase angle between the sequence voltage and the sequence current, without distinguishing whether the crowbar of the DFIG is triggered. The results of digital simulations and experiments show that this method is not affected by the types of renewable energy sources connected to the power grid and can correctly judge the fault direction in different fault types, which has good engineering application value.

Many data sources, such as human poses, lie on low-dimensional manifolds that are smooth and bounded. Learning low-dimensional representations for such data is an important problem. One typical solution is to utilize encoder-decoder networks. However, due to the lack of effective regularization in latent space, the learned representations usually do not preserve the essential data relations. For example, adjacent video frames in a sequence may be encoded into very different zones across the latent space with holes in between. This is problematic for many tasks such as denoising because slightly perturbed data have the risk of being encoded into very different latent variables, leaving output unpredictable. To resolve this problem, we first propose a neighborhood geometric structure-preserving variational autoencoder (SP-VAE), which not only maximizes the evidence lower bound but also encourages latent variables to preserve their structures as in ambient space. Then, we learn a set of small surfaces to approximately bound the learned manifold to deal with holes in latent space. We extensively validate the properties of our approach by reconstruction, denoising, and random image generation experiments on a number of data sources, including synthetic Swiss roll, human pose sequences, and facial expression images. The experimental results show that our approach learns more smooth manifolds than the baselines. We also apply our approach to the tasks of human pose refinement and facial expression image interpolation where it gets better results than the baselines.

This paper presents a general method to obtain the improved common ground quasi-Z-source series dc-dc converters. Utilizing the negative output characteristic of the quasi-Z-source converter (QZSC), the output voltage of the improved converter is the sum of the input voltage and the output voltage of original QZSC, which means that part of the energy is directly supplied by the input voltage source without passing through QZSC. Therefore, compared with the original QZSC, the proposed improved converter has advantages such as lower voltage stress, lower current stress, higher voltage gain and higher efficiency. More importantly, all these improvements are obtained with no obvious drawback. No additional component is required, the input and output remain common ground, and if the input current of original converter and the load current are continuous, the input current of improved converter is also continuous. In order to prove the feasibility of proposed method, firstly, the improved converters of three existing QZSCs are obtained. Then, the operation principle, parameter design, and comparison are introduced in sequence. Finally, the above analyses are verified by simulation and experiment results.

In this paper, a noisy output-based direct learning tracking control is proposed for stochastic linear systems with nonuniform trial lengths. The iteration-varying trial length is modeled using a Markov chain for demonstration of the iteration-dependence. The effect of the noisy output is asymptotically eliminated using a prior given decreasing gain sequence in the learning algorithm. Two alternative adaptive gains are presented for improving the tracking performance and the convergence speed. Both the mean square and almost sure convergence are provided. Numerical simulations on a 4 DoF robot arm are presented to illustrate the effectiveness of the proposed scheme.

This paper focuses on the design of on-line optimal input sequence for robust active fault diagnosis (AFD) of discrete-time linear parameter varying (LPV) systems using set-theoretic methods. Instead of the traditional set-separation constraint conditions leading to the design of off-line input sequence, the proposed approach focuses on on-line (re)shaping of the input sequence based on the real-time information of the output to discriminate system modes at each time instant such that the conservatism of robust AFD has the potential to be further reduced. The criterion on the design of optimal input is characterized based on a non-convex fractional programming problem at each time instant, which is shown to be efficiently solved within a convex optimization framework. Aside this main contribution, by exploiting Lagrange duality, the optimal input is explicitly obtained by solving a characteristic equation. At the end, a physical circuit model is provided to illustrate the effectiveness of the proposed method.

The meaningful patterns embedded in high-dimensional multi-view data sets typically tend to have a much more compact representation that often lies close to a low-dimensional manifold. Identification of hidden structures in such data mainly depends on the proper modeling of the geometry of low-dimensional manifolds. In this regard, this article presents a manifold optimization-based integrative clustering algorithm for multi-view data. To identify consensus clusters, the algorithm constructs a joint graph Laplacian that contains denoised cluster information of the individual views. It optimizes a joint clustering objective while reducing the disagreement between the cluster structures conveyed by the joint and individual views. The optimization is performed alternatively over k -means and Stiefel manifolds. The Stiefel manifold helps to model the nonlinearities and differential clusters within the individual views, whereas k -means manifold tries to elucidate the best-fit joint cluster structure of the data. A gradient-based movement is performed separately on the manifold of each view so that individual nonlinearity is preserved while looking for shared cluster information. The convergence of the proposed algorithm is established over the manifold and asymptotic convergence bound is obtained to quantify theoretically how fast the sequence of iterates generated by the algorithm converges to an optimal solution. The integrative clustering on benchmark and multi-omics cancer data sets demonstrates that the proposed algorithm outperforms state-of-the-art multi-view clustering approaches.

The aim of steganography detection is to identify whether the multimedia data contain hidden information. Although many detection algorithms have been presented to solve tasks with inconsistent distributions between the source and target domains, effectively exploiting transferable correlation information across domains remains challenging. As a solution, we present a novel multiperspective progressive structure adaptation (MPSA) scheme based on active progressive learning (APL) for JPEG steganography detection across domains. First, the source and target data originating from unprocessed steganalysis features are clustered together to explore the structures in different domains, where the intradomain and interdomain structures can be captured to provide adequate information for cross-domain steganography detection. Second, the structure vectors containing the global and local modalities are exploited to reduce nonlinear distribution discrepancy based on APL in the latent representation space. In this way, the signal-to-noise ratio (SNR) of a weak stego signal can be improved by selecting suitable objects and adjusting the learning sequence. Third, the structure adaptation across multiple domains is achieved by the constraints for iterative optimization to promote the discrimination and transferability of structure knowledge. In addition, a unified framework for single-source domain adaptation (SSDA) and multiple-source domain adaptation (MSDA) in mismatched steganalysis can enhance the model's capability to avoid a potential negative transfer. Extensive experiments on various benchmark cross-domain steganography detection tasks show the superiority of the proposed approach over the state-of-the-art methods.

In this essay, I discuss one direction for developing a systematic, data-grounded analysis of categories in talk-in-interaction. This framework is developed around two main analytical foci. The first examines how the participants themselves work to publicly associate some set of normatively and morally accountable actions, rights, obligations, entitlements, attributes, etc. (i.e., category-bound predicates; see, e.g., Jayyusi 1984; Sacks 1972a, 1972b, 1979, 1992; Watson 1978) to the various turn- and sequence-generated categories built up by their actions-in-talk, and to explicit categorial formulations (i.e., labels, metonyms, descriptions, etc.) and their indexers. The second is concerned with how the participants recognizably and relevantly accomplish the sequential organization and turn by turn management of their categorization work. The notions of rhetorical (see Edwards 1991, 1997, 1998), conditional (Schegloff 1968, 1972), and retro-relevance (see Schegloff 2007a on ‘retro-sequences’), along with response priority (Bilmes 1993, 1995; see also Bilmes 1988) are introduced as sequential analytical tools for developing a systematic, data-based analysis of these practices.

We obtain the optimal execution strategy for two sequential trades in the presence of a transient price impact. We first present a novel and general solution method for the case of a single trade (a metaorder) that is executed as a sequence of sub-trades (child orders). We then analyze the case of two sequential metaorders, including the case where the size and direction of the second metaorder are uncertain at the time the first metaorder is initiated. We obtain the optimal execution strategy under two different cost functions. First, we minimize the total cost when each metaorder is benchmarked to the price at its initiation, the total separate costs approach widely used by practitioners. Although simple, we show that optimizing total separate costs can lead to a significant understatement of the real costs of trading whilst also adversely impacting order scheduling. We overcome these issues by introducing a new cost function that splits the second metaorder into two parts, one that is predictable when the first metaorder is initiated and a residual that is not. The predictable and residual parts of the second metaorder are benchmarked using the initiation prices of the first and second metaorders, respectively. We prove existence of an optimal execution trajectory for linear instantaneous price impact and positive definite decay, and derive the explicit form of the minimizer in the special case of exponentially decaying impact. Various numerical examples are included for illustration, each of which admits a unique optimal solution, however uniqueness in general remains unproven.

In this paper we develop an algorithm for designing an optimal control sequence in Petri nets which drives a plant net from a source marking to a set of target markings without passing any pre-given forbidden markings. Such control sequences are useful in flexible, reconfigurable automated systems where a plant necessarily respond promptly to a request of reconfiguration. We develop a Dijkstra searching algorithm that is carried out in the basis marking space of a plant net instead of the conventional reachability space. Hence, only a small subset of the reachability set is explored while the unpromising branches are reduced. Moreover, we propose a transition selecting rule to expose all forbidden trajectories and all first-met target markings during the searching process. The main advantage of the proposed method is wide applicability and low computational effort.

In the natural language processing family, learning representations is a pioneering study, especially in sequence-to-sequence tasks where outputs are generated, totally relying on the learning representations of source sequence. Generally, classic methods infer that each word occurring in the source sequence, having more or less influence on the target sequence, should all be considered when generating outputs. As the summarization task requires the output sequence to only retain the essence, classic full consideration of the source sequence may not work well on it, which calls for more suitable methods with the ability to discard the misleading noise words. Motivated by this, with both relevance retaining and redundancy removal in mind, we propose a summarization learning model by implementing an encoder with copious contextual information represented and a decoder with a selecting mechanism integrated. Specifically, we equip the encoder with an asynchronous bi directional parallel structure, in order to obtain abundant semantic representation. The decoder, different from the classic attention-based works, employs a self-aware context selecting mechanism to generate summary in a more productive way. We evaluate the proposed methods on three benchmark summarization corpora. The experimental results demonstrate the effectiveness and applicability of the proposed framework in relation to several well-practiced and state-of-the-art summarization methods.

As an increasing number of asteroids are being discovered, detecting them using limited propulsion resources and time has become an urgent problem in the aerospace field. However, there is no universal fast asteroid sequence selection method that finds the trajectories for multiple low-thrust spacecraft for detecting a large number of asteroids. Furthermore, the calculation efficiency of the traditional trajectory optimization method is low, and it requires a large number of iterations. Therefore, this study combines Monte Carlo tree search (MCTS) with spacecraft trajectory optimization. A fast MCTS pruning algorithm is proposed, which can quickly complete asteroid sequence selection and trajectory generation for multispacecraft exploration of multiple asteroids. By combining the Bezier shape-based (SB) method and MCTS, this study realizes the fast search of the exploration sequence and the efficient optimization of the continuous transfer trajectories. In the simulation example, compared with the traversal algorithm, the MCTS pruning algorithm obtained the global optimal detection sequence of the search tree in a very short time. Under the same conditions, the Bezier SB method obtained the transfer trajectory with a better performance index faster than the finite Fourier series SB method. Performances of the proposed method are illustrated through a complex asteroid multiflyby mission design.

N6,2'-O-dimethyladenosine (m6Am) is a reversible modification widely occurred on varied RNA molecules. The biological function of m6Am is yet to be known though recent studies have revealed its influences in cellular mRNA fate. Precise identification of m6Am sites on RNA is vital for the understanding of its biological functions. We present here m6AmPred, the first web server for in silico identification of m6Am sites from the primary sequences of RNA. Built upon the eXtreme Gradient Boosting with Dart algorithm (XgbDart) and EIIP-PseEIIP encoding scheme, m6AmPred achieved promising prediction performance with the AUCs greater than 0.954 when tested by 10-fold cross-validation and independent testing datasets. To critically test and validate the performance of m6AmPred, the experimentally verified m6Am sites from two data sources were cross-validated. The m6AmPred web server is freely accessible at: https://www.xjtlu.edu.cn/biologicalsciences/m6am, and it should make a useful tool for the researchers who are interested in N6,2'-O-dimethyladenosine RNA modification.