Consecutive Ones Research Articles

Safe online optimization of motor speed synchronization control with incremental Q-learning

Reinforcement learning (RL) is promising for online controller optimization. However, its practical application has been hindered by safety issues. This paper proposes an algorithm named Incremental Q-learning (IQ) and applies it to the online optimization of motor speed synchronization control. IQ ensures safe learning by adopting so-called incremental action variables which represent incremental change rather than absolute magnitude, and dividing the one-round learning process in the classic Q-learning (in this paper referred to as Absolute Q-learning, AQ) into multiple consecutive ones with the Q table getting reset at the beginning of each round. Since the permitted interval of change is restricted to be very small, the agent can learn its way safely, steadily, and robustly towards the optimal policy. Simulation results show that IQ is advantageous to AQ in optimality, safety, and adaptability. IQ converges to better final performances with significantly smaller performance variance along the whole learning process, smaller torque trajectory deviation between consecutive episodes and adapts to unknown disturbances faster. It is of great potential for online controller optimization/tuning in practical engineering projects. Source code and demos are provided.

Group control for procedural rules: parameterized complexity and consecutive domains

We consider GROUP CONTROL BY ADDING INDIVIDUALS (GCAI) in the setting of group identification for two procedural rules—the consensus-start-respecting rule and the liberal-start-respecting rule. It is known that GCAI for both rules are NP-hard, but whether they are fixed-parameter tractable with respect to the number of distinguished individuals remained open. We resolve both open problems in the affirmative. In addition, we strengthen the NP-hardness of GCAI by showing that, with respect to the natural parameter the number of added individuals, GCAI for both rules are W[2]-hard. Notably, the W[2]-hardness for the liberal-start-respecting rule holds even when restricted to a very special case where the qualifications of individuals satisfy the so-called consecutive ones property. However, for the consensus-start-respecting rule, the problem becomes polynomial-time solvable in this special case. We also study a dual restriction where the disqualifications of individuals fulfill the consecutive ones property, and show that under this restriction GCAI for both rules turn out to be polynomial-time solvable. Our reductions for showing W[2]-hardness also imply several algorithmic lower bounds.

Enumerating grammar-based extractions

We propose a new grammar-based language for defining information extractors from documents (text) that is built upon the well-studied framework of document spanners for extracting structured data from text. While previously studied formalisms for document spanners are mainly based on regular expressions, we use an extension of context-free grammars, called extraction grammars, to define the new class of context-free spanners. Extraction grammars are simply context-free grammars extended with variables that capture interval positions of the document, namely spans. While regular expressions are efficient for tokenizing and tagging, context-free grammars are also efficient for capturing structural properties. Indeed, we show that context-free spanners are strictly more expressive than their regular counterparts. We reason about the expressive power of our new class and present a pushdown-automata model that captures it. We show that extraction grammars can be evaluated with polynomial data complexity. Nevertheless, as the degree of the polynomial depends on the query, we present an enumeration algorithm for unambiguous extraction grammars that, after quintic preprocessing, outputs the results sequentially, without repetitions, with a constant delay between every two consecutive ones.

Column-coherent matrix decomposition

Matrix decomposition is a widely used tool in machine learning with many applications such as dimension reduction or visualization. In this paper we consider decomposing X, a matrix of size n times m, to a product WS where we require that S, a matrix of size n times k, needs to have consecutive ones property. More specifically, we require that each row of S needs to be in the form of 0, ldots , 0, 1, ldots , 1, 0, ldots , 0. Such decompositions are particularly meaningful if X is a matrix where each row represents a time series; in such a case the ones in each row in S represent a time segment. We show that the optimization problem is inapproximable. To solve the problem we propose 5 different algorithms. The first two algorithms are based on solving iteratively S while keeping W fixed and then solving W while keeping S fixed. The next two algorithms are based on greedily optimizing a single row in S and the corresponding column in W. The last algorithm first finds the optimal decomposition of with 2k - 1non-overlapping rows, and then greedily combines the rows until k rows remain. We compare the algorithms experimentally, focusing on the quality of the decomposition as well as the computational time. We show experimentally that our algorithms yield interpretable results in practical time.

Reliability of Y Balance Test in Runners With Intellectual Disability.

The Y Balance Test (YBT) is a simple, reliable, cost-effective screening test. It is used to evaluate dynamic balance as well as to determine the potential risk of injury of the lower limbs. The reliability of YBT has been widely reported in the general population. However, there are no studies evaluating the reliability of YBT use in athletes with intellectual disability (ID). The aim of the study was to examine the reliability of the YBT in runners with ID. A reliability study. Twelve male runners (short-distance running) with ID (age 25.1 [4.50]y, height 169.1 [4.2]cm, weight 69.5 [5.5]kg, and intelligence quotient 60.8 [2.4]). The YBT was used to measure participants' dynamic balance in the anterior, posteromedial, and posterolateral reach directions. The analysis used the normalized values to the relative length of the lower limbs. A 1-way (trial) repeated-measures (5)analysis of variance for each direction was used. Intraclass correlation coefficient, standard error of measurement, and minimal detectable change were computed to assess the reliability of the YBT between trials. After 6 practice trials, 3 out of 5 consecutive ones achieved results stabilization for all directions and both legs (P < .05). The intraclass correlation coefficient, standard error of measurement, and minimal detectable change values for all trials ranged from .76 to .87, 5% to <7%, and 11% to <15%, respectively. The YBT is a highly reliable tool to measure the dynamic balance of male runners with ID. Therefore, it is recommended to perform 6 practice trials and 3 measurements in these runners.

A General FRI Sampling System for Pulse Streams and the Multichannel Synchronization Method

Pulse streams are a kind of finite rate of innovation (FRI) signal that widely exist in many engineering applications. According to the FRI theory, nonconsecutive sampled Fourier coefficients (FCs) have better parameter estimation effects than the consecutive ones. In this brief, we propose a general FRI sampling system for pulse streams based on multichannel random modulation (MRM). Jointly using multichannel samples, multiple sets of consecutive FCs of distinct frequency bands can be analytically calculated by solving the least-squares problems. The minimum sampling rate for each channel is determined by the number of pulses rather than the pulse bandwidth. The proposed sampling system is general for pulses of distinct frequency bands, and we extend it to the frequency-agile radar (FAR). Besides, the multichannel synchronization problem is analyzed, and a calibration method when the pseudo-random sequences (PRSs) are not synchronized is proposed. Simulation experiments demonstrate the effectiveness of the proposed sampling system.

Reponse of second-line treatment in focal status epilepticus: A tertiary hospital experience

ObjectiveTo investigate the response to various antiseizure medications (ASMs) in the treatment of focal status epilepticus (SE) in the established phase, and the effect of administering several ASMs prior to sedation. MethodsAll SE cases in patients aged > 16 years treated with non-BZDs ASMs were prospectively collected in our centre from February 2011 to April 2019. In total, 281 episodes were analysed. ResultsMedian age at SE onset was 65.1 years; 47 % were focal motor and 53 % focal non-motor episodes. SE cessation was achieved in 79 % episodes with second-line drugs, whereas a third line (anesthetics) was required in 47 episodes. SE cessation was achieved in only 27 % with the first ASM, 48 % with the second, and 51 % with the third. Prompt resolution of the SE episode with a first or second ASM was associated with a better outcome than episodes requiring a larger number of drugs (p = 0.024). The first option in our sample was levetiracetam in 70 % of cases. Among the total of non-responding SE cases treated with levetiracetam as the first ASM option, 107 were subsequently given lacosamide (seizure cessation in 53.3 %) and 34 valproic acid (seizure cessation in 29.4 %) (p = 0.015). ConclusionOur findings further support the notion that early termination of SE with a first or second ASM confers a better functional outcome. The large difference in response between the first ASM and consecutive ones suggests that the sum of different ASMs might be the key to resolving focal SE.

Exploiting Graph and Geodesic Distance Constraint for Deep Learning-Based Visual Odometry

Visual odometry is the task of estimating the trajectory of the moving agents from consecutive images. It is a hot research topic both in robotic and computer vision communities and facilitates many applications, such as autonomous driving and virtual reality. The conventional odometry methods predict the trajectory by utilizing the multiple view geometry between consecutive overlapping images. However, these methods need to be carefully designed and fine-tuned to work well in different environments. Deep learning has been explored to alleviate the challenge by directly predicting the relative pose from the paired images. Deep learning-based methods usually focus on the consecutive images that are feasible to propagate the error over time. In this paper, graph loss and geodesic rotation loss are proposed to enhance deep learning-based visual odometry methods based on graph constraints and geodesic distance, respectively. The graph loss not only considers the relative pose loss of consecutive images, but also the relative pose of non-consecutive images. The relative pose of non-consecutive images is not directly predicted but computed from the relative pose of consecutive ones. The geodesic rotation loss is constructed by the geodesic distance and the model regresses a Lie algebra so(3) (3D vector). This allows a robust and stable convergence. To increase the efficiency, a random strategy is adopted to select the edges of the graph instead of using all of the edges. This strategy provides additional regularization for training the networks. Extensive experiments are conducted on visual odometry benchmarks, and the obtained results demonstrate that the proposed method has comparable performance to other supervised learning-based methods, as well as monocular camera-based methods. The source code and the weight are made publicly available.

Hamiltonian problems in directed graphs with simple row patterns

We define a row pattern of a matrix as a model (or a condition) to be satisfied by each row of the matrix, independently of the other rows. When applied to adjacency matrices of digraphs, a row pattern allows to define a class of digraphs characterized by a local condition on the out-neighborhood of each vertex, once an appropriate linear ordering of the vertices has been found.We show that the Hamiltonian Cycle and Hamiltonian Path problems are NP-complete on digraphs with simple row patterns, namely C1P-digraphs (where the row pattern describes the consecutive ones property), 2-out-regular digraphs (where each row contains exactly two values 1) and sym-digraphs (where the 1s in each row are symmetric with respect to the main diagonal, as soon as the row contains at least two 1s).Classes with simple row patterns thus show a real structural complexity, despite their simple definitions. Moreover, they raise interesting class recognition problems.

A Heuristic Approach to the Consecutive Ones Submatrix Problem

Given a matrix, the Consecutive Ones Submatrix (C1S) problem which aims to find the permutation of columns that maximizes the number of columns having together only one block of consecutive ones in each row is considered here. A heuristic approach will be suggested to solve the problem. Also, the Consecutive Blocks Minimization (CBM) problem which is related to the consecutive ones submatrix will be considered. The new procedure is proposed to improve the column insertion approach. Then real world and random matrices from the set covering problem will be evaluated and computational results will be highlighted.

Indoor Localization With Adaptive Signal Sequence Representations

Indoor location-based services (LBS) have exhibited large commercial and social values in smart cities, and urgent demands of which have spurred many localization techniques. Existing indoor localization approaches mostly rely on fingerprint techniques, leveraging either spatially discrete fingerprints or temporally consecutive ones for localization. However, these approaches often suffer from large errors or high time overhead in practice due to signal ambiguities or long input sequences. To overcome these drawbacks, this paper proposes a framework utilizing multiple adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">representations</i> of signal sequences for localization, where each representation indicates a corresponding signal structure with underlying location clues. As an example, the proposed approach takes geomagnetic signal sequences as input and infers location features from two intuitive representations, e.g., spatial and temporal ones. With adaptive signal representations, the proposed approach takes specifically optimized neural networks to extract corresponding location clues respectively and fuses them to generate more distinguishing features for more accurate localization. Furthermore, the ensemble learning mechanism is adopted in the approach and a weighted k-NN based location estimation algorithm is devised to enhance the robustness. Extensive experiments in three different trial sites demonstrate that the proposed approach outperforms state-of-the-art competing schemes by a wide margin, reducing mean localization error by more than 46%.

A Scalable Bidimensional Randomization Scheme for TLC 3D NAND Flash Memories.

Data randomization has been a widely adopted Flash Signal Processing technique for reducing or suppressing errors since the inception of mass storage platforms based on planar NAND Flash technology. However, the paradigm change represented by the 3D memory integration concept has complicated the randomization task due to the increased dimensions of the memory array, especially along the bitlines. In this work, we propose an easy to implement, cost effective, and fully scalable with memory dimensions, randomization scheme that guarantees optimal randomization along the wordline and the bitline dimensions. At the same time, we guarantee an upper bound on the maximum length of consecutive ones and zeros along the bitline to improve the memory reliability. Our method has been validated on commercial off-the-shelf TLC 3D NAND Flash memory with respect to the Raw Bit Error Rate metric extracted in different memory working conditions.

Optimal patchings for consecutive ones matrices

We study a variant of the weighted consecutive ones property problem. Here, a 0/1-matrix is given with a cost associated to each of its entries and one has to find a minimum cost set of zero entries to be turned to ones in order to make the matrix have the consecutive ones property for rows. We investigate polyhedral and combinatorial properties of the problem and we exploit them in a branch-and-cut algorithm. In particular, we devise preprocessing rules and investigate variants of “local cuts”. We test the resulting algorithm on a number of instances, and we report on these computational experiments.

Hausdorff dimension of frequency sets of univoque sequences

We study the set consisting of univoque sequences, the set consisting of sequences in which the lengths of consecutive zeros and consecutive ones are bounded, and their frequency subsets , , and , , consisting of sequences respectively in and with frequency, lower frequency and upper frequency of zeros equal to some . The Hausdorff dimension of all these sets are obtained by studying the dynamical system where is the shift map and for integer , studying the Bernoulli-type measures on and finding out the unique equivalent -invariant ergodic probability measure.

Iterated local search for consecutive block minimization

A 1-block in a binary matrix is any maximal sequence of 1s occurring consecutively in the same row. Consecutive block minimization (CBM) requires a permutation of the matrix columns that minimizes the number of 1-blocks. CBM is NP-hard for matrices with a maximum of two 1s per row and an arbitrary number of 1s per column. First, we prove that it is NP-hard even when it is restricted to binary matrices having a maximum of two 1s per row and a maximum of three 1s per column. In these circumstances, a reasonable and traditional approach to solve the problem is to design a heuristic capable of finding good configurations within acceptable computation times. This paper proposes an Iterated Local Search (ILS) metaheuristic. A binary matrix satisfies the strong consecutive ones property (is strongly C1P) if its non-zero entries occur consecutively in each row. It is C1P if there is a permutation of its columns such that the resulting matrix is strongly C1P. In this study, two necessary conditions for a binary matrix to be C1P are provided, each allowing to define a neighborhood for local search (LS). In addition, a third large-scale neighborhood is proposed where the local optimal solution can be directly obtained by solving a Traveling Salesman Problem (TSP). It is noteworthy that the three neighborhoods are being proposed for the first time. The ILS algorithm is tested on a dataset from the OR-library, composed of thirty large-scale set covering instances of varying sizes and densities. Comparison with two TSP solvers as well as with a recently published metaheuristic, from both accuracy and computing time points of view, allows to conclude that the proposed method competes favorably with the methods available in the literature.

Consecutive and temporally distant perseverations after right brain damage: A prospective study.

Objective: Right brain-damaged patients may show omissions and/or additional marks in target cancellation. The latter is classified as perseverative behavior and has been attributed to defective response inhibition or attentional disengagement deficit. This study aimed at (a) verifying that consecutive (immediate) and return (temporally distant) motor perseverations could be due to different mechanisms; (b) investigating the relationships among different types of perseveration (e.g., consecutive, return, scribble), spatial neglect and the impairment in specific components of executive functioning. Method: Seventeen right brain-damaged patients underwent letter, star, bell, and apple cancellation tasks. A global index for each type of perseveration found and Mean Position of Hits, as a neglect index, were calculated. The following components of executive functioning were evaluated: motor programming (Frontal Assessment Battery [FAB] subtest), inhibitory control FAB, interference sensitivity (FAB and Stroop color-word interference test), set-shifting (Weigl sorting test, Phonemic/semantic alternate fluencies), and working memory (Backward Digit span). Results: Ten patients out of 17 showed some degree of perseveration. Regularized linear regression analyses demonstrated that interference sensitivity and Stroop test performances were related to return perseverations and backward digit to scribble ones. No significant relationships were found for consecutive perseverations and between neglect and any type of perseverations. Conclusions: The present study showed that return perseverations might have a distinct etiology from consecutive ones, being related to an inability to update and shift between action programs according to the visual stimuli. A finer classification of perseverations could help in unveiling the neuropsychological mechanisms underlying each type of behavior. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A Metaheuristic Approach to the C1S Problem

Given a binary matrix, finding the maximum set of columns such that the resulting submatrix has the Consecutive Ones Property (C1P) is called the Consecutive Ones Submatrix (C1S) problem. There are solution approaches for it, but there is also a room for improvement. Moreover, most of the studies of the problem use exact solution methods. We propose an evolutionary approach to solve the problem. We also suggest a related problem to C1S, which is the Consecutive Blocks Minimization (CBM). The algorithm is then performed on real-world and randomly generated matrices of the set covering type.

Явный вид распределения избранных двумерных и трехмерных статистик (0,1)-последовательности

Рассмотрены совместимые распределения заданного числа 2-цепочек и заданного числа 3-цепочек фиксированного вида случайной битовой последовательности, позволяющие осуществлять статистический анализ локальных участков этой последовательности. В качестве 2-цепочек выступают все конфигурации, состоящие из двух подряд или нулей, или единиц битовой последовательности заданной длины. В свою очередь, 3-цепочками являются все конфигурации, состоящие из трех подряд или единиц (при условии, что 2-цепочки являются нулевыми), или нулей (при условии, что 2-цепочки единичны), а также в качестве 3 -цепочек рассматриваются все конфигурации, состоящие либо из трех подряд цифр: один, ноль и один (при условии, что 2-цепочки нулевые), либо из трех подряд цифр: ноль, один и ноль (при условии, что 2-цепочки единичные). Установлены явные выражения двумерных и трехмерных совместимых распределений событий, отражающих количество некоторых комбинаций указанных цепочек в конечной случайной битовой последовательности. Одно из основных предположений состоит в том, что нули и единицы в битовой последовательности — независимые одинаково распределенные случайные величины. Доказательства формул для распределений указанных событий построены на подсчете числа ответных благоприятных событий при условии, что битовая последовательность содержит фиксированное количество нулей и единиц. В качестве примеров использования явных выражений совместимых распределений приведены таблицы, в которых размещены значения вероятностей вышеперечисленных событий для случайной битовой последовательности длины 40 (табл. 1–3) и длины 24 (табл. 4) для некоторых фиксированных значений числа 2-цепочек и числа 3-цепочек в предположении, что нули и единицы появляются независимо и равновероятно. табл. 1–3 проиллюстрированы пузырьковыми диаграммами. Найденные формулы могут представлять интерес для задач тестирования локальных участков, формируемых на выходе генераторов псевдослучайных чисел, для некоторых задач защиты информации от несанкционированного доступа, а также в других сферах, где возникает необходимость в анализе битовых последовательностей.

An integrated approach to assessing students’ emotional stability under the COVID-19 pandemic

In contemporary unpredictably threatening environmental conditions, many students, like individuals of other walks of life who realise the potential danger of corona virus disease for their health and even life, are likely to experience higher than usual levels of emotional stress and consequent behavioural changes, like proneness to self-handicapping, escapism, giving preference to avoidance-type of coping strategies etc. This article presents the description and the expediency of using, for the monitoring of the emotional states of the students, a computer-assisted technique for assessing the mode of information processing preferred by the individual in the stressful conditions. The unified holistic-synthetic mode is found to be pertinent to emotionally stable individuals and consecutive analytical ones compared to the emotional vulnerable ones. The technique can be used for monitoring the emotional states of students and accounting for them in the process of teaching.&#x0D; &#x0D; Keywords: Emotional stability, cognitive processes, strategy of informational processes, consecutive analytical, holistic-synthetic mode.

Characterization of complex data functions through local persistence of increments

The local persistence of increments is a new concept defined in the present paper by the quantity of consecutive ups or consecutive downs in a function until it reverses. Each degree of persistence occurs in a given quantity for a given complex data function, namely higher consecutive ups/downs occur less often than lower consecutive ones. Moreover the decay of the frequency distribution of local persistences is exponential. The slope of the associated log plot leads to the definition of the local persistence coefficient which characterizes and differs one data from another. The method was tested in Weierstrass-Mandelbrot deterministic functions as well as in random fractional Brownian motions with varying fractal dimensions. An empiric expression relating the corresponding fractal dimensions and the defined persistence coefficient is proposed where the latter coincides with the corresponding Hurst exponent. Thus it is conjectured that the frequency distribution of the persistences of increments is related with long-range correlations. An empiric application of the method in data from S&P 500 index is presented. In this case the coefficient associated with ups is higher than that associated with downs. Thus while increasing S&P index persists more than while decreasing and the quantity of unitary negative persistences is higher than the positive ones. The method does not take into account the intensity of the increments and then the fractal dimensions may have no relation with the intensities of the increments. The method may be useful for (1) characterization of complex data; (2) estimation of Hurst coefficient or fractal dimension of data functions and (3) as a tool to predict the probability of the next behavior (up/down) of a complex data function.