Time Space Complexity Research Articles

Constant-space, constant-randomness verifiers with arbitrarily small error

We study the capabilities of probabilistic finite-state machines that act as verifiers for certificates of language membership for input strings, in the regime where the verifiers are restricted to toss some fixed nonzero number of coins regardless of the input size. Say and Yakaryılmaz showed that the class of languages that could be verified by these machines within an error bound strictly less than 12 is precisely NL, but their construction yields verifiers with error bounds that are very close to 12 for most languages in that class when the definition of “error” is strengthened to include looping forever without giving a response. We characterize a subset of NL for which verification with arbitrarily low error is possible by these extremely weak machines. It turns out that, for any ε>0, one can construct a constant-coin, constant-space verifier operating within error ε for every language that is recognizable by a linear-time multi-head nondeterministic finite automaton (2nfa(k)). We discuss why it is difficult to generalize this method to all of NL, and give a reasonably tight way to relate the power of linear-time 2nfa(k)'s to simultaneous time-space complexity classes defined in terms of Turing machines.

Prophage loci predictor for bacterial genomes.

This paper proposes a new algorithm for prophage loci prediction in bacteria. Prophages are defined in Bioinformatics as viral nucleotide sequences that are found intermixed with host nucleotide sequences in bacteria. The proposed algorithm uses machine learning patterns and processing methodologies in order to provide a highly efficient system for loci prediction, thereby reducing the time-space complexity required unlike others of its class. In the training phase, a pattern database is constructed from raw nucleotide sequences of both bacteria and viruses obtained from a training set. In the prediction phase, the aforementioned database is used along with Particle Swarm Optimization (PSO) to predict the probable loci of prophages in a test set of bacterial nucleotide sequences. Testing this method on raw sequences consisting of both partial and complete nucleotide sequences of various bacteria has yielded good results in predicting the loci of prophages in them. This algorithm and connected processes compare favorably in terms of predictive performance with others of its class such as PhiSpy and ProphET, while outperforming others in terms of raw processing speed, suggesting that a data-centric approach can yield comparable results while using a fraction of the resources.

A Mixed-Pruning Based Framework for Embedded Convolutional Neural Network Acceleration

Convolutional neural networks (CNN) have been proved to be an effective method in the field of artificial intelligence (AI), and large-scale deploying CNN to embedded devices, no doubt, will greatly promote the development and application of AI into the practical industry. However, mainly due to the space-time complexity of CNN, computing power, memory bandwidth and flexibility are performance bottlenecks. In this paper, a framework containing model compression and hardware acceleration is proposed to solve the above problems. This framework consists of a mixed pruning method, data storage optimization for efficient memory utilization and an accelerator for mapping CNN on field programmable gate array (FPGA). The mixed pruning method is used to compress the model, and data bit-width is reduced to 8-bit by data quantization. Accelerator based on FPGA makes it flexible, configurable and efficient for CNN implementation. The model compression is evaluated on NVIDIA RTX2080Ti, and the results illustrate that the VGG16 is compressed by 30× and the fully convolutional network (FCN) is compressed by 11× within 1% accuracy loss. The compressed model is deployed and accelerated on ZCU102, which is up to 1.7× and 24.5× better in energy efficiency compared with RTX2080Ti and Intel i7 7700.

Solutions to Space-Time Fractional Complex Ginzburg-Landau Equations With the Complete Discrimination System for Polynomial Method

The space-time fractional complex Ginzburg-Landau equation was studied. Firstly, the space-time fractional complex Ginzburg-Landau equation was transformed into the ordinary differential equation through the fractional complex transform. Secondly, the ordinary differential equation was reduced to an elementary integral form. Finally, a series of exact solutions including solitary wave solutions, rational function type solutions, triangle function type periodic solutions, and Jacobian elliptic function doubly-periodic solutions, were constructed with the complete discrimination system for polynomial method.

Distributed and scalable platform architecture for smart cities complex events data collection: Covid19 pandemic use case.

For decades, numerous names have been given to boost an urban city: digital city, green city, smart cities and the list goes on. They are all accompanied with ideas and propositions to enrich citizens life quality, by employing latest information technology to improve environment’s sustainability, through better energy usage, targeting problems affecting infrastructure costs, automation and efficient human resources distribution. Consequently, cities governors provide plans and conceive laws so society including individuals and organizations collaborate in a cycle of providers and consumers to make steps ahead toward smarti-fication of the city in which they all operate. Hence, hundreds of cities around the world are living example of what a smart cities could be resembling in terms of information technology advancement and everyday usage. Each application or to be general system serve and exist for a specific purpose, using mobile applications and small sensors together to cooperate and deliver a value imposing a huge economical and social value and significant source of data. However, most of this applications are tied to specific domains and solely designed to solve predefined problems. Thus, for a decision maker point of view, decisions’ the cost becomes high to correlate multiple data flow in different shapes. As a solution, in this paper we propose a system that is based on abstracting city events of different backgrounds—social, urban and natural, we chose to call them complex space time events. Furthermore, we present its architecture and how it plays with its external actors, and finally, we explain a use case instance made specifically to counter Covid19 pandemic spread and retain public order.

Adaptable Reduced-Complexity Approach Based on State Vector Machine for Identification of Criminal Activists on Social Media

Security agencies face an emerging challenge of identifying and counter the malicious contents spread on the social media by the terrorists. However, text classification techniques are limited by visualization, pre-processing, features extraction, and larger features space. Additionally, change in criminal content require the learning models to identify altered malicious textual contents which poses extra challenge. This study proposes simplified yet adaptable framework that uses a novel features extraction algorithm for extracting features from the textual part of social media contents. The feature extraction considers selective features from only 8 dimensions and follows a six step process. The extracted features are suitably used to train the state vector machine for the classification of the malicious content. The performance of the proposed method is evaluated against other popular feature selection/extraction algorithms like term frequency-inverse document frequency, Gini Index (GI), Chi square statistics, and PCA. Additionally, machine learning classifiers like decision tree, random forest, and Naïve Bayes are also used for classification. Results suggest that the proposed approach consumes less energy on text visualization, pre-processing, and dimensionality reduction. It also reduces the time-space complexity of the features extraction process and is capable to steer according to the changing strategies of the active criminal groups. In addition, it can effectively analyze the propaganda material published by the extremists. It automatically identifies the radical text on social media platforms allowing understanding of the behaviors, characteristics and subsequent blockage of such content.

Depthwise Spatio-Temporal STFT Convolutional Neural Networks for Human Action Recognition.

Conventional 3D convolutional neural networks (CNNs) are computationally expensive, memory intensive, prone to overfitting, and most importantly, there is a need to improve their feature learning capabilities. To address these issues, we propose spatio-temporal short-term Fourier transform (STFT) blocks, a new class of convolutional blocks that can serve as an alternative to the 3D convolutional layer and its variants in 3D CNNs. An STFT block consists of non-trainable convolution layers that capture spatially and/or temporally local Fourier information using an STFT kernel at multiple low frequency points, followed by a set of trainable linear weights for learning channel correlations. The STFT blocks significantly reduce the space-time complexity in 3D CNNs. In general, they use 3.5 to 4.5 times less parameters and 1.5 to 1.8 times less computational costs when compared to the state-of-the-art methods. Furthermore, their feature learning capabilities are significantly better than the conventional 3D convolutional layer and its variants. Our extensive evaluation on seven action recognition datasets, including Something 2 v1 and v2, Jester, Diving-48, Kinetics-400, UCF 101, and HMDB 51, demonstrate that STFT blocks based 3D CNNs achieve on par or even better performance compared to the state-of-the-art methods.

Identifying weather regimes for regional‐scale stochastic weather generators

AbstractWeather regime based stochastic weather generators (WR‐SWGs) have recently been proposed as a tool to better understand multi‐sector vulnerability to deeply uncertain climate change. WR‐SWGs can distinguish and simulate different types of climate change that have varying degrees of uncertainty in future projections, including thermodynamic changes (e.g., rising temperatures, Clausius‐Clapeyron scaling of extreme precipitation) and dynamic changes (e.g., shifting circulation and storm tracks). These models require the accurate identification of WRs that are representative of both historical and plausible future patterns of atmospheric circulation, while preserving the complex space–time variability of weather processes. This study proposes a novel framework to identify such WRs based on WR‐SWG performance over a broad geographic area and applies this framework to a case study in California. We test two components of WR‐SWG design, including the method used for WR identification (Hidden Markov Models (HMMs) vs. K‐means clustering) and the number of WRs. For different combinations of these components, we assess performance of a multi‐site WR‐SWG using 14 metrics across 13 major California river basins during the cold season. Results show that performance is best using a small number of WRs (4–5) identified using an HMM. We then juxtapose the number of WRs selected based on WR‐SWG performance against the number of regimes identified using metastability analysis of atmospheric fields. Results show strong agreement in the number of regimes between the two approaches, suggesting that the use of metastable regimes could inform WR‐SWG design. We conclude with a discussion of the potential to expand this framework for additional WR‐SWG design parameters and spatial scales.

Quantum computation with generalized dislocation codes

In this paper, we study quantum computing with twists. Twists are yet another form of defects in a lattice. They arise from dislocations in the lattice and can be used to encode and process quantum information. Surface codes with twists are also called dislocation codes. Hastings and Geller showed that dislocation codes could provide gains in space-time complexity of quantum computation. In this paper, we undertake a detailed study of generalized dislocation codes. We develop the theory of qubit dislocation codes over arbitrary four-valent and bicolorable lattices. We give a construction to introduce twists in such lattices and also study the structure of logical operators. We then study dislocation codes over odd prime dimensions in square lattices. Using the theory developed, we present protocols for implementing a universal gate set in qubit dislocation codes. We also show how to implement the generalized Clifford group in qudit dislocation codes in odd prime dimensions.

Short-Term Passenger Flow Forecast of Rail Transit Station Based on MIC Feature Selection and ST-LightGBM considering Transfer Passenger Flow

To solve the problems of current short-term forecasting methods for metro passenger flow, such as unclear influencing factors, low accuracy, and high time-space complexity, a method for metro passenger flow based on ST-LightGBM after considering transfer passenger flow is proposed. Firstly, using historical data as the training set to transform the problem into a data-driven multi-input single-output regression prediction problem, the problem of the short-term prediction of metro passenger flow is formalized and the difficulties of the problem are identified. Secondly, we extract the candidate temporal and spatial features that may affect passenger flow at a metro station from passenger travel data based on the spatial transfer and spatial similarity of passenger flow. Thirdly, we use a maximal information coefficient (MIC) feature selection algorithm to select the significant impact features as the input. Finally, a short-term forecasting model for metro passenger flow based on the light gradient boosting machine (LightGBM) model is established. Taking transfer passenger flow into account, this method has a low space-time cost and high accuracy. The experimental results on the dataset of Lianban metro station in Xiamen city show that the proposed method obtains higher prediction accuracy than SARIMA, SVR, and BP network.

Flow field induced by a dielectric barrier discharge plasma actuator analyzed with bi–orthogonal decomposition

A typical dielectric barrier discharge (DBD) plasma actuator generates complex periodic flow structures in burst mode. Efficient use of these actuators depends on clear understanding of the relationship between the operational parameters of the actuator and flow structure. The present study reports the temporal and spatial evolution of these flow structures by utilizing the bi-orthogonal decomposition (BOD) technique. The flow induced by the actuator is captured using a time resolved particle image velocimetry (2D–2C–TR–PIV) system. The BOD of flow field is carried out using instantaneous velocity field data. The DBD plasma actuator is operated at different combinations of duty cycle, α (50% ≤ α ≤ 90%), and burst frequency, fb (10 Hz ≤ fb ≤ 90 Hz). The modal energy content is used to characterize the flow field as a function of operating variables, i.e., α and fb of the actuation signal. The mean mode of the decomposition successfully approximates the time averaged behavior of the induced flow field. The mean mode energy level increases with the increase in both α and fb with a more pronounced effect observed as a function of fb. The coherent structures are located close to the near wall at high burst frequency. The non-dimensional entropy decreases with the increase in both α and fb with a more pronounced effect of fb than that of α. The decrease in entropy value indicates that space–time complexities are reduced at higher burst frequency. The topos of higher order modes reveal the presence of coherent structures that grow in time and convect along the wall like a train of vortices. The chronos of mode 2 and mode 3 is locked in with respect to the burst frequency. However, the chronos of mode 4 and mode 5 shows frequency doubling at lower burst frequency actuation and frequency halving at higher burst frequency actuation. The entropy value or space–time complexity of flow structures generated by DBD plasma actuator is related to the nonlinear vortex interaction mechanism, i.e., period doubling and period halving of chronos.

An Efficient Two-Level Dictionary-Based Technique for Segmentation and Compression Compound Images

Image data compression algorithms are essential for getting storage space reduction and, perhaps more importantly, to increase their transfer rates, in terms of space-time complexity. Considering that there isn't any encoder that gives good results across all image types and contents, this paper proposed an evolvable lossless statistical block-based technique for segmentation and compression compound or mixed documents that have different content types, such as pictures, graphics, and/or texts.
 
 Derived from the number of detected colors and to achieve better compression ratios, a new well-defined representation of the image is created which nonetheless retains the same image components. With the effort of reducing noise or other variations inside the scanned image, some primary operations are implemented. Thereafter, the proposed algorithm breaks down the compound document image into equal-size-square blocks. Next, inspired by the number of colors detected in each block, these blocks are categorized into a set of six-image objects, called classes, where each one contains a set of closely interrelated pixels that share the same common relevant attributes like color gamut and number, color occurrence, grey level, and others. After that, a new representation of these coherent classes is formed using the Lookup Dictionary Table (LUD), which is the real essence of this proposed algorithm. In order to form distinguishable labeled regions sharing the same attributes, adjacent blocks of similar color features are consolidated together into a single coherent whole entity, called segments or regions. After each region is encoded by one of the most off-the-shelf applicable compression techniques, these regions are eventually fused together into a single data file which then subjects to another compression stage to ensure better compression ratios. After the proposed algorithm has been applied and tested on a database containing 3151 24-bit-RGB-bitmap document images, the empirically-based results prove that the overall algorithm is efficient in the long run and has superior storage space reduction when compared with other existing algorithms. As for the empirical findings, the proposed algorithm has achieved (71.039 %) relative reduction in the data storage space.

Derivation of a new multiscale model: I. Derivation of the model for the atomic, molecular and nano material scales

In the present work, a generalized form for the energy equation is derived following the concepts of a new theory called the time of events theory. This theory assumes a dynamic space-time in addition to the static space-time assumed explicitly in the special theory of relativity and implicitly in quantum mechanics. Assuming two spacetimes necessitates the complex space-time. Therefore, the energy equations are rederived assuming a real space-time, represented by the dynamic space-time, and an imaginary space-time represented by the static space-time. An angle θ is assumed between the particle’s world line and the real axis. The θ value depends on the particle’s mass and directly proportional to it. The heavy particles have larger θ, therefore have larger imaginary component, and the reverse is true for the light particles. The energy equations in the complex space-time were written in terms of complex velocities’ squares, each equation is written independently for each particle. Then, the equations are combined to give the final equation for the assembly that is composed of atoms, ions, or molecules. Applying this procedure for the hydrogen atom led to an equation similar to the Schrodinger equation. Based on the above procedure, a general equation for more complicated systems (atoms or molecules) is derived. We find that there is a difference in the leading factors for the electron-electron term from the traditional forms of quantum mechanics. For the case of ions and non-neutral molecules, a different equation is predicted by the current work, due to differences between electrons and protons numbers.

REBATE: A REpulsive-BAsed Traffic Engineering protocol for dynamic scale-free networks

Nowadays the abundance of IoT devices has the potential of changing our lives dramatically, but brings new routing and traffic orchestration challenges for the next-generation Internet providers: core routers are already overwhelmed, see e.g, the routing table size growth problem. Although some researchers still argue whether or not the next-generation networks should feature scale-free properties, recent results have shown benefits of embedding such scale-free networks in a hyperbolic space of negative curvature. Specifically, this allows geometrically route packets by using only a local topology knowledge (i.e., with average O∗(1) space–time complexity) at no extra communication overhead (i.e., without routing protocols). To our knowledge, however, there is no Traffic Engineering (TE) protocol with the aforementioned properties that can be used in dynamic scale-free networks. In this paper, we propose the first to our knowledge REpulsive-BAsed Traffic Engineering (REBATE) protocol for dynamic scale-free networks. REBATE is built upon dual principles of the demand-aware TE and fundamentals properties of hyperbolic spaces. Using trace-driven numerical simulations, we then show how REBATE can reduce the maximum link utilization up to 25% when compared to a common geometric routing-based traffic steering. Although REBATE can perform worse than common demands-aware and oblivious TE approaches, we think that our work should pave the way for more efficient TE in the next-generation dynamic scale-free networks.

Semi-Supervised Gated Spectral Convolution on a Directed Signed Network

A complex network is a powerful tool that enables a complex system in the real world to be represented as a network structure. Due to the heterogeneous edges and nodes implying rich semantic information, network representation has received considerable attention in both research and industrial domain. Over the recent years, the graph convolutional network (GCN) has provided a novel approach for learning network embeddings. However, this primarily supports undirected unsigned networks; that is, it cannot be directly applied to directed signed networks because it is challenging to effectively depict the direction and signs of edges in such models. In this paper, we therefore propose a method for semi-supervised gated spectral convolution in directed signed networks. We first extend the concept of the GCN to directed signed networks, which not only preserves the advantages of the traditional GCN but also properly describes the significance of the directions and signs of the edges. We then innovatively define sign (label) propagation rules in directed signed networks, rendering the networks semi-supervised. Furthermore, we enhance the balance theory to constrain the process of sign propagation to obtain network embedding with better interpretability. To satisfy the needs of large-scale complex networks, we propose a gating mechanism to adaptively forget sign information, which significantly reduces the time-space complexity of the sign propagation process. Finally, we compare the proposed method with state-of-the-art baselines using four real-world data sets for the classical link sign prediction task. Experimental results demonstrate that the proposed method is competitive.

Associated Attribute-Aware Differentially Private Data Publishing via Microaggregation

Releasing raw data sets with sensitive personal information will leak privacy. Therefore, various differential privacy methods have been proposed for efficient data sharing while preserving privacy. However, they focus on noise processing of all quasi-identifier attributes, which results in high time-space complexity and low data utility. In this paper, we propose a Differential Privacy Protection model considering the Correlations between Attributes, denoted DPPCA. DPPCA first computes the degree of correlations between the quasi-identifier attributes and the sensitive attributes, and determines the pair of attributes with maximal degree of correlation. Then based the attributes with the maximal degree of correlations, it uses microaggregation to partition the data set into clusters of size $k$ ( $k\geq 2$ ) according to three types of attributes, i.e., numerical, non-numerical, and hybrid attributes, such that there are $l$ ( $l ) values of sensitive attributes in a cluster. Finally, noise is added to each cluster separately such that each cluster satisfies $\varepsilon $ -differential privacy. While keeping the same degree of preserving privacy, our experimental results demonstrate that DPPCA substantially reduces the amount of added noise to 11% for the Census data set and the Adult data set. Therefore, DPPCA greatly improve the data utility while reaching the same degree of differential privacy.

Multi-soliton solutions for a nonlocal complex coupled dispersionless equation

Coupled dispersionless equation is an important model in quantum physics. Complex coupled dispersionless equation has valuable applications in geomerty. Recently, Ablowitz and Musslimani introduced and investigated a large class of reverse space, reverse time and reverse space-time nonlocal integrable equations. In this paper, we investigate a reverse space-time nonlocal complex coupled dispersionless equation, which was proposed in our paper [1]. By means of the Darboux transformation, we obtain its multi-soliton solutions from zero seed and nonzero seed. The asymptotic behavior of these solutions is discussed.

Symmetry broken and symmetry preserving multi-soliton solutions for nonlocal complex short pulse equation

Abstract In this article we consider a general system of complex short pulse equation (CSPE) that under certain nonlocal symmetry reduction yields a reverse space-time nonlocal complex short pulse equation (NL-CSPE). We apply matrix Darboux transformation to the associated Lax pair and construct multi-soliton solutions. K-soliton solution is expressed in terms of quasideterminant formula which enable us to compute explicit expressions of symmetry broken and symmetry preserving one- and two-soliton solutions for NL-CSPE. In addition to these solutions, we also obtain one-bright and interaction of two-bright solitons for classical CSPE. All these investigations end up with the conclusion that both symmetry preserving and broken solutions exist for NL-CSPE.

Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity.

Due to the fast speed of data generation and collection from advanced equipment, the amount of data obviously overflows the limit of available memory space and causes difficulties achieving high learning accuracy. Several methods based on discard-after-learn concept have been proposed. Some methods were designed to cope with a single incoming datum but some were designed for a chunk of incoming data. Although the results of these approaches are rather impressive, most of them are based on temporally adding more neurons to learn new incoming data without any neuron merging process which can obviously increase the computational time and space complexities. Only online versatile elliptic basis function (VEBF) introduced neuron merging to reduce the space-time complexity of learning only a single incoming datum. This paper proposed a method for further enhancing the capability of discard-after-learn concept for streaming data-chunk environment in terms of low computational time and neural space complexities. A set of recursive functions for computing the relevant parameters of a new neuron, based on statistical confidence interval, was introduced. The newly proposed method, named streaming chunk incremental learning (SCIL), increases the plasticity and the adaptabilty of the network structure according to the distribution of incoming data and their classes. When being compared to the others in incremental-like manner, based on 11 benchmarked data sets of 150 to 581,012 samples with attributes ranging from 4 to 1,558 formed as streaming data, the proposed SCIL gave better accuracy and time in most data sets.

Inverse scattering transform for the complex reverse space-time nonlocal modified Korteweg-de Vries equation with nonzero boundary conditions and constant phase shift.

Recently, the complex reverse space-time (RST) nonlocal modified Korteweg-de Vries equation (mKdV) was introduced and shown to be an integrable infinite-dimensional dynamical system. The inverse scattering transform (IST) for zero boundary condition was studied by Ablowitz and Musslimani in 2016. In this paper, the IST for the complex RST nonlocal mKdV equation with nonzero boundary conditions at infinity is presented. The direct and inverse scattering problems are analyzed. The method to carry out the inverse problem employs two Riemann surfaces associated with square root branch points in the eigenfunctions/scattering data, which is more complicated than that for the zero boundary condition. Four cases are considered, special soliton solutions are discussed, and an explicit 1-soliton and two 2-soliton are found for three cases. In another case, there are no solitons.