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An Algebraic Circuit for a multivariate polynomial P is a computational model for constructing the polynomial P using only additions and multiplications. It is a syntactic model of computation, as opposed to the Boolean Circuit model, and hence lower bounds for this model are widely expected to be easier to prove than lower bounds for Boolean circuits. Despite this, we do not have superpolynomial lower bounds against general algebraic circuits of depth 3 (except over constant-sized finite fields) and depth 4 (over any field other than F2), while constant-depth Boolean circuit lower bounds have been known since the early 1980s. In this paper, we prove the first superpolynomial lower bounds against algebraic circuits of all constant depths over all fields of characteristic 0. We also observe that our super-polynomial lower bound for constant-depth circuits implies the first deterministic sub-exponential time algorithm for solving the Polynomial Identity Testing (PIT) problem for all small-depth circuits using the known connection between algebraic hardness and randomness. [ABSTRACT FROM AUTHOR]

The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general, priority inversion occurs in computing systems when computations that are "less important" are performed together with or ahead of those that are "more important." Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system's ability to react to critical inputs, while at the same time reducing platform cost. [ABSTRACT FROM AUTHOR]

To investigate accurately unique and advanced wave properties in elastic metamaterials advanced and efficient numerical methods are needed. The paper presents an extended boundary integral equation method for simulating elastic wave scattering by doubly periodic arrays of rectangular crack-like voids. The convergence of the arising double series is proved and the convergence rate is carefully analyzed. Employing the results of the investigation of the convergence rate, an algorithm for fast numerical calculation of double series is proposed to reduce computational costs through preliminary analytical evaluation and the proposed summation strategy. The efficiency of the extended boundary integral equation method is demonstrated and examples of the calculated transmission coefficients exhibiting the influence of the introduction of the doubly periodic array are given. [ABSTRACT FROM AUTHOR]

The article discusses a research paper that applies online resource allocation algorithms to refugee resettlement, aiming to improve refugees' integration into local communities and employment prospects. By utilizing concepts from algorithm design, such as balancing resource utilization and maintaining capacity for future refugees, the authors were able to enhance the employability metric for resettlement agencies like the Hebrew Immigrant Aid Society (HIAS) by approximately 10%. This research not only addresses critical societal issues but also highlights the potential of algorithms to positively impact real-world outcomes for vulnerable populations, encouraging collaboration between algorithm designers and practitioners on important societal problems.

Multidimensional fuzzy sets (MFS) is a new extension of fuzzy sets on which the membership values of an element in the universe of discourse are increasingly ordered vectors on the set of real numbers in the interval [ 0 , 1 ]. This paper aims to investigate fuzzy negations on the set of increasingly ordered vectors on [ 0 , 1 ] , i.e. on L ∞ ([ 0 , 1 ]) , MFN in short, with respect to some partial order. In this paper we study partial orders, giving special attention to admissible orders on L n ([ 0 , 1 ]) and L ∞ ([ 0 , 1 ]). In addition, we study the possibility of existence of strong multidimensional fuzzy negations and some properties and methods to construct such operators. In particular, we define the ordinal sums of n-dimensional negations and ordinal sums of multidimensional fuzzy negations on a multidimensional product order. A multi-attribute group decision making algorithm is presented. [ABSTRACT FROM AUTHOR]

This paper introduces a related model of membrane calculation in the defect detection and positioning of industrial components. It has the characteristics of distributed and parallel computing, and can efficiently search for better solutions in a given feature space. Inspired by the membrane clustering algorithm, this paper proposes a greedy membrane clustering algorithm and names it GMCA. GMCA is applied after the extraction of local features of normal samples. It uses a greedy strategy to construct a sub-feature set that describes the local characteristics of normal samples. During training, GMCA can learn the membrane cluster center of normal image blocks and each sub-feature within the cluster. At test time, the anomaly map is obtained by calculating the distance from the test sample block to the corresponding cluster center and the maximum distance from the cluster center to the nearest neighbor in the training sample. This solves the limitation of traditional algorithms requiring dataset alignment. In the unsupervised dataset MvTec AD, samples can be divided into object categories and texture categories according to the background of images. The pixel-level anomaly location index (AUROC) of this method on object category data reaches 98.3%. The image-level anomaly detection index (AUROC) on texture category data reaches 99.1%. • We design a computational model of membrane clustering using the evolutionary mechanisms and communicative mechanisms of cells. • GMCA has the global optimization characteristics of high accuracy and fast convergence of the membrane clustering algorithm. • GMCA has the local optimization characteristics of the greedy strategy. • GMCA solves the limitation of traditional defect detection and positioning methods that require dataset alignment. • Numerous experiments show the proposed GMCA performs competitively in industrial defect detection and location prediction. [ABSTRACT FROM AUTHOR]

This paper presents a novel and robust framework for state estimation of switched finite-field networks (SFFNs), which are a class of multi-valued logical networks that can model complex systems. The paper uses the semi-tensor product (STP) method to transform the dynamical model of SFFNs into an algebraic form, which can be easily manipulated and analyzed. The paper then applies a Monte-Carlo-based sequential importance sampling (SIS) filter to estimate the state of SFFNs from noisy and incomplete observations. The paper also introduces a resampling algorithm (RA) to prevent the particle degeneracy problem (PDP), which can affect the accuracy and efficiency of the filter. Finally, the paper demonstrates the performance and advantages of the proposed multi-valued particle filter (MVPF) framework through three examples. [ABSTRACT FROM AUTHOR]

This paper proposes an interactive forest algorithm for planning single-tuned harmonic filters for electric power distribution systems. The planning of harmonic filters is a complex problem that must consider multiple objectives, including minimizing the total cost of installed filters and the individual and total harmonic distortion while adhering to operational constraints, such as the voltage drop, the current loading and the harmonic limits. Moreover, system perturbations must be considered, such as frequency variations. This paper proposes a new method for multi-objective optimization of planning single-tuned harmonic filters utilizing the proposed interactive forest algorithm with a non-differentiable multi-objective function involving continuous and discrete variables. A multi-objective optimization technique based on the proposed interactive forest algorithm is proposed. In the interactive approach, planners set expectations for each objective and use the forest algorithm to figure out the optimization problems. The results of the Pareto Front are analyzed and the answers are searched through the interactive process to identify a solution that meets the planner's expectations, allowing for practical compromise or satisfactory solutions. A case study is reported to show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Solving nonlinear equation systems (NESs) is one of the most important tasks in numerical computing. The NESs usually have multiple roots, and quickly locating their roots in a single algorithm run with a limited number of iterations has always been the algorithm improvement direction. In order to further enhance the efficiency of the existing methods, a density clustering-based differential evolution algorithm (DCDE) for the NESs problem solving is proposed in this paper. Firstly, density-based spatial clustering of applications with noise (DBSCAN) is used to divide the population into clusters and noises, which provides a better direction for population evolution. Secondly, a cluster evaluation factor is proposed, which not only divides the clusters into excellent clusters and non-excellent clusters, but also prevents the migration of excellent clusters and maintain the diversity of populations. Then, a migration strategy and individual generation mechanism are proposed to guide non-excellent clusters to migrate to promising regions. Finally, combined with an archive technique, the individuals which satisfy the solution requirements are archived and randomly initialized to further improve population diversity. To verify the effectiveness of the proposed algorithm, comparative experimental results of the propsoed DCDE with other state-of-the-art algorithms for thirty NESs problems solving show that the proposed DCDE is the most effective algorithm to locate more roots in a single run. [ABSTRACT FROM AUTHOR]

Clustering analysis is a powerful tool for discovering potential knowledge in datasets. However, numerous existing clustering algorithms suffer from heavy reliance on parameter settings and cannot cluster complex manifold data very well. Moreover, although non-parameter clustering algorithms aim to lower the usage threshold, their actual performance in clustering is often unsatisfactory. Addressing how to achieve similar clustering effects for numerous data points with only a few core data points during clustering is a valuable consideration. To alleviate these challenges, a non-parameter clustering algorithm is proposed and named N on- p arameter C lustering Algorithm based on C hain P ropagation and N atural Neighbor (NPCCPN) in this paper, by jointly using chain propagation and natural neighbor. Specifically, NPCCPN identifies core points through chain propagation and clusters them using the saturated neighborhood graph. This makes the core data extraction and clustering process efficient and non-parameter. Finally, the performance of the method is validated on 15 complex synthetic datasets and 10 real datasets from public UCI database. The experimental results show that the effectiveness and superiority of the proposed algorithm. Purity scores first. ACC scores second without parameters, but the score first algorithm needs to set the parameters. [ABSTRACT FROM AUTHOR]

Clustering, as an essential technique in unsupervised learning, plays a pivotal role in the fields of data mining and machine learning. However, the classic K -means clustering algorithm has intrinsic drawbacks such as sensitivity to initial cluster centers, susceptibility to a local optimal solution, and challenges in handling data uncertainty. To address these problems, this paper proposes an artificial hummingbird algorithm (AHA)-based three-way K -means clustering algorithm, called AHA-3WKM. First, AHA is introduced to address the problems of sensitivity to initial cluster centers and local optima. Second, a fitness function of AHA is specifically constructed to find the best initial clustering centers so that the hummingbirds can search for high-quality food sources, i.e., the global optimum cluster centers. Third, a three-way clustering approach is utilized to capture information about data uncertainty. In this way, the results of clustering are divided into three distinct regions based on the relationship between objects and clusters. The experimental results demonstrate that AHA-3WKM has good performance, and enhances the stability and the accuracy of clustering results. • AHA is introduced to address the problems of the sensitivity to initial cluster centers and the proneness to local optima. Hummingbirds are treated as data points, which dynamically update their strategies and effectively find the optimal cluster centers during multiple iterations. • A fitness function is designed based on the clustering principle of "birds of a feather flock together", with the aim of simplifying calculations, which enhances the specificity and practicality of K-means algorithm. • An AHA-based three-way K-means clustering algorithm (i.e., AHA-3WKM) is proposed. The clustering process is initialized with cluster centers optimized by AHA, and the results are represented in three regions, which can capture the uncertainty within the datasets. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative bilevel optimization approach to elevate the deblurring process. By integrating a weights variable nonlocal model with a spatially varying attached term, the methodology aims to achieve enhanced restoration outcomes. Theoretical scrutiny is dedicated to unraveling the solution of the model, paving the way for the development of an efficient algorithm meticulously crafted to compute the clean image. This algorithm excels in learning both the weights parameter and the balanced L 2 - L 1 attached parameter concurrently, thereby ensuring optimal performance. Through careful parameter selection, the proposed nonlocal deblurring model showcases superior effectiveness, surpassing existing models in terms of both performance and efficacy. [ABSTRACT FROM AUTHOR]

• A fusion positioning scenario: closed corridor between buildings (With the development of urbanization, the positioning scene is gradually complicated.). • A novel fusion localization algorithm. (The adaptive factor function is established by using the median of residual, and the prior error covariance matrix is weighted to realize the balances the trust of the filter estimation value to the observation value and the state prediction value, then change the optimal estimation of the position.). • The proposed algorithm is compared with LS-KF, EKF, UKF and the improved LS-AKF algorithm proposed in reference [20] , and the results show that the proposed algorithm in this paper has a better effect. • Simulation experiment. To verify the stability of the fusion algorithm in the face of interference. According to the minimum detectable bias (MDB) obtained by internal reliability, different interference types and sizes are introduced, the proposed algorithm has stronger anti-interference ability. Due to the blocking and reflection of buildings, the received Beidou satellite navigation signal (BDS) in the closed corridor will be getting weaker, which results in poor positioning accuracy or positioning failure. Introducing Ultra-Wide Band (UWB) positioning technology and establishing BDS/UWB integrated positioning system is an effective method to achieve seamless positioning. The positioning accuracy obtained by the Least Square-Kalman Filter (LS-KF) algorithm is below m level. However, the prior error covariance matrix of the KF correction process is easily contaminated, which affects the proportion of system observations and state prediction values to the optimal position estimation. A LS-KF fusion positioning algorithm based on adaptive error covariance matrix is proposed, the algorithm adjusts the state prior error covariance matrix by constructing an adaptive factor, improves the Kalman gain and the optimal estimation of the position, balances the trust of the filter estimation value to the observation value and the state prediction value of the integrated positioning system. Theoretical analysis and experimental results show that compared with LS-KF, EKF, UKF and improved LS-KF algorithm, the proposed LS-AKF algorithm not only has higher positioning accuracy, stronger anti-interference ability, but also has faster convergence speed. [ABSTRACT FROM AUTHOR]

In conventional sparse Space-Time Adaptive Processing (STAP) algorithms, the l 0 -norm is often used instead of the l 1 -norm to relax convexity constraints. The approach presented in this paper can be mathematically reformulated as a linear problem, which can be efficiently solved using various convex optimization techniques. While this method effectively circumvents the NP-Hard complexity associated with the l 0 -norm, it does come with certain potential challenges. For example, the observation matrix must meet the criteria of the Restricted Isometry Property (RIP) and other stringent conditions. In this research, a novel STAP algorithm is introduced, which is based on a non-convex penalty function. This innovative algorithm substitutes the traditional l 0 -norm with a custom-designed non-convex penalty function and is applied to the Direct Filter Processor (DFP), solved using the recursive least squares (RLS) algorithm. The resulting algorithm, known as g p -RLS, is demonstrated through Monte Carlo simulations to outperform other l 1 -based and reduced-rank STAP algorithms in terms of faster convergence, improved signal-to-clutter-plus-noise ratio (SCNR), and enhanced target detection performance. [ABSTRACT FROM AUTHOR]

As a blind equalization algorithm, the fractional lower-order statistics based multimodulus algorithm (FLOS-MMA) is suitable for equalizing multilevel quadrature amplitude modulation (QAM) signals in impulsive noise. However, the equalization performance of FLOS-MMA is severely degraded in strong impulsive noise because the first-order gradient of the cost function constrains the ability of FLOS to suppress large outliers in the equalizer output signal and is sensitive to large outliers in the equalizer input signal. To enhance the robustness of multimodulus blind equalization algorithm against impulsive noise, a FLOS based momentum fractional-order multimodulus algorithm (FLOS-MFOMMA) is proposed in this paper. The proposed algorithm mitigates the adverse effects of impulsive noise on the updating of equalizer coefficients by utilizing the fractional-order gradient and accelerates the convergence speed by incorporating a momentum term. Then, the steady-state excess mean square error and the computational complexity of FLOS-MFOMMA are analyzed theoretically. Finally, numerical simulations, for equalizing 16-QAM signals under impulsive noise environments, show that the proposed algorithm is superior to FLOS-MMA in terms of robustness and convergence speed. [ABSTRACT FROM AUTHOR]

• An improved gaining-sharing knowledge-based algorithm named MSGSK is proposed. • A parameter adjustment strategy is developed to adjust relative parameters of MSGSK. • A backtracking differential mutation strategy is designed to enrich the population diversity. • A strategy selection mechanism is presented to integrate the former two strategies. • MSGSK extracts more accurate parameters for five PV models. The determination of photovoltaic (PV) model parameters has essential theoretical and practical significance for the performance evaluation, power monitoring, and power generation efficiency calculation of PV systems. In this paper, a multi-strategy gaining-sharing knowledge-based algorithm (MSGSK) is developed to determine these parameters. In our previous work, it has been demonstrated that gaining-sharing knowledge-based algorithm (GSK) is well suited for solving the concerned problem. To enhance its performance, a parameter adjustment strategy is developed to adjust the knowledge rate and knowledge ratio of GSK. Besides, a backtracking differential mutation strategy by combining the mutation scheme of differential evolution and the updating scheme of backtracking search optimization algorithm is developed to enrich the population diversity. Furthermore, a strategy selection mechanism is introduced to integrate the former two strategies to balance exploration and exploitation in different stages of the evolutionary process. The suggested MSGSK algorithm is applied to five PV cases (SDM, DDM, Photowatt-PW201, STM6-40/36, and STP6-120/36). From the experimental data, it can be observed that MSGSK extracts the PV model parameters more precisely than the basic GSK. Furthermore, it exhibits faster convergence speed and higher accuracy compared to other advanced algorithms found in the literature. [ABSTRACT FROM AUTHOR]

In a modern distributed learning framework, the speeds of intra-worker calculation and inter-worker communication may differ by 1,000 times. It is advisable to perform expensive calculations on worker machines and communicate as few rounds as possible. In this paper, we propose three novel distributed sparse learning algorithms (EDSL-ET, EDSL-IBCD, and EDSL-IBCD-ET) in high dimensions, which are efficient in both computation and communication. Algorithm EDSL-ET utilizes the Top k sparse technique to reduce communication costs and adopts error feedback to guarantee convergence. Algorithm EDSL-IBCD greatly reduces communication costs by introducing an independent block coordinate descent method. Algorithm EDSL-IBCD-ET takes advantage of EDSL-ET and EDSL-IBCD algorithms for extra-high dimensional feature learning, which is the best one whether in communication, computation, and stability. We give theoretical guarantees of the three algorithms under mild conditions, which match the performance of the centralized algorithm. Extensive experiments on simulated and real data validate our theoretical analysis and demonstrate that the proposed algorithms perform well with just a few rounds of communications. [ABSTRACT FROM AUTHOR]

• Analyze DE algorithm two key issues: parameter sensitivity and search imbalance. • A complementary mutation strategy based on complementary symmetric selection and a Gaussian distribution mutation is proposed. • Data fusion-based parameter adaptation method proposed for parameter rationalization. • A novel variant of DE called DACDE is designed and results shown competitive edge. As an excellent optimization algorithm widely used to solve various practical problems, differential evolution (DE) algorithm has few parameters, yet its performance is significantly affected by these parameters. To address this issue, this paper presents a novel variant of DE called DACDE, which utilizes data fusion-based parameter adaptation and a complementary mutation strategy. Most parameter adaptation methods based on successful history only analyze the mean of parameters and ignore their degree of dispersion. In DACDE, the successful parameter distribution is recorded and described by both the mean and variance. Data fusion is then used to combine records and generate an estimated distribution, which is applied to a Gaussian distribution to generate new parameters. Inspired by opposition-based learning, we introduce a complementary mutation strategy. This strategy employs a symmetric selection mechanism to adapt to the varying search abilities required by the algorithm at different stages. The new variant is verified on 32 single-objective functions from CEC 2011 and 2014 benchmark suites, and the results show that DACDE is competitive compared to other 29 evolutionary algorithms. [ABSTRACT FROM AUTHOR]

Federated learning (FL) enables the full utilization of decentralized training without raw data. However, various attacks still threaten the training process of FL. To address these concerns, differential privacy (DP) and secure multi-party computation (SMC) are applied, but these methods may result in low accuracy and heavy training load. Moreover, the high communication consumption of FL in resource-constrained devices is also a challenging problem. In this paper, we propose a novel SMC algorithm for the FL (FL-IPFE) to protect the local gradients. It does not require a trusted third party (TTP) and is more suitable for FL. Furthermore, we propose a secure and efficient FL algorithm (SEFL), which applies compressed sensing (CS) and all-or-nothing transform (AONT) to minimize the number of transmitted and encrypted model updates. Additionally, our FL-IPFE is used to encrypt the last element of the preprocessed parameters for guaranteeing the security of the entire local model updates. Meanwhile, the issue of participant dropouts is also taken into account. Theoretical analyses demonstrate that our proposed algorithms can aggregate model updates with high security. Finally, experimental evaluation reveals that our SEFL possesses higher efficiency compared to other state-of-the-art works, while providing comparable model accuracy and strong privacy guarantees. [ABSTRACT FROM AUTHOR]

Many action recognition methods require significant computational resources to achieve good results on unedited videos. However, their performance on infrared videos, which contain less information, is often unsatisfactory. In this paper, we propose a multi-scale difference joint key frame extraction algorithm for action recognition in infrared surveillance videos. To evaluate our algorithm, we have created a self-built dataset comprising 1200 unedited infrared surveillance videos categorized into 10 action categories. Our algorithm extracts key frames by jointly analyzing the global and local differences between adjacent frames. Experimental analysis demonstrates that by using only 10 frames, our algorithm improves the accuracy of generic action recognition algorithms by more than 10% on both our self-built dataset and the Infrared-Visible dataset. Moreover, our proposed method achieves high recognition accuracy with minimal computational cost, even when using a small number of frames. It outperforms state-of-the-art methods by 1.82% on the Infrared-Visible dataset and 1.35% on the InfAR dataset. These results highlight the effectiveness of our algorithm as a preprocessing module to significantly enhance the accuracy of action recognition algorithms prior to employing generic models. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of identifying ship motion parameters and wave peak frequency. Utilising the Euler discretisation principle, we establish a discrete-time auto-regressive moving average model with exogenous input (ARMAX) for the ship-wave system. Furthermore, we develop a filtering-based stochastic gradient algorithm for the system by applying filtering techniques and auxiliary model identification idea. A filtering-based multi-innovation stochastic gradient algorithm, utilizing the multi-innovation identification theory, was developed to enhance the convergence rate and accuracy of parameter identification. This approach was found to be more effective than the filtering-based stochastic gradient algorithm. Simulation results validate the efficacy of the proposed algorithm in parameter identification. • Based on the Eulerian discretization idea, a ship-wave discrete-time autoregressive moving average model with exogenous inputs is derived. • Introducing the filtering technique and the auxiliary model identification idea, a filtered stochastic gradient algorithm is proposed. • A filtering-based multi-innovation stochastic gradient algorithm is proposed based on filtered stochastic gradient identification. [ABSTRACT FROM AUTHOR]

Mining co-location patterns hidden in spatial data is crucial for spatial association discovery, and it has broad prospects in many applications. H igh U tility C o-location P attern M ining (HUCPM) further takes the utility factor of spatial features into consideration, so it is more realistic compared with the traditional co-location pattern mining. However, HUCPM is more difficult, since the Apriori-like pruning technique does not apply. To address this problem, we firstly suggest two novel pruning strategies to trim the pattern search space. Then, a series of optimizing techniques are presented to speed up the pattern utility ratio calculation of each candidate. Based on above techniques, a fast HUCPM algorithm is proposed, which searches for high utility co-locations involved in each pattern branch via a depth-extending manner and equips with a heuristic strategy to enhance the effect of pruning techniques. Moreover, we theoretically prove the completeness and correctness of the proposed algorithm, and discuss its algorithmic complexity. On multiple spatial datasets, we conduct substantial experiments to reveal the superiority of our algorithm in efficiency and scalability, as well as the effectiveness of the proposed technique. Particularly, the proposed algorithm in this paper runs faster than other baselines for several times to several orders of magnitude. [ABSTRACT FROM AUTHOR]

In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables that optimise multiple objectives, leveraging the relational data of a set of variables and multiple objectives. For these problems, this paper focuses on the following two issues: one is the demand for a unified computational model to solve this problem; the other is how to improve the algorithm's deep intelligent search capability. In this regard, this paper designs a dual data-driven multi-objective optimisation method. The method used consisted of four parts: elimination of redundant variables (ERV), objective function construction (OFC), selection evolution operator (SEO), and multi-objective evolutionary algorithm (MOEA). MOEA was the main focus of the method. ERV is data preparation and variable selection according to multiple objectives. OFC involves constructing the relationship model between variables and objectives, and a high-accuracy model is important for guaranteeing reliable results. Furthermore, SEO can adjust the evolution operator during a deep search. This is an important guarantee for deep, intelligent search. MOEA combined OFC and SEO to form the final solution algorithm—Dual Data Driven Multi-Objective Evolutionary Algorithm (DDMOEA). DDMOEA was explored using two different disciplinary problems of drug compound optimisation and wild blueberry cultivation and benchmarks were selected. The first two problem domains are distinct. The first problem is more complex than the second; however, both encompass redundant variables and indefinite objective functions. Benchmarks are utilised independently to gauge the profound intelligent search capability. The experiments affirm that the dual data-driven optimization approach proposed in this paper is effective, practical, and scalable. • Provide a unified solution for optimisation problems with redundant variables and indefinite objective functions. • Propose a data-driven multi-objective optimisation algorithm. • Designing an adaptive optimiser to enhance the algorithm's depth search capability. [ABSTRACT FROM AUTHOR]

Clustering methods are being applied to a wider range of scenarios involving more complex datasets, where the shapes of clusters tend to be arbitrary. In this paper, we propose a novel Path-based Valley-seeking clustering algorithm for arbitrarily shaped clusters. This work aims to seek the valleys among clusters and then individually extract clusters. Three vital techniques are used in this algorithm. First, path distance (minmax distance) is employed to transform the irregular boundaries among clusters, that is density valleys, into perfect spherical shells. Second, a suitable density measurement, k -distance, is employed to make adjustment on Minimum Spanning Tree, by which a robust minmax distance is calculated. Third, we seek the transformed density valleys by determining their centers and radii. Based on the vital techniques, the main contributions of the proposed algorithm can be summarized as follows. First, the clusters are wrapped in spherical shells after the distance transformation, making the extraction process efficient even with clusters of arbitrary shape. Second, adjusted Minimum Spanning Tree enhances the robustness of minmax distance under different kinds of noise. Last, the number of clusters does not need to be inputted or decided manually due to the individual extraction process. After applying the proposed algorithm to several commonly used synthetic datasets, the results indicate that the Path-based Valley-seeking algorithm is accurate and efficient. The algorithm is based on the dissimilarity of objects, so it can be applied to a wide range of fields. Its performance on real-world datasets illustrates its versatility. [ABSTRACT FROM AUTHOR]

In this paper, a new differential evolution (DE) algorithm is presented for solving global optimization problems by guiding the individuals adaptively to explore different decision regions and setting the control parameters properly for individuals. To consolidate algorithm efficiency and preserve population diversity, we first introduce a proration-based mutation strategy. This strategy dynamically selects promising individuals from population as guiders and proportionately allocates the individuals following them based on their fitness values and unsuccessful updates. Besides, we design a multi-segment mixed parameter setting to provide suitable parameters for individuals, considering feedback information from the population, individual requirements, and parameters' interaction. Additionally, we devise an adaptive population maintaining mechanism to refresh the population by replacing individuals with higher unsuccessful updates and removing those with poor fitness values. Unlike previous DE versions, our new algorithm dynamically and appropriately explores promising areas, refines parameter suitability, and accelerates convergence by updating and removing unpromising individuals. This enhances the algorithm search efficiency and achieves a good balance between exploration and exploitation. Finally, we evaluate the proposed algorithm's performance by comparing it with 17 typical or state-of-the-art algorithms on IEEE CEC2014 and CEC2017 test suites. Experimental results indicate that the proposed algorithm is a more promising optimizer. [ABSTRACT FROM AUTHOR]

Multi-view subspace clustering aims to leverage the consensus and complementary information embedded within the data and effectively fuse the multiple views. However, existing methods have limitations in fully exploring the complementary information. Tensor-based methods tend to overlook the underlying structures, while latent representation-based methods struggle to capture high-order correlations, both of which are important components of complementary information. In this paper, we introduce a novel method called complete multi-view subspace clustering (CMVSC) using visible and latent views. The latent view with the underlying structures is obtained by the latent representation, along with visible views (i.e., original features) constitute the complete multi-view data. Then, the low-rank tensor constraint is imposed on the complete views to comprehensively explore the consensus and complementary information. Besides, to effectively combine the advantages of visible and latent views, we devise an auto-weighted strategy that can automatically assign the ideal weights to each view. Finally, an efficient alternating iterative algorithm using the augmented Lagrange multiplier method is designed to solve the CMVSC model. Experimental results show that our model outperforms the state-of-the-art counterparts while achieving almost perfect clustering performance on several benchmark datasets. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative methodology to establish consensus among distributed information processing nodes (DIPNs) in the context of multi-target tracking (MTT) within environments characterized by resource constraints and communication uncertainties. Through the integration of the event-triggered (ET) strategy with a consensus-based algorithm, existing approaches foster consensus among DIPNs while simultaneously conserving communication resources. Nonetheless, a systematic investigation into the comprehensive analysis of data reliability from each node has not been conducted. Combining anomalous data resulting from communication uncertainties with other data on an equal footing leads to inaccurate results. To address this issue, we apply the multiple-model algorithm, assigning consensus weight to each DIPN based on the motion model distribution of the same target observed by different DIPN. Additionally, we introduce an auxiliary ET marker, considering the divergence in the motion model distribution between two consecutive moments of a certain target. This marker assists in determining whether local information must be transmitted to other DIPNs. The proposed approach yields more accurate and congruent output results from each DIPN in comparison to conventional methods, given the same triggering frequency. Numerical simulations demonstrate the efficacy of the suggested approaches in a distributed MTT scenario. • We apply the multiple-model generalized labeled multi-Bernoulli filter. • We combine event-triggered strategy with consensus approach to reduce communication. • We check data reliability from each node to improve multi-target tracking accuracy. • We suggest an additional event-triggered mark to locate the message for transmitting. [ABSTRACT FROM AUTHOR]

The presence of noise is a pervasive issue that significantly impacts the performance of adaptive filtering algorithms. To address this challenge, the bias compensation technique has recently emerged, involving the incorporation of an additional term into the update equation. Despite the apparent simplicity of the bias-compensated least-mean-squares algorithm, conducting a comprehensive theoretical analysis of its performance is a complex task. In this paper, we undertake an exact expectation analysis to demonstrate the asymptotic unbiasedness of the algorithm, even when disregarding the commonly assumed theory of independence between adaptive coefficients and input data. Furthermore, to improve the understanding of the algorithm's stability, we employ a stochastic model that assumes independence between the radial and angular distributions of the input vector. The resulting model is intricate, necessitating heuristic approximations to derive practical insights. Notably, our analysis reveals that the upper bound on the step size of the bias-compensated least-mean-squares algorithm consistently remains smaller than that of the least-mean-squares algorithm. These findings gain robust support from extensive simulations. [ABSTRACT FROM AUTHOR]

Constrained multi-objective optimization problems (CMOPs) require a delicate balance between satisfying constraints and optimizing objectives. Existing constrained multi-objective evolutionary algorithms (CMOEAs) often struggle to balance convergence, diversity, and feasibility, especially when dealing with CMOPs that have complex feasible regions. This paper proposes a multi-task-based self-organizing mapping evolutionary algorithm (MTSOM) to tackle this challenge, which includes a main and auxiliary task. Two populations independently optimize two tasks without considering constraints in the early stage. Subsequently, in the middle stage, both tasks explore the distribution structure of the population in parallel by employing a novel constraint-to-constraint self-organizing mapping (SOM) approach. In the late stage, the main task fully considers feasibility, while the auxiliary task focuses solely on the highest priority constraints. This approach enables rapid convergence toward feasible regions. To evaluate MTSOM's effectiveness, we conducted a series of experiments on five benchmark suites. Results indicate that MTSOM is competitive when compared to other state-of-the-art CMOEAs. Additionally, our proposed constraint-to-constraint SOM is superior in handling complex CMOPs. [ABSTRACT FROM AUTHOR]

Large-volume and high-dimensional big datasets are being generated quickly. They are expected to provide data-driven solutions for various pressing challenges such as cybersecurity, credit card fraud, network intrusion detection, etc. The visual assessment of clustering tendency (VAT) family of algorithms provides an excellent tool for assessing clustering tendency and subsequent clustering of these novel datasets to extract groups and anomalies to better understand the data generation phenomenon. However, VAT and improved VAT (iVAT) algorithms are plagued with O (n 2) time and space complexity as they use Prim's algorithm for building Euclidean minimum spanning tree (EMST) of the data points, leaving it inapplicable to large datasets. This paper develops three novel time- and memory-scalable algorithms for fast computation of VAT EMST by reducing the search space of the following possible EMST edge and using an efficient data structure, the k-d tree. Next, we develop a novel approach to compute iVAT reordered dissimilarity image (RDI) in a novel memory-preserving manner using the EMST edge lengths calculated previously without having to compute memory intensive n × n VAT RDI. We experimented on several synthetic and real-life datasets to showcase that the proposed approaches can help quickly assess their clustering tendency in a memory-saving manner. We also demonstrate the applicability of the proposed methods for the anomaly detection task in large volumes of high-dimensional data. [ABSTRACT FROM AUTHOR]

This paper investigates the efficient solution of penalized quadratic regressions in high-dimensional settings. A novel and efficient algorithm for ridge-penalized quadratic regression is proposed, leveraging the matrix structures of the regression with interactions. Additionally, an alternating direction method of multipliers (ADMM) framework is developed for penalized quadratic regression with general penalties, including both single and hybrid penalty functions. The approach simplifies the calculations to basic matrix-based operations, making it appealing in terms of both memory storage and computational complexity for solving penalized quadratic regressions in high-dimensional settings. [ABSTRACT FROM AUTHOR]

Algorithm selection technology, aimed at choosing the most suitable algorithm for a given machine learning task, has achieved significant success in the domain of 2D vision. However, few studies have explored its application to the 3D point cloud domain. The rapid proliferation and development of 3D point cloud classification algorithms underscore the urgent need for exploration in selecting these algorithms. In this paper, we propose a novel meta-learning-based 3D classification approach, termed 3D meta-classification , to address this gap. The approach operates at both the base-level and meta-level phases. At the base level, candidate 3D classification algorithms constantly classify various 3D datasets, recording their classification performance as empirical knowledge. As for the meta-level phase, it specifically tailors the 3D meta-knowledge generator, 3D meta-feature extractor, and 3D meta-database constructor to establish the 3D meta-database, capturing the relationship between the 3D meta-features and empirical knowledge. Leveraging the 3D meta-database, the 3D classification meta-learner trains the meta-model and predicts suitable algorithms for new incoming 3D datasets. Extensive experiments are conducted to select the best-performing classification algorithm for specific 3D datasets from ModelNet40. The results demonstrate the effectiveness of the proposed 3D meta-classification model; the accuracies of one-from-two and one-from-three algorithm selection tasks reach over 98% and 80%, respectively. [ABSTRACT FROM AUTHOR]

As a population-based stochastic optimization algorithm, Gravitational Search Algorithm (GSA) has attracted numerous interests and has been applied in various applications. However, GSA has drawbacks such as uneven search and premature convergence in practical applications. This paper specifically explains the inherent characteristic of GSA in prioritizing the center position. Correspondingly, an improvement strategy of fitness normalization with mass shift is proposed, creating a situation where gravity and repulsion are mixed. Then, the global best mechanism with weights is incorporated into the particle's velocity update formula, which compensates for the difficulties in the later exploitation stage. Finally, an empirical formula for the initial gravitational constant related to the size of the solution space is proposed, which enhances the global search ability together with the former strategy. 12 shifted benchmark functions are used to construct 20 optimization problems ranging from 2 to 120 dimensions. The average performance of the proposed algorithm, other GSA and well-known algorithms are compared under the same budget. The results demonstrate that the proposed GSA not only effectively addresses the drawbacks of GSA and maintains good performance, but also exhibits strong competitiveness compared to various similar algorithms. [ABSTRACT FROM AUTHOR]

Traditional nonprofiling side-channel analysis frequently adopts divide-and-conquer strategy to recover the secret key of a cryptographic algorithm. Only a single key byte is used, whereas the remaining bytes are considered extraneous noise. If the cryptographic algorithm is implemented in hardware with parallel processing, the strategy will lead to the inefficient use of information. The combination of intelligent algorithms and side-channel analysis offers a different idea for nonprofiling methods for parallel hardware implementations. It transforms the problem of key recovery into key optimization. In this paper, we adopt this idea and propose an efficient heuristic framework to assist correlation power analysis. The multipoint hill-climbing algorithm will be used to find the optimal key candidates with higher scores, and the correct key will be found in the optimal key candidates by key enumeration. Multiple bytes of the secret key are used to take full advantage of the helpful information. Besides, two search strategies are introduced to overcome the efficiency problem, and two key enumeration strategies are introduced to solve the key recovery problem when the traces are insufficient. Experimental results on the public DPA Contest V1 dataset show that the heuristic framework performs better than other classical methods, verifying its effectiveness. [ABSTRACT FROM AUTHOR]

The application of semantic technologies, particularly ontologies, in the realm of multi-objective evolutionary algorithms is overlook despite their effectiveness in knowledge representation. In this paper, we introduce MOODY, an ontology specifically tailored to formalize these kinds of algorithms, encompassing their respective parameters, and multi-objective optimization problems based on a characterization of their search space landscapes. MOODY is designed to be particularly applicable in automatic algorithm configuration, which involves the search of the parameters of an optimization algorithm to optimize its performance. In this context, we observe a notable absence of standardized components, parameters, and related considerations, such as problem characteristics and algorithm configurations. This lack of standardization introduces difficulties in the selection of valid component combinations and in the re-use of algorithmic configurations between different algorithm implementations. MOODY offers a means to infuse semantic annotations into the configurations found by automatic tools, enabling efficient querying of the results and seamless integration across diverse sources through their incorporation into a knowledge graph. We validate our proposal by presenting four case studies. • A semantic model is proposed for consolidating multi-objective optimization knowledge. • A knowledge graph is presented, as well as the tools to include new RDF data. • The model allows for semantic reasoning on evolutionary algorithm configurations. • The proposal is validated on three use cases with data from experiments. • Moody enables knowledge-based recommendations of configurations for algorithms. [ABSTRACT FROM AUTHOR]

Despite the huge success of reinforcement learning (RL) in solving many difficult problems, its Achilles heel has always been sample inefficiency. On the other hand, in RL, taking advantage of prior knowledge, intentionally or unintentionally, has usually been avoided, so that, training an agent from scratch is common. This not only causes sample inefficiency but also endangers safety –especially during exploration. In this paper, we help the agent learn from the environment by using the pre-existing (but not necessarily exact or complete) solution for a task. Our proposed method can be integrated with any RL algorithm developed based on policy gradient and actor-critic methods. The results on five tasks with different difficulty levels by using two well-known actor-critic-based methods as the backbone of our proposed method (SAC and TD3) show our success in greatly improving sample efficiency and final performance. We have gained these results alongside robustness to noisy environments at the cost of just a slight computational overhead, which is negligible. [ABSTRACT FROM AUTHOR]

In this paper, we design a low-complexity direction-of-arrival (DOA) estimation algorithm based on the unitary approximate message passing (UAMP) and Bernoulli-Gaussian (BG) prior. We first show that the estimation of DOA can be transferred into a sparse signal recovery problem, where we turn to UAMP with damping technique to solve this problem. Furthermore, we assume the BG prior on the sparse vector to be estimated, resulting in the fast estimation of the positions of non-zero elements. Moreover, expectation maximum (EM) is leveraged to automatically learn the BG parameters. Compared to the state-of-the-art UAMP-based algorithm with sparse Bayesian learning (SBL), the proposed approach can achieve the same DOA mean square error (MSE) performance with a much faster convergence speed. [ABSTRACT FROM AUTHOR]

Nonlinear adaptive filters, such as Volterra and functional link adaptive filter (FLAF), face increased computational complexity with rising input dimensionality or expansion order. To enhance computational efficiency, this paper introduces a nonlinear adaptive algorithm based on Rectified Linear Unit (ReLU). Applying ReLU to a tonal signal generates components corresponding to the original frequency and even harmonics. Additionally, applying ReLU to the sum of two tonal signals yields a spectrum encompassing intermodulation distortion frequencies. These traits enable the algorithm to mitigate harmonic and intermodulation distortions while maintaining judicious computational complexity. The numerical simulation results for the identification of nonlinear systems validate the algorithm's ability to fit harmonic and intermodulation nonlinear distortions. In simulations of nonlinear echo cancellation with diverse test signals and various distortion models, our proposed algorithm consistently demonstrates competitive performance, notably excelling in music and speech scenarios. [ABSTRACT FROM AUTHOR]

The Elo algorithm, renowned for its simplicity, is widely used for rating in sports tournaments and other applications. However, despite its widespread use, a detailed understanding of the convergence characteristics of the Elo algorithm is still lacking. Aiming to fill this gap, this paper presents a comprehensive (stochastic) analysis of the Elo algorithm, considering round-robin tournaments. Specifically, analytical expressions are derived describing the evolution of the skills and performance metrics. Then, taking into account the relationship between the behavior of the algorithm and the step-size value, which is a hyperparameter that can be controlled, design guidelines and discussions about the performance of the algorithm are provided. Experimental results are shown confirming the accuracy of the analysis and illustrating the applicability of the theoretical findings using real-world data obtained from SuperLega, the Italian volleyball league. [ABSTRACT FROM AUTHOR]

The existing methodologies for hybrid narrow-band active noise control (HNANC) systems utilises variants of the FXLMS algorithm, which typically resulted in degraded convergence and steady-state performance. In this paper, a class of variable step size (VSS) adaptive algorithms are explicitly developed for a HNANC system analyses and the convergence as well as the steady-state performance of the proposed framework are evaluated in comparison to the existing state-of-the-art. For an HNANC system designed to operate in a multiple noise environment, the VSS filtered-x LMS (VSSFXLMS) and momentum VSS filtered- x weight accumulated least mean square (MVSS-FXWALMS) algorithms are proposed. The proposed algorithm employs a variable step size that is inspired by the variable momentum factor, and combines these two parameters to drive the FXWALMS algorithm for an HNANC system in order to accomplish maximum noise reduction. In a multiple noise environment setup for an HNANC system, the proposed algorithm surpasses the state-of-the-art in nearly all conventional operating conditions. A detailed performance analysis of the proposed algorithm has been carried out using mean noise reduction ratio, steady-state power spectra at different conditions. The results demonstrate a significant improvement in noise reduction performance compared to the counterparts. [ABSTRACT FROM AUTHOR]

This paper introduces a novel algorithm for solving the point-to-point shortest path problem in a static regular 8-neighbor connectivity (G8) grid. This algorithm can be seen as a generalization of Hadlock algorithm to G8 grids, and is shown to be theoretically equivalent to the relaxed A ⁎ (R A ⁎) algorithm in terms of the provided solution's path length, but with substantial time and memory savings, due to a completely different computation strategy, based on defining a set of lookup matrices. Through an experimental study on grid maps of various types and sizes (1290 runs on 43 maps), it is proven to be 2.25 times faster than R A ⁎ and 17 times faster than the original A ⁎ , in average. Moreover, it is more memory-efficient, since it does not need to store a G score matrix. [ABSTRACT FROM AUTHOR]

As one of the main research tasks in software testing, automated test case generation based on path coverage (ATCG-PC) aims to achieve maximum path coverage with a minimized set of test cases. In ATCG-PC, the correlation among the dimensions of test cases is widely utilized in academia to minimize the search efforts of the search-based algorithm. Nevertheless, the information related to target path selection is not utilized, which leads to blind decision-making by the search-based algorithm during the target path selection. Therefore, this paper proposes an enhanced scatter search strategy by using opposition-based learning. An arithmetic optimization algorithm is also proposed to solve ATCG-PC based on the enhanced scatter search strategy, namely, ESSENT. The ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path, and generates new test cases that cover the target path by modifying the dimensions of the existing test cases. The performance of the ESSENT algorithm is evaluated on six iFogSim subprograms and six Stanford coreNLP subprograms. Experiment results show that the ESSENT algorithm achieves a higher convergence rate than other state-of-the-art algorithms. Furthermore, it enables maximum path coverage with fewer test cases. [ABSTRACT FROM AUTHOR]

The joint order batching and picker routing problem (JOBPRP) is a combinatorial optimization problem that occurs during in the order-picking operation of warehouse management. It consists of simultaneous assignment of customer orders to batches and routing of picking operations in the batches such that the total travel distance is minimized. In this paper, we develop a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the JOBPRP. MA-FPC takes advantage of the hierarchical nature of the problem to design a batch exchange crossover (BEX) operator and a two-level local improvement procedure. BEX transfers complete batches from parent solutions to offsprings. Local improvement systematically employs a combined heuristic and exact routing approach to solve the picker routing problem. Furthermore, fuzzy-based population control is used to dynamically regulate population diversity during the search, which removes individuals from or adds individuals to the population. In numerical experiments, MA-FPC significantly outperforms state-of-the-art point-based and population-based algorithms developed for the order batching problem and JOBPRP with respect to solution quality in reasonable running times. Our algorithm improves the previous best-known solutions for 57 of the 64 benchmark instances. [ABSTRACT FROM AUTHOR]

A variety of algorithms has been proposed to mitigate the overestimation bias of Q-learning. These algorithms reduce the estimation of maximum Q-value, i.e., homogeneous update. As a result, some of these algorithms such as Double Q-learning suffer from the underestimation bias. Different from these algorithms, this paper proposes a heterogeneous update idea. It aims to enlarge the normalized gap between Q-value corresponding to the optimal action and that corresponding to the other actions. Based on heterogeneous update , we design HetUp Q-learning. More specifically, HetUp Q-learning increases the normalized gap by overestimating Q-value corresponding to the optimal action and underestimating Q-value corresponding to the other actions. However, one limitation is that our HetUp Q-learning takes the optimal action as input to decide whether a state-action pair should be overestimated or underestimated. To address this challenge, we apply a softmax strategy to estimate the optimal action and obtain HetUpSoft Q-learning. We also extend HetUpSoft Q-learning to HetUpSoft DQN for high-dimensional environments. Extensive experiment results show that our proposed methods outperform SOTA baselines drastically in different settings. In particular, HetUpSoft DQN improves the average score per episode over SOTA baselines by at least 55.49% and 32.26% in the Pixelcopter and Breakout environments, respectively. [ABSTRACT FROM AUTHOR]

Balancing multiple objectives and various constraints is crucial for effectively solving constrained multi-objective optimization problems (CMOPs). Excessive focus on either convergence or feasibility may not result in favorable outcomes of the algorithm. To confront this challenge, this paper proposes a cooperative evolutionary algorithm named SaE-CMO, which aims to achieve a harmonious balance between convergence and feasibility by extracting valuable information from both feasible and infeasible regions. To achieve this, SaE-CMO employs a dual-population approach to enhance search progress, consisting of a main population, Population1, and an auxiliary population, Population2. These two populations complement each other to achieve optimal results. A newly proposed self-adaptive epsilon method is employed in both Population1 and Population2, using different comparison criteria to select next population from mating pools, respectively. Population2 can retain some solutions that are well-constrained but poorly converged, thereby preserving information about both the constrained and the unconstrained Pareto front. This property enables Population2 to assist Population1 in maintaining diversity in certain complex CMOPs. To verify the effectiveness of SaE-CMO, we conduct experiments on three benchmark test instances and four real-world CMOPs with some related state-of-the-art constrained multi-objective optimization algorithms, experimental results prove that the proposed algorithm outperforms the compared algorithms. [ABSTRACT FROM AUTHOR]

Differential privacy (DP), as a promising privacy-preserving model, has attracted great interest from researchers in recent years. At present, research on the combination of deep learning and DP is active. In contrast, another widely used artificial intelligence technique, swarm intelligence (SI), has received little attention in the context of DP even though it also triggers privacy concerns. For this reason, this paper attempts to combine DP and SI for the first time and proposes a general differentially private swarm intelligence algorithm framework (DPSIAF). By utilizing the exponential mechanism, this framework can easily develop existing SI algorithms into the private versions. As examples, we apply it to four popular SI algorithms, and the related analyses demonstrate the effectiveness of our DPSIAF. More importantly, our private algorithms also exhibit some interesting experimental phenomena. In some cases, their performance is not strictly affected by the privacy budget, and one of them even outperforms its non-private version. These findings are different from the conventional cognition, which indicates DP owns the potential to serve as an optimization tool for some SI algorithms. Our study may provide a new perspective on DP and promote the synergy between metaheuristic optimization community and privacy computing community. [ABSTRACT FROM AUTHOR]

Border-Peeling algorithm is a recently proposed density based clustering algorithm. The method of peeling off border points by continuous iteration and calculating the density influence value by using the Gaussian kernel distance makes the algorithm more complex. At the same time, there is a risk of excessive peeling of small clusters on unbalanced datasets, which leads to a large number of noise misidentification. In order to reduce the time consumption and improve the clustering performance on unbalanced datasets, this paper proposes a non-iterative border-peeling clustering algorithm. First, the potential core points are determined by the centroids of k-nearest neighbor. Secondly, the points with lower local relative density in the core points and the points with higher relative density in the border points are exchanged to complete the distinction between the core points and the border points. Then basic DBSCAN method is used to cluster core points and noise points. Finally, the associations between the border points and the core points are based on the number of reverse nearest neighbors of the border points in the core points. Our method has achieved competitive results on 10 synthetic datasets and 8 UCI real-world datasets. [ABSTRACT FROM AUTHOR]

The time series database is a specialized type of database specifically designed for storing and analyzing time series data. Compression of time series data is crucial for its performance. However, efficiently compressing time series data, particularly floating-point data, remains challenging. Existing compression algorithms are efficient for only a limited range of data patterns, indicating a lack of self-adaptation. In this paper, we propose an effective and A daptive lossless F loating-point C ompression algorithm AFC for time series databases. We devise four unique compression strategies, and based on the data patterns, AFC dynamically selects the appropriate strategy. These strategies handle data compression for diverse data patterns, enhancing the compression ratio and efficiency. The most suitable strategy is employed to achieve an optimal compression ratio. We compared our AFC algorithm with four state-of-the-art compression algorithms, namely Gorilla, FPC, TSXor, and Chimp, as well as various general-purpose compression algorithms such as LZ4 and Snappy. Experimental results demonstrate that our algorithm achieves an improvement of at least 20% in compression ratio and even up to 100% on certain datasets. [ABSTRACT FROM AUTHOR]

Optimal granularity selection is a key issue in rough set, by which decision rules can be generated to assign corresponding decision labels for new samples. The current evaluation criteria of optimal granularity mainly focus on the experimental effect of prediction algorithms, thus ignoring the theoretical relationship between granularity and the generalization ability of algorithms. In this paper, we study the issue overlooked above and propose a novel framework for optimal granularity selection with theoretical guarantee. In our framework, a rough set-based prediction algorithm that incorporates a global confidence as scoring function is introduced, which is characterized by the granularity-based loss function. On the bias, the relationship between granularity and the generalization ability of algorithm is studied by introducing the stability theory in machine learning. The generalization error bound of algorithm is derived as a theoretical guarantee for optimal granularity selection. Finally, a novel optimal granularity selection strategy with theoretical guarantee is proposed, which is combined with existing attribute reduction methods to design the attribute re-reduction algorithms for generate optimal granularity by reducing the relatively unimportant attributes. Numerical experiments verify the rationality of optimal granularity selection framework and prove the effectiveness of the optimal granularity generated by the attribute re-reduction algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we focus on robust sparse M -estimation over decentralized networks in the presence of Byzantine attacks. In particular, a decentralized network is modeled as an undirected graph without a central node, while a small fraction of nodes usually behave arbitrarily and send erroneous information due to system breakdowns, cyber attacks and so on. To address the Byzantine issue, some pre-determined robust aggregation rules are applied. Moreover, the gradient tracking and proximal algorithm are combined to ensure convergence and sparsity simultaneously. Theoretically, our proposed algorithms are provably robust against Byzantine attacks and achieve linear convergence rates. The finite-sample performance is studied through numerical experiments under various settings and an application to Communities and Crime Data is also presented. [ABSTRACT FROM AUTHOR]