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High-quality underwater optical images are essential for various applications of underwater vision. However, these images often suffer from severe degradation, complex noise, low contrast, and color cast, leading to poor image quality. To address these issues and accomplish related underwater vision tasks more smoothly, researchers have made many efforts to improve the quality of underwater optical images. This paper presents a comprehensive review of recent research, focusing on specific algorithms published between 2013 and 2023, while also predicting future research trends in this field. In light of the observed ambiguities and inconsistencies in previous review studies' classification frameworks, this paper introduces a novel classification framework, grounded in the specific challenges in this domain, including issues unique to the imaging medium and imaging environment. Additionally, the proposed framework offers a more detailed categorization based on the algorithmic ideology of the authors. Furthermore, this paper addresses a significant gap in the literature by providing an in-depth assessment of the methods for removing marine snow noise from underwater optical images. By complementing existing research, this paper aims to enhance understanding of this subject and provide a clearer, more comprehensive, and in-depth exploration of underwater image quality improvement. [ABSTRACT FROM AUTHOR]

This paper introduces a novel application of subloading surface concept to effectively model the elastoplastic behavior of materials in the finite strain regime. The subloading surface approach allows for a smoother and more seamless transition from the elastic to the plastic region. Two time integration schemes are presented in this study, one in the intermediate configuration and the other in the current configuration. In the first time integration scheme, co-rotational objective time rates are not required, and the formulation utilizes the Mandel stress. In the second time integration scheme, implemented in Eulerian description, the Kirchhoff stress defined in the current configuration is utilized. The results obtained for the simple shear deformation case demonstrate the absence of oscillations. Additionally, these results show excellent agreement with experimental data and existing findings from the literature. Furthermore, a computational comparison between the time integration schemes using the subloading surface model and a model discussed in the literature reveals that the proposed algorithms in this paper require significantly fewer increments in the given time integrations. This indicates a higher computational efficiency and reduced computational time for the proposed methods. Overall, the predicted responses of all the analyzed cases from the provided time integrations exhibit excellent agreement with each other, confirming the accuracy and effectiveness of the presented model. • Novel use of subloading surface in finite strain elastoplasticity was introduced. • Proposed algorithms were reduced the increments and computational cost remarkably. • Smoother and more realistic transition from elastic to plastic region was captured. • Proposed time integrations effectively simulated saturation and softening behavior. • No oscillations were seen in simple shear elastoplastic deformation. [ABSTRACT FROM AUTHOR]

The enclosed multistory poultry housing is a type of poultry enclosure widely used in industrial caged chicken breeding. Accurate identification and detection of the comb and eyes of caged chickens in poultry farms using this type of enclosure can enhance managers' understanding of the health of caged chickens. However, the accuracy of image detection of caged chickens will be affected by the enclosure's entrance, which will reduce the precision. Therefore, this paper proposes a cage-gate removal algorithm based on big data and deep learning Cyclic Consistent Migration Neural Network (CCMNN). The method achieves automatic elimination and restoration of some key information in the image through the CCMNN network. The Structural Similarity Index Measure (SSIM) between the recovered and original images on the test set is 91.14%. Peak signal-to-noise ratio (PSNR) is 25.34dB. To verify the practicability of the proposed method, the performance of the target detection algorithm is analyzed both before and after applying the CCMNN network in detecting the combs and eyes of caged chickens. Different YOLOv8 detection algorithms, including YOLOv8s, YOLOv8n, YOLOv8m, and YOLOv8x, were used to verify the algorithm proposed in this paper. The experimental results demonstrate that compared to images without CCMNN processing, the precision of comb detection of caged chickens is improved by 11, 11.3, 12.8, and 10.2%. Similarly, the precision of eye detection for caged chickens is improved by 2.4, 10.2, 6.8, and 9%. Therefore, more complete outline images of caged chickens can be obtained using this algorithm and the precision in detecting the comb and eyes of caged chickens can be enhanced. These advancements in the algorithm offer valuable insights for future poultry researchers aiming to deploy enhanced detection equipment, thereby contributing to the accurate assessment of poultry production and farm conditions. [ABSTRACT FROM AUTHOR]

This paper introduces a family of subdivision schemes that generate curves over manifolds from manifold-Hermite data. This data consists of points and tangent directions sampled from a curve over a manifold. Using a manifold-Hermite average based on the De Casteljau algorithm as our main building block, we show how to adapt a geometric approach for curve approximation over manifold-Hermite data. The paper presents the various definitions and provides several analysis methods for characterizing properties of both the average and the resulting subdivision schemes based on it. Demonstrative figures accompany the paper's presentation and analysis. [ABSTRACT FROM AUTHOR]

Constrained multitasking optimization (CMTO) obtains increasing attention recently. The goal of CMTO is to handle multiple constrained tasks simultaneously. There are two limitations of existing studies on CMTO: (i) existing knowledge transfer techniques for CMTO may not work due to non-intersecting feasible domains and the unconsidered relationship between constraints and objectives; and (ii) knowledge diversity is lacking in existing CMTO algorithms because the representation of knowledge is biased to feasible solutions. To address these limitations, this paper proposes a co-evolution and domain adaptation (CEDA) method for CMTO. First, a new constraint relaxation-based domain adaptation technique for knowledge transfer is devised. Domain adaptation can effectively address the limitations imposed by non-intersecting feasible domains. In addition, as the population evolves, the knowledge representation is biased to different kinds of solutions. Second, a co-evolutionary strategy is proposed to improve the knowledge diversity. The two proposed techniques are with generality and can be readily integrated into different multitasking frameworks. In this paper, the CEDA method is combined with two popular multitasking (i.e., multifactorial-based and multi-population-based) frameworks. The constructed CEDA-based algorithms are compared with fifteen state-of-the-art algorithms on a CMTO benchmark suite and a real-world application. Experimental results demonstrate the superiority of the proposed CEDA method. [ABSTRACT FROM AUTHOR]

Elevator systems in buildings face challenges due to unpredictable passenger flow, which can make scheduling elevators complicated to optimize their operation. Most of the existing algorithms are developed based on pattern recognition and may not be effective in scenarios where patterns are difficult to classify, especially when elevator operations involve uncertain human behaviors. To address this issue, this paper proposes a time-dependent optimization model that considers long-term and multi-floor peaks, with a focus on peak floors. The proposed model accounts for dynamic scheduling patterns of elevators and represents passenger flow direction as a relation matrix. The proposed direction optimization method contains several functions to guide iteration direction and improve the efficiency of an iteration process based on classical algorithms. This method also ensures the stability of Markov chains by adjusting an iteration process. The feasibility of the proposed method is supported by the relevant theory, and experimental results show that the direction-optimized algorithms outperform classical algorithms, resulting in the superb operating efficiency of elevators at a lower cost. This paper contributes to the development of efficient algorithms for scheduling elevators in complex traffic patterns, which can improve performance of elevator group systems in buildings. The proposed method is not only limited to elevators but can also be extended to other transportation systems with flow requirements. • Estimating the relation between peak floors without pattern recognition. • Four relations are defined to classify the passenger flow. • Dynamically modifying three metaheuristic algorithms' iteration law. • The application for transportation systems with unpredictable flow. [ABSTRACT FROM AUTHOR]

• A novel water transparency calculation algorithm is introduced, employing deep neural network and image processing technology. This algorithm obviates the need for a water gauge, allowing the calculation of water transparency solely through the analysisof Secchi disk videos. Notably, it demonstrates heightened detection efficiency and success rates. • An enhanced Siamese tracker, termed SiamDCFF (Siamese tracker based on dual correlation feature fusion), is proposed for Secchi disk recognition. Distinguishing features of SiamDCFF include a dual correlation module and a feature fusion module. On the Secchi disk dataset, SiamDCFF achieves the highest success rate, outperforming other methods detailed in this paper. • The simplified 3D-ResNet [41] is leveraged to assess the state of the Secchi disk, transforming the critical position determination problem into an optimization challenge. The objective function involves maximizing the number of visible and invisible Secchi disk images on both sides of the critical position. This method facilitates adaptive critical position determination. • A novel Gated Recurrent Unit based on auto-regression (ARGRU) is proposed, taking into account the motion characteristics of the Secchi disk. ARGRU achieves a higher success detection rate for water transparency compared to the water gauge recognition method. Notably, ARGRU outperforms RNN [42], GRU, and LSTM [43] on the dataset presented in this paper. Water transparency is traditionally assessed through visual inspection using a lowered Secchi disk (SD) into the water, with the disappearance depth of the SD recorded as the measure of transparency. However, manual and visually-dependent frequency measurements of the SD render this process labor-intensive and time-consuming. This paper presents a comprehensive machine-vision-based algorithm designed for the automatic computation of water transparency using Secchi disk videos. The algorithm leverages multiple deep neural networks, including an enhanced Siamese tracking model, a 3D-ResNet, and an improved GRU network. Trained models directly identify the SD in the video, calculating water transparency by processing pixel information. Experimental results demonstrate the algorithm's efficacy in estimating water transparency across diverse natural environments, achieving commendable accuracy (MAE = 3.6 cm MSE = 21.5 cm RMSE = 4.6 cm), rapid processing speed (average 6.87 s), and robust stability. In comparison with the former water-gauge-based algorithm, our proposed algorithm exhibits heightened efficiency and a superior detection success rate. [ABSTRACT FROM AUTHOR]

The fair k -center problem has been paid lots of attention recently. In the fair k -center problem, we are given a set X of points in a metric space and a parameter k ∈ Z + , where the points in X are divided into several groups, and each point is assigned a color to denote which group it is in. The goal is to partition X into k clusters such that the number of cluster centers with each color is equal to a given value, and the k -center problem objective is minimized. In this paper, we consider the fair k -center problem with outliers and capacity constraints, denoted as the fair k -center with outliers (F k CO) problem and the capacitated fair k -center (CF k C) problem, respectively. The outliers constraints allow up to z outliers to be discarded when computing the objective function, while the capacity constraints require that each cluster has size no more than L. In this paper, we design an Fixed-Parameter Tractability (FPT) approximation algorithm and a polynomial approximation algorithm for the above two problems. In particular, our algorithms give (1 + ϵ) -approximations with FPT time for the F k CO and CF k C problems in doubling metric space. Moreover, we also propose a 3-approximation algorithm in polynomial time for the F k CO problem with some reasonable assumptions. [ABSTRACT FROM AUTHOR]

In the last years, the massive deployment of new photovoltaic (PV) power plants has launched the connection of PV inverters to the electrical network. A single medium-sized ground-mounted PV plant may have thousands of these inverters linked to the grid and even more PV panels on the DC side. Upon reaching such a substantial magnitude of devices involved in grid-connected installations, the effective operation, management, predictive maintenance, and fault detection becomes increasingly challenging without integrating advanced prediction and automated anomaly detection systems. Artificial intelligence algorithms, grounded in data measurements, can be pivotal in addressing this challenge. This paper proposes several regression-based methods to predict PV plants' energy generation, which is useful for detecting transient and long-term anomalies. These models are trained using a Recursive Least Squares (RLS) method and require a minimum number of variables to yield satisfactory outcomes, which is one of the paper's contributions. They mainly rely on energy generation measurements and geolocation. Within the scope of this research, two distinct algorithms have been implemented and validated. The first algorithm, a simplified model, is engineered to analyse the daily efficiency variation, prioritizing the identification of faults and abnormal operational profiles in PV plants. On the other hand, the second algorithm adopts a more intricate model tailored to facilitate long-term diagnosis, enabling the assessment of PV efficiency degradation. In this work, both algorithms are described and their performance is validated using the historical data from more than 20 PV plants placed in different climatic regions. • Photovoltaic systems' modelling using linear regression models. • Photovoltaic fault detection using linear regression coefficients evaluation. • Automatic fault detection for photovoltaic plants with low sensors' availability. • Short-term and long-term efficiency trend evaluation of photovoltaic plants. [ABSTRACT FROM AUTHOR]

Robust classification algorithms for high-dimensional, small-sample datasets are valuable in practical applications. Faced with the infrared spectroscopic dataset with 568 samples and 3448 wavelengths (features) to identify the origins of Chinese medicinal materials, this paper proposed a novel embedded multiclassification algorithm, ITabNet, derived from the framework of TabNet. Firstly, a refined data pre-processing (DP) mechanism was designed to efficiently find the best adaptive one among 50 DP methods with the help of Support Vector Machine (SVM). Following this, an innovative focal loss function was designed and joined with a cross-validation experiment strategy to mitigate the impact of sample imbalance on algorithm. Detailed investigations on ITabNet were conducted, including comparisons of ITabNet with SVM for the conditions of DP and Non-DP, GPU and CPU computer settings, as well as ITabNet against XGBT (Extreme Gradient Boosting). The numerical results demonstrate that ITabNet can significantly improve the effectiveness of prediction. The best accuracy score is 1.0000, and the best Area Under the Curve (AUC) score is 1.0000. Suggestions on how to use models effectively were given. Furthermore, ITabNet shows the potential to apply the analysis of medicinal efficacy and chemical composition of medicinal materials. The paper also provides ideas for multi-classification modeling data with small sample size and high-dimensional feature. • A TabNet-based deep learning method for high-dimensional small-sample datasets. • A rapid data preprocessing search strategy using Support Vector Machine is established. • An adaptive Focal loss function with adjusting parameters counters sample imbalance. • Chinese herb origin identification via infrared spectroscopy shows impressive results. • Suggestions for Chinese herb further research and method generalization are offered. [ABSTRACT FROM AUTHOR]

Rough set is an important method for dealing with incomplete information systems. In incomplete information systems, the most common way to determine the relation between two samples is the tolerance relation. However, the condition for the tolerance relation to determine those samples may belong to the same category is very lenient, which makes the reduction rate low when using the rough set generated by this relation to select features. In response to the above problems, we design the neighborhood equivalence tolerance relation to solve them. Different from other improved tolerance relations, firstly, the relation designed in this paper does not require additional threshold to accomplish the above goals, which will avoid the trouble caused by the given threshold. Secondly, we notice that most of the current improvements for this kind of problems are computationally cumbersome, and the relation designed in this paper is simple and effective. Based on this, we construct a neighborhood rough set model that handles incomplete information by using this relation, introduce its properties, expound the properties that a reduction set should satisfy, quantify the importance of conditional attributes with attribute dependence degree, which provides the basis for the design of feature selection algorithm. Finally, the greedy strategy is used to design a forward feature selection algorithm. Experimental results show that the model is effective in dealing with incomplete information systems. The feature selection algorithm has the smallest size of the average reduced subset on twelve datasets, and maintains the accuracy of the classifier, which verifies that the feature selection algorithm can effectively deal with incomplete information systems. ● As the judgment of tolerance relation is very lenient, the existing improvement methods either rely on the probability distribution of attribute values in the information system, or need to give a additional threshold. To solve the above problems, this paper improves the tolerance relation, and the improved binary relation only relies on the existing information system itself, without giving additional threshold. ● Rough set in incomplete information systems can only select more attributes to refine the knowledge granules when making feature selection due to the lenient judgment of tolerance relation, which is equivalent to making up for the problems caused by tolerance relation when selecting attributes, but this will lead to low reduction rate. In this paper, we use the newly proposed binary relation to construct neighborhood rough set model in incomplete information systems, and use this model to make feature selection, which improves the problem of low reduction rate above, while maintaining the accuracy of the classifier. [ABSTRACT FROM AUTHOR]

The problem of transforming a single-input single-output nonlinear discrete-time state equations into the extended observer form is studied in this paper. Unlike previous solutions which compute the injection terms from the input–output equations, in this paper the computations utilize directly the given state equations. An algorithm is given to find step-by-step the necessary transformation by computing backward-shifts of functions computed in previous step. It is shown that the integrability conditions presented in the algorithm are necessary and sufficient for solvability of the problem. Various examples have been studied to demonstrate the usefulness of the algorithm. [ABSTRACT FROM AUTHOR]

• An edge detection algorithm for thermal infrared weak texture images. • Construct a high-quality thermal infrared image edge dataset RI-BSDS500 for training to improve the training convergence speed and generalization ability of the model. • Different thermal infrared image datasets are tested under different algorithms, and the results are analyzed from the perspectives of objective parameters and subjective effects, which proves that the proposed algorithm in this paper is more effective on thermal infrared images. In order to solve the problems such as difficulties in extracting image edge information caused by low contrast and blurred edges of thermal infrared images, a thermal infrared image edge detection algorithm PiDiNet-TIR constructed based on the PiDiNet model is proposed. On the basis of the visible light image dataset BSDS500, the visible light image is grayed out, noise is added, and contrast is reduced to construct the thermal infrared image edge dataset I R-BSDS500, which provides image data for model training and testing. Deeply study the theory of PiDiNet model, add rich convolution mechanism to its backbone network, improve the activation function, pooling layer and up-sampling module, study the SimAM attention mechanism and add it to the side network. Using the open-source thermal infrared image dataset and homemade thermal infrared images for testing, we compare and analyze the mean square error (MSE), structural similarity of image (SSIM), feature similarity of image (FSIM), and Frames Per Second (FPS)of different detection algorithms, such as HED, RCF, DexiNet, PiDiNet, and the algorithm proposed in this paper. The results of image evaluation parameters prove that the edge detection algorithm proposed in this paper has high feature extraction efficiency and feature expression ability, and the algorithm detection performance is higher than other algorithms. [ABSTRACT FROM AUTHOR]

The emergence of IoT has introduced new security concerns, particularly the detection of large-scale network attacks initiated by compromised IoT devices. The diverse nature of IoT devices adds complexity to attack detection, necessitating the classification of devices based on their unique characteristics and behavior patterns. This paper presents a novel device classification method grounded in traffic differentials before and after IoT devices succumb to compromise. The study defines the concept of attack sensitivity and designs a method for calculating the value. The K-means algorithm is used to classify devices into predefined categories based on their attack sensitivity values. The effectiveness of this method is demonstrated through experiments on a public dataset. Additionally, a multi-class classification model is developed to identify newly added devices to the IoT environment, with good results in accuracy, precision, recall, F1 score, and FPR (0.999, 0.997–1,0.997–1,0.998–1,0–0.001). Overall, The device classification method proposed in this paper, based on attack sensitivity, not only enables a rational definition of device categories but also ensures precise classification for devices newly integrated into the IoT environment. This lays the foundation for achieving more precise attack detection and protection, advancing the field towards enhanced cybersecurity measures. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative simplified two-stage algorithm for precise and durable regulation of the buck power systems. A critically damped of response is captured and synthesized in the first stage via a second order model. In the second stage, the desired robust response of the system is enforced and stirred by a new parameterless controller. The main feature of this controller is simplicity, which is facilitated by its independence from tunable control parameters. Practical numerical simulations that take into account the impact of plant uncertainties and noise measurements with varying variances are used to thoroughly assess the feasibility of the proposed controller. To highlight the main contribution of this paper, the devised control algorithm is juxtaposed with established controllers, namely the PSO-tuned versions of the sliding mode, backstepping, and PID controllers, alongside other cut cutting-edge controllers, revealing its prowess performance superiority and heightened structural simplicity. Finally, the suggested structure is integrated in a standalone photovoltaic system for appraisal of PV emulation potency. It is found to preserved a superior performance in the context of PV emulation as compared to existing emulators, achieving a dynamic efficiency of 97.85 % in the specific case of MPPT. [ABSTRACT FROM AUTHOR]

Discrete orthogonal polynomials (DOPs) have gained significant research attention owing to their crucial role in digital signal processing applications such as computer vision, pattern recognition, and compression. However, the computation of DOP coefficients often incurs a substantial computational burden, exacerbating for higher-degree moments along with the resulting numerical errors. To address this challenge, this paper exploits the inherent parallelism in Charlier polynomial coefficient calculations to achieve enhanced polynomial performance. Independent calculations are distributed among threads, making efficient use of the available processing resources. Two algorithms are presented, the first algorithm evenly distributes the rows in a sequential manner (straightforward). Additionally, to achieve a more equitable distribution of coefficient calculations, this paper proposes alternative distribution approaches, aimed at balancing processing load among threads. Through extensive comparative experiments, we have confirmed that the proposed approaches achieved high performance across different degrees (1540 to 7370) and at different numbers of threads (2 to 256). The results show processing time in the multithreaded case is improved by up to 9.1 times with respect to the unthreaded case. Furthermore, by increasing the number of threads from 2 to 256, the trend indicates that the most significant improvement occurs in the range of 32 to 128 threads, confirming the robustness of the proposed algorithm. These findings signify the importance of this paper. [ABSTRACT FROM AUTHOR]

In this paper, we aim at the cross-period matching problem on a third-party shared manufacturing platform (TSMP) that provides matching services for capacity demanders (CDs) and capacity suppliers (CSs). Considering the impact of delay in meeting demand, we formulate a bi-level multi-objective optimization model (BMOM) with the objective functions of maximizing the manufacturers' matching rate and the TSMP's commission revenue, in which the lower-level model optimizes the matching strategy for the current period, and the upper-level model matches the cross-period demand (i.e., the demand with delay). Then, we propose a method that combines the ε -constraint and a three-stage solution algorithm to solve the BMOM, and the performance of the proposed method is verified by comparing it with the optimization solver CPLEX in numerical experiments. The results of numerical experiments show that the method proposed in the paper has advantages in computational effectiveness and efficiency and can solve the current-period and cross-period joint matching problem of the TSMP, which provides a reference method for TSMPs to optimize the matching strategies. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile communications, the dense deployment of small base stations (SBSs) in heterogeneous wireless networks (HW-Nets) has met the explosive growth in demand from terminals. However, due to the irregular deployment of SBSs, users frequently handover base stations in pursuit of high-quality services, which inevitably leads to load imbalance among SBSs. To alleviate load imbalance in HW-Nets and improve the efficiency of seamless handover, this paper proposes a distributed auction algorithm scheme based on dual active protocol stack (DAPS), which transforms the wireless access selection of terminals into the terminal-base station association process during movement to globally optimize network resource utilization. At the same time, this paper constructs a terminal-base station association graph and uses auction algorithms to solve the maximum weighted matching of the graph. The DAPS-based scheme can effectively avoid the problem of excessively long outage time caused by handovering outages. Simulation results show that compared with general distributed algorithms, the distributed auction algorithm based on DAPS can effectively balance the load of base stations and reduce the mobile outage latency by 59%. [ABSTRACT FROM AUTHOR]

Ad-hoc sensor networks (known also as citizen or resident networks) are gaining in popularity. They tend to utilize inexpensive sensors; they are deployed and operated in ad-hoc, opportunistic manner, consequently often delivering results of higher uncertainty. Reputation-based algorithms are used for data reconstruction to contain the input of faulty, or unreliable, nodes on the network. This paper discusses the question whether it is feasible to introduce high quality, expensive reference nodes to the network to significantly decrease its uncertainty, in situations where the reconstruction algorithm run by the network is not altered. This paper uses five leading cases, subsequently analyzed with the help of a simulator that executes the reputation-based EWMA algorithm. It identifies that the simple introduction of reference nodes alone does not help in addressing uncertainty without making alteration to the algorithm or to the design of the network. [ABSTRACT FROM AUTHOR]

In this paper, we present a new volumetric ambient occlusion algorithm called Contextual Ambient Occlusion (CAO) that supports real-time clipping. The algorithm produces ambient occlusion images of exactly the same quality as Local Ambient Occlusion (LAO) while enabling real-time modification to the shape used to clip the volume. The main idea of the algorithm is that clipping only affects the ambient value of a small number of voxels, so by identifying these voxels and recalculating the ambient factor only for them, it is possible to significantly increase the rendering performance (by 2-5x) without decreasing the quality of the rendered image. Due to its fast performance, the algorithm is suitable for interactive environments where clipping changes could occur every frame. Additionally, the algorithm does not have any stereoscopic inconsistency, which makes it suitable for mixed reality environments. This paper is an extended version of the "Contextual Ambient Occlusion" article presented during the 2023 Graphics Interface conference, and includes, among other additions, the source code of the algorithm. [Display omitted] • Clipping only affects the ambient value of a small number of voxels. • It is more efficient to perform recalculation only on voxels affected by clipping. • Contextual Ambient Occlusion achieves same quality as Local Ambient Occlusion. • Contextual Ambient Occlusion is suitable for interactive environments. [ABSTRACT FROM AUTHOR]

• This paper proposes a WL Hammerstein model and develops a robust recursive adaptive algorithm. • This paper also gives the simplified version of the proposed algorithm. • The performance analysis is derived and verified by simulations. • The superior behavior is demonstrated by example of system identification and SAEC. The Hammerstein model is a crucial model frequently employed in the field of signal processing. This paper focuses on the widely-linear (WL) complex-valued adaptive filtering and proposes a WL Hammerstein system model. Firstly, a cost function which minimizes the complex correntropic loss criterion is constructed. Subsequently, by extending the alternate direction method of multipliers (ADMM) optimization and non-convex projection method to the WL model, and leveraging the special structure of the autocorrelation matrix in the WL Hammerstein system, a robust recursive adaptive filtering algorithm for the WL Hammerstein system is proposed, along with its simplified version. More importantly, the performance analysis is studied, in which the convergence performance and the mean-square performance are provided. Simulations about system identification and stereophonic acoustic echo cancelation (SAEC) are carried out to validate the theoretical analysis and demonstrate the superior performance of the algorithms in this paper. [ABSTRACT FROM AUTHOR]

This comprehensive review focuses on the Autonomous Driving System (ADS), which aims to reduce human errors that are the reason for about 95% of car accidents. The ADS consists of six stages: sensors, perception, localization, assessment, path planning, and control. We explain the main state-of-the-art techniques used in each stage, analyzing 275 papers, with 162 specifically on path planning due to its complexity, NP-hard optimization nature, and pivotal role in ADS. This paper categorizes path planning techniques into three primary groups: traditional (graph-based, sampling-based, gradient-based, optimization-based, interpolation curve algorithms), machine and deep learning, and meta-heuristic optimization, detailing their advantages and drawbacks. Findings show that meta-heuristic optimization methods, representing 23% of our study, are preferred for being general problem solvers capable of handling complex problems. In addition, they have faster convergence and reduced risk of local minima. Machine and deep learning techniques, accounting for 25%, are favored for their learning capabilities and fast responses to known scenarios. The trend towards hybrid algorithms (27%) combines various methods, merging each algorithm's benefits and overcoming the other's drawbacks. Moreover, adaptive parameter tuning is crucial to enhance efficiency, applicability, and balancing the search capability. This review sheds light on the future of path planning in autonomous driving systems, helping to tackle current challenges and unlock the full capabilities of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Despite overwhelming evidence to the contrary, the concept of 'race' as a biological unit continues to persist in various scientific disciplines, notably in the field of medicine. This paper explores the persistence of 'race as biology' in medical research via examining select citational practices that have perpetuated this problematic concept. Citations serve as a cornerstone in scientific literature, signifying reliability and expert affirmation. By analyzing citation threads and historical patterns, we aim to shed light on the creation and perpetuation of false scientific truths and their impact on medical research, training, and practice. We focus on two prominent examples, eGFR and Spirometry, and trace key articles' citational histories, highlighting the flawed evidence in support of racial corrections in medical assessments. The eGFR equation incorporates 'race' as a factor based on the erroneous belief that Black individuals have higher muscle mass than white individuals. Our analysis reveals that key cited sources for this belief lack robust and well-developed datasets. Similarly, Spirometry measurements incorporate racial correction factors, relying on questionable evidence dating back to the Civil War era. Citations serve as a cornerstone in scientific literature, signifying reliability and expert affirmation. They play a crucial role in shaping theoretical positions and validating data and assumed knowledge. Evaluating citation threads and key articles consistently referenced over time can reveal how falsehoods and erroneous assertions are constructed and maintained in scientific fields. This study underscores the need for critical examination of citational practices in medical research and urges a shift toward a more cautious approach when citing sources that support 'race as biology.' The paper calls for a reevaluation of pedagogical approaches and assigned readings in medical education to prioritize an anti-racist perspective in future research endeavors. • Algorithms in contemporary medicine often involve "race" as a biological variable. • We offer a review of the citation threads in eGFR and Spirometry algorithms. • Support for race as variable relies on faulty, weak, and unsupportive citations. • Medical training must attend to and use caution around "truths" about racialized bodies. [ABSTRACT FROM AUTHOR]

The electric power distribution system (PDS) and the water distribution system (WDS) are coupled with each other through electricity-driven water facilities (EdWFs), such as pumps, water desalination plants, and wastewater treatment facilities. However, they are generally owned and operated by different utilities, and there does not exist an operator that possesses full information of both systems. As a result, centralized methods are not applicable for coordinating the operation of the two systems. This paper proposes a decentralized framework where the PDS and WDS operators solve their own operation problems, respectively, by sharing only limited information. Nevertheless, the boundary variables (i.e., the variables shared between two systems) are discontinuous due to their dependence on the on/off nature of EdWFs. Unfortunately, mature decentralized/distributed optimization algorithms like the alternating direction method of multipliers (ADMM) cannot guarantee convergence and optimality for a case like this. Therefore, this paper develops a novel algorithm that can guarantee convergence and optimality for the decentralized optimization of PDS and WDS based on a recently developed algorithm called the SD-GS-AL method. The SD-GS-AL method is a combination of the simplicial decomposition (SD), gauss–seidel (GS), and augmented Lagrangian (AL) methods, which can guarantee convergence and optimality for mixed-integer programs (MIPs) with continuous boundary variables. Nonetheless, the original SD-GS-AL algorithm does not work for the PDS-WDS coordination problem where the boundary variables are discontinuous. This paper modifies and improves the original SD-GS-AL algorithm by introducing update rules to discontinuous boundary variables (called the Auxiliary Variables Update step). The proposed mixed-integer boundary compatible (MIBC) SD-GS-AL algorithm has the following benefits: (1) it is capable of handling cases whose boundary variables are discontinuous with convergence and optimality guaranteed for mild assumptions, and (2) it only requires limited information exchange between PDS and WDS operators, which will help preserve the privacy of the two utilities and reduce the investment in building additional communication channels. Simulations on two coupled PDS and WDS test cases (Case 1: IEEE-13 node PDS and 11-node WDS, and Case 2: IEEE-37 node PDS and 36-node WDS) show that the proposed MIBC algorithm converges to the optimal solutions while the original SD-GS-AL does not converge for both test cases. The ADMM does not converge for the first test case while it converges to a sub-optimal solution, 63 % more than the optimal solution for the second test case. • We propose a decentralized optimization of the water-energy nexus. • The proposed algorithm handles mixed-integer boundary variables. • The optimality and convergence of the proposed algorithm are guaranteed. • Outperforms mature distributed algorithms like ADMM. [ABSTRACT FROM AUTHOR]

• A feature selection method based on binary dandelion algorithm is proposed. • The algorithm applies a sine-cosine operator and a restart strategy. • Mutual information and quick bit mutation improve the performance of the algorithm. • The algorithm performs well on datasets with larger dimensions. Feature selection (FS) is an important data preprocessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search function. This paper presents an improved Binary Dandelion Algorithm using Sine Cosine operator and Restart strategy (SCRBDA) for feature selection. First, the sine cosine operator is used in the radius formula of the core dandelions (CD), which significantly enhances the ability of algorithm development and exploration. Secondly, the algorithm uses a restart strategy to increase its ability to get rid of local optimum. Thirdly, mutual information is used to guide the generation of some dandelions, which pays more attention to the correlation between the selected features and categories. Finally, quick bit mutation is used as the mutation strategy to improve the diversity of the population. The SCRBDA proposed in this paper was tested on 18 datasets of different sizes from UCI machine learning database. The SCRBDA was compared with 8 other classical feature selection algorithms, and the performance of the proposed algorithm was evaluated through feature subset size, classification accuracy, fitness value, and F1-score. The experimental results show that SCRBDA achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on most datasets. Especially on large-scale datasets, SCRBDA can obtain extremely smaller feature subsets while maintaining much higher classification accuracy, and satisfactory F1-score. [ABSTRACT FROM AUTHOR]

• A model was created to examine the reliability of maintenance budget allocation. • The model was applied to network-level pavement maintenance in Canada. • The impacts of algorithm selection and parameter tuning were studied. • The significance of pavement clustering in increasing reliability was highlighted. • The importance of incorporating actual pavement improvement curves was clarified. The purpose of this paper is to increase the reliability of the network-level pavement maintenance budget allocation by reducing uncertainties of algorithm selection and parameter tuning. In this paper, reliability is defined as the ability of a network-level pavement maintenance plan to improve the condition of a pavement network within a certain budget. In order to quantify reliability, the reliability index is defined as the ratio between the post-maintenance network condition and the net maintenance cost. With this purpose in mind, a two-objective optimization model was developed. To test its applicability, the model was applied to a pavement network in Canada. The model was solved using the Non-Dominated Sorting Genetic Algorithm II and the differential evolution algorithms. Finally, the reliability indices of algorithms and termination criteria were computed. The results indicated that the differential evolution algorithm recommended less frequent but more intense interventions that made the solutions expensive and less reliable. This paper contributed to the network-level pavement maintenance body of knowledge by (1) developing a multi-objective optimization model to reduce uncertainties of parameter tuning and algorithm selection; (2) increasing the reliability of budget allocation; (3) showcasing the significance of pavement clustering in increasing reliability; and (4) developing and incorporating actual pavement improvement curves. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents an adaptive and new region proposal algorithm for generating high-quality regions. The main aim of this algorithm is to investigate different features in the proposal generation process. This algorithm is based on bottom-up image segmentation and deep learning techniques. Each primary region is represented by features derived from a convolutional neural network (CNN). The adjacent and similar regions are merged based on a new proposed searching algorithm and a distance function in a hierarchical way. Additionally, various hand-crafted texture features are examined for representing each region. These texture features are not being utilized previously in region proposal generation. This method also applied both texture-based and deep learning-based features, contemporaneously. Furthermore, the proposed region proposal algorithm was evaluated on two significant challenging datasets, including VOC2012 and COCO2017. The resulting proposals show more high-quality regions in high overlaps in comparison to previous region proposal algorithms. More importantly, the results verified that the deep learning-based features together with handcrafted texture features are complement and this fusion can overcome the shortcoming of other approaches. Additionally, the proposed region proposal algorithm is employed in weakly supervised semantic segmentation. The new generated proposals are used to create some new labeled masks. These masks are very useful and effective in the training phase of deep learning. This approach is evaluated on VOC2012 and compared with previous region-based and free region-based methods. The results show the efficacy of the proposed method. • This paper proposes a new region proposal generation based on a hierarchical deep learning-based merging algorithm. • The effectiveness and quality of some known texture-based descriptors are explored in the proposed algorithm. • A new algorithm is introduced to generate pseudo training data using weak supervision and region proposal. • The proposed region proposal algorithm was evaluated on two significant challenging datasets, including VOC2012 and COCO2017. [ABSTRACT FROM AUTHOR]

In this paper, we apply the machine learning clustering algorithm Density Based Spatial Clustering of Applications with Noise (DBSCAN) to study the membership of stars in twelve open clusters (NGC 2264, NGC 2682, NGC 2244, NGC 3293, NGC 6913, NGC 7142, IC 1805, NGC 6231, NGC 2243, NGC 6451, NGC 6005 and NGC 6583) based on Gaia DR3 Data. This sample of clusters spans a variety of parameters like age, metallicity, distance, extinction and a wide parameter space in proper motions and parallaxes. We obtain reliable cluster members using DBSCAN as faint as G ∼ 20 mag and also in the outer regions of clusters. With our revised membership list, we plot color-magnitude diagrams and we obtain cluster parameters for our sample using ASteCA and compare it with the catalog values. We also validate our membership sample by spectroscopic data from APOGEE and GALAH for the available data. This paper demonstrates the effectiveness of DBSCAN in membership determination of clusters. [ABSTRACT FROM AUTHOR]

We consider estimating a factor model for high-dimensional time series that contains structural breaks in the factor loading space at unknown time points. We first study the case when there is one change point in factor loadings, and propose a consistent estimator for the structural break location, whose convergence rate is shown to depend on an interplay between the dimension of the observed time series and the strength of the underlying factor structure. Our results reveal that the asymptotic behavior of the proposed estimator can be asymmetric in the sense that a larger estimation error can occur toward the regime with weaker factor strength. Based on the proposed estimator for the structural break location, we also consider the problem of estimating the factor loading spaces before and after the structural break. We show that the proposed estimators for change-point location and loading spaces are consistent when the numbers of factors are correctly estimated or overestimated. The algorithm for multiple change-point detection is also developed in the paper. Compared with existing results on change-point factor analyses of high-dimensional time series, a distinguished feature of the current paper is that the noise process is not necessarily assumed to be idiosyncratic and as a result we allow the noise process with potentially strong cross-sectional dependence. Another advantage for the proposed method is that it is specifically designed for the changes in the factor loading space and the stationarity assumption is not imposed on either the factor or noise process, while most existing methods for change-point detection of high-dimensional time series with/without a factor structure require the data to be stationary or 'close' to a stationary process between two change points, which is rather restrictive. Numerical experiments including a Monte Carlo simulation and a real data application are presented to illustrate the proposed estimators perform well. • Factor models for high-dimensional time series with structural breaks are studied. • The noise process in the setting can exhibit a strong notion of cross-sectional dependence. • The factor and noise processes are allowed to be non-stationary between change points. • The methods for change-point detection and estimation are proposed. • The asymptotic properties of proposed estimators are investigated. [ABSTRACT FROM AUTHOR]

Object detection techniques are the foundation for the artificial intelligence field. This research paper gives a brief overview of the You Only Look Once (YOLO) algorithm and its subsequent advanced versions. Through the analysis, we reach many remarks and insightful results. The results show the differences and similarities among the YOLO versions and between YOLO and Convolutional Neural Networks (CNNs). The central insight is the YOLO algorithm improvement is still ongoing.This article briefly describes the development process of the YOLO algorithm, summarizes the methods of target recognition and feature selection, and provides literature support for the targeted picture news and feature extraction in the financial and other fields. Besides, this paper contributes a lot to YOLO and other object detection literature. [ABSTRACT FROM AUTHOR]

Traditional recommendation algorithms have problems such as data sparseness and not paying attention to the diversity of recommendation results. In this paper, we use LDA to extract topics of comments about movies, and identify the emotional tendencies related to topics. As a result, we enrich user interest model and product feature model based on emotional tendencies to improve content-based recommendation algorithms. Most of prior work on applying sentiment classification to recommendation systems only consider the use of sentiment dictionaries to judge polarity, and adopt pattern matching methods to identify features. This paper uses BERT to train sentiment classification models and uses LDA to extract topics. The algorithm is run on the movie review database crawled from Douban, and the experimental result showed that the diversity of recommendation lists had been significantly improved. [ABSTRACT FROM AUTHOR]

Graph is a widely used data structure which can be regarded as a generalized measure metirc space. Since a graph not only includes points but also relation between points, the distance between two graphs is difficult to measure. In recent years, Gromov-Wasserstein discrepancy has been proposed as a pseudometirc on graphs which has a complete theoretical basis, but has few applications. Therefore, this paper reviews the basic ideas, applications and improvements of Gromov-Wasserstein. Since Gromov-Wasserstein discrepancy is a quadratic programming and difficult to calculate, this paper focuses on the iterative algorithm for solving this discrepancy. At the end, we look forward to the development of Gromov-Wasserstein discrepancy. [ABSTRACT FROM AUTHOR]

• A robust method is proposed to alleviate the effect of traffic data with outliers. • A comprehensive traffic flow prediction indicator system is established in this paper. • We hybridize polynomial and Gaussian kernel to build nonlinear version of our method. • Parameters of the proposed method are optimized by Fruit Fly Optimization Algorithm. A novel least squares twin support vector regression method is proposed based on the robust L 1 -norm distance to alleviate the negative effect of traffic data with outliers. Although there is some known work for the short-term traffic flow prediction problems, their efficacy depends heavily on the collected traffic data, which are often affected by various external factors (e.g. weather, traffic jam or accident), leading to errors and missing data. This makes it difficult to pick an effective method that accurately predicts the traffic state. As a contribution of this paper, an iterative algorithm is designed to solve the non-smooth L 1 -norm terms of our method; its convergence also proved. Further, a comprehensive traffic flow indicator system based on speed, traffic flow, occupancy and ample degree is utilized in this paper. We also extend the proposed method to a nonlinear version by hybridizing the polynomial kernel and radial basis function kernel, where the weight coefficient of hybrid kernel is determined by the change tendency of traffic data. To promote the prediction performance, the parameters of our nonlinear method are optimized by adaptive fruit fly optimization algorithm. Extensive experiments on real traffic data are performed to evaluate our model. The results indicate that the newly constructed model yields better prediction performance and robustness than other models in various experimental settings. [ABSTRACT FROM AUTHOR]

• Gait, the 6th vital sign, needs multi-modal methodologies for robust measurement. • Contemporary wearables enable multi-modal patient assessment. • A novel gait fusion approach is presented for use in multiple environments. • This can inform holistic gait assessment beyond the clinic during free-living. Gait abnormalities are typically derived from neurological conditions or orthopaedic problems and can cause severe consequences such as limited mobility and falls. Gait analysis plays a crucial role in monitoring gait abnormalities and discovering underlying deficits can help develop rehabilitation programs. Contemporary gait analysis requires a multi-modal gait analysis approach where spatio-temporal, kinematic and muscle activation gait characteristics are investigated. Additionally, protocols for gait analysis are going beyond labs/clinics to provide more habitual insights, uncovering underlying reasons for limited mobility and falls during daily activities. Wearables are the most prominent technology that are reliable and allow multi-modal gait analysis beyond the labs/clinics for extended periods. There are established wearable-based algorithms for extracting informative gait characteristics and interpretation. This paper proposes a multi-layer fusion framework with sensor, data and gait characteristics. The wearable sensors consist of four units (inertial and electromyography, EMG) attached to both legs (shanks and thighs) and surface electrodes placed on four muscle groups. Inertial and EMG data are interpreted by numerous validated algorithms to extract gait characteristics in different environments. This paper also includes a pilot study to test the proposed fusion approach in a small cohort of stroke survivors. Experimental results in various terrains show healthy participants experienced the highest pace and variability along with slightly increased knee flexion angles (≈1°) and decreased overall muscle activation level during outdoor walking compared to indoor, incline walking activities. Stroke survivors experienced slightly increased pace, asymmetry, and knee flexion angles (≈4°) during outdoor walking compared to indoor. A multi-modal approach through a sensor, data and gait characteristic fusion presents a more holistic gait assessment process to identify changes in different testing environments. The utilisation of the fusion approach presented here warrants further investigation in those with neurological conditions, which could significantly contribute to the current understanding of impaired gait. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient's safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints. [ABSTRACT FROM AUTHOR]

In this paper, the design and implementation of low power and high-efficiency filterbank for MPEG-2/4 AAC system is presented. Since filterbank represents the most computation-intensive kernel of AAC codec, we design it with algorithm and architecture aspects. We supply the dedicated algorithm for filterbank. The derived algorithm and the hardware shared engine (HSE) we proposed for AAC can reduce the computation power and implement as a codec used in encoder and decoder. We also optimize the performance, hardware resources, and power consumption thoroughly. It is designed as an intellectual property (IP) to construct the overall decoder in an embedded system. The hardware cost is with 16.1 k logic gates, 2 k-word local memory, and 1 K-word coefficient ROM. The proposed design has a real-time operation at only 1.25 MHz with a sampling rate of 48 kHz. It can achieve 0.70 mW power consumption in TSMC 0.18 μm CMOS technology. Furthermore, we use a programmable chip (SOPC) platform which includes the software and hardware engine. Throughout the computation analysis, the bitstream parser and lower complexity part are performed by a software solution, and the higher complexity part is computed by a hardware solution. Several design techniques are needed including the wrapper design, embedded CPU, and IP to construct the system. Based on this co-design approach, a whole AAC audio decoder is also established. • This paper presents the algorithm and hardware design for MPEG-2/4 AAC. • Real demonstration is constructed in the embedded system. • VLSI design of the proposed chip is performed with various power-saving techniques. • The proposed design outperforms other designs on gate-count usage and power consumption. [ABSTRACT FROM AUTHOR]

The Two-Dimensional Fast Fourier Transform (2D-FFT) algorithm is used for the study of many modern systems applied for security and biometrics. The adoption of this algorithm, which is a compute intensive task, is limited due to its hardware design complexity. The first objective of this paper is to underline the effect of the hardware/software co-design (Hw/Sw co-design) for the reduction of the processing time and power consumption. Secondly, we propose an innovative architecture for the 2D-FFT algorithm tested on Zynq Soc, which requires less processing time and memory compared to the traditional algorithm. Three implementations (one software and two Hw/Sw co-designs) of the 2D-FFT algorithm using the Zynq SoC are presented in this paper. The first is based on ARM processor. A speedup of 29x is obtained compared to the original implementation thanks to many optimizations. The second is a Hw/Sw co-design solution of the traditional 2D-FFT algorithm introduced on a hybrid platform combining an ARM Cortex-A9 processor with an FPGA. The third is also a Hw/Sw co-design solution using our optimized 2D-FFT algorithm to reach the real-time contraints for high-resolution images (1920 × 1080). It provides a speedup of 1.13x, 3.31x and 96.21x faster than the Hw/Sw co-design implementation of the traditional RC algorithm, the pure software implementations with and without optimizations, respectively. • Underline the effect of the Hw/Sw co-design of the 2D-FFT algorithm for the reduction of the processing time and power consumption. • Innovative architecture for the 2D-FFT algorithm tested on Zynq Soc respecting real-time constraints. • A comparative study between four existing architectures are used to implement the FFT IP to select the best one. • Study the impact of directives on resource usage and performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fast time-frequency mask technique that relies on the sparseness of source signals for blind source separation (BSS) to separate a mixture of two input sounds in a single signal automatically. Due to the sparseness of source signals, the signal can be distinguished when it is transformed into the time-frequency domain. Most previous methods did not mention the effect of different angles on accuracy. To overcome such problems, we first define two features which are normalized level-ratio and phase-difference. Next, we use our method to decrease the variance of Direction of Arrival (DOA). This can reduce the variance of features so that it can reduce the iterations of k -means. Finally, a mask is generated according to the clustered features. Our method does not require any prior information or parameter estimation. The motivation of the proposed design is to incorporate the BSS system with some smart voice appliances. In the application scenario, all the non-human voices may appear and regard as interference. We use Signal to Distortion Ratio (SDR) and Signal to Interference Ratio (SIR) to make some comparison. Based on the proposed system, then we present a hardware design. We use the TSMC 90-nm CMOS process. As a cost-effective result, it consumes about 120 K gates and executes with a frequency of 10 MHz. The power consumption is only 2.92 mW with low power design considerations. • This paper presents the algorithm and hardware design for blind source separation. • The experimental results with various mixtures are simulated in real environments to verify the effectiveness. • Real demonstration is constructed in the embedded system. • VLSI design of the proposed chip is performed. [ABSTRACT FROM AUTHOR]

With continuously decreasing circuit scale, more and more test patterns (test contents) are added to test set to achieve acceptable defect levels, which seriously affect test time and, consequently, test cost. Theoretically, dropping invalid (pattern that can make the test pass) test patterns can reduce test cost. In fact, When the valid (pattern that can make the test fail) and invalid patterns overlap seriously, identifying effective patterns performs poorly, which will not achieve the expected results. This paper proposes a kind of eliminate redundancy method, so that this LDA-MRMR algorithm which can provide test cost reduction without increasing the defect level obviously. Experimental results demonstrate that the proposed method sacrifices 3% of the predictive accuracy in exchange for 3.7 times time saving compared with traditional methods. In addition, the algorithm is completely software-based and does not require any additional hardware overhead and is directly compatible with traditional integrated circuit testing process. • This paper provides test cost reduction without increasing the defect level obviously. • We find the optimal solution between test cost and test quality. • Mathematical model was proposed to make it is possessed of stronger applicability and generality. • Test time and test quality have been greatly improved. [ABSTRACT FROM AUTHOR]

One of the main aims of the paper is to develop the mesh geometry technique for corank-two edge-bipartite graphs Δ with n + 2 ≥ 3 vertices, and the mesh algorithms introduced in [30,33] and successfully studied in our recent article [42]. We introduce and study the concept of a self-duality of mesh geometries Γ ( R ˆ Δ , Φ Δ) viewed as Φ Δ -mesh translation quivers. We show how self-dualities of mesh geometries Γ ( R ˆ Δ , Φ Δ) and the mesh geometry technique is applied to an affirmative algorithmic solution of so called Horn-Sergeichuk type problem [9, Problem 4.3] on the self-congruency of square integer matrices A ∈ M n + 2 (Z) , for the class of non-symmetric Gram matrices A = G ˇ Δ of corank-two loop-free edge-bipartite graphs Δ, with n + 2 ≤ 6 vertices. More precisely, we show that each of the mesh geometries Γ ( R ˆ Δ , Φ Δ) is self-dual, we construct its dual form Γ ( R ˆ Δ , Φ Δ) o p = Γ ( R ˆ Δ , Φ Δ − 1) isomorphic with Γ ( R ˆ Δ , Φ Δ) , and we construct a canonical self-duality isomorphism f Δ : Γ ( R ˆ Δ , Φ Δ) → Γ ( R ˆ Δ , Φ Δ) o p of mesh translation quivers. Using the self-duality f Δ we construct combinatorial algorithms such that, given a square Gram matrix A = G ˇ Δ ∈ M n + 2 (Z) of Δ lying in this class, they are able to compute a Z -invertible matrix B ∈ M n + 2 (Z) that coincide with its inverse B − 1 and defines the congruence of A with A t r , i.e., the equation A t r = B t r ⋅ A ⋅ B is satisfied. An idea of our solution is outlined in Section 8 of our recent article [42] , where among others two of our 13 algorithms solving the problem are constructed. The remaining 11 algorithms are constructed in the present article. We do it by means of the structure of the standard self-dual Φ Δ -mesh translation quiver Γ ( R ˆ Δ , Φ Δ) (called a geometry) canonically associated with Δ, consisting of Φ Δ -meshes of Φ Δ -orbits O Δ (w) of vectors w ∈ R ˆ Δ ⊆ Z n + 2 , where Φ Δ : Z n + 2 → Z n + 2 is the Coxeter transformation of Δ. We construct in the paper such self-dual Φ Δ -mesh geometry Γ ( R ˆ Δ , Φ Δ) , for each of the corank-two loop-free edge-bipartite graphs Δ, with n + 2 ≤ 6 vertices. [ABSTRACT FROM AUTHOR]

• The accuracy is greatly improved after replacing SCS module with the CSC module in Shift-GCN. • Inspired by shift CNNs, we replace shift space convolution in Shift-GCN with a sparse shift and named SparseShift-GCN. • The accuracy of algorithm was further improved after introduction of OHEM into SparseShift-GCN. Skeleton-based action recognition is widely used due to its advantages of lightweight and strong anti-interference. Recently, graph convolutional networks (GCNs) have been applied to action recognition and have made breakthrough progress. The shift convolution operator can effectively replace the spatial convolution and greatly reduce the computational complexity of the algorithm. This article first applies the Conv-Shift-Conv (CSC) module and the Shift-Conv-Shift-Conv (SC2) module to replace the Shift-Conv-Shift (SCS) module in spatial graph convolution of Shift-GCN respectively. This design can reorder the shifted channels more effectively. The experimental results show that the CSC module has the best effect and effectively improves accuracy of model. After that, this article proposes to replace the shift module in the original Shift-GCN with a sparse shift module and named SparseShift-GCN. This structure can reduce the redundancy of features, prevent overfitting and improve the generality of the model. Based on the improvement in the previous step, better results have been achieved. Finally, this paper uses OHEM Loss and Weighted Loss to carefully design the loss function of the model and introduces it into the model proposed in this paper. Experimental results show that OHEM Loss further improves the accuracy of algorithm. After a series of improvements, our proposed model has improved the accuracy of 4 different streams to varying degrees, which improves the overall performance of the network. [ABSTRACT FROM AUTHOR]

Abstract When analyzing the computational complexity of well-known puzzles, most papers consider the algorithmic challenge of solving a given instance of (a generalized form of) the puzzle. We take a different approach by analyzing the computational complexity of designing a "good" puzzle. We assume a puzzle maker designs part of an instance, but before publishing it, wants to ensure that the puzzle has a unique solution. Given a puzzle, we introduce the FCP (fewest clues problem) version of the problem: Given an instance to a puzzle, what is the minimum number of clues we must add in order to make the instance uniquely solvable? We analyze this question for the Nikoli puzzles Sudoku, Shakashaka, and Akari. Solving these puzzles is NP -complete, and we show their FCP versions are Σ 2 P -complete. Along the way, we show that the FCP versions of Triangle Partition , Planar 1- in -3 SAT , and Latin Square are all Σ 2 P -complete. We show that even problems in P have difficult FCP versions, sometimes even Σ 2 P -complete, though "closed under cluing" problems are in the (presumably) smaller class NP ; for example, FCP 2 SAT is NP -complete. [ABSTRACT FROM AUTHOR]

Abstract: -Means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and inefficient for solving clustering problems in large data sets. Recently, a new version of the -means algorithm, the global -means algorithm has been developed. It is an incremental algorithm that dynamically adds one cluster center at a time and uses each data point as a candidate for the -th cluster center. Results of numerical experiments show that the global -means algorithm considerably outperforms the -means algorithms. In this paper, a new version of the global -means algorithm is proposed. A starting point for the -th cluster center in this algorithm is computed by minimizing an auxiliary cluster function. Results of numerical experiments on 14 data sets demonstrate the superiority of the new algorithm, however, it requires more computational time than the global -means algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents a novel algorithm for computing graph edit distance (GED) in image categorization. This algorithm is purely structural, i.e., it needs only connectivity structure of the graph and does not draw on node or edge attributes. There are two major contributions: (1) Introducing edge direction histogram (EDH) to characterize shape features of images. It is shown that GED can be employed as distance of EDHs. This algorithm is completely independent on cost function which is difficult to be defined exactly. (2) Computing distance of EDHs with earth mover distance (EMD) which takes neighborhood bins into account so as to compute distance of EDHs correctly. A set of experiments demonstrate that the newly presented algorithm is available for classifying and clustering images and is immune to the planar rotation of images. Compared with GED from spectral seriation, our algorithm can capture the structure change of graphs better and consume 12.79% time used by the former one. The average classification rate is 5% and average clustering rate is 25% higher than the spectral seriation method. [Copyright &y& Elsevier]

Abstract: Phylogenetic trees are an important tool to help in the understanding of relationships between objects that evolve through time, in particular molecular sequences. In this paper, we efficiently solve two subtree-comparison problems on a set of phylogenetic trees which have practical applications to analyze the evolution and co-evolution genes clustering of genomic sequences. Let be a set of k phylogenetic trees such that the leaves of each tree are drawn from and the leaves for two arbitrary trees are not necessary the same, where n is the maximum number of the leaves among the k trees. We present a linear-time algorithm to find all the leaf-agreement descendant subtrees. By further extending this result, we present a linear-time algorithm to find all the leaf-agreement isomorphic descendant subtrees. Based on our algorithms, a web-based system using input tree files from TreeBASE is also implemented. [Copyright &y& Elsevier]

In this paper, we propose several algorithms for determining an optimal length tree-like refutation of linear feasibility in systems of Unit Two Variable Per Inequality (UTVPI) constraints. Given an infeasible UTVPI constraint system (UCS), a refutation certifies its infeasibility. The problem of finding refutations in a UCS finds applications in domains such as program verification and operations research. In general, there exist several types of refutations of feasibility in constraint systems. In this paper, we focus on a specific type of refutation called a tree-like refutation. Tree-like refutations are complete , in the sense that if a system of linear constraints is infeasible, then it must have a tree-like refutation. Associated with a refutation is its length, which equals the total number of constraints (accounting for the reuse of constraints) that are used to establish the infeasibility of the corresponding linear constraint system. Our goal in this paper is to find an optimal length tree-like refutation (OTLR) of an infeasible UCS. We describe two deterministic algorithms that find an OTLR of a UCS. If m is the number of constraints, n is the number of variables in the system, and k is the length of an OTLR, then our two algorithms run in O (n 3 ⋅ log k) time and O (m ⋅ n ⋅ k) time respectively. We also propose a true-biased, randomized algorithm for this problem. This algorithm runs in O (m ⋅ n ⋅ log n) time, and returns the length of an OTLR with probability (1 − 1 e). We extend our work to weighted UTVPI constraint systems (WUCS), where a positive weight is associated with each UTVPI constraint. The weight of a tree-like refutation is defined as the sum of the weights of the constraints used by the refutation. The problem of finding a minimum weight refutation in a WUCS is called the weighted optimal length tree-like refutation (WOTLR) problem and is known to be NP-hard. We propose a pseudo-polynomial time algorithm for the WOTLR problem and convert it into a fully polynomial time approximation scheme (FPTAS). [ABSTRACT FROM AUTHOR]

Carsten Thomassen and I proved that in any graph G , the number of cycles containing a specified edge as well as all the vertices of odd degree is odd if and only if G is eulerian. Where all vertices have even degree this is a theorem of Shunichi Toida and where all vertices have odd degree it is Andrew Thomason's extension of Smith's Theorem. Our proof was not algorithmic. In this paper, I extend Thomason's algorithm to one which, in a non-eulerian graph, finds a second cycle containing a specified edge and all the odd-degree vertices. Kenneth Berman extended Thomason's Theorem to spanning trees: he proved that if T is a spanning tree such that the degree of every vertex in G − E (T) is odd, then there is an even number of spanning trees of G with the same degree as T at each vertex. In this paper, I give a common generalization of all of these theorems to a parity theorem about "good" trees in general graphs. My proof provides an algorithm for finding a second "good" tree. [ABSTRACT FROM AUTHOR]

This paper considers the theoretical properties of iteratively reweighted least squares algorithm for noisy block sparse recovery problem (BIRLS for short). Li et al. used numerical experiments to show the remarkable performance of BIRLS algorithm for recovering a block sparse signal in noiseless measurement case, but no convergence analysis was given. In this paper, we focus on providing convergence, convergence rate and stability analysis of BIRLS algorithm for block sparse recovery in the presence of noise. The convergence of BIRLS is proved strictly. Furthermore, when the linear measurement matrix A satisfies the block restricted isometry property (abbreviated as block RIP), we show that BIRLS algorithm is stable and give the error analysis of BIRLS algorithm. We also characterize the convergence rate of the BIRLS algorithm, which implies global linear convergence for p = 1 and local super-linear convergence for 0 < p < 1. The simplicity of BIRLS algorithm, along with the theoretical guarantees provided in this paper, make a compelling case for its adoption as a standard tool for block sparse recovery. [ABSTRACT FROM AUTHOR]