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In this paper, we consider to solve the problem of reducing the end-to-end quantum entanglement establishment latency in quantum networks. In some existing schemes, "doubling" entanglement swapping (ES) method is usually utilized in a nested quantum repeater structure, while we investigate parallel-measurement techniques. We propose a protocol for end-to-end entanglement establishment in a quantum network where heterogeneous Bell pairs are previously shared on physical links. The protocol is proven to be feasible theoretically in a generalized scenario with multiple quantum repeater nodes. A general decision mechanism is developed for the destination node to read out the state of end-to-end quantum entanglement locally based on knowledge of the received measurement outcomes and entangled links information. Numerical evaluation shows that our protocol outperforms the "doubling" ES scheme regarding to the end-to-end entanglement establishment latency. [ABSTRACT FROM AUTHOR]

Existing weakly supervised named entity recognition (NER) research only deals with flat entities and ignores nested entities. This paper proposes a multi-stage nested entity recognition method (MNR) that utilizes weakly labeled data to recognize nested entities. However, weak labels generated through external knowledge bases have two problems: incompleteness and labeling bias. To address this challenge, the MNR comprises two models. First, we propose a neural transition-based attention model (NTAM) to solve the problem of weak-label incompleteness by learning the correlation between words. Simultaneously, the NTAM obtains candidate entities, including nested entities. Second, we propose a multi-marker fusion attention judgment model (MAJM) for selecting candidate entities through context semantics, candidate entities' meanings, and their boundary information, thereby solving the labeling bias problem. The boundary information of candidate entities is enhanced by fusing their type markers. To our knowledge, we are the first to recognize nested entities under weak supervision by alleviating the noise of weakly labeled data. Experiments on three public nested NER datasets prove the effectiveness of our proposed method under weak supervision and demonstrate that the method outperforms previous state-of-the-art models under supervision. [ABSTRACT FROM AUTHOR]

The geometric distortion in panoramic images significantly mediates the performance of saliency detection method based on traditional CNN. The strategy of dynamically expanding convolution kernel can achieve good results, but it also produces a lot of computational overhead in the process of reading the adjacency list, which decreases the computational efficiency. The appearance of graph convolution provides a new way to solve such problems. Although using graph convolution can effectively extract the structural features of the graph, it reduces the accuracy of the model resulting from ignoring the spatial features of the image signal. To this end, this paper proposes a construction method of the multi-scale graph structure of the panoramic image and a panoramic image saliency detection model composed of an image saliency feature extraction network and multi-scale saliency feature fusion network combining the image structure information and spatial information in the panoramic image. First, we establish a graph structure consisting of root and leaf nodes obtained by super-pixel segmentation at different scales and spherical Fibonacci sampling, respectively. Then, a feature extraction network composed of two graph convolution layers and two one-dimensional auto-encoders with the same parameterization is used to extract the salient features of the multi-scale graph structure. Finally, the U-Net network fuses the multi-scale saliency features to get the final saliency map. The results show that the proposed model performs better than the state-of-the-art models in terms of calculation speed and accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a detector that focuses on multi-scale detection problems and effectively enhances the detection performance to solve the problem that is hard to detect minor traffic signs. This detector, called YOLOF-F (you only look one-level feature fusion), is a single-stage detector that extracts multi-scale feature information from a single layer of fusion feature. First, we propose FFM (feature fusion module) to fuse different scales. Next, we offer a new encoder CDE (corner dilated encoder) to enhance the angular point information in the feature map, improve position regression accuracy, and maintain a faster detection speed. Finally, YOLOF-F achieved 74.57% and 77.23% of the AP on the GTSDB and CTSD datasets and reached 32 FPS. Extensive experiments validate that YOLOF-F is faster and more effective than most traffic sign detection methods. [ABSTRACT FROM AUTHOR]

To solve the problems of complex surface texture of magnetic tile, cumbersome process of traditional detection algorithm and low detection accuracy, this paper proposes a deep learning-based detection model: RC-YOLOv5s. The model incorporates two new structures: Res-Head and Drop-CA, where Res-Head enhances the feature fusion and information exchange between different layer structures and Drop-CA alleviates the case that the model pays too much attention to the defect target and reduces the missed detection rate of the model. Compared with YOLOv5s, the detection accuracy of the proposed model is improved by 1.83%, the missing rate is reduced from 1.673 to 0.372%, and the average detection frame rate of a single image reaches 41.67, which meets the requirements of real-time and accuracy of tile surface detection. [ABSTRACT FROM AUTHOR]

Large-scale natural frequency-related topology optimization problems pose a great challenge due to the high computational cost required by the iterative computation of the system eigenvalues and their sensitivities in each design iteration. This paper presents a new framework based on a multi-step relay strategy in conjunction with the idea of successive iteration of analysis and design (SIAD), to find high-quality solutions at affordable computational costs. The method starts from a relatively coarse finite element discretization, and then use gradually refined meshes to improve the design resolution. For a given level of mesh resolution, the method interleaves the eigenvalue solution routine with the optimization iterations to achieves sequential approximations of the eigenpairs along with the structural design evolution, thus avoiding the time-consuming eigenpair analysis in each optimization iteration. By sequentially solving the optimization problem and projecting intermediate designs and the corresponding approximate eigenmodes from a coarser mesh onto finer meshes, the proposed multi-step relay method can further substantially alleviate the computational burden and generate high-resolution boundaries in the final design. Numerical examples show that this method can be used to solve natural frequency maximization topology optimization problems with millions of degrees of freedom on a desktop workstation, and is much more efficient than the conventional double-loop method. [ABSTRACT FROM AUTHOR]

In the Bayes paradigm, given a loss function and an n-sample, we present the construction of a new type of posterior distribution, that extends the classical Bayes one. The loss functions we have in mind are either those derived from the total variation and Hellinger distances or some L j -ones for j > 1 . We prove that, with a probability close to one, this new posterior distribution concentrates its mass in a neighbourhood (for the chosen loss function) of the law of the data, provided that this law belongs to the support of the prior or, at least, lies close enough to it. We therefore establish that the new posterior distribution enjoys some robustness properties with respect to a possible misspecification of the prior, or more precisely, its support. We also show that the posterior distribution is stable with respect to the equidistribution assumption we started from. Besides, when the model is regular and well-specified and one uses the squared Hellinger loss, we show that our credible regions possess, at least for n sufficiently large, the same ellipsoidal shapes and approximately the same sizes as those we would derive from the classical Bayesian posterior distribution by using the Bernstein–von Mises theorem. Then we use our Bayesian-like approach to solve the following problems. We first consider the estimation of a location parameter or both the location and scale parameters of a density in a nonparametric framework. Then we tackle the problem of estimating a density, with the squared Hellinger loss, in a high-dimensional parametric model under some sparsity conditions on the parameter. Importantly, the results established in this paper are nonasymptotic and provide, as much as possible, bounds with explicit constants. [ABSTRACT FROM AUTHOR]

The mechanical parameters of a rock mass are the basis for studying the deformation and failure mechanisms and control methods of the surrounding rock in underground engineering. At present, these parameters are mainly obtained through laboratory tests of cores obtained from surrounding rock in the field. It is difficult to core the broken surrounding rock, and a rock core is detached from an environment with on-site stress. The rock mass drilling test can provide a new method to solve the above problems. This paper has developed a rotary cutting test system for rock mass (RCT system) for research on the relationships between drilling parameters and rock mechanics parameters. The basic characteristics and structure of the RCT system were introduced, digital drilling tests of different rock mass specimens were conducted for various control parameters and different bit types. The relationships between drilling parameters, and the response law of drilling parameters to rock mechanics parameters and drill bit structural parameters during rock drilling were systematically analyzed. The research lays a foundation of equipment and theory for implementing testing of rock mass parameters while drilling. [ABSTRACT FROM AUTHOR]

Aiming at the problem of multi-object detection such as target occlusion and tiny targets in road scenes, this paper proposes an improved YOLOv5 multi-object detection model based on ML-AFP (multi-level aggregation feature perception) mechanism. Since tiny targets such as non-motor vehicle and pedestrians are not easily detected, this paper adds a micro target detection layer and a double head mechanism to improve the detection ability of tiny targets. Varifocal loss is used to achieve a more accurate ranking in the process of non-maximum suppression to solve the problem of target occlusion, and this paper also proposes a ML-AFP mechanism. The adaptive fusion of spatial feature information at different scales improves the expression ability of network model features, and improves the detection accuracy of the model as a whole. Our experimental results on multiple challenging datasets such as KITTI, BDD100K, and show that the accuracy, recall rate and mAP value of the proposed model are greatly improved, which solves the problem of multi-object detection in crowded road scenes. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology serves many industries to improve their performance. As such, utilizing far distant cloud datacenters to run time-sensitive IoT applications has become a great challenge for the sake of real-time interaction and accurate service delivery time requests. Therefore, the fog computing as a deployment approach of IoT applications has been presented in the edge network. However, inefficient deployment of applications' modules on the fog infrastructure leads to physical resource and bandwidth dissipations, and debilitation of quality of service (QoS), and also increases the power consumption. When all application's modules are highly utilized on a single fog node owing to the reduction in the power consumption, the level of service reliability is decreased. To obviate the problem, this paper takes the concept of fault tolerance threshold into account as a criterion to guarantee applications' running reliability. This paper formulates deployment of IoT applications' modules on fog infrastructure as a multi-objective optimization problem with minimizing both bandwidth wastage and power consumption approach. To solve this combinatorial problem, a multi-objective optimization genetic algorithm (MOGA) is proposed which considers physical resource utilization and bandwidth wastage rate in their objective functions along with reliability and application's QoS in their constraints. To validate the proposed method, extensive scenarios have been conducted. The result of simulations proves that the proposed MOGA model has 18, 38, 9, and 43 percent of improvement against MODCS, MOGWO-I, MOGWO-II, and MOPSO in terms of total power consumption (TPC) and it has 6.4, 15.99, 28.15, and 15.43 dominance percent against them in term of link wastage rate (LWR), respectively. [ABSTRACT FROM AUTHOR]

With the development of formal concept analysis, classical concept lattice cannot solve some problems in practice because of its strict conditions. Protoconcept, which is an extension of the formal concept, provides a new method of data processing. Three-way decisions is more in line with human cognition. Not like the decision of two ways, three-way cognition expands its scope of application. However, up to now, protoconcept is influenced by two-way decisions which leads to the restrictions of its applications. Hence, it is urgent to find a method to generalize protoconcept from the framework of two-way decisions. To give the solution to this urgent problem, the main contributions of this paper are the following: First, using the combination of protoconcept and three-way decisions, two models of three-way protoconcept are obtained. The algorithms for searching three-way protoconcept are built. Second, we investigate the constructions of three-way protoconcepts in double Boolean algebra. Third, the relationships between protoconcepts and three-way protoconcepts are found. Moreover, using examples that come from the real world, we explain the main results obtained above and show some applications. The two models for protoconcepts provided in this paper generalize the fields of thought from two-way decisions to three-way decisions. This follows that they are more in line with human thinking. Hence, the two models will certainly generalize the applications of protoconcept theory in the future. [ABSTRACT FROM AUTHOR]

The finite-difference frequency domain (FDFD) method is widely applied for simulating seismic wavefields, and a key to achieving successful FDFD simulation is to construct FDFD coefficients that can effectively suppress numerical dispersion. Among the existing FDFD coefficients for seismic wavefield simulation, adaptive FDFD coefficients that vary with the number of wavelengths per grid can suppress numerical dispersion to the maximum extent. The current methods for calculating adaptive FDFD coefficients involve numerical integration, conjugate gradient (CG) optimization, sequential initial value selection, and smooth regularization, which are difficult to implement and inefficient in calculations. To simplify the calculation of adaptive FDFD coefficients and improve the corresponding computational efficiency, this paper proposes a new method for calculating adaptive FDFD coefficients. First, plane-wave solutions with different discrete propagation angles are substituted in the FDFD scheme, and the corresponding least-squares problem is constructed. As this problem is ill-conditioned and obtaining smooth adaptive FDFD coefficients by the conventional solving method based on normal equations is difficult, this paper proposes solving the least-squares problem by solving the corresponding overdetermined linear system of equations through QR matrix decomposition. Compared with the existing methods for calculating adaptive FDFD coefficients based on numerical integration, CG optimization, and sequential initial value selection, the proposed method allows for a simplified computational process and considerably higher computational efficiency. Numerical wavefield simulation results show that the adaptive-coefficient FDFD method based on QR matrix decomposition can achieve the same accuracy as those based on numerical integration, CG optimization, and sequential initial value selection while requiring less computation time. [ABSTRACT FROM AUTHOR]

Flexible flat cable (FFC) detection is the premise of robot 3C assembly and is challenging because FFCs are often non-axis aligned with arbitrary orientations having cluttered surroundings. However, to date, the traditional robotic object detection methods mainly regress the object horizontal bounding box, in which the size and aspect ratios do not reflect the actual shape of the target object and hardly separate the FFCs in dense. In this paper, rotated object detection was introduced into FFC detection, and a YOLO-based arbitrary-oriented FFC detection method named YOLOOD was proposed. Firstly, oriented bounding boxes are used to reflect the object's physical size and angle information and better separate the FFCs from the dense background. Secondly, the circular smooth label angular classification algorithm is adopted to obtain the angle information of FFCs. Finally, the head point regression branch is introduced to distinguish between the head and the tail of the FFC and expand the range of FFC detection angle to 0 ∘ , 360 ∘ . The proposed YOLOOD can reach the detection performance with an average precision of 90.82% and a detection speed of 112 FPS on an FFC dataset. Meanwhile, an actual FFC grasping experiment demonstrated the proposed YOLOOD's effectiveness and feasibility in practical assembly scenarios. In conclusion, this paper innovatively introduces rotating object detection into robot object detection, and the proposed YOLOOD solves the problem of detecting and locating non-axis aligned FFCs and has particular significance for robot 3C assembly. [ABSTRACT FROM AUTHOR]

We study the problem of describing the triples , , where is the (co)metric associated with a symmetric second-order differential operator defined on a domain of and such that there exists an orthonormal basis of consisting of polynomials which are eigenvectors of and this basis is compatible with the filtration of the space of polynomials by some weighted degree. In a joint paper of D. Bakry, M. Zani, and the author this problem was solved in dimension 2 for the usual degree. In the present paper we solve it still in dimension 2 but for a weighted degree with arbitrary positive weights. [ABSTRACT FROM AUTHOR]

The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m3/s, which is much lower than the safe overflow flow rate of 22,000 m3/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs. [ABSTRACT FROM AUTHOR]

In this paper, a node detection method is proposed to detect the damaged parts of armor automatically. The traditional detection of armor damage is judged by experience first, and then the parts are decomposed and confirmed, which makes the problem solving time long and efficiency low. In this paper, the in-mold electronic decoration technology is used on the surface of armor, and the node displacement changes after the surface film is damaged are used to achieve the purpose of damage detection. By using the wavelet analysis, the singularity of the wavelet function can be found and the damaged part can be determined. The Lipschitz index can be used to judge the singularity of the wavelet function to detect the local damage on the armor surface. Finally, the changes of Lipschitz index of the signal with different damage degrees on the helmet deck were simulated. In terms of influencing factors of optimization process, moldex3D was used to simulate and analyze the damaged parts of armor—different injection molding parameter schemes were set to optimize the armor film. In the first stage, the displacement change at the damaged location is detected by the probe node through simulation. In the second stage, the armor was simulated by setting appropriate process parameters such as melt temperature, filling time, filling pressure, and filling time. In the third stage, the wavelet analysis and a Lipschitz index were used to detect the location of damaged nodes. In the fourth stage, the change curve of wavelet analysis is verified by the analysis experiment. Through the experimental verification, it can be seen that the position displacement of the damaged armor changes and the singularity is generated on the wavelet function to achieve the purpose of damage detection. The influencing factors of the film injection molding are temperature and pressure. Through the simulation analysis, we optimize the injection molding parameters of the film, thus improving the damage resistance of the armor. Finally, the optimal parameters of vulnerable parts are obtained and the optimization scheme is determined. [ABSTRACT FROM AUTHOR]

The probabilistic linguistic term set (PLTS) shows great superiority in expressing decision-makers' opinions. The multi-attribute decision-making (MADM) problem under a PLTS environment has gained attention from numerous scholars. However, the majority of current studies are not precise enough in capturing information on PLTS. To address this problem, this paper presents a preference ranking organization method for enrichment of evaluations (PROMETHEE) based on the redefined PLTS and novel score function to solve MADM problems under a PLTS environment. First, an asymmetric normalized PLTS based on prospect theory (ANPLTSPT) is developed. Compared with the PLTS, ANPLTSPT offers a more realistic portrayal of decision-makers' psychological state while ensuring the superiority of the PLTS. Second, regarding the structural complexity of ANPLTSPT, this paper attempts to simplify the computational process through a score function that can embody the characteristics of ANPLTSPT. Inspired by previously formulated score functions, a novel score function called Score-InInHe is developed, the corresponding definitions are given, and some further properties are discussed. With the support of the proposed Score-InInHe, the total score entropy is defined and an objective method to determine the attribute weights is proposed. Finally, the proposed approach is applied to the selection of a green supplier and the determination of air quality. The validity and realistic applicability of the proposed approach are demonstrated through comparative analyses and discussions. [ABSTRACT FROM AUTHOR]

In this paper, we investigate refutability in Difference Constraint Systems (DCS) under the Unit Read-Once Refutation (UROR) system. A difference constraint is a linear relationship of the form: x i - x j ≤ b ij and a DCS is a conjunction of such constraints. In the UROR refutation system, each constraint can be used by at most one inference. Additionally, each inference has to use at least one one-variable (absolute) constraint. Note that an unsatisfiable difference constraint system may not have a UROR. Thus, the UROR refutation system is incomplete for DCSs. The UROR refutation system is useful for proving that the infeasibility of a DCS is caused by the current variable domains. These domains are determined by the absolute constraints in the system. Thus, the UROR refutations of a DCS depend on these variable domains. This is in contrast to unrestricted refutations which do not need to depend on these domain constraints. Investigating weak (incomplete) refutation systems leads to a better understanding of the inference rules required for establishing contradictions in the given constraint system. Thus, this study is well-motivated. Likewise, difference constraint systems arise in a number of application domains such as program verification and scheduling. It follows that efficient refutation systems are of paramount interest. In this paper, we show that problem of checking if a DCS has a unit read-once refutation is NP-complete. Additionally, we provide parameterized and exact exponential algorithms for solving this problem. Finally, we show that the problem of finding the length of the shortest unit read-once refutation is NPO PB-complete. [ABSTRACT FROM AUTHOR]

The efficiency of truck loading-and-unloading tasks has become one of the major problems in logistics transportation, and intelligent loading-and-unloading robots are considered as effective solutions to this problem. Recently, carton detection with deep learning is widely used for those robots; however, lack of large annotated datasets is one of major impediments for rapidly deploying carton detection models. We currently have presented the Sacked Carton Dataset (SCD) that contains carton images from four logistics scenarios, such as Comprehensive Pharmaceutical Logistics Company (CPLC), E-commerce Logistics Company (ECLC), Fruit Market (FM), and Large Furniture Market (LFM). However, due to the domain shift, the carton detection model trained with source domain (which are annotated) has poor generalization ability when applied to the target domain (which are not annotated and from new logistics scenarios). To solve this problem, a novel image synthesis method is proposed to quickly acquire a labeled dataset, which replace the foreground texture of the source datasets with the texture of the target datasets. In this paper, a Local Surfaces Segmentation Algorithm is proposed to extract the carton stacking skeleton and an Invisible Contour Reconstruction Algorithm is proposed to complete the contour of the carton. Our method can keep the context relationship of foreground objects and backgrounds unchanged, and it also can greatly augment the target datasets. Code is available here. [ABSTRACT FROM AUTHOR]

The author of this paper has recently shown that daily-mean self-potential (SP) time series recorded at Urla station, Turkey, exhibit a unit-root nonstationary behavior with weakly correlated stationary differences driven by stable noise of tail index around 1.12–1.15. This work mainly aims to assess whether the Exponentiated-Exponential Cauchy (EEC) family distributions could be utilized as an alternative to the stable distributions. The EEC family distributions have many advantages as they have closed-form representations. Those goodness-of-fit statistics, relative strengths and predictive abilities of the three EEC family distributions with two- (Cauchy), three- (EEC-3) and four (EEC-4) parameters were compared with that of the stable one. The Akaike Information Criterion (AIC)-based relative strength for the EEC-3 candidate indicated that it is approximately 1.85 times more likely to be the best approximating model than the stable one. Additionally, those considerably smaller relative mean absolute errors (RMAE) for the EEC-3 model indicated that the asymptotic tail properties of the empirical distribution could be better represented in comparison with the stable model. The results of this work revealed, on the example of Urla, that an EEC-3 distribution would plausibly the stable one. We hope the findings of this paper would provide many advantages and facilitate the numerical computations in solving problems arising from the use of stable laws. [ABSTRACT FROM AUTHOR]

Graph stream is used to express complex and highly dynamic relationships between entities, such as friendships in social networks. The storage and mining on graph stream are the main research areas of big data research. Triangle listing/counting is an important topic in graph mining research. The triangle, as the simplest circle and clique structure, has many applications in many real-world scenarios. A large amount of related work also exists on the study of triangles on graph streams. However, the existing research has focused on triangle counting on static graphs or graph streams, and there is a lack of research targeting heavy weight triangle listing. This paper formally defines the triangle weight on graph stream. Based on this definition, this paper presents an approximation algorithm for the top-k heavy weight triangle listing problem on graph stream, and proposes various optimized data structures DolhaT, Filtered DolhaT and Double Filtered DolhaT (DFD) to solve this problem. Experiments on real graph stream datasets demonstrate the effectiveness of the proposed optimized structures for the heavy weight triangle listing problem on graph stream. [ABSTRACT FROM AUTHOR]

High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems. [ABSTRACT FROM AUTHOR]

In recent years, the behavior recognition technology based on Wi-Fi devices has been favored by many researchers. Existing Wi-Fi-based human behavior recognition technology mainly uses classification algorithms to construct classification models, which has problems such as inaccurate behavior segmentation, failure to extract deep-level features from the original data and design classification models matching the proposed features in the process of behavior recognition. In order to solve above problems, this paper proposes a window variance comparison method by combining the adaptive thresholds calculated to achieve effective segmentation of multiple discontinuous human behaviors, then uses the short-time Fourier algorithm to extract time-frequency features of individual behavior, and extracts the graph structure data from the autocorrelation matrix of time-frequency features and the features themselves. A graph neural network is built for behavior recognition. The experimental results show that the segmentation accuracy of the behavior segmentation method in the two scenes is 0.964 and 0.993, which is better than the existing threshold-based behavior segmentation methods. In addition, this paper extracts graph structure data by spectral energy change method and builds behavior recognition model by using graph neural network, and the recognition accuracy is significantly improved compared with the traditional classification algorithm. [ABSTRACT FROM AUTHOR]

The problems of constructing accurate and stable algorithms for solving inverse problems of mathematical physics are at the forefront of modern computational mathematics due to the ever-increasing number of applications of such problems in physics and technology, as well as to the properties of these problems, which greatly complicate their numerical solution. In this paper, we consider the problem of numerical solution of one of such problems, known as the inverse problem of heat transfer. The numerical method for solving the inverse problem of heat transfer uses the apparatus of hypersingular integral equations. As far as the authors know, this approach to the construction of methods for solving the inverse problem of heat transfer is used for the first time. The numerical method described in the paper makes it possible to successfully find an approximate solution to the inverse problem of heat transfer, including the case of significant errors in the initial data. The solution of a model example demonstrates the efficiency of the method proposed. [ABSTRACT FROM AUTHOR]

Compared with the current continuous development of the Internet, the unbalanced distribution of medical resources and low efficiency have made the traditional medical system stretched. In order to solve these problems and allow patients to get health advice from doctors at home, this article proposes an Internet-based telemedicine system. This article first elaborates the research background and significance of telemedicine, the development status at home and abroad, and the advantages and disadvantages of telemedicine. In the overall design, the system is divided into basic platform subsystem, application platform subsystem, and specific application subsystem, and the structure and implementation process of each module are designed. Aiming at the reliability of data transmission, this paper improves the traditional follow-up selection algorithm. The performance of the improved algorithm proposed is better than that of the traditional algorithm, which greatly improves the throughput of the system, avoids the repeated use of the same node, and improves the stability of the system. This paper designs a more comprehensive telemedicine system at the functional level, which can meet various functions required by telemedicine, enable patients and doctors to communicate in real time, and effectively improve doctors' work efficiency. [ABSTRACT FROM AUTHOR]

It is very fascinating that the principles of Artificial Intelligence, that were proposed many decades ago, have remained to be the foundations of many of the modern-day techniques, and a brief summary of these principles is given in the paper itself. While this truism is valid for problem solving and game playing, in the context of this paper, we emphasize that it is also pertinent for the so-called partitioning problems. In this paper, we consider the general partitioning problem that has been studied for decades. Unlike the heuristic and search strategies, our attention focuses on learning automata (LA) and reinforcement learning methods, and their powerful ability to solve problems in stochastic environments. These render the latter tools to be applicable to various complex tasks. As we shall explain, LA have been employed for partitioning, and in particular, the paradigm of Object Migration Automata (OMA) has offered state-of-the-art adaptive methods for solving grouping and partitioning problems. However, because the number of possible partitions is combinatorially exponential, and of the NP-hardness of the underlying tasks, the existing state-of-the-art OMA algorithms can only solve problems of equal-sized groups. To resolve this, in this paper, we propose two new OMA variants that can solve both equally and unequally sized partitioning problems. The key here is to provide additional information to the algorithms, and in doing so, to expand the solution space. The first variant, the Greatest Common Divisor OMA (GCD-OMA), relaxes the constraint of having equally sized groups by mapping the actions onto a larger state space, and thus permitting it to solve problems where the group sizes have a GCD that is greater than unity. Subsequently, we propose a second variant named the Partition Size Required OMA (PSR-OMA) to make the algorithm more versatile. The PSR-OMA can handle groups of arbitrary sizes when the group sizes are known a priori. We demonstrate the proposed algorithms' strength in solving stochastic grouping problems through extensive simulations, even with high noise levels. The GCD-OMA and the PSR-OMA represent the current state-of-the-art when it concerns resolving the extremely complex problem of partitioning and can be used in any of the numerous applications in which the OMA itself has been utilized. [ABSTRACT FROM AUTHOR]

Studies the role of automatic control theory that lead to system methodologies of identification for various types of applied problems. System methodologies developed for the practical use of the mathematical apparatus of the theory; Orientation of research activity to realization and detailed description of the rational structure of identification within the framework of various search systems; Provision of the subject of identification with informational support for transformation of the difficult-to-formalize conditions.

Collaboration is a key driver of science and innovation. Mainly motivated by the need to leverage different capacities and expertise to solve a scientific problem, collaboration is also an excellent source of information about the future behavior of scholars. In particular, it allows us to infer the likelihood that scientists choose future research directions via the intertwined mechanisms of selection and social influence. Here we thoroughly investigate the interplay between collaboration and topic switches. We find that the probability for a scholar to start working on a new topic increases with the number of previous collaborators, with a pattern showing that the effects of individual collaborators are not independent. The higher the productivity and the impact of authors, the more likely their coworkers will start working on new topics. The average number of coauthors per paper is also inversely related to the topic switch probability, suggesting a dilution of this effect as the number of collaborators increases. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles are useful tools for many applications. However, autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a challenging problem when facing a series of problems such as poor consistency, high influence by the native controller of the Unmanned Aerial Vehicles. In this paper, we investigate reinforcement learning-based autonomous local path planning methods for Unmanned Aerial Vehicles with high autonomous decision-making capability and locally high portability. We propose an autonomous local path planning algorithm based on the TD3 strategy to solve the problem of local obstacle avoidance and path planning in unfamiliar environments using autonomous decision-making of Unmanned Aerial Vehicles. The simulation results on Gazebo show that our method can effectively realize the autonomous local path planning task for Unmanned Aerial Vehicles, the success rate of path planning with our method can reach 93% under the interference of no obstacles, and 92% in the environment with obstacles. Finally, our method can be used for autonomous path planning of Unmanned Aerial Vehicles in unfamiliar environments. [ABSTRACT FROM AUTHOR]

The paper proposes a methodology for mathematical modeling of the phased development of nodes and transport routes in a hierarchical network with multicommodity discrete flows of correspondences based on solving the problems of optimizing its structure and distribution of flows. As a rule, such networks consist of a decentralized mainline network and networks in the internal service areas of mainline nodes. In a multicommodity network, each node can exchange correspondence (products, goods, cargo, messages) with the other nodes. Correspondence is characterized by a source node, a sink node, and a quantity, which for data transmission networks is given by the number of bytes, kilobytes, etc., and for transport networks by the number of cargo units in a package of uniform size. In the mainline network, all correspondences are transmitted via communication channels or transported by vehicles in transport units of a given size (capacity, volume). The authors considered the main postulates of generating a mathematical model of the perspective development of mainline network. They presented a method of mathematical modeling of the phased development of nodes and transport routes, which includes, for each stage of development, the forecasting of data and network parameters, solving the problem of packing correspondence and choosing the structure of the network, and solving the problem of distribution and routing of transport unit flows. [ABSTRACT FROM AUTHOR]

Extrapolation, restart and stepsize are very powerful strategies for accelerating the convergence rates of first-order algorithms. In this paper, we propose a modified accelerated proximal gradient algorithm (modAPG), which incorporates the adaptive nonmonotone stepsize strategy, extrapolation and a modified rule of setting the extrapolation parameter to zero at some iterations, for solving a minimization problem composed of a smooth (possibly nonconvex) with Lipschitz continuous gradient and nonsmooth (possibly nonconvex) function. Moreover, the proposed algorithm has a simpler form for this problem with convex setting. We show that any cluster point of the sequence generated by modAPG is a critical point of the problem, and analyze the convergence rates of function values and iterates under the assumption that objective function has the Kurdyka-Łojasiewicz property. Finally, we conduct some preliminary numerical experiments for solving the convex problems, such as image deblurring problem and sparse logistic regression problem; and the nonconvex regularization problem, such as the L 1 2 penalty and the smoothly clipped absolute deviation (SCAD) penalty problem. Numerical experiments demonstrate the promising performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we consider the resolvent via Busemann functions introduced by Bento, Cruz Neto, Melo (J Optim Theory Appl 195:1087–1105, 2022), and we present a proximal point method for equilibrium problems on Hadamard manifold. The resolvent in consideration is a natural extension of its counterpart in linear settings, proposed and analyzed by Combettes and Hirstoaga (J Nonlinear Convex Anal 6:117–136, 2005). The advantage of using this resolvent is that the term performing regularization is a convex function in general Hadamard manifolds, allowing us to explore the asymptotic behavior of the proximal point method to solve equilibrium problems. [ABSTRACT FROM AUTHOR]

This paper presents sum-of-squares (SOS) relaxation results to a difference-of-convex-max (DC-max) optimization involving SOS-convex polynomials in the face of constraint data uncertainty and their applications to robust feature selection. The main novelty of the present work in relation to the recent research in robust convex and DC optimization is the derivation of a new form of minimally exact SOS relaxations for robust DC-max problems. This leads to the identification of broad classes of robust DC-max problems with finitely exact SOS relaxations that are numerically tractable. They allow one to find the optimal values of these classes of DC-max problems by solving a known finite number of semi-definite programs (SDPs) for certain concrete cases of commonly used uncertainty sets in robust optimization. In particular, we derive relaxation results for a class of robust fractional programs. Also, we provide a finitely exact SDP relaxation for a DC approximation problem of an NP-hard robust feature selection model which gives computable upper bounds for the global optimal value. [ABSTRACT FROM AUTHOR]

A risk-aware decision-making problem can be formulated as a chance-constrained linear program in probability measure space. Chance-constrained linear program in probability measure space is intractable, and no numerical method exists to solve this problem. This paper presents numerical methods to solve chance-constrained linear programs in probability measure space for the first time. We propose two solvable optimization problems as approximate problems of the original problem. We prove the uniform convergence of each approximate problem. Moreover, numerical experiments have been implemented to validate the proposed methods. [ABSTRACT FROM AUTHOR]

A multi-dimensional signal on graph has different features along different directions/dimensions. For example, image signal is isotropic along horizontal and vertical directions, and time-varying signal presents different correlation characters in the time and vertex domains. Nevertheless, the current graph signal processing concentrates on the single shift, which makes it difficult to differentiate the correlation features along different directions. In this paper, we define a multi-shift notion for the time-varying signals on graphs enabling us to separately analyze the multi-dimensional signal along different directions captured by diverse shifts. Furthermore, the multi-shift notion is leveraged to formulate the inpainting problem for time-varying signals on the strong product graph, which can be exploited to characterize three different kinds of elements interaction of time-varying signals by using three different kinds of shifts. The conjugate gradient descent algorithm is further deployed to solve the inpainting problem. Numerical experiments conducted on the synthetic signal and real-world data show the potentiality of the multi-shift representation and the effectiveness of the inpainting algorithm. [ABSTRACT FROM AUTHOR]

Obtaining 3D coordinates of fire by binocular vision is beneficial for intelligent fire extinguishing. However, binocular vision technology has a small field of view (FOV) for long-distance targets, which results in the fire not appearing in the FOV of binocular cameras. To solve this problem, an automatic fire alignment system with monocular and binocular cameras is constructed, and an alignment control method, including a one-time alignment method and visual servo is proposed. First, the one-time alignment method based on the monocular camera is designed to solve the problem of time. The alignment angle independent of depth information ensures that the binocular camera can reach the vicinity of the fire in a short time with only a single camera. Then, image-based visual servo (IBVS) methods based on a monobinocular vision system are designed to improve the alignment precision of the binocular camera, and a logic controller is designed for automatically switching the vision servo control method based on different cameras. Finally, experiments are designed to compare the proposed method with other methods. Results demonstrate that the method in this paper is better than other methods in terms of both time and accuracy and contributes to improving the efficiency of fire extinguishing. [ABSTRACT FROM AUTHOR]

In this paper, fixed-time convergent RNNs with logarithmic settling time are proposed for solving time-variant quadratic programming (QP), where the domain of attraction of the RNN models can be reasonably estimated a priori. Two novel activation functions are designed, and the exact expressions of the settling time functions for each given initial condition are given; and upper bounds of the settling times for any given initial condition can be obtained. In addition, the generic activation functions are constructed and analyzed to achieve logarithmic fixed-time settling. The RNNs based on the generic activation function are designed, which have a faster convergence rate and a fixed-time convergence property. By estimating the bound of the settling time function under given initial conditions in the bounded region, the semi-global fixed-time convergence of the RNN model is in turn established. It is shown that the predefined-time convergence of modified RNN models can be assured by adopting the inverse of the bound. Simulation results verify effectiveness of the proposed computing schemes for the time-variant QP problem solving and the repetitive motion planning of a redundant manipulator. [ABSTRACT FROM AUTHOR]

Businesses nowadays often feel impelled to rush and implement Big Data and Artificial Intelligence initiatives in their organizations without clarity on their business problems, nor the appropriate methodologies for extracting actionable insights from the data. In contrast, this paper presents a process that starts with an articulated business problem instead of a "data rush". The presented process, dubbed the Business-Driven Data-Supported (BDDS) process, is rigorously anchored in concepts from Theory of Constraints and Information Theory. BDDS guides businesses in solving their problems by stating observed performance gaps, uncovering their underlying root cause, formulating the "right question", utilizing only the "right data", and choosing the "right methodology" to extract the "right information" from the data, leading to the "right actionable insights." Also provided is a prescriptive framework, dubbed the Data-to-Information-Extraction-Methodology (DIEM), for selecting the "right methodology", based on the understanding level of relevant system dependencies and the availability of relevant data. The BDDS process is illustrated by an example from the healthcare industry, and the efficacy and applicability of the DIEM framework are supported by eleven case studies from a broad range of industries. [ABSTRACT FROM AUTHOR]

The image stitching process often produces many undesirable effects. Solving problems such as discontinuity and dislocation of pictures has always been the focus of people's research. From the perspective of human vision, this dislocation situation can be easily perceived and found. In this paper, we propose a stitching strategy based on the human visual system (HVS) and scale-invariant feature transform (SIFT) algorithm. We preprocess the brightness difference and contrast of the stitched images, combining SIFT algorithm and HVS to divide the overlapping areas of the stitched images and establish an attribute relationship model. We use dynamic programming to find the optimal seamline according to the attribute relationship model, and the final result makes the optimal seamline almost invisible under the discriminative vision of human eyes. The experimental results show that our method has more advantages in the HVS. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven solver (DDS) for the growth-induced deformation problems of soft materials. Contrary to the over-reliance of traditional numerical methods on material constitutive models, the DDS requires only a discrete material dataset of stress–strain pairs to describe the stress–strain relationship. With the multiplicative decomposition of the gradient tensor, the growth effects are introduced. The growth-induced deformation problems are then solved as the pre-strain problems. To overcome the difficulty in determining the growth-induced pre-stress without an explicit constitutive model, a strain-driven searching scheme is thus proposed. Several numerical examples demonstrate the robustness and its good performance of the proposed DDS. In particular, the reason for choosing the strain-driven searching scheme is also illustrated according to the numerical example results. [ABSTRACT FROM AUTHOR]

The novelty of additive manufacturing (AM) involves new requirements, restrictions and rules, that are considerably different to those of conventional manufacturing methods. Therefore, designers often lack experience and knowledge about identifying AM-suited components. However, to ensure profitability, it is essential to choose components, that are well suited for additive manufacturing. State-of-the-art user-support methods for identifying AM potential widely focus on either economic potential or manufacturability but fail to address both aspects. While machine learning solutions are considered highly efficient, the assessment outcome lacks process transparency, inhibiting troubleshooting, plausibility checks and problem-oriented considerations. This paper proposes a holistic, yet detailed and transparent approach to identify conventionally manufactured parts for AM from an existing product portfolio, enabling decision-making based on quantifiable results. It combines and advances state-of-the-art methods, considering manufacturability, economic profitability and socio-ecological aspects. Besides evaluating AM potential, the method additionally assesses the components' potential for re-design-based enhancement for AM suitability. Besides understanding product functions and present production processes, users are expected to have a basic understanding of company goals. The approach involves inquiries regarding company- and product-specific priorities, enabling a weighted assessment. The weights are determined based on individual company philosophies regarding AM value propositions such as differing stakeholder interests and priorities. Additionally, the approach allows users to investigate different development goals by weighting opportunistic and restrictive assessment. The method application is demonstrated via an assembly comprising 11 parts in a scenario focusing on serviceability, eventually determining suitability statements. [ABSTRACT FROM AUTHOR]

The emergence of digital computers has profoundly reshaped our interactions with technology and the processing of information. Despite excelling in data processing and arithmetics, these computers face limitations in tackling complex nondeterministic-polynomial (NP) problems. In response, researchers have started searching for new computational paradigms that possess the natural tendency of solving these problems. Oscillator-based optimizers are one such paradigm, where the idea is to exploit the parallelism of oscillators networks in order to efficiently solve NP problems. This involves a process of mapping a given optimization task to a quadratic unconstrained binary optimization program and then mapping the resulting program onto an inter-oscillator coupling circuit encoding its coefficients. This paper presents a comprehensive approach to constructing oscillator-based optimizers, offering both the rationale for employing oscillator networks and formulas for linking optimization coefficients to inter-oscillator coupling. Here, we cover most aspects of oscillator-based optimization starting from the design of the network up to its technical implementation. Moreover, we provide a platform-independent wave digital algorithm, which allows for emulating our network's behavior in a highly parallel fashion. [ABSTRACT FROM AUTHOR]

The traditional behavior recognition model has the disadvantage that it can't get the internal relationship between similar behaviors, such as smoking, pen, chin and the clamped objects, which limits the actual landing of such fine and complex behaviors as smoking recognition. To solve these problems, this paper puts forward the heterogeneous algorithm HMMA-NET (Heterogeneous multi-task smoking behavior recognition model combined with Attention), which consists of two modules: behavior prior and local detection, aiming at establishing the relationship between behavior and behavior objects. CNN combined with channel attention mechanism is used in both behavior prior module and local detection module. The former uses sign language semantic features to complete the primary prior of behavior according to the obtained behavior affinity vector field, while the latter designs network optimization such as fast Edgebox to obtain candidate areas, so as to transfer component information and achieve the goal of fast fine-grained detection. Finally, the two modules use SaaS mode to complete association recognition. Experiment shows that the algorithm can recognize complex actions effectively, and its accuracy is still equal to or even better than that of a single model, in which the accuracy of detecting smoking behavior scenes is 96.10%, and the false detection rate is 3.6%. The algorithm has been commercialized and applied to the actual monitoring of petrochemical scenes. The running results show that the algorithm can maintain good real-time performance and generalization ability. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the network scale, network technology is also constantly developing. However, with the continuous deterioration of the security environment, the problem of network safety is improving. The traditional single security method has greatly improved the network's stability, but due to the lack of effective cooperation, it becomes increasingly difficult to understand the state changes of the entire network at all times. In such a large environment, research on network security situational awareness can obtain theoretical value and has certain application prospects. The current understanding of cybersecurity situational awareness is not deep enough. Most cases are built in a single-source environment and cannot accurately reflect the perception of attack phases and sequences. To solve this problem, a new model of network safety situation awareness based on multisource data fusion was proposed. The model can effectively perceive the attack stages and sequences and provide an early warning, which is of great importance to improve the network security situation awareness and maintain the network security environment. On the basis of extracting the degree of dissimilarity, in this paper, the fusion-based method is used to generate the attack trajectory, thus forming the multisource data fusion and reconstruction algorithm and finally forming the network security situational awareness model. Compared with the single-source data fusion and reconstruction algorithm, this method has better performance. The final result shows that when the original number of alarms was 1237, after multisource data fusion, the number of alarms was reduced to 124. Moreover, on the basis of multisource data fusion, the detection rate of the number of alarms reached 86.67%, which was 26.67% higher than that of single-source data fusion; the false alarm rate was 5.63%, which was 1.19% lower than that of single-source data fusion. In addition, when using the trajectory reconstruction method to reconstruct the trajectory, the accuracy of the multisource data fusion algorithm was also 1.18% higher than that of the single source, and the completeness also increased by 2.53% compared with the single source. Therefore, the proposed algorithm has higher efficiency, and it is helpful to establish and study the network safety situation consciousness model. [ABSTRACT FROM AUTHOR]

Currently, there is rapid development of thermophysics of solids associated with the need to create models with a high degree of predictive reliability. This paper presents new approaches to solve relevant issues related to the study of heat transfer in semiconductors and dielectrics, mainly concerning nanostructures. The first of the considered tasks is the creation of a statistical model of the processes of interaction of heat carriers, phonons, with the rough surfaces of solids. For the first time, the authors propose a method based on the statistics of the slopes of the profile of a random surface. The calculation results are the mean free paths of phonon between the opposite boundaries of the sample, which are necessary for calculating the effective thermal conductivity in the ballistic and diffusion–ballistic regime of heat transfer, depending on the roughness parameters. The second task is to develop methods for calculating the processes of heat transfer through the contact surfaces of solids. We are able to show that, taking into account the phonon dispersion and the corresponding restrictions on the frequency values, the modified acoustic mismatch model for calculating Kapitza resistance can be extended to temperatures above 300 K. Previously, the limit of applicability of this method was considered to be a temperature of 30 K. Moreover, the proposed method is also generalized to the case of rough interfaces. The third task is a new approach to determine the thermal conductivity of solids. The authors develop a method of direct Monte Carlo simulation of phonon kinetics with strict consideration of their interaction due to the direct use of the laws of conservation of energy and quasi-momentum. The calculations of the thermal conductivity coefficient for pure silicon in the temperature range from 100 to 300 K show close agreement with the experiment and ab initio calculations of other authors, and also allow considering in details the kinetics of phonons. [ABSTRACT FROM AUTHOR]

In the process of facial expression recognition, face detection is the prerequisite, image preprocessing is the foundation, facial expression feature extraction is the key, and facial expression classification is the target. Effective feature extraction in this process can improve the accuracy of facial expression classifications. On the other hand, traditional facial expression recognition methods are not only complicated in the feature extraction process, but also unable to obtain more in-depth high-semantic features and deep features from the original image. To solve the above problems, this paper proposes a facial expression recognition method based on multi-channel fusion and lightweight neural network. First, a cascade classifier based on Haar features is used to detect the face region of the facial expression image. Second, the local binary pattern (LBP) is used to extract the local texture features from the face region. Third, face edge features are simultaneously obtained by performing edge detection in the face region based on the Canny edge detection algorithm. Fourth, the obtained face image, LBP texture feature image, and edge detection Canny image are fused, and the fused image is input into the constructed lightweight neural network for training and recognition. Experiments are carried out on the public image databases Facial Expression Recognition 2013 (Fer2013) and extended Cohn–Kanade (CK +) using the hold-out cross-validation method. The experimental results show that the proposed method effectively extracts more complete image features by combining traditional feature extraction algorithms with deep learning feature extraction algorithms, improves the accuracy and robustness of facial expression recognition, and has better recognition rate and generalization ability compared to other mainstream methods. [ABSTRACT FROM AUTHOR]

The subject of the present paper is the Cauchy problem for the wave equation in a space-time cylinder Ω × ℝ, Ω ⊂ ℝ2, with data on the surface ∂Ω × I, where I is a finite time interval. The algorithm for solving the Cauchy problem with data on S × I, S ⊂ ∂Ω, was obtained previously. Here we adapt this algorithm to the special case S = ∂Ω and show that the solution in this situation is determined with higher stability in comparison with the case S ⊂ ≠ ∂Ω. [ABSTRACT FROM AUTHOR]

The dynamical system under consideration is u tt - u xx + V u = 0 , x > 0 , t > 0 ; u t = 0 = u y t = 0 = 0 , x ≥ 0 ; u x = 0 = f , t ≥ 0 , where V = V (x) is a matrix-valued function (potential); f = f (t) is an ℝN-valued function of time (boundary control); u = uf (x, t) is a trajectory (an ℝN-valued function of x and t). The input/output map of the system is a response operator R : f ↦ u x f (0 , ·) , t ≥ 0. The inverse problem is to determine V from given R. To characterize its data is to provide the necessary and sufficient conditions on R that ensure its solvability. The procedure that solves this problem has long been known and the characterization has been announced (Avdonin and Belishev, 1996). However, the proof was not provided and, moreover, it turned out that the formulation must be corrected. Our paper fills this gap. [ABSTRACT FROM AUTHOR]

In this paper, with motivation from the No Free Lunch theorem, a new human-based metaheuristic algorithm named Preschool Education Optimization Algorithm (PEOA) is introduced for solving optimization problems. Human activities in the preschool education process are the fundamental inspiration in the design of PEOA. Hence, PEOA is mathematically modeled in three phases: (i) the gradual growth of the preschool teacher's educational influence, (ii) individual knowledge development guided by the teacher, and (iii) individual increase of knowledge and self-awareness. The PEOA's performance in optimization is evaluated using fifty-two standard benchmark functions encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, as well as the CEC 2017 test suite. The optimization results show that PEOA has a high ability in exploration–exploitation and can balance them during the search process. To provide a comprehensive analysis, the performance of PEOA is compared against ten well-known metaheuristic algorithms. The simulation results show that the proposed PEOA approach performs better than competing algorithms by providing effective solutions for the benchmark functions and overall ranking as the first-best optimizer. Presenting a statistical analysis of the Wilcoxon signed-rank test shows that PEOA has significant statistical superiority in competition with compared algorithms. Furthermore, the implementation of PEOA in solving twenty-two optimization problems from the CEC 2011 test suite and four engineering design problems illustrates its efficacy in real-world optimization applications. [ABSTRACT FROM AUTHOR]

What is computational explanation? Many accounts treat it as a kind of causal explanation. I argue against two more specific versions of this view, corresponding to two popular treatments of causal explanation. The first holds that computational explanation is mechanistic, while the second holds that it is interventionist. However, both overlook an important class of computational explanations, which I call limitative explanations. Limitative explanations explain why certain problems cannot be solved computationally, either in principle or in practice. I argue that limitative explanations are not plausibly understood in either mechanistic or interventionist terms. One upshot of this argument is that there are causal and non-causal kinds of computational explanation. I close the paper by suggesting that both are grounded in the notion of computational implementation. [ABSTRACT FROM AUTHOR]