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Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper introduces Large Conference Matching (LCM) , a novel reviewer–paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022-2024, and IJCAI 2022-2024. LCM has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer–paper scores; (2) formulating and solving an optimization problem to find good reviewer–paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data—including a comparison with the matching algorithm used in AAAI's previous (2020) iteration—and supplements this with additional numerical experimentation.2 [ABSTRACT FROM AUTHOR]

The chromatic index , χ ′ (G) , is the smallest integer k for which a graph G has a proper edge coloring. The Classification Problem involves determining whether a graph G is Class 1 ( χ ′ (G) = Δ (G)) or Class 2 ( χ ′ (G) = Δ (G) + 1). It is known that subgraph-overfull graphs must be Class 2. In this paper we are concerned with the Classification Problem for split graphs G [ Q ∪ S ] where Q is a clique and S is an independent set. When Δ (G) is odd, G is known to be Class 1. The original proof presented by Chen et al. (1995) has a minor flaw which we detail in this paper while also clarifying that it does not compromise their result. We prove that their technique can be adapted in a non-trivial way to show that some split graphs with even Δ (G) are also Class 1. We show that to solve the Classification Problem for split graphs it suffices to consider that all vertices in Q have degree Δ (G). Considering the subset X of S of the vertices of degree at most Δ (G) / 2 , we show that if the neighborhood of X has at least ⌊ | Q | / 2 ⌋ vertices, then G is Class 1; in the remaining cases we characterize the subgraph-overfull split graphs. • Correction of a flaw in the proof that odd maximum degree split graphs are Class 1. • Proof that certain split graphs with even maximum degree are Class 1. • Characterization of the split graphs which are subgraph-overfull, and thus Class 2. [ABSTRACT FROM AUTHOR]

A well known method to solve the Polynomial Eigenvalue Problem (PEP) is via linearization. That is, transforming the PEP into a generalized linear eigenvalue problem with the same spectral information and solving such linear problem with some of the eigenvalue algorithms available in the literature. Linearizations of matrix polynomials are usually defined using unimodular transformations. In this paper we establish a connection between the standard definition of linearization for matrix polynomials introduced by Gohberg, Lancaster and Rodman and the notion of polynomial system matrix introduced by Rosenbrock. This connection gives new techniques to show that a matrix pencil is a linearization of the corresponding matrix polynomial arising in a PEP. [ABSTRACT FROM AUTHOR]

The last decade has seen Advanced Medicines Manufacturing (AMM) progress from isolated product developments to the creation of industry-academic centres of excellence, regulatory innovation progressing leading to new standards, and product commercialisation across multiple product formats. This paper examines these developments focusing on successful applications and strategies presented at the 2023 Symposium of the International Consortium for Advanced Medicines Manufacturing (ICAMM). Despite these exemplar applications, there remain significant challenges to the sector-wide adoption of AMM technologies. Drawing on Symposium delegate expert responses to open-ended questions, our coding-based thematic analysis suggest three primary enablers drive successful adoption of AMM technologies at scale, namely: the ability to leverage pre-competitive collaborations to challenge-based problem solving; information and knowledge sharing through centres of excellence; and the development of AMM specific regulatory standards. Further analysis of expert responses identified the emergence of a ' Platform creation' approach to AMM innovation; characterised by: i) New collaboration modes; ii) Exploration of common product-process platforms for new dosage forms and therapy areas; iii) Development of modular equipment assets that enable scale-out, and offer more decentralized or distributed manufacturing models; iv) Standards based on product-process platform archetypes; v) Implementation strategies where platform-thinking and AMM technologies can significantly reduce timelines between discovery, approval and GMP readiness. We provide a definition of the Platform creation concept for AMM and discuss the requirements for its systematic development. [ABSTRACT FROM AUTHOR]

In this paper, we study a Cauchy problem of fractional diffusion equation concerning fractional Laplacian. We prove the existence and uniqueness of solution to the problem under investigation in Hölder space. Then we apply the Monte Carlo method to solving this Cauchy problem. For the problem with free force term, we derive an unbiased scheme which only produces the statistic error. For the problem with inhomogeneous force term, we establish the simple and high-order jump-adapted schemes by approximating the trajectory of symmetric stable process. The error estimates of these two jump-adapted schemes are given based on the Hölder regularity of the solution. Numerical experiments including even one hundred dimensional case confirm the theoretical analysis and show the numerical efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we introduce three adaptive extragradient-based algorithms for solving equilibrium problems in Hadamard manifolds. The proposed algorithms can work adaptively without requiring the prior information about the Lipschitz constants of the bifunctions involved. Moreover, the iterative sequences generated by the suggested algorithms converge to the solutions of the equilibrium problems when the bifunctions are pseudomonotone and Lipschitz continuous. We also establish the global error bounds and R -linear convergence rates of the proposed algorithms in the case that the bifunctions involved are strongly pseudomonotone. Finally, a fundamental numerical example is given to illustrate the theoretical findings. [ABSTRACT FROM AUTHOR]

In this paper, a finite-time convergent continuous action iterated dilemma (CAID) with topological optimization is proposed to overcome the limitations of traditional methods. Asymptotic stability in traditional CAID does not provide information about the rate of convergence or the dynamics of the system in the finite time. There are no effective methods to analyze its convergence time in previous works. We made some efforts to solve these problems. Firstly, CAID is proposed by enriching the players' strategies as continuous, which means the player can choose an intermediate state between cooperation and defection. And discount rate is considered to imitate that players cannot learn accurately based on strategic differences. Then, to analyze the convergence time of CAID, a finite-time convergent analysis based on the Lyapunov function is introduced. Furthermore, the optimal communication topology generation method based on the Deep Q-learning (DQN) is proposed to explore a better game structure. At last, the simulation shows the effectiveness of the proposed method. • The dynamic model of Continuous Action Iterated Dilemma (CAID) with continuous strategy is more realistic. • The convergence time of CAID is analyzed by proposed finite-time convergent analysis method based on the Lyapunov function. • The optimal communication topology generation method based on DQN is proposed to enhance the game structure. [ABSTRACT FROM AUTHOR]

In this paper we study the possible Kronecker invariants of an arbitrary matrix pencil obtained by bounded rank perturbation of a regular matrix pencil. We solve this problem in the case of a perturbation of minimal rank. [ABSTRACT FROM AUTHOR]

This paper focuses on investigating a second-order time parallel decoupled algorithm for the mixed Stokes/Darcy model. The main objective of this algorithm is to deal with the issue of low computational efficiency in solving the coupling problem directly. Our scheme not only reduces the computation scale through the decoupling method, but also realizes the parallelism in the time direction through parareal method. Furthermore, we implement a second-order spectral deferred correction method to improve accuracy. For some small time step size, we prove the stability and convergence results of the time parallel decoupled algorithm. Finally, we provide three numerical tests to demonstrate the reliability and computational efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

Quantum neural network (QNN) is a neural network model based on the principles of quantum mechanics. The advantages of faster computing speed, higher memory capacity, smaller network size and elimination of catastrophic amnesia make it a new idea to solve the problem of training massive data that is difficult for classical neural networks. However, the quantum circuit of QNN are artificially designed with high circuit complexity and low precision in classification tasks. In this paper, a neural architecture search method EQNAS is proposed to improve QNN. First, initializing the quantum population after image quantum encoding. The next step is observing the quantum population and evaluating the fitness. The last is updating the quantum population. Quantum rotation gate update, quantum circuit construction and entirety interference crossover are specific operations. The last two steps need to be carried out iteratively until a satisfactory fitness is achieved. After a lot of experiments on the searched quantum neural networks, the feasibility and effectiveness of the algorithm proposed in this paper are proved, and the searched QNN is obviously better than the original algorithm. The classification accuracy on the mnist dataset and the warship dataset not only increased by 5.31% and 4.52%, respectively, but also reduced the parameters by 21.88% and 31.25% respectively. Code will be available at https://gitee.com/Pcyslist/models/tree/master/research/cv/EQNAS , and https://github.com/Pcyslist/EQNAS. [ABSTRACT FROM AUTHOR]

In developing countries like Indonesia, adding a new unit for hydroprocessing facilities is more efficient than modifying the existing equipment. When the addition occurs, refiners should analyze if the existing hydrogen network can supply enough hydrogen to the new unit before constructing a costly hydrogen plant. Current studies on the hydrogen network have yet to consider pressure drop adequately. Therefore, this paper integrates pressure drop estimation and density prediction into the multi-objective MINLP-based hydrogen network. The multi-objective problem is solved sequentially by adding a minor unit to obtain each configuration's maximum flowrate and total annual cost. The optimal configuration based on the combined objective function is to add 1 PSA and compressor for a hydrogen purity of 0.84 and specified pressure requirements. The pressure drop integration shows an insignificant impact averaging 0.004 m3/s of maximum flowrate difference compared to optimization without pressure drop. • The addition of a new hydroprocessing unit is more efficient than existing refiners. • Multi-objective MINLP-based hydrogen network is used to analyze retrofit options. • The pressure drop integration shows a small impact on the overall. • Network. • The optimal configuration is to add one PSA and one compressor. [ABSTRACT FROM AUTHOR]

To explore the rich information contained in multi-modal data and take into account efficiency, deep cross-modal hash retrieval (DCMHR) is a wise solution. But currently, most DCMHR methods have two key limitations, one is that the recommended classification of DCMHR models is conditioned only on the objects in different regions, respectively. Another flaw is that these methods either do not learn the unified hash codes in training or cannot design an efficient training process. To solve these two problems, this paper designs Large-Scale Cross-Modal Hashing with Unified Learning and Multi-Object Regional Correlation Reasoning (HUMOR). For the proposed related labels classified by ImgNet, HUMOR uses Multiple Instance Learning (MIL) to reason the correlation of these labels. When regional correlation reasoning is low, these labels will be through "reduce-add" to rectification from max-to-min (global precedence) or min-to-max (regional precedence). Then, HUMOR conducts unified learning on hash loss and classification loss, adopts the four-step iterative algorithm to optimize the unified hash codes, and reduces bias in the model. Experiments on two baseline datasets show that the average performance of this method is higher than most of the DCMHR methods. The results demonstrate the effectiveness and innovation of our method. • We propose multi-object regional correlation reasoning for CMR tasks. • HUMOR conducts effective unified training on hash loss and classification loss. • We tried to simplify the function of unified learning and reduce redundant bias. • Experiments show that our model has obviously better performance. [ABSTRACT FROM AUTHOR]

Learning the relationship between weak and strong perturbations has been considered a major part of semi-supervised semantic segmentation. We observed two problems with a publicly used perturbation method, which randomly generates a mask with a single large bounding box. The large single bounding box that entirely covers the important object components in an image, hindering the model from capturing partial object information. Furthermore, training the model with a single large bounding box as an image-level perturbation causes the model to be biased towards the shape of the large squared box, rather than the deformable object component shapes. In this paper, we propose Subdivided Mask Dispersion Framework (SMDF) to solve these problems. Our framework disperses the large squared box into small multi-scale boxes, capturing the crucial multi-scaled object components in the image. SMDF achieves state-of-the-art performance on five data partitions of PASCAL dataset and three partitions of Extended SBD dataset. Our extensive ablation studies show the effectiveness of dispersed small multi-scale bounding boxes in semi-supervised semantic segmentation. • We proposed a novel Subdivided Mask Dispersion Framework (SMDF) for semi-supervised semantic segmentation. • We proposed a Subdivided White Mask Disperser (SWMD) that generates small and uniformly distributed bounding boxes. • SMDF outperforms all the semi-supervised semantic segmentation approaches on PASCAL and Extended SBD datasets. • We substantiated the effectiveness of SMDF in different backbone networks and resolutions. [ABSTRACT FROM AUTHOR]

Recent papers have emphasized the primary role of cellular information management in biological and evolutionary development. In this framework, intelligent cells collectively measure environmental cues to improve informational validity to support natural cellular engineering as collaborative decision-making and problem-solving in confrontation with environmental stresses. These collective actions are crucially dependent on cell-based memories as acquired patterns of response to environmental stressors. Notably, in a cellular self-referential framework, all biological information is ambiguous. This conditional requirement imposes a previously unexplored derivative. All cellular memories are imperfect. From this atypical background, a novel theory of aging and death is proposed. Since cellular decision-making is memory-dependent and biology is a continuous natural learning system, the accumulation of previously acquired imperfect memories eventually overwhelms the flexibility cells require to react adroitly to contemporaneous stresses to support continued cellular homeorhetic balance. The result is a gradual breakdown of the critical ability to efficiently measure environmental information and effect cell-cell communication. This age-dependent accretion governs senescence, ultimately ending in death as an organism-wide failure of cellular networking. This approach to aging and death is compatible with all prior theories. Each earlier approach illuminates different pertinent cellular signatures of this ongoing, obliged, living process. [ABSTRACT FROM AUTHOR]

The exponential stabilization of stochastic neural networks in mean square sense with saturated impulsive input is investigated in this paper. Firstly, the saturated term is handled by polyhedral representation method. When the impulsive sequence is determined by average impulsive interval, impulsive density and mode-dependent impulsive density, the sufficient conditions for stability are proposed, respectively. Then, the ellipsoid and the polyhedron are used to estimate the attractive domain, respectively. By transforming the estimation of the attractive domain into a convex optimization problem, a relatively optimum domain of attraction is obtained. Finally, a three-dimensional continuous time Hopfield neural network example is provided to illustrate the effectiveness and rationality of our proposed theoretical results. • The impulsive control sequence is defined by (mode-dependent) impulsive density. • Estimating attractive domain's size is converted into solving optimization problems. • Introduce a convex set to handle mode-dependent saturated impulsive input strategies. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of constrained tensor least squares problems which arises in the image restoration. The alternating direction multiplier method (ADMM) is proposed to solve this problem. The convergence analysis of this method is given. The numerical experiments illustrate that the ADMM method is feasible and effective for solving the constrained tensor least squares problem. The simulation experiments of image restoration are also performed. [ABSTRACT FROM AUTHOR]

• The paper gives a novel combination between sparse signal recovery and moving least squares in the field of outlier detection. • This paper proposes a new method to detect outliers in scattered data for approximation. • The idea of sparse signal recovery in the proposed method provides the theoretical guarantee for its effectiveness. • The proposed method can restore the true values corresponding to outliers in high accuracy while outliers are marked. In the scattered data used for function approximation, there may be some data that deviate greatly from their true values, which are called outliers. The existence of outliers significantly affects the accuracy of some approximation methods, such as moving least squares. How to quickly and accurately find these outliers and restore their true values has not been well resolved. In this paper, a new outlier detection and recovery method is proposed for data processing. The method uses the high accuracy of moving least squares quasi-interpolation scheme, its sensitivity to outliers and the sparse distribution of outliers to construct a ł 0 -minimization problem with an inequality constrain. Under certain assumptions, it is proved theoretically that the deviation vector corresponding to the outliers is a solution to the optimization problem. The classical orthogonal matching pursuit algorithm is introduced to solve the optimization problem efficiently. By solving the problem, the outliers are marked, and the deviations are also estimated approximately, which can restore the true values of outliers. The numerical experiments demonstrate that the proposed method has high computational efficiency, very high detection accuracy, and high recovery accuracy for the scattered data used for function approximation, so it is practical. [ABSTRACT FROM AUTHOR]

In this paper, we study the discrete-time control problem for the refrigeration system with unmodeled dynamics. This paper proposes discrete adaptive neural network controllers for two refrigeration systems, including the water membrane evaporator cooling and compression refrigeration systems. The influence of model uncertainty on system performance can be eliminated effectively by designing the neural network and the corresponding discrete-time adaptive updating strategy. Thus, the model parameters in the refrigeration system are not required in our control algorithms. Simulation results show that the proposed strategy can effectively adjust the refrigeration system temperatures. Compared with the traditional PID control strategy, the system response overshoot under our method can be reduced by 0.3%, and the system settling time is reduced by at least 94.3%. • Discrete-time adaptive neural controller is proposed for refrigeration systems. • Uncertainty problems are solved by using the adaptive scheme and neural networks. • Compared with the PID method, the settling time is reduced by at least 94.3%. [ABSTRACT FROM AUTHOR]

[Display omitted] • Size-dependent assembly of graphene sheets constructs vertical heat pathways across the graphene paper. • GHGP exhibits both high through-plane thermal conductivity and good compressibility. • The cooling efficiency of GHGP was 2.2 times that of the state-of-the-art commercial TIM. Graphene-based papers have recently triggered considerable interests in developing the application as thermal interface materials (TIMs) for addressing the interfacial heat transfer issue, but their low through-plane thermal conductivity (κ ⊥), resulting from the layer-by-layer stacked architecture, limits the direct use as TIMs. Although various hybrid graphene papers prepared by combining the graphene sheets and the thermally conductive insertions have been proposed to solve this problem, achieving a satisfactory κ ⊥ higher than that of commercial TIMs (>5 W m−1 K−1) remains challenging. Here, a strategy aimed at the construction of heat pathways along the through-plane direction inside the graphene paper for achieving a high κ ⊥ was demonstrated through the simultaneous filtration of graphene sheets with two different lateral sizes. The as-prepared graphene paper presented a hierarchical structure composed of loosely stacked horizontal layers formed by large graphene sheets, intercalated by a random arrangement of small graphene sheets. Due to the heat pathways formed by small graphene sheets along the through-plane direction, the hierarchically structured graphene paper exhibited an improved κ ⊥ as high as 12.6 W m−1 K−1 after a common graphitization post-treatment. In the practical test, our proposed paper as an all-graphene TIM achieved an enhancement in cooling efficiency of ≈ 2.2 times compared to that of the state-of-the-art TIM, demonstrating its superior performance to meet the ever-increasing heat dissipation requirement. [ABSTRACT FROM AUTHOR]

The development of ARX-based lightweight block ciphers has been plagued by the difficulty of theoretical security analysis. SAND solves this problem better by obtaining an equivalent representation based on a synthetic S-box. This paper analyzed SAND in terms of diffusivity and found that it can be optimized. SAND has the issue of slow diffusion after the initial plaintext and key are all 0. On the other hand, it takes at least 11 rounds for SAND to reach full diffusion without AddRoundKey. Thus, this paper proposes a high-diffusion architecture SAND-2 to address the above issues. Firstly, issue 1 is solved by replacing the AND operation with a NAND operation and calling the round function dynamically. Then, in order to solve issue 2, P1 and P2 permutations are introduced into G 0 and G 1 , respectively. The full diffusion speed of SAND-2 is 63.7% increased compared to SAND. In addition, the comparison results of hardware indicators show that the hardware resources of SAND-2 are slightly lower, and the throughput is 10% higher than SAND. Finally, the security analysis shows that SAND-2 reaches the upper bound of the resisting differential analysis in fewer rounds. • SAND-2 diffusion speed is 63.7% increased by SAND. • SAND-2 throughput is 10% higher than SAND. • SAND-2 is more secure than SAND. [ABSTRACT FROM AUTHOR]

This paper studies the high telepresence control for the relay communication based space teleoperation. High telepresence requires real-time, accurate force perception and good position tracking, but all of them are destroyed or affected by time delay. To eliminate the influence of time delay, direct recursive generalized predictive control (DR-GPC) is quoted into this paper to realize position tracking. As for the other two properties, the pseudo position-force (Pseudo-PF) feedback strategy (composed of parameter identification and force reconstruction) is a good way as it can provide real-time and precise force feedback when environment change slowly and time delay is relatively small. However, complex and changeable environment and serious and complex time delay in space teleoperation make the environment force parameter available to force reconstruction is outdated and cannot realize accurate force rebuilding. To solve this problem, we propose a dual regression Pseudo-PF feedback strategy. Compared with the traditional one, its biggest change is the introduction of dual regression that consists of time delay prediction and parameter regression. The former predicts time delay and provides it to the latter to determine the forward prediction steps, while the latter regresses the actual latest and precise environment force parameter according to the forward prediction steps. With the cooperation of time delay prediction and parameter regression, the influence of fast changing environment or serious and complex time delay on force reconstruction can be effectively overcome, so as to realize real-time and accurate force feedback. Finally, DR-GPC and dual regression Pseudo-PF feedback strategies cooperate to achieve high telepresence control, and simulation is given to show its effectiveness. • A dual regression Pseudo-PF feedback strategy is proposed. • High teleopresence control for the relay communication based space teleoperation is first studied. • The introduction of dual regression makes the applicability of this method wider. [ABSTRACT FROM AUTHOR]

• Reduce the two-dimensional automated vehicle (AV) trajectory planning to a one-dimensional speed planning problem considering conflicting moving objects (CMOs) in the environment. • Propose a parsimonious and efficient model for the AV trajectory planning problem. • Analyze the conflict areas caused by CMOs in the space-time diagram and reveal upper and lower bounds for feasible trajectories. • Propose a customized dynamic programming algorithm based on the revealed bounds to solve the investigated problem in real time. Trajectory planning for autonomous vehicles (AVs) by considering conflicting moving objects (CMOs) is a challenging problem to AV operations. This paper investigates an AV trajectory planning problem where the AV follows a given spatial path, and the trajectories of CMOs are predictable. With the spatial path fixed, the two-dimensional trajectory planning problem is reduced to a one-dimensional speed planning problem that decides the optimal speeds and accelerations of the AV along the spatial path. This paper first analyzes the conflict area caused by a single CMO in the space-time diagram, which reveals upper and lower bounds to the conflict area. Then a multi-area fusion algorithm is proposed to extend the upper and lower bound analyses to a relatively complex traffic scenario with multiple CMOs. To facilitate the computation of the investigated problem, a customized dynamic programming (DP) algorithm is developed, which employs the revealed upper and lower bounds and arriving time constraints to cut invalid trajectories at each stage. With this, the number of stages and states, as well as the computational time for solving the proposed problem, are largely reduced. A set of numerical experiments are conducted to evaluate the performance of the customized DP-based algorithm. The results show that the proposed customized DP-based algorithm can solve the investigated problem within milliseconds, which enables applications to real-time AV control. This much outperforms a state-of-the-art commercial solver, Gurobi, especially for complex traffic scenarios. We further implemented the proposed method in a real-world case study, and the results show that the trajectory generated by the proposed model has a great potential to enhance the future traffic system. [ABSTRACT FROM AUTHOR]

• To introduce BCOHHS for wrapper-based feature selection • A binary using the X-shaped function of the hybrid, HHO, with chaotic opposition-based initialization and simulated annealing • Evaluated the performance of the BCOHHS using high-dimensional medical data sets Harris Hawks Optimization (HHO) is a relatively new meta-heuristic algorithm that has shown promise in solving various optimization problems. However, HHO suffers from some limitations that can affect its performance. The two main drawbacks of HHO are population diversity and local optima. To overcome these limitations and adapt it to solve feature selection issues, a new meta-heuristic, Chaotic Opposition Harris Hawks Optimization with Simulated Annealing (COHHS), is proposed in this paper. Two main improvements are proposed for the HHO. The first one, the chaotic opposition, is applied at the initialization phase of HHO to improve the population diversity of the search agents. The second one, simulated annealing, is applied to find the optimal solution during each iteration to improve HHO exploitation. The proposed method is a dynamic structure that was originally designed to solve problems with continuous optimization. In this paper, we propose a binary COHHS (BCOHHS) using X-shaped functions to boost the efficiency of feature selection. The KNN classifier is used to evaluate the classification accuracy. The performance of the proposed method is evaluated on nine high-dimensional medical datasets and compared with other optimization methods. The experimental results confirm the superiority of the BCOHHS method over the other methods on the majority of the datasets. [ABSTRACT FROM AUTHOR]

This paper studies the efficient methods for option pricing under multivariate rough volatility models. The characteristic functions of the asset log-price, which play important role in the option pricing under the multivariate rough volatility models, are determined by a system of parametric nonlinear fractional Riccati equations. This paper obtains the results on the existence, uniqueness and regularity of the solutions to the parametric nonlinear fractional Riccati equations, proposes a high-order scheme to solve the system and proves the high-order convergence. The option pricing problem is solved by the Fourier-cosine formula with the fast approximation of the characteristic functions. Numerical examples are carried out to confirm the theoretical results and show efficiency of the methods. [ABSTRACT FROM AUTHOR]

In this paper we discuss different transmission operators for the non-overlapping Schwarz method which are suited for solving the time-harmonic Helmholtz equation in cavities (i.e. closed domains which do not feature an outgoing wave condition). Such problems are heavily impacted by back-propagating waves which are often neglected when devising optimized transmission operators for the Schwarz method. This work explores new operators taking into account those back-propagating waves and compares them with well-established operators neglecting these contributions. Notably, this paper focuses on the case of rectangular cavities, as the optimal (non-local) transmission operator can be easily determined. Nonetheless, deviations from this ideal geometry are considered as well. In particular, computations of the acoustic noise in a three-dimensional model of the helium vessel of a beamline cryostat with optimized Schwarz schemes are discussed. Those computations show a reduction of 46% in the iteration count, when comparing an operator optimized for cavities with those optimized for unbounded problems. [ABSTRACT FROM AUTHOR]

To achieve the carbon peaking and carbon neutrality goals, China promoted county-wide photovoltaic projects (CWPVs) in 2021. A large number of distributed photovoltaic (DPV) installations have expanded the scale of prosumer. It also brings the inequity problem of prosumer, distribution network system operator (DSO) and consumer--DSO and consumer bear the costs such as access cost and depreciation of stock investment caused by DPV access. Combined with China's current electricity tariff system and the promotion of photovoltaic scale development, this paper proposes a standby fee model based on the principle of "beneficiary apportionment". In this model, the standby fee mechanism for self-owned power plants is applied to DPV to reduce the implementation cost of the mechanism, and the principle of "beneficiary apportionment" is adopted to ensure the profitability of DPV. This paper takes the CWPV in Hunan Province, China as an example, calculates the standby fee under various electricity metering methods, and determines the most appropriate standby fee by considering factors such as output uncertainty of DPV. Finally, the difficulties in the implementation of standby fee are sorted out. [ABSTRACT FROM AUTHOR]

We study the minimum spanning tree cycle intersection (MSTCI) problem on outerplanar graphs in this paper. Consider a connected simple graph G = (V , E) and any spanning tree T = (V , E T) of G , it is well-known that each non-tree edge e ∈ E ∖ E T induces a fundamental cycle in T ∪ { e }. We say that two distinct fundamental cycles intersect if they share at least one edge. The MSTCI problem seeks a spanning tree such that the number of pairwise cycle intersections is minimized. We provide a linear-time algorithm to solve the problem on outerplanar graphs by exploiting the cycle-cut duality on plane graphs. Besides, we show that the minimum cycle intersection number is bounded by O (n) for outerplanar graphs with n vertices. [ABSTRACT FROM AUTHOR]

This paper considers a class of multi-agent distributed convex optimization with a common set of constraints and provides several continuous-time neurodynamic approaches. In problem transformation, l 1 and l 2 penalty methods are used respectively to cast the linear consensus constraint into the objective function, which avoids introducing auxiliary variables and only involves information exchange among primal variables in the process of solving the problem. For nonsmooth cost functions, two differential inclusions with projection operator are proposed. Without convexity of the differential inclusions, the asymptotic behavior and convergence properties are explored. For smooth cost functions, by harnessing the smoothness of l 2 penalty function, finite- and fixed-time convergent algorithms are provided via a specifically designed average consensus estimator. Finally, several numerical examples in the multi-agent simulation environment are conducted to illustrate the effectiveness of the proposed neurodynamic approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-timescale projection neural network (PNN) for solving optimization problems with nonconvex functions. We prove the convergence of the PNN with sufficiently different timescales to a local optimal solution. We develop a collaborative neurodynamic approach with multiple such PNNs to search for global optimal solutions. In addition, we develop a collaborative neurodynamic approach with multiple PNNs connected via a directed graph for distributed global optimization. We elaborate on four numerical examples to illustrate the characteristics of the approaches. [ABSTRACT FROM AUTHOR]

The problem of low rank approximation is ubiquitous in science. Traditionally this problem is solved in unitary invariant norms such as Frobenius or spectral norm due to existence of efficient methods for building approximations. However, recent results reveal the potential of low rank approximations in Chebyshev norm, which naturally arises in many applications. In this paper we tackle the problem of building optimal rank-1 approximations in the Chebyshev norm. We investigate the properties of alternating minimization algorithm for building the low rank approximations and demonstrate how to use it to construct optimal rank-1 approximation. As a result we propose an algorithm that is capable of building optimal rank-1 approximations in Chebyshev norm for moderate matrices. [ABSTRACT FROM AUTHOR]

In this paper, a novel spectral method is presented and studied for the fourth order problem with mixed boundary in a cylindrical domain. The basic idea of our approach is to reduce the original problem into a series of decoupled two-dimensional fourth-order problems first, by using the cylindrical coordinate transformation and Fourier expansion, and then adopt the standard spectral method to solve the decoupled problems. A new essential pole condition is proposed to overcome the difficulty caused by the introduction of singularity and variable coefficients in cylindrical coordinate transformation. Existence and uniqueness of the weak solution and the discrete numerical solution are proved, and error estimates of the spectral method are derived. Furthermore, the efficient implementation of our algorithm is discussed, where a set of effective basis functions are constructed to ensure the sparsity of the mass matrix and stiffness matrix. Numerical examples are presented to validate the theoretical findings and the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

Few-shot learning is attracting extensive research because of its ability to classify only a few co-trainable samples. Current few-shot learning approaches focus on learning class prototypes representation to solve problems by a simple averaging approach, but this approach ignores intra-class differences. In this paper, we propose a new weighted prototype network for few-shot learning. Our model consists of two modules, feature extraction and prototype modification. We first construct graphs from the embeddings obtained from the feature extraction module. Then we fed these graphs into graph neural networks in order to explore the contribution of each sample to its class prototype from the graph structure. The experimental results on three benchmark datasets show that our proposed model is comparable to the state-of-the-art few-shot learning approach. • Introduced a novel approach for few-shot image classification. • Innovatively transformed the process of assessing sample importance into graph label propagation. • Achieved performance improvements on few-shot benchmark datasets. [ABSTRACT FROM AUTHOR]

A subset S of vertices in a graph G is an F - isolating set , closely related to the dominating set of G , if G − N [ S ] contains no member of F as a subgraph, where F is a family of connected graphs, and N [ S ] is the closed neighborhood of S. The F - isolation number of G , denoted by ι (G , F) , is the minimum cardinality of an F -isolating set in G. As usual, we simply write ι (G , { P k }) as ι (G , P k). Zhang and Wu (2021) proposed a problem: for a connected graph G of order n , what is the exact upper bound on ι (G , P k) for an integer k ≥ 4 ? Later, the same authors settled the problem for k = 4. In this paper we solve the problem for k = 5 , i.e., if G is a connected graph of order n other than C 8 , then ι (G , P 5) ≤ 2 9 n and this bound is sharp. [ABSTRACT FROM AUTHOR]

An edge-coloring of a graph G is an assignment of colors to the edges of G so that adjacent edges have distinct colors. The chromatic index χ ′ (G) is the smallest number of colors needed in an edge-coloring of G. Clearly, χ ′ (G) ≥ Δ (G) and the celebrated Vizing's Theorem (1964) states that χ ′ (G) ≤ Δ (G) + 1 , for any simple graph G. A simple graph G is Class 1 if χ ′ (G) = Δ (G) , and Class 2 otherwise. The Classification Problem is to determine whether a simple graph is Class 1. The Overfull Conjecture, proposed by Chetwynd and Hilton (1984), states that when Δ (G) > | V (G) | / 3 , the graph G is Class 1 if and only if it is not subgraph-overfull. Figueiredo et al. (2000) conjectured that any chordal graph is Class 1 if and only if it is not subgraph-overfull. Chordal graphs are a superclass of split and of interval graphs. In this paper we prove that both the Overfull and Figueiredo et al.'s Conjectures hold for all split-comparability and split-interval graphs. Our proofs lead to polynomial-time algorithms to solve the Classification Problem in these classes. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamics and digital control issues of deploying a tethered satellite using constrained tether tension. To cater to the requirements of the digital techniques applied in engineering applications, unlike existing results, this study develops a digital feedback control strategy to achieve the rapid and stable tether deployment. The proposed digital control law consists of discretized and quantized tether states and is derived based on continuous-time dynamical model instead of the discrete-time one. To keep tether tension at a positive value when implementing control action, the constraint on tether tension is explicitly accounted for during controller design. Furthermore, the discretization and quantization errors are treated as the uncertain terms in the closed-loop system which can be handled by robust analysis and synthesis techniques. A Lyapunov functional, together with an integral performance index, is made to prove the stability of the system, and the desired controller is obtained by solving an optimization problem. Finally, the effectiveness of the proposed controller is demonstrated by numerical simulation. [ABSTRACT FROM AUTHOR]

This paper presents a decentralized guidance and target assignment (DGTA) strategy for reconfiguring swarms made up of hundreds of modules that have limited communication capabilities. This strategy provides a decentralized onboard computation method to address both near-optimal target assignment and collision-free trajectory planning for swarms of modules. To effectively estimate the transfer cost between modules and targets for target assignment, a multilayer perceptron (MLP) is developed via the backward generation method. The decentralized target assignment problem is solved by greedy algorithm according to the result of the efficient estimating solver. A decentralized model predictive control approach is proposed to guide swarm modules towards their assigned targets, while avoiding collisions with other modules. The feasibility of this guidance framework has been demonstrated through rigorous theoretical analysis. Numerical experiment confirms that the MLP-based method shows an accurate and real-time advantage for estimating transfer cost. Furthermore, the DGTA strategy has been shown to possess robustness and generalization ability, as evidenced by its successful application in a pinpoint assembly task involving 125 and 182 modules. The simulation results confirm the feasibility of this approach for in-orbit assembly application. [ABSTRACT FROM AUTHOR]

Recent multi-domain processing methods have demonstrated promising performance for monaural speech enhancement tasks. However, few of them explain why they behave better over single-domain approaches. As an attempt to fill this gap, this paper presents a complementary single-channel speech enhancement network (CompNet) that demonstrates promising denoising capabilities and provides a unique perspective to understand the improvements introduced by multi-domain processing. Specifically, the noisy speech is initially enhanced through a time-domain network. However, despite the waveform can be feasibly recovered, the distribution of the time–frequency bins may still be partly different from the target spectrum when we reconsider the problem in the frequency domain. To solve this problem, we design a dedicated dual-path network as a post-processing module to independently filter the magnitude and refine the phase. This further drives the estimated spectrum to closely approximate the target spectrum in the time–frequency domain. We conduct extensive experiments with the WSJ0-SI84 and VoiceBank + Demand datasets. Objective test results show that the performance of the proposed system is highly competitive with existing systems. [ABSTRACT FROM AUTHOR]

Multi-person pose estimation is the task of estimating the coordinates of body joints and predicting the body poses for multiple people in an images. This problem has made breakthroughs in recent years, but the solutions still suffer from some shortcomings. A serious weakness of the state-of-the-art models is the number of poses detected by these models which is generally much larger than the actual number of human instances in the input image. This makes the existing models unreliable and thus unusable in real-world tasks. In this paper, we propose a more reliable multi-person pose estimation method consisting of three main blocks: a top-down multi-person pose estimation, a human detection, and a pose selection block. The proposed method incorporates the bounding box of the segmented objects to select the best subset of the initial pose set. We formulate the pose selection problem using Conditional Random Fields. First, we introduce a set of potential functions to form a general probability model. Then an inference algorithm is proposed to select the best poses which maximize the probability function. Finally, the proposed solution is implemented by a neural network. The proposed pose selection model is a model-agnostic method that can be easily used in conjunction with other pose estimation and object detection models. Experiments demonstrate that the reliability and precision of the proposed model are higher than those of the state-of-the-art models. • We solve a serious problem of the state-of-the-art multi-person pose estimation models. • Our method incorporates instance segmentation results to select the best estimated poses. • We formulate the problem using CRFs. We introduce a set of potential functions and use an efficient inference algorithm. • The proposed solution is implemented as a model-agnostic method. • Experiments show that the reliability and precision of the proposed model are higher than other models. [ABSTRACT FROM AUTHOR]

This paper applies a terminal learning strategy to study distributed quadratic optimization problems. Since the optimal state is unknown in advance, the tracking error information is generally unavailable. To achieve the optimal state without the tracking error information, the terminal consensus iterative learning scheme is used to solve the problem. And the terminal consensus state is obtained without the global information of network. On this basis, the optimal target is also achieved by choosing the proper initial state and learning parameters. And the optimization problem is studied with the constraints of state and control input. Results show that our approach is effective. Compared with existing distributed optimization methods, the learning strategy in this paper provides another effective analysis scheme. Last, a numerical example is presented to show the effective aspects of the method. [ABSTRACT FROM AUTHOR]

• This paper studies the scaled consensus problem which is a generalization of usual consensus investigated in the literature. • Innovative impulsive control protocols are proposed to solve the problems of scaled consensus for multi-agent systems. • It also studied the scaled consensus problems of multi-agent systems subject to external perturbations. This paper studies scaled consensus problems of multi-agent systems under fixed and switched topologies. Firstly, the scaled consensus protocols are designed for solving consensus problems of multi-agent systems under fixed topologies. Secondly, the scaled consensus problems of multi-agent systems under switching topologies are considered. Thirdly, scaled consensus of multi-agent systems with external perturbations is investigated via impulsive mechanism. Using matrix theory, graph theory and Lyapunov algorithm, we can show that under appropriate conditions the scaled consensus problems can be solved. Moreover, our results show that the boundedness of the sampling period depending on scalar scales and degree of networks has strong impact for achieving scaled consensus of the systems. Some numerical examples are provided to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

• A high-performance method is proposed for RGB-D salient object detection. • RGB and depth data are adaptively fused to boost the performance. • Features of all levels are refined in an iterative manner. • The proposed method outperforms other state-of-the-art models on six datasets. Existing state-of-the-art RGB-D saliency detection models mainly utilize the depth information as complementary cues to enhance the RGB information. However, depth maps can be easily influenced by environment and hence are full of noises. Thus, indiscriminately integrating multi-modality (i.e., RGB and depth) features may induce noise-degraded saliency maps. In this paper, we propose a novel Adaptive Fusion Network (AFNet) to solve this problem. Specifically, we design a triplet encoder network consisting of three subnetworks to process RGB, depth, and fused features, respectively. The three subnetworks are interlinked and form a grid net to facilitate mutual refinement of these multi-modality features. Moreover, we propose a Multi-modality Feature Interaction (MFI) module to exploit complementary cues between depth and RGB modalities and adaptively fuse the multi-modality features. Finally, we design the Cascaded Feature Interweaved Decoder (CFID) to exploit complementary information between multi-level features and refine them iteratively to achieve accurate saliency detection. Experimental results on six commonly used benchmark datasets verify that the proposed AFNet outperforms 20 state-of-the-art counterparts in terms of six widely adopted evaluation metrics. Source code will be publicly available at https://github.com/clelouch/AFNet upon paper acceptance. [ABSTRACT FROM AUTHOR]

The completion of point cloud directly affects the accuracy of primitive parameter extraction and 3D reconstruction. Due to the limitations of sensor scanning, point cloud are often incomplete due to occlusions during the data collection. To solve the problem of incomplete building point clouds and to support geometric detail LoD2 reconstruction, a point cloud completion method with structural constraint is proposed in this paper. First, based on the idea of cloth simulation, regularity and planarity of the building were used to impose structural constraints on the cloth nodes. Combined with the rigidity of the building, the force rules of nodes under the distribution of the building structure are formulated. Then, the sinking and rebounding stages of the node were simulated to recover the position of point cloud in the area of interest. Finally, the missing surface in each façade direction was completed by attitude adjustment. The solution of a multi-layer structural façade was realized by the selection of parameters during the sinking and rebounding stage. The feasibility and accuracy of the proposed point cloud completion method have been verified through experiments. It is demonstrated that the proposed method can improve the integrity of a building point cloud by achieving more than 90% coverage. Compared with existing methods, the proposed method can deal with the problem of large-scale structure missing from buildings with fewer limitations. The patched points do not rely on the geometric primitives' extraction and are based on the distribution of existing points without redundant observations, so the patched points adapt better to the existing building points. The proposed method has enhanced the utilization of point clouds and improved the detail of LoD2 reconstruction. It provides a new feasible method for point cloud completion. The code of the method is available at https://github.com/bufanzhao/point-cloud-completion.git. [ABSTRACT FROM AUTHOR]

• A computational binary characterization method for convex object(s). • A unified model for convex object(s) segmentation and convex hull computation. • Linearization technique employed to reduce the difficulty of the model solving. • A convergent proximal alternating direction method of multipliers for the linearization problem. • An interactive procedure to improve the segmentation accurac y in terms of shape distance and D ice coefficient. Convexity prior is one of the main cues for human vision and shape completion with important applications in image processing and computer vision. This paper provides a computable characterization method for shape convexity, and illustrates its applications in image segmentation and convex hull computation. We prove that the convexity of a region is equivalent to a series of quadratic inequality constraints on its indicator function. By incorporating this result, models are proposed for image segmentation with convexity prior and convex hull computation of clean and noisy data sets, respectively. Then, these models are summarized into a unified optimization problem on binary function(s) with the quadratic inequality constraints. Numerical method is proposed by linearizing the quadratic constraints. The linearization problem is solved by a proximal alternating direction method of multipliers, the convergence of which is guaranteed under some proper conditions. Numerical experiments demonstrate the efficiency and effectiveness of the proposed methods for image segmentation and convex hull computation. [ABSTRACT FROM AUTHOR]

• The trajectory planning problem is transformed into an optimal control problem. • A loosen formation constraint strategy is integrated to facilitate the convergence. • An improved APF method is constructed to warm-start the trajectory planner. In this paper, an optimal control-based method with efficient warm-start is proposed to solve the trajectory planning problem for fixed-wing unmanned aerial vehicle formations. We first formulate the trajectory planning problem as an optimal control problem by analyzing the motion equations and various constraints. Secondly, an improved artificial potential field method is designed to meet the requirements of eliminating local oscillations, inter-formation coordination, and physical constraints with the objective of a high-quality homotopic warm-start. Last, a loose formation constraint strategy is employed to expand the solution space and thus facilitate the convergence process. Numerical simulations at different complexity levels show that the proposed planner is efficient and robust and that the designed warm-start strategy contributes significantly to the convergence of the method. Also, the proposed planner exhibits promising performance in solving the problem with multiple formations. [ABSTRACT FROM AUTHOR]

This paper is concerned with self-representation subspace learning. It is one of the most representative subspace techniques, which has attracted considerable attention for clustering due to its good performance. Among these methods, low-rank representation (LRR) has achieved impressive results for subspace clustering. However, it only considers the similarity between the data itself, while neglecting the differences with other samples. Besides, it cannot well deal with noise and portray cluster-to-cluster relationships well. To solve these problems, we propose a Contrastive Self-representation model for Clustering (CSC). CSC simultaneously takes into account the similarity/dissimilarity between positive/negative pairs when learning the self-representation coefficient matrix of data while the form of the loss function can reduce the effect of noise on the results. Moreover, We use the ℓ 1 , 2 -norm regularizer on the coefficient matrix to achieve its sparsity to better characterize the cluster structure. Thus, the learned self-representation coefficient matrix well encodes both the discriminative information and cluster structure. Extensive experiments on seven benchmark databases indicate the superiority of our proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we describe weak Galerkin finite element methods for solving hyperbolic problems on polygonal meshes. We propose both semidiscrete and fully discrete schemes to numerically solve the second-order linear wave equation. For the time discretization, we have used implicit second order Newmark scheme. For sufficiently smooth solutions, optimal order error estimate in the L 2 norm is shown to hold as O (h k + 1 + τ 2) , where h is the mesh size and τ the time step. An extensive set of numerical experiments are conducted to demonstrate the robustness, reliability, flexibility, and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper we present a nonoverlapping Robin-type multi-domain decomposition method based on stabilized Q 1 – P 0 mixed approximation (RMDD-Q1P0) for incompressible flow problems. The global Stokes and Navier-Stokes equations are decomposed into a series of local problems through Robin-type domain decomposition, and local problems are solved through the local jump stabilized Q 1 – P 0 approximation. The RMDD-Q1P0 solutions are proven to converge to the standard global Q 1 – P 0 solutions for the Stokes problem. Numerical experiments for both Stokes and Navier-Stokes equations demonstrate that RMDD-Q1P0 significantly improves the scalability of the stabilized Q 1 – P 0 approximation. The codes used to generate the numerical results are available online. [ABSTRACT FROM AUTHOR]

Problems with uniform probabilities on an infinite support show up in contemporary cosmology. This paper focuses on the context of inflation theory, where it complicates the assignment of a probability measure over pocket universes. The measure problem in cosmology, whereby it seems impossible to pick out a uniquely well-motivated measure, is associated with a paradox that occurs in standard probability theory and crucially involves uniformity on an infinite sample space. This problem has been discussed by physicists, albeit without reference to earlier work on this topic. The aim of this article is both to introduce philosophers of probability to these recent discussions in cosmology and to familiarize physicists and philosophers working on cosmology with relevant foundational work by Kolmogorov, de Finetti, Jaynes, and other probabilists. As such, the main goal is not to solve the measure problem, but to clarify the exact origin of some of the current obstacles. The analysis of the assumptions going into the paradox indicates that there exist multiple ways of dealing consistently with uniform probabilities on infinite sample spaces. Taking a pluralist stance towards the mathematical methods used in cosmology shows there is some room for progress with assigning probabilities in cosmological theories. [ABSTRACT FROM AUTHOR]

Hate Speech Detection (HSD) aims to identify whether a text contains hate speech content, which often refers to discrimination and is even associated with a hate crime. The mainstream methods jointly train the HSD problem with relevant auxiliary problems, e.g., emotion detection and sentiment analysis, under the paradigm of Multi-Task Learning (MTL). In this paper, we improve HSD by integrating it with emotion detection, since we take inspiration from the potential correlations between hate speech and certain negative emotion states, which have been studied theoretically and empirically. To be specific, we can concatenate their hateful labels and predicted emotion states as pseudo-multiple labels for hate speech samples, formulating a pseudo-Multi-Label Learning (MLL) problem. Beyond the existing MTL-HSD methods, we further incorporate this pseudo-MLL problem and solve it by capturing the correlations between hate speech and negative emotion states, so as to improve the performance of HSD. Based on these ideas, we propose a novel HSD method named the E motion-correlated H ate S peech Detect OR (EHS or). We conduct extensive experiments to evaluate EHS or , and the results show that it can consistently outperform the existing HSD methods across benchmark datasets. • Validating correlations between hate speech and specific negative emotions. • Proposing a new MTL-based HSD method EHSOR from the perspective of MLL. • EHSOR empirically outperforms baseline methods over three HSD benchmarks. [ABSTRACT FROM AUTHOR]

The past decade has witnessed significant progress in detecting objects by using enormous features of deep learning models. But, most of the existing models are unable to detect x-small and dense objects, due to the futility of feature extraction, and substantial misalignments between anchor boxes and axis-aligned convolution features, which leads to the discrepancy between the categorization score and positioning accuracy. This paper introduces an anchor regenerative-based transformer module in a feature refinement network to solve this problem. The anchor-regenerative module can generate anchor scales based on the semantic statistics of the objects present in the image, which avoids the inconsistency between the anchor boxes and axis-aligned convolution features. Whereas, the Multi-Head-Self-Attention (MHSA) based transformer module extracts the in-depth information from the feature maps based on the query, key, and value parameter information. This proposed model is experimentally verified on the VisDrone, VOC, and SKU-110K datasets. This model generates different anchor scales for these three datasets and achieves higher mAP, precision, and recall values on three datasets. These tested results prove that the suggested model has outstanding achievements compared with existing models in detecting x-small objects as well as dense objects. Finally, we evaluated the performance of these three datasets by using accuracy, kappa coefficient, and ROC metrics. These evaluated metrics demonstrate that our model is a good fit for VOC, and SKU-110K datasets. [ABSTRACT FROM AUTHOR]