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Radial basis function (RBF) neural network training presents a challenging optimization task, necessitating the utilization of advanced algorithms that can fully train the network so as to produce accurate and computationally efficient models. To achieve this goal, this work introduces a new framework where the original RBF training problem is divided into two simpler subproblems; the linear parameters, namely the network weights, are projected out of the problem using variable projection (VP), thus leaving a reduced functional, which depends only on nonlinear parameters, i.e., the RBF centers. The centers are updated using the Levenberg–Marquardt (LM) algorithm, while the optimal values of the synaptic weights are calculated in each iteration of the LM algorithm using linear regression. The proposed VP-LM scheme is coupled with the fuzzy means (FM) algorithm, which helps to select the number of RBF centers and enhances the overall search procedure, thus resulting to a framework that produces parsimonious models with enhanced accuracy in shorter training times. The proposed training scheme is evaluated on 12 both real-world and synthetic benchmark datasets and tested against various RBF training algorithms, as well as different neural network architectures. The experimental results underscore the effectiveness of the VP-FM algorithm in producing neural network models that outperform those generated by alternative methods in many aspects; to be more specific, the proposed approach achieves very competitive model accuracy, while resulting to smaller network sizes and thus lower complexity, which leads to shorter training times. [ABSTRACT FROM AUTHOR]

Automatic face recognition has made significant progress in recent decades, particularly in controlled environments. However, recognizing faces across different modalities, known as Heterogeneous Face Recognition, presents challenges due to variations in modality gaps. This paper addresses the problem of HFR by proposing a two-stage algorithm. In the first stage, a deep stacked PCA descriptor (DSPD) is introduced to extract domain-invariant features from face images of different modalities. The DSPD utilizes multiple convolution layers of domain-trained PCA filters, and the features extracted from each layer are concatenated to obtain a final feature representation. Additionally, pre-processing steps are applied to input images to enhance the prominence of facial edges, making the features more distinctive. The obtained DSPD features can be directly used for recognition using nearest neighbour algorithms. To further improve recognition robustness, a coupled subspace called coupled discriminant neighbourhood embedding (CDNE) is proposed in the second stage. CDNE is trained with a limited number of data samples and can project DSPD features from different modalities onto a common subspace. In this subspace, data points representing the same subjects from different modalities are positioned closely, while those of different subjects are positioned apart. This spatial arrangement enhances the recognition of heterogeneous faces using nearest neighbour algorithms. Experimental results demonstrate the effectiveness of the proposed algorithm on various HFR scenarios, including VIS-NIR, VIS-Sketch, and VIS-Thermal face pairs from respective databases. The algorithm shows promising performance in addressing the challenges posed by the modality gap, providing a potential solution for accurate and robust Heterogeneous Face Recognition. [ABSTRACT FROM AUTHOR]

As control algorithms become increasingly sophisticated, delivering improved performance at the expense of greater complexity, practical experiments often become unfeasible. To address this challenge, this study introduces a novel rapid control prototyping (NRCP) approach based on model-based design (MBD) using MATLAB/Simulink, STM32CubeMX software, and field-oriented control strategies for permanent magnet synchronous motors. Compared with existing rapid control prototyping methods, our NRCP design offers several advantages: it simplifies model construction by utilizing only basic Simulink modules, minimizes dependency on MATLAB/Simulink toolboxes by only requiring Embedded Code conversion to C language, and ensures strong compatibility as the experimental code involves only C language. To demonstrate the feasibility and efficiency of this approach, sensor-based and sensorless control models were developed using the MBD method. The practicality of the NRCP was successfully validated through sensor-based and sensorless experiments using an ARM Cortex-M4-based STM32 microcontroller. [ABSTRACT FROM AUTHOR]

With the popularization of Internet of Things applications, trillions of new devices with different application requirements, such as smart wearables and smart home appliances, will be connected to the network. Hence, it is crucial to ensure the data transmission security of these low-power and multifunctional sensor nodes. In this paper, a novel lightweight block cipher, GFSPX, is proposed for resource-constrained microdevices. The proposed algorithm combines a generalized Feistel structure with the substitution permutation networks structure to design the round function, which effectively addresses the inherent problem of slow diffusion in the traditional Feistel structure. Furthermore, the introduction of Addition or AND, Rotation, XOR operations in the round function to process part of the plaintext reduces the demand for hardware resource of the algorithm. The avalanche test results indicate that the GFSPX algorithm has strong diffusion and can satisfy the avalanche effect in just six rounds. The security analysis results verify the security of the GFSPX algorithm against differential and linear cryptanalysis attacks, algebraic attacks, structural attacks and key scheduling attacks. Finally, the performance analysis results indicate that the hardware implementation cost of GFSPX algorithm is relatively low, requiring only 1715 GE based on 0.13 micron logic process. In addition, the software implementation of this algorithm works well at an encryption rate of 12.31 Mb/s. [ABSTRACT FROM AUTHOR]

The fog computing model is a new computing model that has been proposed in recent years by increasing the number of requests sent to the cloud to reduce the delay and workload of the cloud computing model. In addition to its advantages, the fog computing model also has challenges, among which we can mention the issue of service placement in this computing model, which is very effective in the performance of the computing model. So far, many works have been presented to solve the problem of service deployment by considering different goals such as energy consumption, end-to-end delay, load balancing, resource efficiency, etc. Considering the importance of all the mentioned parameters, it is very important to provide a multi-objective method. In multi-objective problems, the method of evaluating the generated solutions is a separate challenge. Therefore, in this paper, a service placement method is presented by considering end-to-end delay criteria and energy consumption based on the modified Harris hawks algorithm to solve multi-objective problems. To increase accuracy, in the proposed method called multi-objective Harris hawks optimization, a multi-objective problem is modeled as several single-objective problems. The simulation results in CloudSim show that the proposed method has achieved better results than other algorithms in terms of reducing energy consumption, end-to-end delay, and network utilization. [ABSTRACT FROM AUTHOR]

Chaotic systems are often used as random sequence generators due to their excellent pseudo-randomness, but there is limitation that the discretization of complex chaotic systems requires a long computational time. Therefore, a parallel discretization method for chaotic system, and an accelerated chaotic image secure communication system based on the Zynq-7000 platform are proposed in this paper. Firstly, a 3-dimensional (3-D) chaotic system is constructed to generate random sequence, which has high Shannon entropy (SE) complexity. Then, chaotic system is parallelly discretized through finite state machine, which sequences are combined with scrambling and diffusion algorithms to construct an accelerated chaotic image secure communication system. Finally, the secure communication process based on the Zynq-7000 platform is completed, and the analysis of hardware experimental results shows that the system has safe performances, simple structure and excellent operational efficiency. [ABSTRACT FROM AUTHOR]

Incorporating cognition into the design and architecture of the Internet of Things (IoT) has recently been the subject of much research, giving rise to a new subfield known as the cognitive IoT (CIoT). Consequently, CIoT takes on some characteristics and challenges of the IoT. Many applications in CIoT generate a massive amount of data that require meaningful insight in a computationally efficient manner. Therefore, this research proposed a novel way to extract meaningful insight from huge amounts of data using probabilistic values. Along with that, it finds the most informative observation from each cluster based on the amount of information associated with each sensory observation. Subsequently, the modified Lagrangian L1 algorithm is used to extract conscious points, which are further combined, and the highest entropy row is selected as the conscious pattern. Experimenting with 21.25 years of environmental data and cross-validating using many metrics, it outperforms current approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel keyframe extraction extraction method based on the gray wolf optimization (GWO) algorithm, addressing the challenge of information loss in traditional methods due to redundant and similar frames. The proposed method GWOKConvLSTM prioritizes speed, accuracy, and compression efficiency while preserving semantic information. Inspired by wolf behavior, we construct a fitness function that minimizes reconstruction error and achieves optimal compression ratios below 8%. Compared to traditional methods, our GWO method achieves the lowest reconstruction error for a given compression rate, providing a concise and visually coherent summary of keyframes while maintaining consistency across similar motions. Additionally, we propose a template-based method for video classification tasks, achieving the highest accuracy when combined with pre-trained CNNs and ConvLSTM. Our method effectively prevents dynamic background noise from affecting keyframe selection, leading to significantly improve video classification performance using deep neural networks. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) has attracted widespread attention due to its outstanding optimization performance and ease of operation, but it cannot avoid the dilemmas of premature convergence or stagnation when faced with complex optimization problems. To reduce the probability of such difficulties for DE, we sort out the factors that influence the balance between global exploration and local exploitation in the DE algorithm, and we design a novel DE variant (abbreviated as PISRDE) by integrating the corresponding influence factors through a periodic intervention mechanism and a systematic regulation mechanism. The periodic intervention mechanism divides the optimization operations of PISRDE into routine operation and intervention operation, and it balances global exploration and local exploitation at the macro level by executing the two operations alternately. The systematic regulation mechanism treats the involved optimization strategies and parameter settings as an organic system for targeted design, to balance global exploration and local exploitation at the meso or micro level. To evaluate and verify the optimization performance of PISRDE, we employ seven DE variants with excellent optimization performance to conduct comparison experiments on the IEEE CEC 2014 and IEEE CEC 2017 benchmarks. The comparison results indicate that PISRDE outperforms all competitors overall, and its relative advantage is even more significant when dealing with high-dimensional and complex optimization problems. Schematic design philosophy of PISRDE [ABSTRACT FROM AUTHOR]

Uncertainty in the evolution of opponent behavior creates a non-stationary environment for the agent, reducing the reliability of value estimation and strategy selection while compromising security during the exploration process. Previous studies have developed various uncertainty quantification techniques and designed uncertainty-aware exploration methods for multi-agent reinforcement learning (MARL). However, existing methods have gaps in theoretical research and experimental verification of decoupling uncertainty between opponents and environment, which can decrease learning efficiency and lead to an unstable training process. Due to inaccurate opponent modeling, the agent is vulnerable to harm from opponents, which is undesirable in real-world tasks. To address these issues, this study proposes a novel uncertainty-guided safe exploration strategy for MARL that decouples the two types of uncertainty originating from the environment and opponents. Specifically, we introduce an uncertainty decoupling quantification technique based on a novel variance decomposition method for action-value functions. Furthermore, we present an uncertainty-aware policy optimization mechanism to facilitate safe exploration in MARL. Finally, we propose a new adaptive parameter scaling method to ensure efficient exploration by the agents. Theoretical analysis establishes the proposed approach's convergence rate, and its effectiveness is demonstrated empirically. Extensive experiments on benchmark tasks spanning differential games, multi-agent particle environments, and RoboSumo validate the proposed uncertainty-guided method's significant advantages in attaining higher scores and facilitating safe agent exploration. [ABSTRACT FROM AUTHOR]

In the multi-focus image fusion process, generating a focus decision map is typically a requisite step before the actual fusion of images. Many multi-focus image fusion methods often require optimization through post-processing techniques such as hole filling and removal of small regions in the decision map. To solve these problems and obtain better fusion images, we propose a multi-focus image fusion algorithm based on an end-to-end decision optimization model called EDOM-MFIF. Firstly, we design a cross-scale feature extraction encoder based on the joint attention fusion of upper and lower information, which can fully extract the shallow texture information and high-level semantic information of images. Secondly, we design a feature fusion module using convolution and spatial attention for comprehensive feature fusion and maximal retention of global image information. Finally, we achieve end-to-end training and optimization of the focusing decision map by integrating the decision map's optimization into the whole image fusion architecture. This prevents evident pixel faults in the immediate area from spreading to other areas during post-processing, which could lead to inaccurate classification findings, in addition to maintaining pixel consistency with the original image to the greatest extent possible. To facilitate training of the multi-focus image fusion model, we construct a large-scale multi-focus image dataset with a supervised decision map and test the algorithm on three different types of public datasets. The experimental results show that the proposed algorithm outperforms other advanced multi-focus image fusion algorithms in both objective evaluation and visual evaluation. [ABSTRACT FROM AUTHOR]

Unsupervised feature selection (UFS) is a fundamental and indispensable dimension reduction method for large amount of high-dimensional unlabeled data samples. Without label information, the manifold learning technique is leveraged to compensate for the lack of discrimination with the selected features. However, it is still a challenging problem to capture the geometrical structure for practical data, which are often contaminated by noises and outliers. Additionally, the predetermined graph embedded UFS models suffer from the parameter tuning problem and the separated model optimization procedures. To generate more compact and discriminative feature subsets, we propose a Robust UFS model with Adaptive and Flexible ℓ 1 -norm Graph (RAFG) embedding. Specifically, the ℓ 2 , 1 -norm is imposed on the flexible regression term to alleviate the adverse effects of both noisy features and outliers, and ℓ 2 , p -norm regularization term is incorporated to ensure that the selected transformation matrix is sufficiently sparse. Moreover, the adaptive ℓ 1 -norm graph learning characterize the clustering distribution via consistent embeddings, which avoids time-consuming distance computations in a high-dimensional feature space. To solve the challenging problem, we propose an efficient alternative updating algorithm with an iterative reweighted strategy, together with the necessary convergence and complexity analyses. Finally, experimental results on two synthetic data and eight benchmark datasets illustrate the effectiveness and superiority of the proposed RAFG method compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Video action recognition with semi-supervised learning is a challenging research topic due to the low-labeling ratio. Previous works mainly tackle the problem with two kinds of approaches: pseudo labeling and contrastive learning. Different from existing approaches that often treat the two parts separately, we propose an integrated learning framework that incorporates pseudo labeling and contrastive clustering in a coherent and mutually beneficial way. On one hand, the contrastive learning aggregates data from the same class into clusters, yielding more reliable pseudo labels for training the classifier; on the other hand, the re-trained classifier predicts categories for unlabeled data, thereby guiding contrastive learning to establish discriminative representations. We theoretically prove that the two iterative operations can be formulated as an E-M algorithm and validate its generalization ability upon the semi-supervised classification task with experiments. Specifically, we construct a MoCo-like structure to implement the proposed learning framework and explore the potential of employing the video tramsformer for semi-supervised action recognition. Furthermore, We also devise a global-local view sampling strategy for video data augmentation, which verifies to facilitate the representation learning and advance the performance. We implement extensive experiments on three video action recognition datasets with a series of data labeling ratios. Compared with state-of-the-art (SOTA) methods, the proposed approach achieves superior or competitive performances. For example, with 1 % labeling ratio, the top-1 accuracy increase to 49.1 % and 52.4 % on UCF-101 and Kinetics-400 datasets, respectively, surpassing SOTA by 2.8 % and 3.3 % . [ABSTRACT FROM AUTHOR]

This paper proposes an integrated approach to group decision-making (GDM) by using stochastic preference analysis (SPA) and best-worst method (BWM). BWM preparation algorithm is proposed to obtain the best and worst of experts and the relative importance degree. Meanwhile, expert weights model and expert's priority vector model are proposed. Furthermore, the stochastic composite rank acceptability index, stochastic composite expected priority vector, stochastic composite expected rank and stochastic composite confidence factor are developed based on SPA to describe the ranks of alternatives based on SPA. Finally, a group stochastic preference analysis-best worst method (GSPA-BWM) algorithm is developed by analyzing the judgments space through Monte Carlo simulation. The experts can use this method to choose some of the outcomes which they find most useful to make reliable decisions. Examples and comparison analyses show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

Surrogate-assisted evolutionary algorithms have been widely employed to solve data-driven optimization problems. However, for offline data-driven optimization, it is very challenging to perform evolutionary search efficiently as well as accurately since no new data is available during the optimization process. To mitigate this issue, a multifidelity surrogates-assisted multi-objective particle swarm optimization (MFSa-PSO) algorithm is proposed in this paper. First, two low-fidelity models with convergence and diversity characteristics separately and a high-fidelity model are constructed to assemble multifidelity surrogate models. Second, by adopting the knowledge transfer strategy, the multifidelity surrogates-assisted two-archive multi-objective particle swarm optimization is conducted to search optimal solutions more exactly and effectively. Third, the output solution set is achieved by associating the solutions of two archives with reference vectors. Finally, the proposed MFSa-PSO is compared with some popular surrogate-assisted evolutionary algorithms on benchmark problems to verify its effectiveness and outperformance. Additionally, a real-world application of the municipal solid waste incineration process is carried out to verify the engineering applicability of MFSa-PSO. [ABSTRACT FROM AUTHOR]

In low voltage distribution systems, series arc fault current is small and hidden, and traditional circuit protection devices cannot effectively identify it. To address this problem, a series arc fault detection method based on multi-domain depth feature association is proposed in this paper. By building an experimental platform, the current signal data on normal and arc fault states under different loads are obtained. The time-domain features, frequency-domain features and wavelet packet energy features of the current signal are extracted. To enhance the quality of the data, the importance of each domain characteristic is ranked using four distinct tree techniques, and the most useful features are chosen. A one-dimensional stacked neural network (1D-SNN) fault detection model is constructed to further extract the depth features of each domain. To achieve series arc defect detection, the depth features are combined and fed into a fully connected neural network. The Radam algorithm is used to optimize the detection model. It is then compared with Adam, SGD, and RMSprop optimization algorithms, which verifies that Radam has a better effect on the optimization of the arc detection model. Experimental results show that the average detection accuracy is 99.63%. [ABSTRACT FROM AUTHOR]

In this study, a general method is developed for the torsional vibration of non-circular-shaped nanorods with varying boundary conditions using second-order strain gradient theory. In most of the studies in the literature, the cross section of the rods is considered to be circular. The reason for this is that the use of warping function is inevitable when the cross section geometry is not circular. For circular cross sections after torsion, the warping is very small and is considered to be non-existent. For non-circular sections, cross section warping should be taken into account in mathematical calculations. The cross section geometry is different from circular in this study, and the boundary conditions are not rigid, contrary to most studies in the literature. In this paper, the second-order strain gradient theory and the most general solution method are discussed. In some specific cases, it is possible to transform the problem into many studies found in the literature. The correctness of the algorithm is tested by comparing the resulting solutions with closed solutions found in the literature. The influence of some variables on the torsional frequencies is illustrated by a series of graphical figures, and the superiority of the applied method is summarized. [ABSTRACT FROM AUTHOR]

In computational fluid dynamics, controlling grid scale is efficiently managed using the Advancing Front Technique (AFT). However, achieving grid generation convergence within a three-dimensional (3D) computational domain remains challenging, primarily due to excessive intersection judgments that significantly reduce efficiency. This paper addresses the non-convergence issues inherent in the 3D AFT and proposes preliminary solutions to enhance algorithm robustness while reducing intersection judgments. We introduce two neural networks trained on the backpropagation (BP) algorithms, Line-ANN and Plane-ANN, specifically designed for integration with AFT. These networks are individually combined with traditional 3D AFT to develop two enhanced methods. We assess these methods by comparing grid quality and time consumption against traditional AFT approaches. The results demonstrate that integrating Plane-ANN and Line-ANN with AFT improves overall efficiency by approximately 55% and 36%, respectively, thereby significantly enhancing grid generation efficiency. [ABSTRACT FROM AUTHOR]

We revisit the seminal Brill–Noether algorithm for plane curves with ordinary singularities. Our new approach takes advantage of fast algorithms for polynomials and structured matrices. We design a new probabilistic algorithm of Las Vegas type that computes a Riemann–Roch space in expected sub-quadratic time. [ABSTRACT FROM AUTHOR]

Nowadays, the use of Artificial Intelligence (AI) and Machine Learning (ML) algorithms is increasingly affecting the performance of innovative systems. At the same time, the advent of the Internet of Things (IoT) and the Edge Computing (EC) as means to place computational resources close to users create the need for new models in the training process of ML schemes due to the limited computational capabilities of the devices/nodes placed there. In any case, we should not forget that IoT devices or EC nodes exhibit less capabilities than the Cloud back end that could be adopted for a more complex training upon vast volumes of data. The ideal case is to have, at least, basic training capabilities at the IoT-EC ecosystem in order to reduce the latency and face the needs of near real time applications. In this paper, we are motivated by this need and propose a model that tries to save time in the training process by focusing on the training dataset and its statistical description. We do not dive into the architecture of any ML model as we target to provide a more generic scheme that can be applied upon any ML module. We monitor the statistics of the training dataset and the loss during the process and identify if there is a potential to stop it when not significant contribution is foreseen for the data not yet adopted in the model. We argue that our approach can be applied only when a negligibly decreased accuracy is acceptable by the application gaining time and resources from the training process. We provide two algorithms for applying this approach and an extensive experimental evaluation upon multiple supervised ML models to reveal the benefits of the proposed scheme and its constraints. [ABSTRACT FROM AUTHOR]

We investigate the concept of adjustability—the difference in objective values between two types of dynamic robust optimization formulations: one where (static) decisions are made before uncertainty realization, and one where uncertainty is resolved before (adjustable) decisions. This difference reflects the value of information and decision timing in optimization under uncertainty, and is related to several other concepts such as the optimality of decision rules in robust optimization. We develop a theoretical framework to quantify adjustability based on the input data of a robust optimization problem with a linear objective, linear constraints, and fixed recourse. We make very few additional assumptions. In particular, we do not assume constraint-wise separability or parameter nonnegativity that are commonly imposed in the literature for the study of adjustability. This allows us to study important but previously under-investigated problems, such as formulations with equality constraints and problems with both upper and lower bound constraints. Based on the discovery of an interesting connection between the reformulations of the static and fully adjustable problems, our analysis gives a necessary and sufficient condition—in the form of a theorem-of-the-alternatives—for adjustability to be zero when the uncertainty set is polyhedral. Based on this sharp characterization, we provide two efficient mixed-integer optimization formulations to verify zero adjustability. Then, we develop a constructive approach to quantify adjustability when the uncertainty set is general, which results in an efficient and tight poly-time algorithm to bound adjustability. We demonstrate the efficiency and tightness via both theoretical and numerical analyses. [ABSTRACT FROM AUTHOR]

The network pricing problem (NPP) is a bilevel problem, where the leader optimizes its revenue by deciding on the prices of certain arcs in a graph, while expecting the followers (also known as the commodities) to choose a shortest path based on those prices. In this paper, we investigate the complexity of the NPP with respect to two parameters: the number of tolled arcs, and the number of commodities. We devise a simple algorithm showing that if the number of tolled arcs is fixed, then the problem can be solved in polynomial time with respect to the number of commodities. In contrast, even if there is only one commodity, once the number of tolled arcs is not fixed, the problem becomes NP-hard. We characterize this asymmetry in the complexity with a novel property named strong bilevel feasibility. Finally, we describe an algorithm to generate valid inequalities to the NPP based on this property, whose numerical results illustrate its potential for effectively solving the NPP with a high number of commodities. [ABSTRACT FROM AUTHOR]

Composite minimization is a powerful framework in large-scale convex optimization, based on decoupling of the objective function into terms with structurally different properties and allowing for more flexible algorithmic design. We introduce a new algorithmic framework for complementary composite minimization, where the objective function decouples into a (weakly) smooth and a uniformly convex term. This particular form of decoupling is pervasive in statistics and machine learning, due to its link to regularization. The main contributions of our work are summarized as follows. First, we introduce the problem of complementary composite minimization in general normed spaces; second, we provide a unified accelerated algorithmic framework to address broad classes of complementary composite minimization problems; and third, we prove that the algorithms resulting from our framework are near-optimal in most of the standard optimization settings. Additionally, we show that our algorithmic framework can be used to address the problem of making the gradients small in general normed spaces. As a concrete example, we obtain a nearly-optimal method for the standard ℓ 1 setup (small gradients in the ℓ ∞ norm), essentially matching the bound of Nesterov (Optima Math Optim Soc Newsl 88:10–11, 2012) that was previously known only for the Euclidean setup. Finally, we show that our composite methods are broadly applicable to a number of regression and other classes of optimization problems, where regularization plays a key role. Our methods lead to complexity bounds that are either new or match the best existing ones. [ABSTRACT FROM AUTHOR]

The minimum linear ordering problem (MLOP) generalizes well-known combinatorial optimization problems such as minimum linear arrangement and minimum sum set cover. MLOP seeks to minimize an aggregated cost f (·) due to an ordering σ of the items (say [n]), i.e., min σ ∑ i ∈ [ n ] f (E i , σ) , where E i , σ is the set of items mapped by σ to indices [i]. Despite an extensive literature on MLOP variants and approximations for these, it was unclear whether the graphic matroid MLOP was NP-hard. We settle this question through non-trivial reductions from mininimum latency vertex cover and minimum sum vertex cover problems. We further propose a new combinatorial algorithm for approximating monotone submodular MLOP, using the theory of principal partitions. This is in contrast to the rounding algorithm by Iwata et al. (in: APPROX, 2012), using Lovász extension of submodular functions. We show a (2 - 1 + ℓ f 1 + | E |) -approximation for monotone submodular MLOP where ℓ f = f (E) max x ∈ E f ({ x }) satisfies 1 ≤ ℓ f ≤ | E | . Our theory provides new approximation bounds for special cases of the problem, in particular a (2 - 1 + r (E) 1 + | E |) -approximation for the matroid MLOP, where f = r is the rank function of a matroid. We further show that minimum latency vertex cover is 4 3 -approximable, by which we also lower bound the integrality gap of its natural LP relaxation, which might be of independent interest. [ABSTRACT FROM AUTHOR]

We consider the oracle complexity of computing an approximate stationary point of a Lipschitz function. When the function is smooth, it is well known that the simple deterministic gradient method has finite dimension-free oracle complexity. However, when the function can be nonsmooth, it is only recently that a randomized algorithm with finite dimension-free oracle complexity has been developed. In this paper, we show that no deterministic algorithm can do the same. Moreover, even without the dimension-free requirement, we show that any finite-time deterministic method cannot be general zero-respecting. In particular, this implies that a natural derandomization of the aforementioned randomized algorithm cannot have finite-time complexity. Our results reveal a fundamental hurdle in modern large-scale nonconvex nonsmooth optimization. [ABSTRACT FROM AUTHOR]

We consider the problem of maximizing a non-negative submodular function under the b-matching constraint, in the semi-streaming model. When the function is linear, monotone, and non-monotone, we obtain the approximation ratios of 2 + ε , 3 + 2 2 ≈ 5.828 , and 4 + 2 3 ≈ 7.464 , respectively. We also consider a generalized problem, where a k-uniform hypergraph is given, along with an extra matroid or a k ′ -matchoid constraint imposed on the edges, with the same goal of finding a b-matching that maximizes a submodular function. When the extra constraint is a matroid, we obtain the approximation ratios of k + 1 + ε , k + 2 k + 1 + 2 , and k + 2 k + 2 + 3 for linear, monotone and non-monotone submodular functions, respectively. When the extra constraint is a k ′ -matchoid, we attain the approximation ratio 8 3 k + 64 9 k ′ + O (1) for general submodular functions. [ABSTRACT FROM AUTHOR]

The Set Packing problem is, given a collection of sets S over a ground set U, to find a maximum collection of sets that are pairwise disjoint. The problem is among the most fundamental NP-hard optimization problems that have been studied extensively in various computational regimes. The focus of this work is on parameterized complexity, Parameterized Set Packing (PSP): Given parameter r ∈ N , is there a collection S ′ ⊆ S : | S ′ | = r such that the sets in S ′ are pairwise disjoint? Unfortunately, the problem is not fixed parameter tractable unless W [ 1 ] = FPT , and, in fact, an "enumerative" running time of | S | Ω (r) is required unless the exponential time hypothesis (ETH) fails. This paper is a quest for tractable instances of Set Packing from parameterized complexity perspectives. We say that the input (U , S) is "compact" if | U | = f (r) · poly (log | S |) , for some f (r) ≥ r . In the Compact PSP problem, we are given a compact instance of PSP. In this direction, we present a "dichotomy" result of PSP: When | U | = f (r) · o (log | S |) , PSP is in FPT, while for | U | = r · Θ (log (| S |)) , the problem is W[1]-hard; moreover, assuming ETH, Compact PSP does not admit | S | o (r / log r) time algorithm even when | U | = r · Θ (log (| S |)) . Although certain results in the literature imply hardness of compact versions of related problems such as Set r -Covering and Exact r -Covering, these constructions fail to extend to Compact PSP. A novel contribution of our work is the identification and construction of a gadget, which we call Compatible Intersecting Set System pair, that is crucial in obtaining the hardness result for Compact PSP. Finally, our framework can be extended to obtain improved running time lower bounds for Compact r -VectorSum. [ABSTRACT FROM AUTHOR]

We study the approximability of the maximum size independent set (MIS) problem in bounded degree graphs. This is one of the most classic and widely studied NP-hard optimization problems. It is known for its inherent hardness of approximation. We focus on the well known minimum-degree greedy algorithm for this problem. This algorithm iteratively chooses a minimum degree vertex in the graph, adds it to the solution and removes its neighbors, until the remaining graph is empty. The approximation ratios of this algorithm have been widely studied, where it is augmented with an advice that tells the greedy algorithm which minimum degree vertex to choose if it is not unique. Our main contribution is a new mathematical theory for the design of such greedy algorithms for MIS with efficiently computable advice and for the analysis of their approximation ratios. Using this theory we obtain the ultimate approximation ratio of 5/4 for greedy algorithms on graphs with maximum degree 3, which completely solves an open problem from the paper by Halldórsson and Yoshihara (in: Staples, Eades, Katoh, Moffat (eds) Algorithms and computations—ISAAC '95, in 2026 LNCS, Springer, Berlin, Heidelberg, 1995). Our algorithm is the fastest currently known algorithm for MIS with this approximation ratio on such graphs. We also obtain a simple and short proof of the (Δ + 2) / 3 -approximation ratio of any greedy algorithms on graphs with maximum degree Δ , the result proved previously by Halldórsson and Radhakrishnan (Nord J Comput 1:475–492, 1994). We almost match this ratio by showing a lower bound of (Δ + 1) / 3 on the ratio of a greedy algorithm that can use an advice. We apply our new algorithm to the minimum vertex cover problem on graphs with maximum degree 3 to obtain a substantially faster 6/5-approximation algorithm than the one currently known. We complement our algorithmic, upper bound results with lower bound results, which show that the problem of designing good advice for greedy algorithms for MIS is computationally hard and even hard to approximate on various classes of graphs. These results significantly improve on the previously known hardness results. Moreover, these results suggest that obtaining the upper bound results on the design and analysis of the greedy advice is non-trivial. [ABSTRACT FROM AUTHOR]

Motivated by an application from geodesy, we study the connected k-center problem and the connected k-diameter problem. The former problem has been introduced by Ge et al. (ACM Trans Knowl Discov Data 2(2):1–35, 2008. https://doi.org/10.1145/1376815.1376816) to model clustering of data sets with both attribute and relationship data. These problems arise from the classical k-center and k-diameter problems by adding a side constraint. For the side constraint, we are given an undirected connectivity graphG on the input points, and a clustering is now only feasible if every cluster induces a connected subgraph in G. Usually in clustering problems one assumes that the clusters are pairwise disjoint. We study this case but additionally also the case that clusters are allowed to be non-disjoint. This can help to satisfy the connectivity constraints. Our main result is an O (log 2 k) -approximation algorithm for the disjoint connected k-center and k-diameter problem. For Euclidean spaces of constant dimension and for metrics with constant doubling dimension, the approximation factor improves to O(1). Our algorithm works by computing a non-disjoint connected clustering first and transforming it into a disjoint connected clustering. We complement these upper bounds by several upper and lower bounds for variations and special cases of the model. [ABSTRACT FROM AUTHOR]

Classical streaming algorithms operate under the (not always reasonable) assumption that the input stream is fixed in advance. Recently, there is a growing interest in designing robust streaming algorithms that provide provable guarantees even when the input stream is chosen adaptively as the execution progresses. We propose a new framework for robust streaming that combines techniques from two recently suggested frameworks by Hassidim et al. (NeurIPS 2020) and by Woodruff and Zhou (FOCS 2021). These recently suggested frameworks rely on very different ideas, each with its own strengths and weaknesses. We combine these two frameworks into a single hybrid framework that obtains the "best of both worlds", thereby solving a question left open by Woodruff and Zhou. [ABSTRACT FROM AUTHOR]

We investigate algorithms for testing whether an image is connected. Given a proximity parameter ϵ ∈ (0 , 1) and query access to a black-and-white image represented by an n × n matrix of Boolean pixel values, a (1-sided error) connectedness tester accepts if the image is connected and rejects with probability at least 2/3 if the image is ϵ -far from connected. We show that connectedness can be tested nonadaptively with O (1 ϵ 2 ) queries and adaptively with O (1 ϵ 3 / 2 log 1 ϵ ) queries. The best connectedness tester to date, by Berman, Raskhodnikova, and Yaroslavtsev (STOC 2014) had query complexity O (1 ϵ 2 log 1 ϵ) and was adaptive. We also prove that every nonadaptive, 1-sided error tester for connectedness must make Ω (1 ϵ log 1 ϵ) queries. [ABSTRACT FROM AUTHOR]

In this study, a new rain type classification algorithm for the Dual-Frequency Precipitation Radar (DPR) suitable over the Tibetan Plateau (TP) was proposed by analyzing Global Precipitation Measurement (GPM) DPR Level-2 data in summer from 2014 to 2020. It was found that the DPR rain type classification algorithm (simply called DPR algorithm) has mis-identification problems in two aspects in summer TP. In the new algorithm of rain type classification in summer TP, four rain types are classified by using new thresholds, such as the maximum reflectivity factor, the difference between the maximum reflectivity factor and the background maximum reflectivity factor, and the echo top height. In the threshold of the maximum reflectivity factors, 30 dBZ and 18 dBZ are both thresholds to separate strong convective precipitation, weak convective precipitation and weak precipitation. The results illustrate obvious differences of radar reflectivity factor and vertical velocity among the three rain types in summer TP, such as the reflectivity factor of most strong convective precipitation distributes from 15 dBZ to near 35 dBZ from 4 km to 13 km, and increases almost linearly with the decrease in height. For most weak convective precipitation, the reflectivity factor distributes from 15dBZ to 28 dBZ with the height from 4 km to 9 km. For weak precipitation, the reflectivity factor mainly distributes in range of 15–25 dBZ with height within 4–10 km. It is also shows that weak precipitation is the dominant rain type in summer TP, accounting for 40%–80%, followed by weak convective precipitation (25%–40%), and strong convective precipitation has the least proportion (less than 30%). [ABSTRACT FROM AUTHOR]

Aiming at the problem of object occlusion in the traditional assembly process, this paper proposes an assisted assembly method based on augmented reality. First, the virtual target model and the on-site assembly scene are used to achieve the initial three-dimensional positioning of the occluded object. Then, an area division method based on bounding box mapping is proposed to efficiently partition the two-dimensional target area required for follow-up tracking registration. Then, a keyframe-based LK optical flow tracking registration method is proposed for real-time pose estimation. This method combines feature matching with the tracking method and realizes smooth switching between them through the tracking failure judgment mechanism. In order to improve the accuracy of tracking registration, a forward and backward error screening mechanism based on the tracking of feature points is added in the feature tracking stage. In the feature matching stage, quadtree homogenization feature extraction and PROSAC mismatching elimination are used. In addition, a jitter correction method based on motion consistency is proposed to further ensure the stability of the algorithm. Finally, according to the tracking registration results, the virtual auxiliary information is fused and superimposed with the actual assembly operation scene, which brings the "perspective" vision beyond reality to the construction personnel and assists the construction personnel in completing the assembly task simply and efficiently. Experimental results show that the proposed method has high tracking accuracy and good stability and can reach 28 frames per second, which can meet the requirements of augmented reality–assisted assembly. [ABSTRACT FROM AUTHOR]

Purpose: Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum complex. We evaluated machine learning to classify individuals as having one of three disorders of the autism spectrum under DSM-IV, or as non-spectrum. Methods: We employed machine learning to analyze retrospective data from 38,560 individuals. Inputs encompassed clinical, demographic, and assessment data. Results: The algorithm achieved AUROCs ranging from 0.863 to 0.980. The model correctly classified 80.5% individuals; 12.6% of individuals from this dataset were misclassified with another disorder on the autism spectrum. Conclusion: Machine learning can classify individuals as having a disorder on the autism spectrum or as non-spectrum using minimal data inputs. [ABSTRACT FROM AUTHOR]

In this research paper, we introduce an adaptive block-matching motion estimation algorithm to improve the accuracy and efficiency of motion estimation (ME). First, we present a block generation system that creates blocks of varying sizes based on the detected motion location. Second, we incorporate predictive tools such as early termination and variable window size to optimize our block-matching algorithm. Furthermore, we propose two distinct search patterns to achieve maximum quality and efficiency. We evaluated the proposed algorithms on 20 videos and compared the results with known algorithms, including the full search algorithm (FSA), which is a benchmark for ME accuracy. Our proposed quality-based algorithm shows an improvement of 0.27 dB in peak signal-to-noise ratio (PSNR) on average for reconstructed frames compared to FSA, along with a reduction of 71.66% in searched blocks. Similarly, our proposed efficiency-based method results in a 0.07 dB increase in PSNR and a 97.93% reduction in searched blocks compared to FSA. These findings suggest that our proposed method has the potential to improve the performance of ME in video coding. [ABSTRACT FROM AUTHOR]

It is essential for meeting the stringent real-time demands encountered in actual production scenarios. Employing the low computational complexity of recursive algorithms, some new schemes are developed for the parameter estimation of a class of time-varying systems. The temporal evolution of parameters is characterized through the autoregressive process, facilitating the construction of the identification model with regard to the autoregressive coefficients. Based on the computational efficiency of the gradient search, a parametric autoregression-based stochastic gradient algorithm is derived with an appropriate step size, achieving a compromise between the steepest descent and convergence rate. In order to address the limitation of the low estimation accuracy in gradient algorithms, a parametric autoregression-based multi-innovation stochastic gradient algorithm is explored by making use of the favorable information for corrections. The simulation results are given to demonstrate the effectiveness of the proposed algorithms. Therefore, for a class of time-varying systems whose parameters become the further insight through the autoregressive process, the proposed gradient methods can obtain the parameter estimates faster and more accurately while ensuring the real-time performance of time-varying systems. [ABSTRACT FROM AUTHOR]

Accurate measurement is an important prerequisite for sound source localization. In the enclosed environments, noise and reverberation tend to cause localization errors. To address these issues, this paper proposes a compound model particle filter algorithm based on multi-feature. Based on a multi-feature observation, the likelihood function of speaker tracking is constructed for particle filter, and multi-hypothesis and frequency selection function are adopted to establish multi-feature optimization mechanism, including time delay selection and beam output energy fusion. It is found that they effectively solved the difficulty in the simultaneous suppression of noise and reverberation by single feature. Moreover, considering the randomness of speaker motion, a compound model for sound source tracking is developed, where the stability of the speaker tracking system is improved by integrating multi-feature observation into the compound model filtering. The experimental results with both simulated and real acoustic data indicate that the proposed method has better tracking performance, compared with the existing ones with low SNR and strong reverberation as well as highly mobile conditions. [ABSTRACT FROM AUTHOR]

In this study, two improved parallel finite element algorithms based on two-grid strategies are developed to approximate the stationary Navier–Stokes equations. Algorithms are devised to improve the existing local and parallel finite element methods to arrive at L 2 optimal velocity approximation by considering one further coarse grid correction. Rigorously numerical analysis is established, and numerical experiments are reported to support the theoretical findings. [ABSTRACT FROM AUTHOR]

In this work, we investigate a contraction-type method for solving monotone variational inclusion problems in real Hilbert spaces. We obtain strong convergence theorems for two algorithms with a self-adaptive step size for solving monotone variational inclusions. The advantage of our algorithms is that we do not require a cocoercivity assumption nor do we need to know the Lipschitz-type constant of the single-valued operator. Moreover, a convergence rate is derived in the case where one of the operators is maximally and strongly monotone, and the other is monotone and Lipschitz continuous. The performance of our proposed methods is illustrated by numerical experiments regarding signal recovery. Our results improve and extend some known results, and our experiments show that our proposed algorithms are efficient and outperform other algorithms which are available in the literature. [ABSTRACT FROM AUTHOR]

We propose and study two new methods based on the golden ratio technique for approximating solutions to variational inequality problems in Hilbert space. The first method combines the golden ratio technique with the subgradient extragradient method. In the second method, we incorporate the alternating golden ratio technique into the subgradient extragradient method. Both methods use self-adaptive step sizes which are allowed to increase during the execution of the algorithms, thus limiting the dependence of our methods on the starting point of the scaling parameter. We prove that under appropriate conditions, the resulting methods converge either weakly or R-linearly to a solution of the variational inequality problem associated with a pseudomonotone operator. In order to show the numerical advantage of our methods, we first present the results of several pertinent numerical experiments and then compare the performance of our proposed methods with that of some existing methods which can be found in the literature. [ABSTRACT FROM AUTHOR]

The stripe noise existing in remote sensing images badly degrades the visual quality and restricts the precision of data analysis. Therefore, many destriping models have been proposed in recent years. In contrast to these existing models, in this paper, we propose a nonconvex model with a DC function (i.e., the difference of convex functions) structure to remove the strip noise. To solve this model, we make use of the DC structure and apply an inexact proximal majorization-minimization algorithm with each inner subproblem solved by the alternating direction method of multipliers. It deserves mentioning that we design an implementable stopping criterion for the inner subproblem, while the convergence can still be guaranteed. Numerical experiments demonstrate the superiority of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we consider the frictional contact model between a piezoelectric body and an electrically conductive foundation. This contact is described by a reduced normal compliance law. To address this problem, we establish a mixed variational formulation and prove the existence of a unique solution. Moreover, we present an efficient algorithm for approximating the weak solution for the contact problem, taking into account both friction and electrical contact conditions. We conclude with numerical examples that demonstrate the practicality of the model. [ABSTRACT FROM AUTHOR]

A multi-objective solid transportation problem that includes source, destination, and mode of transport parameters may have fractional objective functions in real-life applications to maximize the profitability ratio, which could be the profit/cost or profit/time. We refer to such transportation problems as multi-objective fractional solid transportation problems. In addition, although most of the studies in the literature deal with the standard equality-constrained form of the multi-objective fractional solid transportation problem, the mixed-constrained type is addressed in this paper. With these mixed constraints, the multi-objective fractional solid transportation problem offers more flexible modeling of real-world problems that exist in many application areas, but mixed constraints also make the optimization process more challenging. This article presents an iterative fuzzy approach that combines the use of linear programming and the bisection algorithm using linear membership functions to obtain a strongly efficient solution. The algorithm's ability to convert a nonlinear problem into a set of linear problems is one of its main advantages, and it also decreases the amount of time needed to solve large-scale problems. A numerical example from the literature is adopted to illustrate the solution procedure. Moreover, large-scale instances are generated to further test the presented algorithm, and a comparison between the traditional fuzzy approach and the proposed method is presented. [ABSTRACT FROM AUTHOR]

The Particle Swarm Optimization (PSO) algorithm faces several inherent challenges when applied to dynamic and large-scale optimization problems. These challenges encompass the issues of outdated particle memory, inadequate scalability in high-dimensional search spaces, the incapability to detect environmental changes, a continual trade-off between exploration and exploitation, and the potential loss of population diversity within the problem space. To address these challenges, we propose a novel hybrid PSO algorithm, denoted as Parent–Child Multi-Swarm Clustered Memory (PCSCM). PCSCM is explicitly designed to leverage an enhanced memory system, capable of mitigating the issue of outdated particle memory after convergence, and efficiently adapting to changing environmental conditions. This innovative memory system retains and retrieves promising solutions from the past when environmental alterations occur. Additionally, PCSCM introduces clustering mechanisms for particles within each swarm, aimed at augmenting diversity within the problem space. This clustering strategy substantially bolsters the algorithm's performance in tracking evolving optimal solutions and positively contributes to its scalability. Crucially, the clustering approach is implemented not only for the main population but also for stored solutions in memory, which collectively strike a balance between exploration and exploitation. In the proposed method, particle swarms are divided into parent and child swarms, with parent swarms dedicated to preserving diversity; while, child swarms focus on identifying local solutions. These clustering and memory strategies are consistently applied within each sub-swarm to effectively address the challenges posed by high-dimensional search spaces. In addition to addressing challenges related to dynamic optimization, our proposed Parent–Child Multi-Swarm Clustered Memory (PCSCM) algorithm introduces an innovative mechanism for detecting environmental changes. This novel approach enhances the algorithm's adaptability by efficiently identifying moments when the optimization environment undergoes significant shifts. The detection of such changes is a crucial aspect of the PCSCM algorithm, contributing to its robust performance in dynamic scenarios. The effectiveness and robustness of the PCSCM algorithm are substantiated through extensive simulation experiments. These experiments provide insights into PCSCM's behavior in dynamic environments and showcase its ability to scale proficiently in high-dimensional settings. Particularly noteworthy are the results obtained when benchmarked against the Moving Peaks Benchmark and Generalized Moving Peaks Benchmark. These results not only underscore the algorithm's efficiency but also demonstrate its superiority when compared to several existing state-of-the-art optimization methods, including Multi-Swarm PSO, AmQSO, CPSO, Cellular PSO, FMSO, mQSO10 (5 + 5q), and DPSABC. [ABSTRACT FROM AUTHOR]

The primary hyperoxalurias (PH 1, 2, and 3) are rare autosomal recessive disorders of glyoxylate metabolism resulting in hepatic overproduction of oxalate. Clinical presentations that should prompt consideration of PH include kidney stones, nephrocalcinosis, and kidney failure of unknown etiology, especially with echogenic kidneys on ultrasound. PH1 is the most common and severe of the primary hyperoxalurias with a high incidence of kidney failure as early as infancy. Until the recent availability of a novel RNA interference (RNAi) agent, PH care was largely supportive of eventual need for kidney/liver transplantation in PH1 and PH2. Together with the Oxalosis and Hyperoxaluria Foundation, the authors developed a diagnostic algorithm for PH1 and in this report outline best clinical practices related to its early diagnosis, supportive treatment, and long-term management, including the use of the novel RNAi. PH1-focused approaches to dialysis and kidney/liver transplantation for PH patients with progression to chronic kidney disease/kidney failure and systemic oxalosis are suggested. Therapeutic advances for this devastating disease heighten the importance of early diagnosis and informed treatment. [ABSTRACT FROM AUTHOR]

This study introduces delay-aware cloudlet placement with a modified k-means (DACP-mk) algorithm to address the complexities of mobility and optimize cloudlet placement in a mobility-enhanced environment. DACP-mk enhances the traditional k-means algorithm by incorporating modified regional and global delay matrices to enable the simulation of a delay-aware cloudlet movement and placement strategy using a cluster-based approach within a smart healthcare platform. An optimized cloudlet position is generated to achieve a uniform distribution of mobile devices among placed cloudlets, maximize device coverage, and minimize network access delay within the cloudlet coverage distance. The proposed algorithm determines the optimal positions for cloudlets at coordinates (161.90, 103.24), (080.22, 139.52), and (080.29, 051.21) with an increase of 02.42%, 00.54%, 38.71%, and 49.00%, respectively, in terms of global mobile device coverage compared to modified k-means Manhattan (mkMAN), k-means, energy-efficient cloudlet placement method (ECPM) and enhanced adaptive cloudlets placement with covering algorithm (EACP-CA), and a decrease of 00.71% and 58.27% in global network access delay compared to mkMAN and k-means algorithms. The proposed algorithm offers enhanced healthcare support and cost-effective services, improving the Quality of Service (QoS) and ultimately contributing to saving lives. [ABSTRACT FROM AUTHOR]

We address the problem of resource allocation with multi-attribute demand considering heterogeneous servers in mobile edge computing (MEC). A mobile device (MD) may be within the coverage area of multiple access points and can get resources from any server that can establish direct communication. However, the configurations of heterogeneous servers are different, so the demand for each MEC server is different. We tackle this problem and consider multi-attribute demand, where each MD is assigned exactly one of multiple demands. We formulate the allocation problem in an auction-based setting to provide server scalability. We propose a family of greedy mechanisms to solve the resource allocation problem. However, each MD is self-interested and may try to manipulate the system to maximize its utility. We show our mechanisms are truthful, and they drive the system into an equilibrium where no MD can declare an untrue value to obtain higher utility. We also analyze the approximation ratios of our greedy mechanisms. Experimental results demonstrate that the greedy mechanisms obtain the near-optimal allocation in a short period while benefiting MDs and edge cloud providers. [ABSTRACT FROM AUTHOR]

In recent years, the occurrence of task failures are becoming prevalent in cloud computing due to various factors such as the increasing complexity of cloud environments, heterogeneity of resources, resource limitations and inadequate allocation. Task failure due to insufficient allocation poses a significant challenge in cloud computing. When tasks are not allocated effectively, they may not be completed within their deadlines which ultimately leads to failure. Hence, effective allocation strategies combined with appropriate fault tolerance measures are vital for addressing these challenges and mitigating the risk of task failures. This paper proposes a fault-tolerant task allocation algorithm (FTTA) for independent tasks with deadline through preemptive migration in heterogeneous cloud environments to reduce task failure. The proposed algorithm involves three phases: the initial phase decides the priority of tasks in the ready list to minimize the execution time and meet task deadlines, the second phase includes the selection of a suitable virtual machine with minimum execution time and the last phase assigns task on available or non-available (which may available in future) virtual machines to find the best execution time within the deadline limit. During the task allocation process, the algorithm adopts fault-tolerant strategy that includes preemptive migration if necessary which allows the migration of tasks to identify the best suitable virtual machine. An analysis of the proposed algorithm reveals that the overall time complexity is O (n log n + n m 2) where n is the number of tasks and m is the number of virtual machines. Further, the performance of the algorithm is evaluated for different sets of tasks (small to large) while varying the number of virtual machines. The experimental results demonstrate that FTTA outperforms First Come First Served (FCFS), Priority based algorithm, Shortest Job First (SJF), Dynamic Maximum Minimum (Dy max min) and RADL algorithms in terms of number of rejected tasks, makespan, speedup and efficiency. [ABSTRACT FROM AUTHOR]

Developers often encounter challenges with their introductory programming tasks as part of the development process. Unfortunately, rectifying these mistakes manually can be time-consuming and demanding. Automated program repair (APR) techniques offer a potential solution by synthesizing fixes for such errors. Previous research has investigated the utilization of both symbolic and neural techniques within the APR domain. However, these approaches typically demand significant engineering efforts or extensive datasets and training. In this paper, we explore the potential of using a large language model trained on code, specifically, we assess ChatGPT's capability to detect and repair bugs in simple Python programs. The experimental evaluation encompasses two benchmarks: QuixBugs and Textbook. Each benchmark consists of simple Python functions that implement well-known algorithms and each function contains a single bug. To gauge repair performance in various settings, several benchmark variations were introduced including addition of plain English documentation and code obfuscation. Based on thorough experiments, we found that ChatGPT was able to correctly detect and fix about 50% of the methods, when code is documented. Repair performance drops to 25% when code is obfuscated, and 15% when documentation is removed and code is obfuscated. Furthermore, when compared to existing APR systems, ChatGPT considerably outperformed them. [ABSTRACT FROM AUTHOR]

In contemporary datacenter networks, various applications experience the challenge of incast occurrences. Data-intensive applications, particularly in distributed scenarios such as distributed deep learning, frequently encounter incast. While single-path transport protocols are widely deployed in datacenter networks, the available bandwidth is often severely underutilized. MP-RDMA, as a state-of-the-art multi-path transport protocol, has been implemented; however, it faces performance issues during large-scale incast communications. MP-RDMA employs insufficient congestion feedback and fixed rate adjustment for congestion management, making the scheme hardly scalable. In this paper, we introduce MPTD, an RTT-based multi-path transport protocol. MPTD encompasses two primary aspects: (1) a novel congestion control algorithm based on the setting of target delay; (2) an optimized packet scheduling mechanism. Specifically, the receiver dynamically updates the target delay in real-time based on network conditions, and the sender adjusts the sending rate accordingly. A categorized implementation for paths is added to achieve a more accurate identification of path characteristics. Our evaluation demonstrates that, in comparison to MP-RDMA, MPTD can achieve speedups of 1.35x/1.40x in flow completion time for two traffic patterns. [ABSTRACT FROM AUTHOR]