Score prediction is crucial in realistic image sharpness assessment after informative features are collected. Recently, Kolmogorov-Arnold networks (KANs) have been developed and witnessed remarkable success in data fitting. This study presents Taylor series based KAN (TaylorKAN). Then, different KANs are explored on four realistic image databases (BID2011, CID2013, CLIVE, and KonIQ-10k) for score prediction by using 15 mid-level features and 2048 high-level features. When setting support vector regression as the baseline, experimental results indicate KANs are generally better or competitive, TaylorKAN is the best on three databases using mid-level feature input, while KANs are inferior on CLIVE when high-level features are used. This is the first study that explores KANs for image quality assessment. It sheds lights on how to select and improve KANs on related tasks.

This paper investigates the task of tracking multiple objects in clutter under a distributed multi-sensor network with time-varying connectivity. Designed with the same objective as the centralised variational multi-object tracker, the proposed method achieves optimal decentralised fusion in performance with local processing and communication with only neighboring sensors. A key innovation is the decentralised construction of a locally maximised evidence lower bound, which greatly reduces the information required for communication. Our decentralised natural gradient descent variational multi-object tracker, enhanced with the gradient tracking strategy and natural gradients that adjusts the direction of traditional gradients to the steepest, shows rapid convergence. Our results verify that the proposed method is empirically equivalent to the centralised fusion in tracking accuracy, surpasses suboptimal fusion techniques with comparable costs, and achieves much lower communication overhead than the consensus-based variational multi-object tracker.

Performance testing aims at uncovering efficiency issues of software systems. In order to be both effective and practical, the design of a performance test must achieve a reasonable trade-off between result quality and testing time. This becomes particularly challenging in Java context, where the software undergoes a warm-up phase of execution, due to just-in-time compilation. During this phase, performance measurements are subject to severe fluctuations, which may adversely affect quality of performance test results. However, these approaches often provide suboptimal estimates of the warm-up phase, resulting in either insufficient or excessive warm-up iterations, which may degrade result quality or increase testing time. There is still a lack of consensus on how to properly address this problem. Here, we propose and study an AI-based framework to dynamically halt warm-up iterations at runtime. Specifically, our framework leverages recent advances in AI for Time Series Classification (TSC) to predict the end of the warm-up phase during test execution. We conduct experiments by training three different TSC models on half a million of measurement segments obtained from JMH microbenchmark executions. We find that our framework significantly improves the accuracy of the warm-up estimates provided by state-of-practice and state-of-the-art methods. This higher estimation accuracy results in a net improvement in either result quality or testing time for up to +35.3% of the microbenchmarks. Our study highlights that integrating AI to dynamically estimate the end of the warm-up phase can enhance the cost-effectiveness of Java performance testing.

In this paper, we propose an algorithm for registering sequential bounding boxes with point cloud streams. Unlike popular point cloud registration techniques, the alignment of the point cloud and the bounding box can rely on the properties of the bounding box, such as size, shape, and temporal information, which provides substantial support and performance gains. Motivated by this, we propose a new approach to tackle this problem. Specifically, we model the registration process through an overall objective function that includes the final goal and all constraints. We then optimize the function using gradient descent. Our experiments show that the proposed method performs remarkably well with a 40\% improvement in IoU and demonstrates more robust registration between point cloud streams and sequential bounding boxes

Annotation errors are a challenge not only during training of machine learning models, but also during their evaluation. Label variations and inaccuracies in datasets often manifest as contradictory examples that deviate from established labeling conventions. Such inconsistencies, when significant, prevent models from achieving optimal performance on metrics such as mean Average Precision (mAP). We introduce the notion of "label convergence" to describe the highest achievable performance under the constraint of contradictory test annotations, essentially defining an upper bound on model accuracy. Recognizing that noise is an inherent characteristic of all data, our study analyzes five real-world datasets, including the LVIS dataset, to investigate the phenomenon of label convergence. We approximate that label convergence is between 62.63-67.52 mAP@[0.5:0.95:0.05] for LVIS with 95% confidence, attributing these bounds to the presence of real annotation errors. With current state-of-the-art (SOTA) models at the upper end of the label convergence interval for the well-studied LVIS dataset, we conclude that model capacity is sufficient to solve current object detection problems. Therefore, future efforts should focus on three key aspects: (1) updating the problem specification and adjusting evaluation practices to account for unavoidable label noise, (2) creating cleaner data, especially test data, and (3) including multi-annotated data to investigate annotation variation and make these issues visible from the outset.

To address unfairness issues in federated learning (FL), contemporary approaches typically use frequent model parameter updates and transmissions between the clients and server. In such a process, client-specific information (e.g., local dataset size or data-related fairness metrics) must be sent to the server to compute, e.g., aggregation weights. All of this results in high transmission costs and the potential leakage of client information. As an alternative, we propose a strategy that promotes fair predictions across clients without the need to pass information between the clients and server iteratively and prevents client data leakage. For each client, we first use their local dataset to obtain a synthetic dataset by solving a bilevel optimization problem that addresses unfairness concerns during the learning process. We then pass each client's synthetic dataset to the server, the collection of which is used to train the server model using conventional machine learning techniques (that do not take fairness metrics into account). Thus, we eliminate the need to handle fairness-specific aggregation weights while preserving client privacy. Our approach requires only a single communication between the clients and the server, thus making it computationally cost-effective, able to maintain privacy, and able to ensuring fairness. We present empirical evidence to demonstrate the advantages of our approach. The results illustrate that our method effectively uses synthetic data as a means to mitigate unfairness and preserve client privacy.

This paper introduces a new framework for clustering in a distributed network called Distributed Clustering based on Distributional Kernel (K) or KDC that produces the final clusters based on the similarity with respect to the distributions of initial clusters, as measured by K. It is the only framework that satisfies all three of the following properties. First, KDC guarantees that the combined clustering outcome from all sites is equivalent to the clustering outcome of its centralized counterpart from the combined dataset from all sites. Second, the maximum runtime cost of any site in distributed mode is smaller than the runtime cost in centralized mode. Third, it is designed to discover clusters of arbitrary shapes, sizes and densities. To the best of our knowledge, this is the first distributed clustering framework that employs a distributional kernel. The distribution-based clustering leads directly to significantly better clustering outcomes than existing methods of distributed clustering. In addition, we introduce a new clustering algorithm called Kernel Bounded Cluster Cores, which is the best clustering algorithm applied to KDC among existing clustering algorithms. We also show that KDC is a generic framework that enables a quadratic time clustering algorithm to deal with large datasets that would otherwise be impossible.

The Schrödinger bridge problem seeks a process, the Schrödinger bridge, with prescribed initial and terminal distributions, such that the distribution of the Schrödinger bridge minimizes the Kullback-Leibler (KL) divergence to the distribution of a reference process. Schrödinger bridges play a central role in measure transport theory (Marzouk et al., 2016). Notably, it is known that the initial-terminal distribution of a Schrödinger bridge provides a solution to a corresponding entropic optimal transport problem (Peyré & Cuturi, 2020). Schrödinger bridges thus provide an effective framework for finding an alignment between samples from two target distributions. Furthermore, diffusion-based generative models (Ho et al., 2020; Song et al., 2021) can be interpreted as solving trivial instances of the Schrödinger bridge problem (Peluchetti, 2023). Consequently, Schrödinger bridges offer a more general approach to contemporary generative applications. We consider the setting where samples are readily available from both target distributions, and where the reference process is a diffusion process solution to a stochastic differential equation (SDE).

Conventional WENO3 methods are known to be highly dissipative at lower resolutions, introducing significant errors in the pre-asymptotic regime. In this paper, we employ a rational neural network to accurately estimate the local smoothness of the solution, dynamically adapting the stencil weights based on local solution features. As rational neural networks can represent fast transitions between smooth and sharp regimes, this approach achieves a granular reconstruction with significantly reduced dissipation, improving the accuracy of the simulation. The network is trained offline on a carefully chosen dataset of analytical functions, bypassing the need for differentiable solvers. We also propose a robust model selection criterion based on estimates of the interpolation's convergence order on a set of test functions, which correlates better with the model performance in downstream tasks. We demonstrate the effectiveness of our approach on several one-, two-, and three-dimensional fluid flow problems: our scheme generalizes across grid resolutions while handling smooth and discontinuous solutions. In most cases, our rational network-based scheme achieves higher accuracy than conventional WENO3 with the same stencil size, and in a few of them, it achieves accuracy comparable to WENO5, which uses a larger stencil.

In this paper, we address the problem of reducing the computational burden of Model Predictive Control (MPC) for real-time robotic applications. We propose TransformerMPC, a method that enhances the computational efficiency of MPC algorithms by leveraging the attention mechanism in transformers for both online constraint removal and better warm start initialization. Specifically, TransformerMPC accelerates the computation of optimal control inputs by selecting only the active constraints to be included in the MPC problem, while simultaneously providing a warm start to the optimization process. This approach ensures that the original constraints are satisfied at optimality. TransformerMPC is designed to be seamlessly integrated with any MPC solver, irrespective of its implementation. To guarantee constraint satisfaction after removing inactive constraints, we perform an offline verification to ensure that the optimal control inputs generated by the MPC solver meet all constraints. The effectiveness of TransformerMPC is demonstrated through extensive numerical simulations on complex robotic systems, achieving up to 35x improvement in runtime without any loss in performance.