Abstract: This article presents the large-scale Integrated Scheduling Problem of TTC and DDT with Idle Degree Requirements (IS-TTC&DDT-IDR), which involves efficiently allocating antenna resources and scheduling tasks for tracking, telemetry, and command (TTC) as well as digital data transmission (DDT) in satellite ground stations. The problem aims to optimize task completion while managing idle resource capacity. To tackle this challenge, a Multi-Stages Local Search (MSLS) algorithm is proposed. The MSLS algorithm is designed based on the problem’s unique characteristics and is structured in three stages: the first stage uses a Forcibly Insertion Procedure (FIP) to generate a high-quality initial solution for DDT tasks, the second stage also uses the Forced Insertion Procedure (FIP) to optimize the TTC task, and the third stage enhances idle capacity through an Exchanging Procedure (EP). To design the experiments, this paper firstly extends task scale in quasi-real scenarios to ten-thousands level within a multi-satellite system, while current studies conduct their experiments in maximum 1600 tasks. Extensive empirical results based on such scenarios demonstrate that the MSLS algorithm outperforms reference algorithms on optimization value, stability, and convergence.

Abstract: Performance analysis, utilizing video technology and recent technological advancements in soccer stadiums, provides a wealth of data, including player trajectories and real-time game statistics, which are crucial for tactical evaluation and decision-making by coaches and players. This data allows for the definition of metrics that not only enriches the experience for soccer fans through enhanced visual displays, but also empowers coaching staff and managers to make informed, real-time decisions that directly impact match outcomes. Ultimately, it serves as a pivotal tool for improving team strategy based on comprehensive post-match data analysis. In this article, we present a mathematical model to study the concept of pressure between players and, subsequently, between teams. We first explore the concept in a fixed frame of a match, determining what we call {\sl influence areas} between players. We introduce the unit pressure function and analyze the total number of pressure interactions. Then, we apply these concepts to football matches, considering various factors such as players and the radius of the area of influence, examining pressure efficiency through mean unitary pressure. Lastly, a real case study is presented, showcasing visualizations like a heatmap matrix displaying individual and collective pressure, as well as team pressure balance.

Abstract: With the rapid digitization of healthcare, the secure transmission of medical images has become a critical concern, especially given the increasing prevalence of cyber threats and data privacy breaches. Medical images are frequently transmitted via the Internet and cloud platforms, making them susceptible to unauthorized access, tampering, and theft. While traditional cryptographic techniques play a vital role, they are often insufficient to fully ensure the integrity and confidentiality of these sensitive images. In this paper, we present AGFI-GAN, a robust and secure framework for medical image watermarking that leverages attention-guided and feature integration mechanisms within a Generative Adversarial Network (GAN). Specifically, a Feature Integration Module (FIM) is proposed to effectively capture and combine both shallow and deep image features to facilitate multi-layer fusion with the watermark. The dense connections within the module facilitate feature reuse, boosting the system’s robustness. To mitigate distortion from watermark embedding, an Attention Module (AM) is utilized, generating an attention mask by extracting global image features. This attention mask prioritizes features in less prominent and textured regions, allowing for stronger watermark embedding, while other features are downplayed to enhance the overall effectiveness of the watermarking process. The framework is evaluated based on its versatility, embedding capacity, robustness, and imperceptibility, and the results confirm its effectiveness. The study shows a marked improvement over the baseline, thus highlighting the framework’s superiority.

Abstract: This paper shows that the accuracy in T-spline based isogeometric analysis may be substantially improved by increasing the multiplicity of the inner knots up to the polynomial degree. This task can be performed considering the Bézier extraction operator matrix elementwise, and thus easily receiving an increased number of updated control points in the geometrical and computational model. Nevertheless, after the determination of the unique control points, sometimes the Bézier elements near the T-junctions may not be well-shaped, and thus minor automatic interventions are required to ensure full (i.e., C0 and G0) compatibility. The improved IGA solution may be also used as a reference to determine the a-posteriori error estimations in the T-spline elements of the domain. The methodology is shown in BVPs dominated by Laplace-Poisson equations in rectangular and curvilinear domains, while eigenvalues were extracted in a rectangular acoustic cavity.

Abstract: As Unmanned Aerial Vehicles (UAVs) are becoming crucial in modern warfare, research on autonomous path planning is becoming increasingly important. The conflicting nature of the optimization objectives characterizes path planning as a multi-objective optimization problem. Current research has predominantly focused on developing new optimization algorithms. Although being able to find the mathematical optimum is important, one also needs to ensure this optimum aligns with the decision-maker's (DM's) most preferred solution (MPS). In particular, to align these, one needs to handle the DM's preferences on the relative importance of each optimization objective. This paper provides a comprehensive overview of all preference handling techniques employed in the military UAV path planning literature over the last two decades. It shows that most of the literature handles preferences by the overly simplistic method of scalarization via weighted sum. Additionally, the current literature neglects to evaluate the performance (e.g. cognitive validity and modeling accuracy) of the chosen preference handling technique. To aid future researchers handle preferences, we discuss each employed preference handling technique, their implications, advantages, and disadvantages in detail. Finally, we identify several directions for future research, mainly related to aligning the mathematical optimum to the MPS.

Abstract: This essay presents Programmed Conceptual Deconstruction (PCD), an algorithm developed to analyze the foundations of logic, along with the introduction of the concept of Conceptual Quarks (CQs), which identify the basic units of knowledge. Using these tools and experimenting with a custom-built AI model, it is demonstrated that fundamental logical principles, such as the principle of identity or the principle of non-contradiction, are not absolute truths, but constructions that arise from the specific properties of our reality. This work is based on tools and concepts created by the author and combines philosophy with technology precisely to reevaluate traditional perspectives of logic.

Abstract: The integration of multimodal data in retrieval applications, such as text with accompanying images on platforms like Wikipedia, has emerged as a critical area of research. The challenge lies in effectively representing such multimodal data for efficient retrieval tasks. Traditional deep multimodal learning methods generally involve a two-step process: (1) independent extraction of intermediate features for each modality through separate deep models, and (2) subsequent fusion of these intermediate features into a unified representation. However, these approaches are limited by the lack of mutual awareness among the intermediate features during their extraction, which prevents full utilization of inter-modal information. In this work, we introduce a novel Adaptive Kernel-Attention Framework (AKAF) designed to address these limitations. The AKAF framework incorporates a dynamic modal-aware operation as a core building block to capture complex inter-modal dependencies during the intermediate feature learning stage. This operation is composed of a kernel network to model non-linear inter-modal relationships and an attention network to focus on salient regions within the data, optimizing the representations for binary hash code generation. By introducing mutual awareness across modalities at an early stage, our framework significantly enhances the joint representation quality. Through extensive experiments conducted on three benchmark datasets, we demonstrate that AKAF achieves substantial improvements in retrieval performance compared to state-of-the-art methods. Our results underscore the potential of modal-aware learning in advancing multimodal retrieval systems.

Abstract: We give a short proof of the well-known Knuth's old sum and provide some generalizations. Our approach utilizes the binomial theorem and integration formulas derived using the Beta function. Several new polynomial identities and combinatorial identities are derived.

Abstract:

Gender identification of authors in literary texts is a compelling area of research within computational linguistics and natural language processing. Analyzing the gender of authors can uncover biases and socio-cultural dynamics of the past, deepening our understanding of historical texts. Inspired by the historical context where women often used male pseudonyms to navigate the literary world, this study seeks to determine an author's gender, relying on their written works using various classifiers, including language models. Our contributions include compiling a large-scale dataset of literary texts and conducting extensive experiments with different classification models. Our results show that the best-performing model, GPT2, achieved an impressive accuracy of 0.925.

Abstract: This paper introduces a novel method for Semi-Supervised Few-Shot Class Incremental Learning (SSFSCIL) that exhibits virtually no catastrophic forgetting. The method uses a generic feature extractor that was pretrained without supervision on a large image dataset, and a classifier based on a Probabilistic PCA (PPCA) model for each class instead of the standard fully connected layer usually employed as the projection head. The PPCA models are localized around the class means and the models for existing classes are not retrained when new classes are added. The learning algorithm is a modified k-Means that freezes the models on the existing classes and only updates models for the new classes. This makes the approach both computationally efficient and accurate. Extensive experiments on CUB200, CIFAR100, and miniImageNet show the effectiveness of the proposed approach. Additionally, experiments on the ImageNet-1k dataset, which previous methods have avoided due to its size, demonstrate its applicability to large-scale datasets.