Treap is a balanced search tree that relies on a random distribution of keys to keep the tree balanced. This paper introduces a novel data structure that expands the concept of binary treap to N-ary trees. We outline the structural characteristics, including the parent-child relationship, and discuss the relative arrangement of elements with different priorities within this tree data structure. Furthermore, we analyze algorithms for performing operations such as searching, inserting, and deleting. Additionally, to evaluate the runtime of the operations mentioned above, we implemented the proposed data structure in C programming language and conducted several experiments. The proposed data structure achieves insertion and deletion operations in expected logarithmic time in average case. Furthermore, the suggested data structure is much simpler to implement than other n-ary trees like B-Trees [2]. This paper makes a valuable contribution to the field by offering a new approach to N-ary trees, which may be beneficial in the development of storage engines.

This paper considers the problem of tracking a timevarying number of non-cooperative emitters based on singlechannel passive sensors, which is traditionally carried out via sequential processing, i.e., detection, measurement extraction and tracking. However, in such a framework, tracking performance is severely affected by the pre-processing steps especially in low signal-to-noise ratio (SNR) scenarios. To remedy this drawback, in this paper we develop an algorithm that directly performs tracking based on the received signals. The states of multiple emitters are represented by a labeled random finite set (LRFS), which is further modeled by a generalized labeled multi-Bernoulli (GLMB) density. A new likelihood model, which inherently exploits time difference of arrival (TDOA) of signals coming from different sensors, is proposed. Then, a new GLMB filter is derived to iteratively compute the posterior of multiple emitters. Moreover, an efficient implementation strategy is devised for the proposed algorithm and its performance is assessed via simulations.

The proliferation of tobacco-related content on social media platforms poses significant challenges for public health monitoring and intervention. This paper introduces a novel multi-modal deep learning framework named Flow-Attention Adaptive Semantic Hierarchical Fusion (FLAASH) designed to analyze tobacco-related video content comprehensively. FLAASH addresses the complexities of integrating visual and textual information in short-form videos by leveraging a hierarchical fusion mechanism inspired by flow network theory. Our approach incorporates three key innovations, including a flow-attention mechanism that captures nuanced interactions between visual and textual modalities, an adaptive weighting scheme that balances the contribution of different hierarchical levels, and a gating mechanism that selectively emphasizes relevant features. This multi-faceted approach enables FLAASH to effectively process and analyze diverse tobacco-related content, from product showcases to usage scenarios. We evaluate FLAASH on the Multimodal Tobacco Content Analysis Dataset (MTCAD), a large-scale collection of tobacco-related videos from popular social media platforms. Our results demonstrate significant improvements over existing methods, outperforming state-of-the-art approaches in classification accuracy, F1 score, and temporal consistency. The proposed method also shows strong generalization capabilities when tested on standard video question-answering datasets, surpassing current models. This work contributes to the intersection of public health and artificial intelligence, offering an effective tool for analyzing tobacco promotion in digital media.

Magneto-mechanical transmitters (MMTs) are of active interest for ultra-low-frequency (ULF) communication, as they can significantly reduce the power consumption and size requirements compared to conventional ULF antennas. Typical MMTs produce ULF signals through either oscillatory or continuous rotational motion of permanent magnets. In this paper, we present an adaptive driving circuit that uses a negative impedance converter (NIC) to autonomously sustain motion for both resonant and continuous rotation MMTs without the need for sensors or active tuning. The driving circuit adapts seamlessly to a wide range of resonant frequencies and rotational speeds, ensuring consistent performance across different MMT configurations without altering the system's inertia or stiffness. Additionally, it features a saturable gain mechanism that regulates the oscillation amplitude and rotational speed using DC voltage control, which can be used for amplitude and frequency modulation.

An efficient Least Significant Bits (LSB) removal (scaling by a power-of-2) solution in the Residue Number System (RNS) with 2's power residue |𝒙| 𝟐 𝒎 is proposed, which includes flooring, rounding, and ceiling. This LSB removal solution is similar to that in regular binary system, and even with lower latency. This LSB removal solution does not need sign detection and even/odd detection. It also does not alter the base, or the moduli set of the RNS. The only requirement for this solution is no even modulus in the moduli set of the RNS. This paper also describes three alternative algorithms to produce the 2's power residue |𝒙| 𝟐 𝒎. Alternative-3 is the best of three, with which all the operations associated with the 2's power residue |𝒙| 𝟐 𝒎 are efficient 2's power modulo operations.

There is growing tendency toward the application of high frequency electromagnetic waves in in areas such as wireless telecommunications and military technology. High frequency electromagnetic waves like microwave, millimeter wave and terahertz penetrate to the surface layer of a medium depending on the frequency and the conductivity. Moreover, the most important types of interaction between high frequency electromagnetic waves and the surrounding medium - when the medium is represented by a biological tissue model - are predominantly in the form of tissue heating. The objective of this study is to analyze the influence of exposure to electromagnetic fields in the frequency range of 1 MHz – 100 GHz on a biological model coated with soil samples. The biological model consists of three biological tissue: skin, fat and muscle. Further, four soil samples are used as a coating material based on their textual classifications which are silty clay, silty loam, sandy loam and loam. Results show that the different soil samples can reduce the amount of absorbed radiation, and heating in the skin tissue thus proved to be a good shielding material against high frequency electromagnetic waves, particularly for frequencies greater than 10 GHz.

This research note reports the surprisingly accurate approximate exponential regimes observed in the time evolution of Susceptible-Infected-Recovered (SIR) epidemiological models. Focusing on the classic SIR model, we demonstrate this phenomenon graphically and through asymptotic analysis. This seemingly overlooked phenomenon enables deriving approximate closed-form solutions to otherwise intractable estimation and prediction problems in SIR modelling. Building on this key observation, we speculate on the presence of approximate exponential evolution regimes in other more complex SIR model variants, with the potential to simplify analysis and implementation across disciplines where compartmental models are utilised.

A document by Takuma Yoshimura . Click on the document to view its contents.

This study explores the application of machine learning in financial fraud detection and compares the performance of four models: logistic regression, support vector machine, random forest, and XGBoost. By analyzing and processing transaction data from US financial institutions, it is found that the XGBoost model performs well in processing unbalanced data sets and can maintain high precision at high recall, thereby effectively identifying fraudulent behavior and reducing false positives. Experimental results show that the XGBoost model outperforms other models in all indicators, especially in accuracy, precision, recall, and F1 value. Feature importance analysis verifies its sensitivity to key features and helps optimize feature engineering. Through the analysis of the precision-recall curve, the performance of the XGBoost model is more stable at different thresholds, which is of great significance for practical applications in financial fraud detection. The study emphasizes the key role of accurate and fast fraud detection systems in reducing losses in financial institutions and improving customer satisfaction and trust. The results of this study provide effective technical means for financial institutions and promote technological progress and innovation in the entire financial industry in preventing financial fraud.

Large language models (LLMs) have seen rapid advancements in their ability to integrate and generate knowledge from various sources. However, the challenge of efficiently combining information across different modalities-such as text, images, and audio-persists. In this work, we introduce the Iterative Retrieval Augmentation for Multi-Modal Knowledge Integration and Generation (IRAMIG) framework, designed to enhance the integration of multi-modal knowledge. By employing an iterative retrieval mechanism, IRAMIG allows for the dynamic fetching of relevant data informed by context from previous queries. The architecture comprises modality-specific retrieval modules and a fusion component that synthesizes knowledge, ultimately enriching the model's understanding and output generation. Experiments conducted across image captioning and multi-modal question answering tasks reveal significant performance improvements when compared to existing methods, showcasing IRAMIG's potential to refine knowledge integration and enhance the quality of generation in complex multi-modal scenarios.

Understanding the differences and advantages of multi-agent systems (MAS) over single-agent systems is crucial for leveraging their full potential. This paper explores these distinctions, addressing how MAS overcomes the limitations of single-agent systems and highlighting their functional and application-specific benefits.

In recent years, the rapid growth of research on Large Language Models (LLMs) has led to a surge in survey papers, making it difficult for beginners to take in all the latest advancements. This report aims to overcome this challenge by analyzing a collection of LLM survey papers to look at key trends and provide useful insights into the field. We aggregate a dataset containing titles, summaries, categories, and release dates of LLM survey papers, then preprocess the data and apply a Random Forest Model to predict the categories of surveys based on their textual content. Our evaluation demonstrates the use of Random Forest Classifiers, achieving a precision of 0.70 but an accuracy of 0.38, highlighting room for improvement. Overall, this work contributes to making LLM research more accessible by offering a structured exploration of trends in survey publications and laying the foundation for automatic categorization of future research surveys.

Gut microbiome research has made tremendous progress, especially with the integration of machine learning and artificial intelligence that can provide new insights from complex microbiome data and its impact on human health. The use of explainable artificial intelligence is becoming critical in medicine, and adopting it in gut microbiome prediction models is appealing for providing more transparent and trustworthy models in clinical research. This scoping review evaluates the use of machine learning and explainable artificial intelligence techniques and identifies existing gaps in knowledge in this research area to suggest future research directions. A systematic search was conducted in PubMed and Scopus to include 59 articles published between 2018 and 2023. Different clinical applications of machine learning and artificial intelligence techniques in gut microbiome studies were applied in the reviewed articles, with Random Forest as the most popular and the most used algorithm, and a large prevalence of black box non explainable models. Finally, not enough attention was paid to the reproducibility of the research work published. This review presents the opportunities that remain to advance research regarding the explainability of artificial intelligence models in the field of microbiome, supporting and accelerating the application of microbiomebased precision medicine in the future.

This paper presents an innovative framework for enhancing vehicular multimedia communication through adaptive computing offloading and dynamic radio access control, targeting the unique challenges of vehicular environments such as high mobility and variable network conditions. Our approach optimizes multimedia content delivery by intelligently managing computing resources and network bandwidth, focusing on maintaining high quality of service (QoS). The system uses advanced algorithms to make real-time decisions for offloading computational tasks and prioritizing multimedia traffic, ensuring efficient resource utilization and improved communication reliability. We validate our framework with simulations that demonstrate significant performance benefits over existing methods, offering potential enhancements in traffic safety and passenger multimedia experience.

This paper introduces an integrative multi-robot system for advanced manufacturing, presenting an innovative approach that addresses contemporary challenges in the manufacturing landscape. By seamlessly integrating additive and subtractive manufacturing processes, namely wire-based laser deposition welding and robotic milling, within a highly automated cell, the system offers a novel solution with the potential to advance industrial production. Delving into the system's architecture, the paper explores the intricate details, emphasizing key characteristics such as functional integration, joint programming, and digital twin. Specific functionalities are elucidated to provide a clearer understanding of the system's capabilities. Research perspectives outlined in the paper shed light on potential advancements in simultaneous additive and subtractive operations, providing insights into the system's capabilities. Additionally, a comprehensive analysis of the potential and applications of the OPTIMA system is presented, highlighting its versatility in aerospace applications, the automotive industry, and customized products. This paper serves as a detailed guide to the integrative multi-robot system, offering in-depth insights into its design, functionalities, and specific contributions to the field of advanced manufacturing. Exploring both research perspectives and practical applications contributes to a holistic understanding of the system's significance in shaping the future of industrial production.

This study aims to categorize the following dogs' primary emotional states: anger, fear, and happiness. The research team utilized Transfer Learning (TL) and advanced 2D/3D Convolutional Neural Networks (CNNs) to identify these emotions from two small datasets-static images and video clips. The image dataset covers various canine emotional expressions across various contexts, forming the basis for the training of Dog Emotion Recognition-Transfer Learning Neural Network (DER-TLNN) algorithm. DER-TLNN has an accuracy rate of 70% for emotion classification based on static images (400 images). In addition, DER-TLNN is the basis for the Video Dog Emotion Recognition Network (VDERNet) algorithm, which is designed to categorize video clips depicting dogs' emotional states. Trained on video data sourced online (39 videos), VDERNet achieves an accuracy of 96%, highlighting AI's potential in understanding canine emotions in dynamic contexts with non-invasive techniques. These findings demonstrate the efficacy of TL and 3D CNNs in animal behavior analysis and contribute to a better understanding of canine emotions. The research provides promising applications in improving human-dog interactions and enhancing canine welfare, showcasing the immense potential of AI in animal behavior studies. The achievement of notable results with small datasets, along with data augmentation and regularization techniques, underscores our innovative approach and the potential impact of AI in animal behavior studies.

Terahertz (THz) communications is an emerging technology for sixth generation (6G) wireless communication systems and beyond with many challenges. Reaching Terabits per second (Tpbs) channel capacity in THz-band is physically crucial to the number of antennas at the base station (BS) and reconfigurable intelligent surface (RIS) elements, and requires careful innovative planning to manipulate electromagnetic (EM) waves. Throughout this letter, we propose joint beamforming (BF) and reflecting optimization for maximizing downlink (DL) data rate in THz-band communications equipped with RIS-aided orthogonal frequency division multiple access (OFDMA) and ultra-massive multiple-input multiple-output (UM-MIMO) system. Based on UM-MIMO channel model we derive, a non-convex problem is formulated in order to maximize the channel DL data rate. Then, we propose an alternating iterative algorithm to jointly optimize the BS BF weights, and RIS phase shift coefficients. In particular, the BS BF is solved by the gradient descent (GD) algorithm, while the RIS phase shift coefficients are solved by successive convex approximation (SCA) algorithm. Monte-Carlo simulation results are carried out to verify the efficiency and performance of the proposed framework and reveal that the DL channel rate is significantly can be maximized.

Accurate estimation of AC losses is crucial to successfully implement high-temperature superconductors (HTS) in the armature of electric machines. In this estimation, a purely sinusoidal current is often assumed. However, the connection to power electronic converters will introduce harmonics in the transport current, generally leading to higher losses. In this paper, four separate distorted current waveforms produced by space vector modulation were implemented to assess how harmonics from power electronics influence the HTS AC losses in a 2.5 MW superconducting aviation motor. By varying the switching frequency between 5 and 40 kHz, the amount of harmonic distortion was varied between the four cases. The ReBCO and copper layers were both modeled using a multilayer H-A-formulated model. While the harmonic distortion was found to have a minor impact on ReBCO layer hysteresis losses, it increased eddy current losses in the copper stabilizer by between 250 % and 471 %, depending on the amount of distortion. Consequently, the total losses increased by between 60 % and 123 %, with a higher copper loss fraction.