Split Theorems for Join Reporting and Sampling

Neighbor discovery, one of the most fundamental bootstrapping networking primitives, is particularly challenging in decentralized wireless networks where devices have directional antennas. In this paper, we study the following fundamental problem, which we term oblivious neighbor discovery: How can neighbor nodes with heterogeneous antenna configurations discover each other within a bounded delay in a fully decentralised manner without any prior coordination or synchronisation? We establish a theoretical framework on the oblivious neighbor discovery and the performance bound of any neighbor discovery algorithm achieving oblivious discovery. Guided by the theoretical results, we then devise an oblivious neighbor discovery algorithm, which achieves guaranteed oblivious discovery with order-minimal worst case discovery delay in the asynchronous and heterogeneous environment. We further demonstrate how our algorithm can be configured to achieve a desired tradeoff between average and worst case performance.

An iterative approach for achieving consensus when ranking a finite set of alternatives by a group of experts

Multispectral remote sensing images (MRSI) contain rich information about geographical objects and phenomena, such as land use and land cover. To extract such information, classification is normally carried out to yield land use and land cover maps (LULCM). A lot of techniques have been developed for classification, yet such a fundamental problem has not been solved as mathematical models for predicting the upper and lower limits of land cover classification accuracy with a given MRSI. This study aims to tackle this key problem by considering classification as an explicit information transfer process from images to maps and then build a mathematical model (Boltzmann-entropy-based) for the process with Shannon’s information theory and Crooks’ Thermodynamic Fluctuation as theoretical foundation. The model is designed to predict both upper and lower limits of classification accuracy instead of a definite value and is expressed in terms of Boltzmann entropies of MRSI and LULCM, total number of classes, and two basic parameters defined by prior knowledge. Verification experiments are carried out with 1091 images and three well-established classifiers (support vector machine, random forests, and K-nearest neighbors). The results demonstrate that (i) the values of information in MRSI and LULCM are strongly correlated, and (ii) the Boltzmann-entropy-based model can predict both upper and lower limits of classification accuracy. This study provides a novel perspective for understanding land cover classification and opens the door for the establishment of new theories in remote sensing.

Texture analysis is a fundamental problem in image processing and computer vision, providing a means of characterizing surfaces and spatial patterns through the statistical, structural, and spectral behavior of pixel intensities [1][2][5].It plays a vital role in applications such as medical diagnosis, industrial inspection, remote sensing, document processing, and content-based image retrieval [2][3][4].Over the past three decades, diverse techniques have been proposed and are commonly grouped into statistical, structural, model-based, and transform-domain categories [1][2].This paper reviews these approaches, emphasizing their theoretical foundations, computational properties, and practical limitations.A comparative analysis summarizes their strengths and weaknesses, followed by a discussion of emerging hybrid and deep-learning-based strategies that suggest promising future directions [4].

Solving nonlinear equations is a fundamental problem across science and engineering. Many real-world problems – from weather forecasting to satellite orbit determination – boil down to finding roots of nonlinear equations​. In most practical cases these equations cannot be solved analytically, so iterative numerical methods are employed to obtain approximate solutions​. The motivation for this research is the widespread importance of efficient and reliable solvers for nonlinear equations in diverse fields (physics, biology, finance, engineering, etc.). Effective root-finding algorithms enable modeling and simulation of complex systems where closed-form solutions are impossible. This study aims to analyze and optimize iterative methods for nonlinear equations. We focus on classical methods (like Newton-Raphson, Secant, and bisection) as well as modern improvements, examining their convergence, stability, and performance. Key objectives include: (1) reviewing existing iterative algorithms and their theoretical convergence properties, (2) developing and discussing strategies to accelerate or stabilize these methods, and (3) implementing the algorithms in Python to compare performance on representative nonlinear problems. In particular, we ask: Which iterative methods converge fastest for a given problem, and how can their efficiency or robustness be improved? We also explore how recent techniques (e.g. adaptive step-sizing and AI-based enhancements) can address the limitations of classical approaches. The structure of our research splits into a theoretical foundation (Sections 1–4) followed by practical experimentation (Sections 5–8). (Ahmed & Khan, 2011)

Properties of various semiconductors

A comparison of techniques for finding components with simple structure

Major reform efforts in the field of teacher education have occurred during the past decade. This article provides a description of one institution's response to reform efforts within the context of a post‐baccalaureate second language teacher education program, which combines the preparation of foreign language and English as a second language (ESL) teachers. The description begins with a brief overview of the theoretical and philosophical foundations that guide practice within the program. These foundations include a brief analysis of what the authors perceive as fundamental problems in second language education as well as their beliefs that teachers and students both act as knowers and learners in an active, experiential, and integrative process; that teaching is context sensitive; and that reflection is a cornerstone in teacher development. The program description details how the various components–underlying themes, coursework, experiences, and demonstrations of growth–interact to form an integrated whole. The article also highlights challenges that face second language teacher educators and invites colleagues in the profession to continue dialogue on the issues.

The article investigates the reasons for the need for improvement and the factors of the development of the accounting system in the conditions of the formation of the knowledge-based economy (new economic conditions for the functioning of enterprises; new normative requirements for enterprise reporting; society's requirements for the operation of enterprises on the basis of sustainable development). The views of researchers regarding the need to develop the accounting system in new economic conditions, characterized by the priority of innovation and intellectual capital in the activities of enterprises, have been analyzed. The requirements of the management report in terms of research and development of enterprises have been disclosed. The modern requirements for disclosing information about innovative activities of enterprises in reporting aimed at ensuring sustainable development have been revealed. The accounting system's problems related to the enterprise's innovative activity in the conditions of the knowledge-based economy, which need to be solved by scientists (fundamental problem, problems of a theoretical, methodological, and organizational nature), have been identified and characterized. The necessity of developing the theoretical and methodological foundations of the accounting of innovative capital as a comprehensive means of solving the identified problems has been substantiated.

During the last forty years ethyl carbamate, or urethane, has been the subject of many toxicology studies. Particular importance has been attached to this chemical because of widespread human exposure, experimental animal data indicating carcinogenic and teratogenic properties and an alluringly simple chemical structure. The simple chemical structure suggests an increased possibility of unveiling mechanistic pathways leading to its pathology. Consequently, a variety of studies have been concerned with ethyl carbamate’s potential for interacting with DNA. In fact, ethyl carbamate became one of the earliest examples of a chemical mutagen when it was demonstrated to cause chromosome translocations in plants (1). However, the increasing appreciation of its carcinogenic capability has not been accompanied by very revealing patterns of mutagenic potential. While in vivo mammalian cytogenetic assays are clearly sensitive to this agent, urethane is negative in most short-term genetic tests for carcinogens. Thus, a fundamental problem is whether the relevant biological activity is due to ethyl carbamate itself or to a more potent host-generated metabolite. The enigmatic nature of ethyl carbamate has been alluded to in several excellent reviews (2–5). The present treatise provides an abbreviated perspective of ethyl carbamate, with updated genetic and, in particular, mammalian cytogenetic findings.

In recent years, the evidence suggesting that honey bees communicate with a "dance language" has been stronly attached on both theoretical and experimental grounds. An alternative theory has been proposed by which bees are supposed to use only odors to locate sources of food. A review of the evolution of the controversy isolates and analyzes the main issues. Early experiments which she fundamental problem in this important dispute has been that dancing bees advertise a food location with site-specific odoer information as well as symbolic distance and direction coordinates. A new technique has overcome this problem and demonstrated that von Frisch's dance language theory is, on the whole, correct. The apparently contradictory results of Wenner and his colleagues are shown to be due to their techniques for training bees. The dance-language controversy raises issues beyond how bees communicate. These include whether and when "evolutionary" arguments are useful, and to what extent Kuhn's scientific revolution paradign fits the dispute.

Modal approach to obtain the coupling loss factors between structural systems and the surrounding fluid

Chapter 15 - Geometric Shortest Paths and Network Optimization

Object tracking is a fundamental problem in the field of computer vision. The object tracking methods proposed so far can be divided into a ‘discriminative correlation filter’ and a ‘deep learning’ based methods with a complex structure and a lot of computation. In this paper, we propose an algorithm for tracking objects with a simple structure while maintaining tracking performance using a convolutional variational auto-encoder, external memory, and a Siamese network. As a result of an experiment with an RT (real-time) data set to measure real-time, the result was a precision of 0.546 and a success rate of 0.527.

OrderSketch: An Unbiased and Fast Sketch for Frequency Estimation of Data Streams