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Random Tessellations -- An Overview of Models

Random tessellations are a prominent class of models in stochastic geometry. In this chapter, we give an overview of mechanisms that have been used to formulate random tessellation models. First, the ...

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The article provides a comprehensive overview of random tessellations, integrating various existing models and methods, which is crucial for both theoretical understanding and practical applications in stochastic geometry. The inclusion of simulations and model fitting techniques enhances its applicability and methodological rigor.

Bringing Order Amidst Chaos: On the Role of Artificial Intelligence in Secure Software Engineering

Context. Developing secure and reliable software remains a key challenge in software engineering (SE). The ever-evolving technological landscape offers both opportunities and threats, creating a dynam...

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The article presents a comprehensive examination of the intersection between AI and Secure Software Engineering, a field facing increasing challenges due to evolving cybersecurity threats. The empirical methodologies utilized in the study, alongside the exploration of SASTTs and AI models, contribute to a nuanced understanding of vulnerability management. The focus on domain-specific differences further enhances the relevance of the findings, emphasizing the need for tailored approaches, thus marking the work as both novel and applicable for future developments in SSE.

Tree Models Machine Learning to Identify Liquid Metal based Alloy Superconductor

Superconductors, which are crucial for modern advanced technologies due to their zero-resistance properties, are limited by low Tc and the difficulty of accurate prediction. This article made the init...

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The article presents a novel application of machine learning to a critical issue in superconductor research—predicting critical temperatures of liquid metal alloy superconductors. Its use of the SuperCon dataset and the demonstrated performance of the Extra Trees model underline methodological rigor. The findings are highly applicable and could significantly accelerate materials discovery in superconductors.

Explainable AI based System for Supply Air Temperature Forecast

This paper explores the application of Explainable AI (XAI) techniques to improve the transparency and understanding of predictive models in control of automated supply air temperature (ASAT) of Air H...

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The article introduces a novel application of Explainable AI techniques specifically to the domain of predictive modeling for air temperature control, making it relevant in both AI and HVAC systems. Its emphasis on interpretability via Shapley values adds to its value for industries needing accountability and transparency in AI systems. However, further empirical validation and comparison with other predictive models could enhance its applicability.

A Key Conditional Quotient Filter for Nonlinear, non-Gaussian and non-Markovian System

This paper proposes a novel and efficient key conditional quotient filter (KCQF) for the estimation of state in the nonlinear system which can be either Gaussian or non-Gaussian, and either Markovian ...

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The article introduces a novel filtering technique (KCQF) that enhances state estimation in complex systems with greater efficiency by focusing on key measurement conditions. This represents significant methodological innovation within the field. Its rigor is supported by comparisons with existing models, indicating substantial improvements. The applicability to a wider range of systems (nonlinear, non-Gaussian, and non-Markovian) broadens its significance, making it relevant for future research and applications.

Modulation of nanowire emitter arrays using micro-LED technology

A scalable excitation platform for nanophotonic emitters using individually addressable micro-LED-on-CMOS arrays is demonstrated for the first time. Heterogeneous integration by transfer-printing of s...

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The article presents a novel approach to integrate nanophotonic emitters with micro-LED technology, showcasing methodological rigor and the potential for significant advancements in photonic devices. The approach addresses scalability and precision in assembly, which are critical for practical applications. The high-frequency modulation capability provides an additional layer of utility, making it relevant for communications and sensor technologies.

Joint Detection and Angle Estimation for Multiple Jammers in Beamspace Massive MIMO

In this paper, we study the joint detection and angle estimation problem for beamspace multiple-input multiple-output (MIMO) systems with multiple random jamming targets. An iterative low-complexity g...

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This article presents a novel approach to a significant problem in beamspace MIMO systems, specifically targeting joint detection and angle estimation of multiple jammers. Its methodological rigor, demonstrated by the proposed low-complexity GLRT and rigorous simulations, enhances its relevance. The potential to outperform existing benchmarks adds to the article's impact, particularly in scenarios with high jamming noise. This research could stimulate further developments in MIMO system optimization and interference management.

Quantifying Traffic Patterns with Percolation Theory: A Case Study of Seoul Roads

Urban traffic systems are characterized by dynamic interactions between congestion and free-flow states, influenced by human activity and road topology. This study employs percolation theory to analyz...

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This article is particularly relevant due to its application of percolation theory, a mathematical framework that is not typically used in traffic analysis. The study's focus on real-world data and its implications for urban transportation optimization highlight its practical applicability and novelty. Additionally, its rigorous quantitative methodologies support strong conclusions, making it potentially influential for future research in both traffic dynamics and urban planning.

An Efficient Mixed-Integer Formulation and an Iterative Method for Optimal Control of Switched Systems Under Dwell Time Constraints

This paper presents an efficient Mixed-Integer Nonlinear Programming (MINLP) formulation for systems with discrete control inputs under dwell time constraints. By viewing such systems as a switched sy...

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The paper addresses a significant problem in control systems involving switched systems under dwell time constraints, which is a relevant area in both theoretical and practical applications. The introduction of a Mixed-Integer Nonlinear Programming formulation represents a novel approach that enhances computational efficiency. Additionally, the iterative algorithm for solving the problem adds to its practical implications, making the results notable for real-time applications. However, while the methodological rigor appears strong, potential limitations in generalizability and application to a wider range of systems must be acknowledged.

Design for light-based spherical aberration correction of ultrafast electron microscopes

We theoretically demonstrate that ponderomotive interactions near the electron cross-over can be used for aberration correction in ultrafast electron microscopes. Highly magnified electron shadow imag...

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This article presents a novel theoretical framework for correcting spherical aberration in ultrafast electron microscopes, a significant advancement in imaging technology. The use of ponderomotive interactions and the application of gradient descent algorithms to optimize beam shapes represent a strong methodological focus, enabling practical implementations of the findings. The combination of simulation results with experimental relevance may inspire further research and upgrades in aberration correction techniques.

State-Based Disassembly Planning

It has been shown recently that physics-based simulation significantly enhances the disassembly capabilities of real-world assemblies with diverse 3D shapes and stringent motion constraints. However, ...

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The article introduces a novel approach to disassembly planning that effectively improves computational efficiency and success rates in complex simulations. The proposed State-Based Disassembly Planning (SBDP) method showcases methodological rigor through innovative evaluation functions and a solid experimental basis. The application of physics-based simulations to practical industrial assembly scenarios represents significant novelty and potential for real-world impact.

Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier

Document-Level Biomedical Relation Extraction (Bio-RE) aims to identify relations between biomedical entities within extensive texts, serving as a crucial subfield of biomedical text mining. Existing ...

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The proposed framework addresses significant limitations in existing Bio-RE methods, particularly regarding cross-sentence inference and the integration of external knowledge. Its novel approach to synthetic data generation and fine-tuning demonstrates methodological rigor and offers practical solutions to common challenges in the field, marking it as a noteworthy contribution. The use of large language models (LLMs) reflects contemporary advancements in AI, enhancing its relevance. Overall, it shows promise for high impact and applicability in advancing biomedical text mining research.

Detection of binary companions below the diffraction limit with lucky imaging

We provide a technique for resolving intermediate-separation binaries stars with medium-sized telescopes (i.e. diameter less than or equal to 2.5 m) at wavelengths around 825 nm in the super-resolutio...

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This article presents a novel technique for resolving binary stars below the diffraction limit, representing significant advancement in optical astrophysics. The development of the Lucky Imaging Super Resolution Technique (LIST) combines established algorithms in a way that enhances observational capabilities, particularly for medium-sized telescopes. This methodological innovation, paired with demonstrations of results that improve measurement accuracy, indicates high potential for future applications in astronomy.

Assisting MoCap-Based Teleoperation of Robot Arm using Augmented Reality Visualisations

Teleoperating a robot arm involves the human operator positioning the robot's end-effector or programming each joint. Whereas humans can control their own arms easily by integrating visual and pro...

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The article presents a novel approach to enhancing the teleoperation of robot arms by integrating motion capture and augmented reality, addressing a significant challenge in the field. The use of AR visualisations to aid human operators by providing intuitive feedback is innovative and could have practical applications in various robotics fields. The methodology seems sound, with potential for further exploration. Impactful findings on user learning and control suggest a promising direction for future research.

Magnetism and electronic dynamics in $CuCr_{2-x}Sn_xS_4$ spinels studied by transferred hyperfine fields at $^{119}Sn$ and muon spin rotation and relaxation

We investigated magnetization, muon spin rotation ( $μ$ SR), and $^{119}Sn$ Mössbauer spectroscopy on Sn substituted $CuCr_{2-x}Sn_xS_4$ (x=0.03 and 0.08) spinel compounds. The m...

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This article presents novel findings on the magnetic properties of Sn-substituted $CuCr_{2-x}Sn_xS_4$ spinels, incorporating advanced techniques like muon spin rotation and Mössbauer spectroscopy. The investigation offers insights into complex magnetic state transitions and is methodologically rigorous, utilizing multiple experimental approaches to support its findings. The implications for the understanding of spin dynamics in these materials are significant, indicating potential advancements in the field of magnetism and materials science.

Heat transport model for the transition between scaling regimes in quasistatic and full magnetoconvection

In magnetoconvection, the flow is governed by the interplay between gravitational buoyancy and the Lorentz force, with one of these forces dominating in different regimes. In this paper, we develop a ...

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The article presents a novel heat transport model with a single adjustable parameter that effectively describes the transition between different scaling regimes in magnetoconvection. Its methodological rigor is reinforced by validation against direct numerical simulations and experimental data, which enhances its credibility. The model's applicability to both quasistatic scenarios and finite magnetic Reynolds numbers, as well as the potential extension to rotating convection, significantly contributes to its relevance. This work is likely to inspire future research in related areas of fluid dynamics and magnetohydrodynamics.

Anisotropic active Brownian particle in two dimensions under stochastic resetting

We analytically investigate the dynamic behavior of an an-isotropic active Brownian particle under various stochastic resetting protocols in two dimensions. The motion of shape-asymmetric active Brown...

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The article explores the dynamic behavior of anisotropic active Brownian particles under various stochastic resetting protocols, representing a novel approach to understanding diffusion processes in non-equilibrium systems. The analytical results complemented by simulations enhance the validity and robustness of the findings. The implications for statistical physics and biological systems make this work particularly relevant.

D-Antimagic Labelings of Oriented Star Forests

For a distance set $D$ , an oriented graph $\overrightarrow{G}$ is $D$ -antimagic if there exists a bijective vertex labeling such that the sum of all labels of $D$ -out-n...

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The article explores the novel concept of $D$-antimagic labelings within oriented star forests, which presents a new avenue of research in graph theory. The methodology appears both robust and rigorous as it provides necessary and sufficient conditions for these concepts. However, while interesting, the specificity of the focus might limit broader applicability across other graph structures.

A Systematic Literature Review on Deep Learning-based Depth Estimation in Computer Vision

Depth estimation (DE) provides spatial information about a scene and enables tasks such as 3D reconstruction, object detection, and scene understanding. Recently, there has been an increasing interest...

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This systematic literature review addresses a crucial gap in the existing literature by providing a comprehensive overview of deep learning methods for depth estimation, which is fundamental for many applications in computer vision. The thorough methodology, including the selection of high-quality studies and evaluation metrics, enhances the robustness of the findings. Additionally, the identification of challenges such as the lack of ground truth data is significant for future research directions. The novelty lies in its broad scope covering various deep learning approaches rather than focusing on specific categories like monocular or stereo methods.

Polymorphism and Magnetism in a Kitaev Honeycomb Cobaltate KCoAsO $_4$

We report the synthesis, crystal structure, and magnetic properties of a new Kitaev honeycomb cobaltate, KCoAsO $_4$ , which crystallizes in two distinct forms: $P2/c$ and $R\bar{3}&...

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The article presents novel findings on a new Kitaev honeycomb cobaltate, KCoAsO$_4$, distinguishing two polymorphic forms, which adds significantly to the existing literature on quantum materials. The combined focus on crystal structure, synthesis, and magnetic properties underlines the rigor of the methodology. The correlation drawn between structural differences and magnetic properties is likely to inspire further investigations into similar materials and their applications in spintronics and quantum computing.