Thesis Tide - Find relevant and interesting papers

On the distribution of the statistical sum related to BSC

The distribution function of the sum of i.i.d. random variables of the special form is considered. Such sum describes messages posterior probabilities for random coding in binary symmetric channel. Cl...

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The article addresses a specific aspect of statistical distributions related to binary symmetric channels, which is significant in the fields of information theory and communications. The derivation of interesting lower and upper bounds for distribution functions adds methodological rigor and could lead to improved performance in coding strategies for communications. The relevance is enhanced by its potential application in practical scenarios involving error-correcting codes and their optimization. However, more application-focused validation would boost its relevance further.

NEBULA: A National Scale Dataset for Neighbourhood-Level Urban Building Energy Modelling for England and Wales

Buildings are significant contributors to global greenhouse gas emissions, accounting for 26% of global energy sector emissions in 2022. Meeting net zero goals requires a rapid reduction in building e...

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The NEBULA dataset presents a novel and comprehensive resource for urban building energy modelling, addressing a significant gap in high-resolution data availability in Europe. Its focus on neighbourhood-level data offers potential insights for energy consumption patterns that can inform regional and national energy policies, particularly regarding net zero targets. The methodological rigor in integrating diverse datasets enhances its applicability, making it a valuable asset for researchers and policymakers alike.

The $α$ condensate states of atomic nuclei ${}^{12}C$ , ${}^{16}O$ and ${}^{20}Ne$ in an analytical solvable model

The $α$ condensation in the ${}^{12}C$ , ${}^{16}O$ and ${}^{20}Ne$ nuclei is investigated within an analytical solvable model. It is found that the calculated ratio of ...

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The article presents novel insights into $\alpha$ condensation within atomic nuclei, which has significant implications for nuclear structure physics. The use of an analytical solvable model allows for a clearer understanding of complex phenomena, and the results align closely with experimental values, enhancing the reliability of the findings. However, the discrepancy in the ground state energy for ${}^{20}Ne}$ suggests there are limitations and areas for further exploration.

Artificial Intelligence, Ambient Backscatter Communication and Non-Terrestrial Networks: A 6G Commixture

The advent of Non-Terrestrial Networks (NTN) represents a compelling response to the International Mobile Telecommunications 2030 (IMT-2030) framework, enabling the delivery of advanced, seamless conn...

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The article presents a novel intersection of three cutting-edge technologies: Artificial Intelligence (AI), Ambient Backscatter Communication (AmBC), and Non-Terrestrial Networks (NTN). This integration addresses important challenges within the evolving landscape of 6G communication networks, indicating strong potential for significant advancements in both theoretical and practical applications. The focus on energy efficiency and the adaptability of network parameters through AI suggests methodological rigor and immediate applicability, which are crucial for future research development.

Agent-Based Simulation of a Perpetual Futures Market

I introduce an agent-based model of a Perpetual Futures market with heterogeneous agents trading via a central limit order book. Perpetual Futures (henceforth Perps) are financial derivatives introduc...

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The paper presents a novel agent-based model addressing a specific and increasingly relevant area in finance, namely Perpetual Futures markets. Its methodological rigor in adapting existing models to a new context, including the ability to successfully simulate key market features, shows potential for significant contributions to both theoretical understanding and practical applications in market dynamics. However, while robust, it does not fully explore potential real-world implications of its findings, which slightly limits its overall impact.

PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI

Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet, reducing acquisition time, and thereby availa...

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The article introduces a novel self-supervised loss function that addresses a critical issue in dynamic MRI concerning overfitting due to limited training data. Its high relevance is marked by the promising results it achieves in significantly improving reconstruction quality at high acceleration factors, showcasing methodological rigor and potential for widespread application. The integration of PISCO not only enhances existing architectures but also offers a versatile tool for future developments in the field.

A practical review on promoting connectivity in topology optimization

Topology optimization facilitates the automated design of high-performance structures across various engineering fields but, if unconstrained, often produces designs that are complex and difficult to ...

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This article provides a thorough review of connectivity constraints in topology optimization, highlighting their importance in generating manufacturable designs. It merges practical engineering challenges with theoretical advancements, making the findings relevant for real-world applications. The comparative analysis of different constraints offers valuable insights for future research, fostering further exploration in the field.

Towards a Framework for Enterprise Architecture in Mobile Government: A Case Study

Mobile government (m-government) represents a distinct paradigm shift from electronic government (e-government), offering a new avenue for governments worldwide to deliver services and applications to...

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The article addresses a significant gap in the literature regarding the application of Enterprise Architecture (EA) in the context of mobile government (m-government), particularly in developing countries. This is a novel area given the shift from e-government, and the proposed framework could help streamline the integration of services, thus enhancing the efficiency of public sector IT infrastructure. The methodology indicates rigor in developing the framework, and the practical implications could lead to better resource allocation and less redundancy in investments. However, the effectiveness of the proposed framework in practice remains to be seen, which slightly affects the score.

Joint Antenna Selection and Beamforming Design for Active RIS-aided ISAC Systems

Active reconfigurable intelligent surface (A-RIS) aided integrated sensing and communications (ISAC) system has been considered as a promising paradigm to improve spectrum efficiency. However, massive...

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This article addresses significant challenges in the integration of active reconfigurable intelligent surfaces (A-RIS) in wireless systems, particularly in the context of integrated sensing and communications (ISAC). The proposed solutions contribute both theoretically and practically to the efficiency of resource allocation in these systems. The novelty of combining antenna selection with advanced optimization techniques enhances its value for future research in ISAC and related fields.

Fast Searching of Extreme Operating Conditions for Relay Protection Setting Calculation Based on Graph Neural Network and Reinforcement Learning

Searching for the Extreme Operating Conditions (EOCs) is one of the core problems of power system relay protection setting calculation. The current methods based on brute-force search, heuristic algor...

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The article presents a novel approach to a significant problem in power system engineering, integrating advanced techniques such as graph neural networks and reinforcement learning. The proposed method shows substantial improvements in computation speed while maintaining accuracy, which is crucial given the increasing complexity of power systems with renewable energy sources. The methodological rigor is highlighted by extensive case studies. Its applicability in real-world scenarios and potential to influence future developments in related fields makes it highly relevant.

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

Graphics Processing Units (GPUs) have become the standard in accelerating scientific applications on heterogeneous systems. However, as GPUs are getting faster, one potential performance bottleneck wi...

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The article tackles a significant issue in GPU performance optimization, providing a novel approach by utilizing CUDA Graphs for batching kernel launches. The research presents a clear methodology, rigorous analysis, and demonstrates tangible performance gains through real-world application examples, enhancing its credibility and relevance for further exploration in the field.

AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and Spatial Relation-based Referring

3D visual grounding (3DVG), which aims to correlate a natural language description with the target object within a 3D scene, is a significant yet challenging task. Despite recent advancements in this ...

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The article presents a novel method that tackles a fundamental limitation in 3D visual grounding by enhancing training data diversity through cross-modal augmentation. The use of foundation models for creating semantically rich text-3D pairs and the introduction of a language-spatial adaptive decoder demonstrates methodological rigor and innovative thinking. This work holds promise for significantly improving 3DVG, which is critical for various applications in computer vision and natural language processing.

Collision Risk Analysis for LEO Satellites with Confidential Orbital Data

The growing number of satellites in low Earth orbit (LEO) has increased concerns about the risk of satellite collisions, which can ultimately result in the irretrievable loss of satellites and a growi...

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This article addresses a highly relevant issue in space research with significant implications due to the increasing density of satellites in LEO. The innovative application of fully homomorphic encryption (FHE) to preserve the confidentiality of orbital data while conducting collision risk analysis is both novel and methodologically rigorous. The insights it provides could pave the way for safer satellite operations and stimulate further research into secure data handling in similar contexts.

Joint Transmission and Deblurring: A Semantic Communication Approach Using Events

Deep learning-based joint source-channel coding (JSCC) is emerging as a promising technology for effective image transmission. However, most existing approaches focus on transmitting clear images, ove...

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The article presents a novel approach that leverages advanced deep learning techniques to address a significant gap in existing image transmission paradigms, specifically targeting motion blur in real-time scenarios. The integration of event cameras and JSCC is innovative and aligns with contemporary challenges in the field of image processing and transmission, making it highly relevant and impactful for future research. The methodological rigor, demonstrated through empirical simulations that outperform existing systems, further supports its high relevance score.

ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines

Deep Operator Networks (DeepONets) are among the most prominent frameworks for operator learning, grounded in the universal approximation theorem for operators. However, training DeepONets typically r...

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The article introduces a novel approach to training Deep Operator Networks using Extreme Learning Machines, significantly reducing computational costs while maintaining or improving accuracy. The backpropagation-free methodology adds to its novelty, making it particularly relevant for resource-constrained environments. The rigorous validation on benchmark problems enhances its credibility, demonstrating direct applicability in scientific computing.

Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments

The proliferation of Internet of Things (IoT) devices equipped with acoustic sensors necessitates robust acoustic scene classification (ASC) capabilities, even in noisy and data-limited environments. ...

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The article presents a novel approach to acoustic scene classification using quantum-inspired methods, leveraging techniques such as superposition and entanglement, which adds significant novelty to the field. The methodological rigor is supported by comprehensive evaluations against existing models, demonstrating quantifiable improvements in accuracy. This work has strong applicability in the growing field of IoT and could inspire further research in quantum applications for machine learning.

SVIA: A Street View Image Anonymization Framework for Self-Driving Applications

In recent years, there has been an increasing interest in image anonymization, particularly focusing on the de-identification of faces and individuals. However, for self-driving applications, merely d...

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The SVIA framework presents a novel approach to enhancing privacy in self-driving applications through comprehensive street view image anonymization. Its methodological rigor, encompassing multiple components for segmentation and image processing, signifies an important advancement in addressing privacy concerns in this technology. The combination of visual coherence with privacy preservation suggests significant practical applicability.

New Approaches to the Monotonicity Inequality for Linear Stochastic PDEs

The Monotonicity inequality is an important tool in the understanding of existence and uniqueness of strong solutions for Stochastic PDEs. In this article, we discuss three approaches to establish thi...

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The article addresses a significant aspect of stochastic partial differential equations (PDEs) by focusing on the monotonicity inequality, which is essential for solution existence and uniqueness. The introduction of three approaches to establish this inequality suggests methodological innovation, contributing valuable theoretical insights to the field. However, the potential impact may depend on the applicability of these approaches in broader contexts.

Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, pho...

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This paper presents a novel integration of contract theory, reinforcement learning, and generative models for image generation specifically in mobile edge computing contexts, addressing a significant gap in current research. The methodology shows rigor in both theoretical underpinnings and empirical validation, suggesting high applicability for enhancing user experience in the Metaverse.

The Mini SiTian Array: Prospects of Searching for Tidal Disruption Events

We assess the detectability of tidal disruption events (TDEs) using mock observations from the Mini SiTian array. We select 100 host galaxy samples from a simulated galaxy catalog based on specific cr...

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This article presents a novel approach to quantify the detectability of tidal disruption events using a specific observational strategy, making it highly relevant for future studies in this emerging field. The integration of mock observations and a well-defined methodology highlights the potential impact of the Mini SiTian array on TDE research. However, the abstract lacks detailed information about the robustness of the simulations, leaving some uncertainty about the conclusions drawn.