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Thesis Tide

Thesis Tide ranks papers based on their relevance to the fields, with the goal of making it easier to find the most relevant papers. It uses AI to analyze the content of papers and rank them!

Exact VC-Dimensions of Certain Geometric Set Systems

The VC-dimension of a family of sets is a measure of its combinatorial complexity used in machine learning theory, computational geometry, and even model theory. Computing the VC-dimension of the $...

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The article provides significant advancements in understanding the VC-dimensions of unions of geometric set systems, addressing an open combinatorial problem that has implications for machine learning and computational geometry. Its methodological rigor and clear characterizations add to its impact. The results can inspire further research into VC-dimension in related geometric contexts and other complexities of set systems.

MuFF: Stable and Sensitive Post-training MutationTesting for Deep Learning

Rapid adoptions of Deep Learning (DL) in a broad range of fields led to the development of specialised testing techniques for DL systems, including DL mutation testing. However, existing post-training...

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The article introduces a novel mutation testing technique, MuFF, that addresses key weaknesses in existing post-training mutation testing methods for deep learning applications, notably issues of stability and sensitivity of the mutants generated. The robustness of the methodology is highlighted by the empirical validation that shows significant improvements in sensitivity and stability metrics, bonding these improvements with a notable efficiency gain over previous methods. This could fundamentally enhance the testing process for deep learning models, thus having far-reaching implications for quality assurance in various applications.

Adaptive Weighted Total Variation boosted by learning techniques in few-view tomographic imaging

This study presents the development of a spatially adaptive weighting strategy for Total Variation regularization, aimed at addressing under-determined linear inverse problems. The method leverages th...

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This article introduces a novel approach by integrating neural networks with Total Variation regularization, addressing a significant challenge in few-view tomographic imaging. The development of a spatially adaptive weighting strategy is particularly relevant as it enhances image reconstruction quality from limited data, which is critical in applications like medical imaging. The methodological rigor is underpinned by theoretical analysis that strengthens the validity of the proposed approach, making it a solid contribution to the field.

Design-based causal inference in bipartite experiments

Bipartite experiments are widely used across various fields, yet existing methods often rely on strong assumptions about modeling the potential outcomes and exposure mapping. In this paper, we explore...

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This article presents a novel approach to causal inference specifically in the context of bipartite experiments, addressing a significant gap in the literature. The methodological rigor shown through the development of point and variance estimators, alongside the establishment of a central limit theorem, supports its potential impact. Its applicability across various fields further enhances its relevance.

Graphs that predict exciton delocalization

The field of molecular excitons and related supramolecular systems has largely focused on aggregates where nearest-neighbour couplings dominate. We propose that radically different states can be produ...

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The article presents a novel approach by integrating concepts from discrete mathematics (specifically, graph theory) into the study of excitons in molecular systems. This cross-disciplinary perspective can significantly advance understanding in both fields. The potential implications for developing exciton systems that are robust to disorder add high relevance. However, the practical implementation and verification of the proposed models would benefit from further empirical studies, hence not a perfect score.

The semi-inducibility problem

Let $H$ be a $k$ -edge-coloured graph and let $n$ be a positive integer. What is the maximum number of copies of $H$ in a $k$ -edge-coloured complete graph on ...

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The article addresses a significant problem in combinatorial graph theory with implications for edge-coloured graphs, specifically focusing on the semi-inducibility problem. The results presented are sharp, enhancing the understanding of the graph's structure under edge-colouring constraints. The connection to established problems like the inducibility problem adds depth to its relevance. However, while the findings are impactful, they may cater to a niche within the broader field of graph theory.

General relativistic particle trajectories via quantum mechanical weak values and the Schwarzschild-Alcubierre spacetime

We show that the average trajectories of relativistic quantum particles in Schwarzschild spacetime, obtained via quantum mechanical weak measurements of momentum and energy, are equivalent to the pred...

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This article presents a novel framework integrating quantum mechanics with general relativity by linking weak values to particle trajectories in curved spacetime. The equivalence derived between quantum and classical geodesics is a significant theoretical advancement that could pave the way for new insights into quantum gravity. The methodological rigor, through the generalized application of weak measurements and hybrid spacetimes, adds to its credibility and interdisciplinary impact.

Dirt/Detector/Dump: Complementary BSM production at Short-Baseline Neutrino Facilities

Short-baseline neutrino (SBN) facilities are optimal for new-physics searches, including the possible production of new particles in and along the neutrino beamline. One such class of models considers...

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The article presents a novel approach to exploring new physics in neutrino interactions by focusing on the underutilized beam dump in SBN facilities. The methodology appears rigorous, and it addresses a pertinent gap in the current understanding of neutrino physics related to heavy neutral leptons (HNLs). By highlighting the complementarity of different locations for detecting interactions, the research could significantly inform future experiments and searches for new particles. Additionally, it connects well with ongoing discussions in the field about unexplained phenomena like the MiniBooNE low-energy excess. However, its impact will heavily depend on subsequent empirical validations.

Coarse tree-width

We prove two theorems about tree-decompositions in the setting of coarse graph theory. First, we show that a graph $G$ admits a tree-decomposition in which each bag is contained in the union o...

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This article addresses a complex area of graph theory and provides significant theoretical advancements by relating tree-decompositions, quasi-isometries, and bounded tree-width. The findings not only extend existing results but also deepen understanding in coarseness within graphs, making the work highly relevant. However, the applicability may be somewhat specialized, which slightly limits its broader impact compared to more interdisciplinary contributions.

CrossModalityDiffusion: Multi-Modal Novel View Synthesis with Unified Intermediate Representation

Geospatial imaging leverages data from diverse sensing modalities-such as EO, SAR, and LiDAR, ranging from ground-level drones to satellite views. These heterogeneous inputs offer significant opportun...

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This article presents a novel approach to multi-modal view synthesis that addresses a significant gap in geospatial imaging, specifically the challenges posed by heterogeneous sensing modalities and the lack of precise ground truth data. The proposed framework is methodologically rigorous, employing advanced techniques such as modality-specific encoders and volumetric rendering. Its ability to provide a unified representation for multiple input modalities is highly innovative and broadens the applicability of view synthesis across different fields. The validation on the ShapeNet dataset further supports its relevance and effectiveness, enhancing its potential impact on future research in the area.

Complex-Valued Neural Networks for Ultra-Reliable Massive MIMO

In the evolving landscape of 5G and 6G networks, the demands extend beyond high data rates, ultra-low latency, and extensive coverage, increasingly emphasizing the need for reliability. This paper pro...

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This article introduces innovative methodologies, such as the combination of quasi-orthogonal space-time block coding with neural network-based decoding, which represent significant advances in the field of ultra-reliable massive MIMO systems. It effectively addresses critical challenges in 5G and 6G communications, namely reliability and computational complexity, and provides robust simulation results to support the proposed methods. The potential for application in next-generation communication networks underscores its impact and utility.

Experimental signatures of Kalb-Ramond-like particles

We analyse phenomenological signatures of Kalb-Ramond-like particles, described by an antisymmetric rank-2 tensor, when coupled to fermionic matter. The latter is modelled by a tensor current coupled ...

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The article provides a thorough examination of phenomenological signatures of Kalb-Ramond-like particles, demonstrating methodological rigor and potential for significant contribution to particle physics. It explores the implications of new particle types in existing frameworks and offers concrete experimental limits based on LEP data, suggesting future avenues for research with upcoming lepton colliders. This combination of theoretical advancement and practical applicability enhances its relevance.

Consistencies of Beliefs

Type spaces are analysed, and we identify three types of consistency of the players' beliefs in a type space: consistency, universal consistency, and strong consistency. Furthermore, we propose a ...

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The article presents a novel interpretation of type spaces, which is a fundamental concept in game theory and decision theory. The identification of different types of consistency in players' beliefs enhances the theoretical understanding of these interactions. Moreover, the critique of traditional concepts like the common prior adds depth and may inspire significant re-evaluation of existing models, making it highly relevant for future research.

Observation of discontinuities in the periodic modulation of PSR B1828-11

PSR B1828-11 is a radio pulsar that undergoes periodic modulations (~500 days) of its spin-down rate and beam width, providing a valuable opportunity to understand the rotational dynamics of neutron s...

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This article provides a detailed study of PSR B1828-11, offering significant insights into the rotational dynamics of neutron stars through rigorous analysis of periodic modulations and a glitch. The use of advanced modeling techniques and the analysis of high-resolution data enhances its novelty and methodological rigor. Additionally, its findings challenge existing theories, potentially reshaping current understandings and inspiring new research directions in astrophysics.

EraseBench: Understanding The Ripple Effects of Concept Erasure Techniques

Concept erasure techniques have recently gained significant attention for their potential to remove unwanted concepts from text-to-image models. While these methods often demonstrate success in contro...

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The article presents a novel multi-dimensional benchmark, EraseBENCH, for evaluating concept erasure techniques in text-to-image models. Its systematic investigation of failure modes and the introduction of the new benchmark addresses a critical gap in existing literature, increasing its potential impact. Its rigorous methodology and exploration of real-world applicability suggest significant implications for future research.

Crossover-BPSO Driven Multi-Agent Technology for Managing Local Energy Systems

This article presents a new hybrid algorithm, crossover binary particle swarm optimization (crBPSO), for allocating resources in local energy systems via multi-agent (MA) technology. Initially, a hier...

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The novelty of introducing a crossover binary particle swarm optimization method combined with a multi-agent framework for local energy systems offers significant potential for efficiency improvements in energy management. The methodological rigor is evidenced by comparative simulation results showing substantial cost savings over existing methods. However, its practical applicability may depend on the scalability of the algorithm and the generalizability of the findings across diverse energy systems.

Serverless Computing: Architecture, Concepts, and Applications

Recently, serverless computing has gained recognition as a leading cloud computing method. Providing a solution that does not require direct server and infrastructure management, this technology has a...

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This article offers a thorough examination of serverless computing, a rapidly evolving paradigm in cloud computing. Its comprehensive approach to outlining both advantages and challenges provides valuable insights into practical applications and future trends, making it relevant for researchers and industry practitioners alike. However, the novelty could be stronger if it included more empirical or case study data.

Visualizing Double-Slit Interference on a Shoestring

Student misconceptions of the double-slit experiment are abundant. We have designed an exercise that helps to overcome them.

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The article addresses a common educational challenge in physics, particularly surrounding quantum mechanics and the double-slit experiment. Its focus on student misconceptions is timely and relevant, making it likely to have a positive impact on physics education. The novelty lies in the specific exercise designed to address these misconceptions, potentially offering a practical tool for educators, though more detail on its application and efficacy would further strengthen the assessment.

Sur certains faisceaux de formes méromorphes sur un espace complexe réduit

The purpose of this is the study of certain coherent sheaves of meromorphic forms on reduced complex space and particularly their behavior with respect to pull back and higher direct image.

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The article focuses on coherent sheaves of meromorphic forms, an area that is both technically intricate and offers significant insights into complex geometry. Its examination of pullbacks and higher direct images is particularly relevant for mathematicians working with sheaf theory and complex analysis, suggesting a potential for impactful advancements in these fields. The work appears to be methodologically sound and builds upon established theories, which indicates its relevance for future research.

On the Complexity of p-Order Cone Programs

This manuscript explores novel complexity results for the feasibility problem over $p$ -order cones, extending the foundational work of Porkolab and Khachiyan. By leveraging the intrinsic struc...

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The article presents significant advancements in understanding the complexity of feasibility problems related to $p$-order cones, an area that has direct applications in optimization and convex analysis. Its contributions to theoretical bounds and practical insights enhance its relevance. The methodological rigor and the introduction of novel results indicate a solid foundation, suggesting potential for future exploration in related fields.