Thesis Tide - Find relevant and interesting papers

Partially massless spin 2 and supersymmetry

The very existence of partially massless spin 2 supermultiplet tell us that partially massless spin 2 has two natural superpartners: massless spin 3/2 and massive spin 3/2 with some special value of m...

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The article presents significant advancements in the understanding of partially massless spin 2 particles and their supersymmetry properties, which are foundational for both theoretical particle physics and cosmology. The development of minimal vertices and the exploration of self-interaction and localization of global supersymmetry contribute to hybrid models in supergravity. The novelty and methodological approach are noteworthy, yet further empirical validation may be required.

Causal discovery with endogenous context variables

Causal systems often exhibit variations of the underlying causal mechanisms between the variables of the system. Often, these changes are driven by different environments or internal states in which t...

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The article addresses a significant gap in causal discovery research by specifically targeting endogenous context variables, which is a novel contribution. The proposed adaptive constraint-based algorithm shows methodological rigor and provides empirical validation through numerical experiments. Its applicability to real-world scenarios in fields like environmental science enhances its impact, though some limitations remain acknowledged in the discussion.

Matrix-Free Parallel Scalable Multilevel Deflation Preconditioning for Heterogeneous Time-Harmonic Wave Problems

We present a matrix-free parallel scalable multilevel deflation preconditioned method for heterogeneous time-harmonic wave problems. Building on the higher-order deflation preconditioning proposed by ...

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The article presents a novel and efficient approach to solving heterogeneous time-harmonic wave problems using a matrix-free multilevel deflation method. Its emphasis on parallel scalability and improved convergence rates is particularly relevant for computational applied mathematics and engineering applications. However, the potential impact largely depends on further validation in diverse real-world scenarios and cross-discipline applicability.

A Key Encapsulation Mechanism from Low Density Lattice Codes

Key Encapsulation Mechanisms (KEMs) are a set of cryptographic techniques that are designed to provide symmetric encryption key using asymmetric mechanism (public key). In the current study, we concen...

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The paper introduces a novel approach to key encapsulation mechanisms using low-density lattice codes, which may provide significant advancements in cryptographic methods, especially in terms of reducing key size while maintaining security. The focus on practical aspects such as computational complexity and security against attacks adds to its relevance. However, further experimental validations may be required to confirm real-world applicability.

Boundary regularity for quasiminima of double-phase problems on metric spaces

We give a sufficient condition for Hölder continuity at a boundary point for quasiminima of double-phase functionals of $p,q$ -Laplace type, in the setting of metric measure spaces equipped wit...

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This article presents a novel approach to the boundary regularity of quasiminima in double-phase problems, which is a significant and relatively underexplored area. The methodological rigor, particularly the use of a variational approach and pointwise estimates, indicates a solid foundation that could inspire further research in related fields. The focus on metric measure spaces adds depth, potentially broadening the applicability of the findings.

ORKG ASK: a Neuro-symbolic Scholarly Search and Exploration System

Purpose: Finding scholarly articles is a time-consuming and cumbersome activity, yet crucial for conducting science. Due to the growing number of scholarly articles, new scholarly search systems are n...

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The article presents a novel neuro-symbolic approach to scholarly search, integrating advanced technologies like LLMs and knowledge graphs, which indicates a high degree of innovation and interdisciplinary applicability. It addresses a significant pain point in the research process—literature retrieval—making it highly relevant and impactful for various research fields. The open-source nature ensures wide accessibility and potential for further development, enhancing its relevance.

Bounds for Kloosterman Sums for $\mathrm{GL}_n$

In this paper power saving bounds for general Kloosterman sums for all Weyl elements for $\mathrm{GL}_n$ for n>2 are proven, improving the trivial bound by Dąbrowski and Reeder. T...

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This article presents significant improvements to existing bounds for Kloosterman sums, which are of great importance in number theory and have implications in areas such as the theory of automorphic forms and representation theory. The methodological rigor in deriving these bounds through established techniques like the Weil bound enhances its credibility and usefulness. The results could inspire further research in both theoretical aspects and applications involving Weyl elements and similar sums.

PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning

Few-shot learning and parameter-efficient fine-tuning (PEFT) are crucial to overcome the challenges of data scarcity and ever growing language model sizes. This applies in particular to specialized sc...

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The article presents a novel approach to improving few-shot learning and parameter-efficient fine-tuning in NLP, which is increasingly vital as language model sizes grow. It addresses practical challenges in specialized scientific domains, enhancing accessibility for researchers. The validation on benchmark datasets illustrates the methodological rigor, and the claims of improved performance and efficiency suggest significant potential impact. However, further independent validation and broader application studies would strengthen its contribution.

Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Task

In previous research, we developed methods to train decision trees (DT) as agents for reinforcement learning tasks, based on deep reinforcement learning (DRL) networks. The samples from which the DTs ...

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The article presents a novel application of decision trees in the context of reinforcement learning, addressing both theoretical and practical challenges in real-world robotic tasks. The methodology is rigorous, and the results showcase a successful implementation that balances performance with model simplicity. This could inspire further research in model interpretability and efficiency in AI systems.

On the Laplace equation with non-local dynamical boundary conditions

Aim of the paper is to study non-local dynamic boundary conditions of reactive-diffusive type for the Laplace equation from analytic and probabilistic point of view. In particular, we provide compact ...

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The paper presents a novel approach to the Laplace equation by incorporating non-local dynamic boundary conditions. This expands the current understanding of boundary value problems and introduces new methodologies in both analytic and probabilistic contexts. The implications of the findings could lead to further exploratory research in applied mathematics and related fields.

Tripling Fluctuations and Peaked Sound Speed in Fermionic Matter

A crossover involving three-fermion clusters is relevant to the hadron-quark crossover, which, if occurring in a neutron star, could naturally reproduce the dense-matter equation of state recently ded...

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The article addresses a highly relevant issue in astrophysics involving the behavior of fermionic matter in extreme conditions, such as those found in neutron stars. The investigation of three-fermion clusters provides novel insights into the hadron-quark crossover, a critical phenomenon for understanding dense matter, which is of great interest following recent astronomical observations. The unique combination of methodological approaches and theoretical constructs further enhances its potential impact, though its applicability could benefit from more extensive validation in higher-dimensional scenarios.

CMSO-transducing tree-like graph decompositions

We show that given a graph G we can CMSO-transduce its modular decomposition, its split decomposition and its bi-join decomposition. This improves results by Courcelle [Logical Methods in Computer Sci...

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The article presents an advancement over existing methodologies in graph theory by providing CMSO-transductions for important graph decompositions. The improvement over previous results signifies a potentially impactful contribution, especially as it involves refining logical frameworks used in graph analysis, demonstrating both robustness and novelty. The implications for various applications show significant applicability in theoretical and practical aspects of graph theory.

From Theory to Practice: Demonstrators of FAIR Data Spaces Across Different Sectors

The principles of data spaces for sovereign data exchange across trusted organizations have so far mainly been adopted in business-to-business settings, and recently scaled to cloud environments. Mean...

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This article offers a significant contribution to the understanding and application of FAIR data principles in bridging research and industry, which is critical for enhancing data interoperability. Its detailed examination of demonstrators across various sectors indicates strong methodological rigor and presents practical implications that can inspire further research and application in diverse fields.

On higher fermions in supergravity

We show that the generalised geometry formalism provides a new approach to the description of higher-fermion terms in gauged $\mathcal N=1$ supergravity in ten dimensions, which does not appea...

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The article presents a novel approach to higher-fermion terms in supergravity using generalised geometry, diverging from traditional methods. Its methodological rigor and the introduction of a compact framework for understanding these terms are significant. The focus on higher-fermions in gauged supergravity is timely, given the increasing interest in non-linear realizations of supersymmetry, which adds to its relevance and potential future impact.

Deterministic Algorithms to Solve the $(n,k)$ -Complete Hidden Subset Sum Problem

The Hidden Subset Sum Problem (HSSP) involves reconstructing a hidden set from its multiple subset sums. Initially introduced as a cryptographic problem of accelerating the key generation process in s...

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This article addresses a well-defined and known problem in cryptography and AI privacy, presenting novel deterministic algorithms that reduce limitations of existing methods. Its methodological rigor and relevance to both theory and practical applications contribute significantly to its potential impact.

Stochastic modeling of blob-like plasma filaments in the scrape-off layer: Time-dependent velocities and pulse stagnation

A stochastic model for a super-position of uncorrelated pulses with a random distribution of and correlations between amplitudes and velocities is analyzed. The pulses are assumed to move radially wit...

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The article presents a novel stochastic model that enhances the understanding of blob-like plasma filaments in the scrape-off layer, which is significant for fusion energy research. Its methodological rigor and alignment with experimental data underscore its relevance, while its potential implications for plasma-surface interactions suggest impactful applications in the field.

Succinct Data Structures for Segments

We consider succinct data structures for representing a set of $n$ horizontal line segments in the plane given in rank space to support \emph{segment access}, \emph{segment selection}, and \em...

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This article presents a significant advancement in the design of succinct data structures for geometric data, specifically for querying segments. The introduction of a novel segment wavelet tree is particularly notable, as it extends existing structures in a meaningful way, providing both optimal space and query times. The theoretical results are robust and grounded in formal mathematical proofs which enhance their credibility and reliability.

Flash Communication: Reducing Tensor Parallelization Bottleneck for Fast Large Language Model Inference

The ever-increasing sizes of large language models necessitate distributed solutions for fast inference that exploit multi-dimensional parallelism, where computational loads are split across various a...

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This article addresses a critical challenge in the deployment of large language models (LLMs) by proposing a novel communication technique that significantly enhances performance. Its findings contribute to both theoretical understanding and practical applications within the field, offering a solution to bandwidth limitations that many researchers face. The empirical evidence supporting its claims indicates robustness and applicability, enhancing its relevance.

Krylov Complexity in Mixed Phase Space

We investigate the Krylov complexity of thermofield double states in systems with mixed phase space, uncovering a direct correlation with the Brody distribution, which interpolates between Poisson and...

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The article presents a novel application of Krylov complexity as a diagnostic tool for quantum chaos, expanding the understanding of complex quantum systems in mixed phase spaces. By uncovering correlations with established statistical distributions, the work not only addresses fundamental questions in quantum chaos but also contributes to the broader discourse on the behavior of complex systems. The rigorous analysis across various models enhances its robustness and applicability, making it relevant to future research.

Divisible design graphs from symplectic graphs over rings with precisely three ideals

In this paper we construct two new infinite families of divisible design graphs based on symplectic graphs over rings with precisely three ideals.

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The article presents a novel construction of divisible design graphs, which builds on the existing theory of symplectic graphs. This indicates a substantial contribution to the field of graph theory, particularly in combinatorial designs, where the study of graph constructions can lead to deeper insights and applications. However, the specificity of the topic may limit its broader impact within more general mathematical contexts.