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A $k^{\frac{q}{q-2}}$ Lower Bound for Odd Query Locally Decodable Codes from Bipartite Kikuchi Graphs

A code $C \colon \{0,1\}^k \to \{0,1\}^n$ is a $q$ -query locally decodable code ( $q$ -LDC) if one can recover any chosen bit $b_i$ of the message $b \in \{0,1\}^k...

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This paper introduces a novel approach to establishing lower bounds for $q$-query locally decodable codes (LDCs), particularly for odd queries, through the use of bipartite Kikuchi graphs. The methodological rigor shown in simplifying the analysis of odd arity XOR and extending existing lower bounds is significant for advancing the understanding of LDCs. The results not only fill a gap in current literature but also potentially stimulate new lines of research in coding theory and related algorithms. Its implications for other areas such as theoretical computer science and information theory also enhance its relevance.

Exploring the Impact of Quizzes Interleaved with Write-Code Tasks in Elementary-Level Visual Programming

We explore the role of quizzes in elementary visual programming domains popularly used for K-8 computing education. Prior work has studied various quiz types, such as fill-in-the-gap write-code questi...

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This article presents novel insights into an educational approach in K-8 computing, specifically focusing on the interplay between quizzes and coding tasks. Its methodological rigor, stemming from a large-scale study involving 405 students, enhances credibility. The findings suggest practical implications for curriculum development in visual programming education, suggesting that interleaved quizzes can improve long-term retention of coding skills, which is valuable for educators and researchers alike. However, while the insights are significant for educational contexts, their broader applicability beyond elementary education may be limited.

Quantum Vacuum Self-Propulsion and Torque

This article summarizes our recent efforts to understand spontaneous quantum vacuum forces and torques, which require that a stationary object be out of thermal equilibrium with the blackbody backgrou...

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The article presents a novel investigation into quantum vacuum forces and their implications for propulsion and torque, which could have significant implications for theoretical physics and engineering applications. The systematic approach taken adds rigor, and the potential for real-world applications through the identification of examples is notable. However, future empirical validations and broader applicability could further elevate its impact.

Examining the evolution of the Supersoft X-ray Source RX J $0513.9-6951$

Supersoft X-ray sources (SSS) are thought to be accreting white dwarfs (WDs) in close binary systems, with thermonuclear burning on their surfaces. The SSS RX J $0513.9-6951$ in the Large Magel...

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The article presents a detailed analysis of the spectral data of a specific Supersoft X-ray source, contributing to our understanding of the behavior of white dwarfs in binary systems. The methodology employed is robust, utilizing high-resolution spectral data and a comprehensive modeling approach. The findings challenge existing models and raise new questions that may lead to further exploration, enhancing the novelty and potential impact within the field of astrophysics. However, the local study of a specific source may limit broader application across different types of celestial phenomena.

Efficient Aspect-Based Summarization of Climate Change Reports with Small Language Models

The use of Natural Language Processing (NLP) for helping decision-makers with Climate Change action has recently been highlighted as a use case aligning with a broader drive towards NLP technologies f...

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The article addresses a pressing issue by applying NLP to climate change, specifically through Aspect-Based Summarization (ABS) techniques. It introduces a new dataset which is crucial for further research and presents a novel approach by evaluating small language models alongside large models, highlighting energy efficiency—a growing concern in AI. The methodological rigor appears sound, and the insights can significantly impact decision-making in climate action.

The properties of the interstellar medium in dusty, star-forming galaxies at $z \sim 2-4$ : The shape of the CO spectral line energy distributions

The molecular gas in the interstellar medium (ISM) of star-forming galaxy populations exhibits diverse physical properties. We investigate the $^{12}$ CO excitation of twelve dusty, luminous st...

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This article addresses the properties of interstellar medium in high-redshift star-forming galaxies, a relatively under-explored area that can greatly enhance our understanding of galaxy formation and evolution. The innovative combination of multiple data sources and the focus on molecular gas properties in an epoch of active star formation provide significant methodological rigor. The findings on the relationship between CO excitation and star-formation metrics could influence future observational strategies and theoretical models in astrophysics.

Y Gem, a Symbiotic Star Outshined by its Asymptotic Giant Branch Primary Component

A considerable number of asymptotic giant branch (AGB) stars exhibit UV excess and/or X-ray emission indicative of the presence of a binary companion. AGB stars are so bright that they easily outshine...

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The article presents a detailed multi-wavelength analysis of the symbiotic star Y Gem, culminating in the identification of its components and clarification of its nature as an S-type symbiotic star. The use of diverse observational data enhances the robustness of the findings, and the clear implications for the understanding of AGB stars and their companions address a significant gap in the literature. The novelty of presenting an optical spectrum of Y Gem for the first time contributes to its importance in the field. Additionally, insights into the accretion processes and stellar characteristics have broad implications for theoretical models and observational campaigns.

Guided MRI Reconstruction via Schrödinger Bridge

Magnetic Resonance Imaging (MRI) is a multi-contrast imaging technique in which different contrast images share similar structural information. However, conventional diffusion models struggle to effec...

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This article introduces a novel approach to MRI reconstruction by leveraging the Schrödinger Bridge framework. Its methodological rigor in combining guided priors with advanced diffusion models presents a significant advancement in the field. The experimental results showcasing substantial performance improvements further enhance its impact and applicability. Overall, it has strong implications for both clinical and research settings in medical imaging.

Supercritical Tradeoffs for Monotone Circuits

We exhibit a monotone function computable by a monotone circuit of quasipolynomial size such that any monotone circuit of polynomial depth requires exponential size. This is the first size-depth trade...

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The article presents a novel and significant breakthrough in understanding the tradeoffs related to monotone circuits, an important area in computational complexity theory. It provides new insights that have direct implications for both theoretical computer science and proof complexity, making it highly relevant. The methodological rigor in establishing the size-depth tradeoff adds to its impact, indicating strong applicability in future research and potential cross-disciplinary connections.

Truly Supercritical Trade-offs for Resolution, Cutting Planes, Monotone Circuits, and Weisfeiler-Leman

We exhibit supercritical trade-off for monotone circuits, showing that there are functions computable by small circuits for which any circuit must have depth super-linear or even super-polynomial in t...

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This article presents significant advancements in computational complexity and proof theory by demonstrating supercritical trade-offs that exceed established upper bounds. The novelty arises from the application of new techniques, particularly in relation to the Cop-Robber game, which may influence future research in circuit complexity and related areas. The robust approach taken in establishing theoretical results, coupled with the potential implications for various complexity classes, suggests a high relevance to the field.

Quantitative Propagation of Chaos for 2D Viscous Vortex Model with General Circulations on the Whole Space

We derive the quantitative propagation of chaos in the sense of relative entropy for the 2D viscous vortex model with general circulations, approximating the vorticity formulation of the 2D Navier-Sto...

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The article presents a significant advance in the understanding of chaos propagation in fluid dynamics through a rigorous mathematical framework. The extension to cases with changing sign vorticity is novel, addressing previously unexplored areas, and the methodological approach is robust, relying on well-established mathematical principles. The results provide optimal convergence rates, which are beneficial for theoretical and practical applications alike.

A Bayesian mixture model for Poisson network autoregression

In this paper, we propose a new Bayesian Poisson network autoregression mixture model (PNARM). Our model combines ideas from the models of Dahl 2008, Ren et al. 2024 and Armillotta and Fokianos 2024, ...

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This article presents a novel Bayesian mixture model that addresses a significant gap in modeling multivariate count time series within the context of network structures. The integration of autoregression with the Bayesian framework and the application to real-world data, such as COVID-19 cases, enhances its relevance and applicability. The methodology appears robust, leveraging recent advancements in the field, which adds to its novelty and potential impact.

Explainable Multi-Agent Reinforcement Learning for Extended Reality Codec Adaptation

Extended Reality (XR) services are set to transform applications over 5th and 6th generation wireless networks, delivering immersive experiences. Concurrently, Artificial Intelligence (AI) advancement...

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This article presents a significant advancement in the area of explainability in multi-agent reinforcement learning (MARL) algorithms, specifically applying these methods to enhance codec adaptation for extended reality (XR) applications. The introduction of novel enhancements and a new explainability metric indicates a strong potential for improving trust in AI systems. The methodological rigor shown in architectural modifications and simulation results adds to the robustness of the research.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders

In predictive process monitoring, predictive models are vulnerable to adversarial attacks, where input perturbations can lead to incorrect predictions. Unlike in computer vision, where these perturbat...

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This article addresses a significant gap in the literature regarding adversarial attacks on predictive models in business process monitoring. Its methodology is innovative, implementing novel latent space attacks that differ from traditional approaches in both application and execution. The use of real-life event logs and a range of adversarial methods enhances the rigor of the study, while its focus on maintaining realistic constraints within business processes signifies high applicability in practical scenarios. There is potential for broad impact across the field, particularly in improving the security and reliability of predictive models.

Accelerating Construction of Non-Intrusive Nonlinear Structural Dynamics Reduced Order Models through Hyperreduction

We present a novel technique to significantly reduce the offline cost associated to non-intrusive nonlinear tensors identification in reduced order models (ROMs) of geometrically nonlinear, finite ele...

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The article presents a novel approach to enhance the efficiency of constructing reduced order models for nonlinear structural dynamics, addressing a significant challenge within the field. The methodology includes innovative techniques that combine hyperreduction and tensor identification, showcasing both theoretical advancements and practical applicability. The focus on real-world applications, such as the dynamics of structures under acoustic loading, increases its relevance. The detailed comparisons with existing methods also demonstrate methodological rigor, providing valuable insights for future research in this domain.

On braid statistics versus parastatistics

I report the recent advances in applying (graded) Hopf algebras with braided tensor product in two scenarios: i) paraparticles beyond bosons and fermions living in any space dimensions and transformin...

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The article presents innovative applications of Hopf algebras in the context of non-standard particle statistics, primarily focusing on paraparticles and anyons. The exploration of how these models can differentiate between various types of particles could fundamentally challenge accepted beliefs in quantum physics. The introduction of the braided Majorana qubit also indicates practical implications for quantum computing, suggesting a strong potential impact in both theoretical frameworks and practical applications.

Existence and global behaviour of solutions of a parabolic problem involving the fractional $p$ -Laplacian in porous medium

In this paper, we prove the existence and the uniqueness of a weak and mild solution of the following nonlinear parabolic problem involving the porous $p$ -fractional Laplacian: \begin{equati...

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The paper addresses a complex and emerging area that combines fractional calculus with nonlinear parabolic problems, which is both innovative and of significant mathematical interest. The proof of existence and uniqueness contributes to the theoretical foundation required for further developments in the field. Moreover, the study of global behavior and stabilization adds practical relevance. The rigorous methodology enhances the credibility of the results, making it useful for researchers tackling similar issues in related areas.

Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning

Quantum differential equation solvers aim to prepare solutions as $n$ -qubit quantum states over a fine grid of $O(2^n)$ points, surpassing the linear scaling of classical solvers. Howe...

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This article addresses a significant limitation in the field of quantum computing by proposing a novel solution to the readout problem in quantum differential equation algorithms. The use of quantum scientific machine learning (QSML) to extract low-dimensional features represents a substantial methodological advancement. The applicability of this work extends to critical areas such as fluid dynamics and quantum data analysis, potentially influencing future research directions. The rigorous approach and clear implications for practical applications in real-world scenarios add to its relevance.

Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) based applications including automated text generation, question answering, chatbots, and others. However, they face ...

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This article presents a timely investigation at the intersection of knowledge graphs and large language models, major areas in NLP. Its focus on addressing hallucinations is crucial, as reliability is a central concern when deploying LLMs in practical applications. The discussion of open challenges combined with potential future directions adds significant novelty, making it a valuable contribution to the field. The methodological rigor in exploring datasets and benchmarks further enhances its relevance.

Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models

Hallucinations in large language models are a widespread problem, yet the mechanisms behind whether models will hallucinate are poorly understood, limiting our ability to solve this problem. Using spa...

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This article presents a novel approach to understanding the mechanisms behind hallucinations in large language models, utilizing sparse autoencoders for interpretability. The research provides valuable insights into entity recognition and self-knowledge within models, which are crucial for improving the reliability of language models. The methodological rigor in exploring causal relationships and the implications for model design are especially impactful, making this work highly relevant for future research.