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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!

On polynomial automorphisms commuting with a simple derivation

Let $D$ be a simple derivation of the polynomial ring $\mathbb{k}[x_1,\dots,x_n]$ , where $\mathbb{k}$ is an algebraically closed field of characteristic zero, and denote by ...

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The article presents novel results regarding the structure of polynomial automorphisms in the context of simple derivations, which offers significant insights into algebraic groups and their properties. The methodological rigor in dealing with specific cases (n=3 and n=4) adds to its relevance. The findings may open avenues for further exploration in related areas of algebraic geometry and group theory.

Clifford Perturbation Approximation for Quantum Error Mitigation

Quantum error mitigation (QEM) is critical for harnessing the potential of near-term quantum devices. Particularly, QEM protocols can be designed based on machine learning, where the mapping between n...

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The article presents a novel learning-based framework for quantum error mitigation that addresses significant challenges in utilizing near-term quantum devices. The novelty of the Clifford Perturbation Data Regression (CPDR) method, along with its superior performance in simulations and real-world application on IBM's quantum processor, highlights its methodological rigor and impactful contributions to quantum computing. Its potential to generalize across different error mitigation techniques enhances its applicability and relevance in advancing the field of quantum computing.

Geometric Deep Learning for Realized Covariance Matrix Forecasting

Traditional methods employed in matrix volatility forecasting often overlook the inherent Riemannian manifold structure of symmetric positive definite matrices, treating them as elements of Euclidean ...

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The article presents a novel methodology that significantly enhances existing models for forecasting covariance matrices by incorporating Riemannian geometric principles. This is a substantial advancement in the field of financial econometrics, as traditional methods often fail to effectively model the complex structures inherent in covariance matrices. The rigorous application of deep learning and the demonstration of superior accuracy bolster the methodological rigor of the paper. Furthermore, the practical implications for finance, particularly in risk assessment and portfolio management, underscore its relevance. The approach's ability to handle high-dimensional data is increasingly pertinent in today's data-rich financial environment.

Search for Inelastic Boosted Dark Matter with the ICARUS Detector at the Gran Sasso Underground National Laboratory

We present the result of a search for inelastic boosted dark matter using the data corresponding to an exposure of 0.13 kton $\cdot$ year, collected by the ICARUS T-600 detector during its 2012-...

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This article presents a search for inelastic boosted dark matter using a specific experimental setup, contributing novel experimental results to the field of particle physics. The methodological rigor is high due to the detailed analysis of data from the ICARUS detector, although the findings return zero events, which is significant in itself for setting exclusion limits. The exploration of dark matter interactions and the introduction of the dark photon as a portal between the dark and visible sectors demonstrates its relevance in theoretical frameworks. These aspects make it impactful for future research in dark matter and related theories.

Skew Laurent Series Ring Over a Dedekind Domain

We show that the formal skew Laurent series ring $R = D(\! ( x; σ)\! )$ over a commutative Dedekind domain $D$ with an automorphism $σ$ is a noncommutative Dedekind domain. If ...

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The article presents significant findings about the structure of skew Laurent series rings over Dedekind domains, a relatively unexplored area. It combines noncommutative algebra with commutative algebra, showcasing rigorous mathematical methods. The results regarding the Grothendieck group and dimensions contribute to foundational knowledge in algebra, potentially leading to further exploration and applications in both algebraic geometry and representation theory.

Agent-based Video Trimming

As information becomes more accessible, user-generated videos are increasing in length, placing a burden on viewers to sift through vast content for valuable insights. This trend underscores the need ...

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The proposed Agent-based Video Trimming (AVT) method introduces a novel task and methodology in the field of video content management, which is critical as user-generated content continues to grow. Its focus on segment relevance and narrative coherence adds significant value to video summarization techniques. The robust evaluation against benchmarks and user studies enhances its practical applicability, suggesting a real potential for impact in both user experience and algorithmic advancement.

On least energy solutions to a pure Neumann Lane-Emden system: convergence, symmetry breaking, and multiplicity

We consider the following Lane-Emden system with Neumann boundary conditions \[ -Δu= |v|^{q-1}v \text{ in } Ω,\qquad -Δv= |u|^{p-1}u \text{ in } Ω,\qquad \partial_νu=\partial_νv=0 \text{ on } \partial...

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The article presents novel insights into the convergence and multiplicity of least energy solutions to a Neumann Lane-Emden system, which is a significant and relatively unexplored area. The application of alternative characterizations within nonlinear eigenvalue problems also indicates methodological rigor. The results on symmetry breaking could stimulate further investigations into related equations, enhancing the article's relevance.

GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency

Identifying affordance regions on 3D objects from semantic cues is essential for robotics and human-machine interaction. However, existing 3D affordance learning methods struggle with generalization a...

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The article introduces a novel framework (GEAL) that addresses significant limitations in existing 3D affordance learning methods, notably generalization and robustness against noise and data corruption. By utilizing pre-trained 2D models and implementing advanced dual-branch and consistency alignment techniques, GEAL shows a promising approach to enhance 3D object interaction insights crucial for robotics applications. The introduction of new benchmarks further strengthens its contribution to the field.

Higgs boson production in association with massive bottom quarks at NNLO+PS

We study the production of a Higgs boson in association with a bottom-quark pair ( $b \bar b H$ ) at hadron colliders. Our calculation is performed in the four-flavour scheme with massive bottom...

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This article offers a significant advancement in Higgs boson production calculations, particularly through its introduction of NNLO corrections combined with parton shower simulations. The novelty of employing the MiNNLO$_{ m PS}$ method alongside addressing tensions between different theoretical predictions enhances its impact. The methodological rigor is high, and the implications for both phenomenological studies and experimental configurations at the LHC are profound.

Proposal of realizing the Sachdev-Ye-Kitaev model from a Kitaev Spin Chain

The Sachdev-Ye-Kitaev (SYK) model is zero-dimensional model simulating quantum chaos using interacting Majorana fermions.Previously proposals have been made to realize the SYK model in fermionic syste...

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This article presents a novel approach to simulating the Sachdev-Ye-Kitaev model, which is significant in the study of quantum chaos and condensed matter physics. The methodological rigor in incorporating a Kitaev spin chain to realize the SYK model and exploring the implications of Majorana fermions from spin operators demonstrates a promising advancement. The work is likely to stimulate further research into quantum chaotics and nonlocal interactions in other systems, although the study might benefit from more robust experimental validation to enhance real-world applicability.

L^2-Betti numbers of branched covers of hyperbolic manifolds

We show that Gromov-Thurston branched covers satisfy the Singer conjecture whenever the degree of the cover is not divisible by a finite set of primes determined by the base manifold and the branch lo...

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This article presents significant findings in the field of mathematical topology by addressing a major conjecture related to L^2-Betti numbers and their connection to Gromov-Thurston branched covers of hyperbolic manifolds. The novelty of extending results concerning the Singer conjecture in this context adds a rich layer to existing knowledge, creating potential for further exploration of the interaction between algebraic topology and geometric structures. The rigorous mathematical analysis can serve as a foundation for future studies in hyperbolic geometry and branched covers, enhancing its relevance in both applied and theoretical aspects.

Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction

Vision Transformers (ViTs) have demonstrated remarkable success in achieving state-of-the-art performance across various image-based tasks and beyond. In this study, we employ a ViT-based neural netwo...

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This article presents a novel application of Vision Transformers in the domain of radio map prediction, demonstrating both methodological rigor and significant advancements over traditional approaches. The use of extensive data augmentation and pretrained models indicates a robust strategy for improving model performance, particularly in diverse and challenging conditions. The broader implications of improving pathloss predictions are substantial, particularly for indoor network planning and optimization.

The Extended Crosswise Model Adjusted for Random Answering

The Extended Crosswise Model is a popular randomized response design that employs a sensitive and a randomized innocuous statement, and asks respondents if one of these statements is true, or that non...

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This article presents novel methodological advancements to improve the Extended Crosswise Model, which is significant in addressing response biases in sensitive research topics. The application of these methods to survey data on doping use among elite athletes adds practical relevance and addresses potential limitations in current methodologies. Consequently, these enhancements hold promise for improving data integrity in sensitive surveys.

Integrating Vision Systems and STPA for Robust Landing and Take-Off in VTOL Aircraft

Vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) are versatile platforms widely used in applications such as surveillance, search and rescue, and urban air mobility. Despite their ...

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The article presents a novel integration of vision systems and STPA, addressing a critical area in VTOL UAV operations where safety is paramount. The methodological rigor is notable, and the focus on comprehensive hazard analysis enhances its relevance. This approach not only advances the state-of-the-art in UAV stability and safety but also sets a foundation for future research in unmanned vehicle systems, making it highly impactful.

High Precision Binding Energies from Physics Informed Machine Learning

Twelve physics informed machine learning models have been trained to model binding energy residuals. Our approach begins with determining the difference between measured experimental binding energies ...

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The article presents a novel integration of physics-informed machine learning techniques to improve the modeling of binding energy residuals, showcasing a promising application of machine learning within nuclear physics. The methodology is rigorous, employing multiple machine learning models and effectively demonstrating their efficacy and extrapolation capabilities, which is critical in predicting binding energies accurately. The combination of physical features with machine learning enhances the applicability and accuracy of predictions, making it a valuable contribution. However, further validations with broader datasets or comparison to existing traditional methods would strengthen the findings.

AdS amplitudes as CFT correlators

We show that AdS amplitudes are CFT correlators to all orders in the loop expansion by showing that they obey the conformal Ward identities. In particular, we provide explicit formulas for the constan...

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This article presents a significant theoretical advancement by proving that AdS (Anti-de Sitter) amplitudes are equivalent to conformal field theory (CFT) correlators, expanding our understanding of the relationship between these two frameworks in quantum gravity and theoretical physics. The approach is methodologically rigorous, demonstrating the adherence to conformal Ward identities and offering explicit formulations that could impact future research in both gravitational theories and quantum field theories. Its implications are likely to inspire further explorations into holographic principles and their applications.

Nonlinear control and stability analysis of a unified Tethered UAV-winder system

This paper presents the development of a comprehensive dynamics and stabilizing control architecture for Tethered Unmanned Aerial Vehicle (TUAV) systems. The proposed architecture integrates both onbo...

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The paper presents a novel approach to controlling tethered UAV systems through a well-developed control architecture. It integrates nonlinear backstepping control techniques, which contributes to the originality of the study. The focus on both onboard and ground-based control is essential for practical applications, and the validation through simulations adds to the methodological rigor. Its applicability in areas such as precision agriculture, surveillance, and logistics enhances its potential impact in developing UAV technologies.

Lyra: An Efficient and Speech-Centric Framework for Omni-Cognition

As Multi-modal Large Language Models (MLLMs) evolve, expanding beyond single-domain capabilities is essential to meet the demands for more versatile and efficient AI. However, previous omni-models hav...

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This paper presents a highly novel approach to integrating speech with multi-modal language models, addressing a significant gap in current research. The methodological innovations, including the use of low-rank adaptation (LoRA) and efficient training strategies, are likely to impact both theoretical advancements and practical applications of AI. The extensive dataset and improved performance metrics also suggest a strong capability for real-world deployment and further advancements in MLLM research.

Loss function to optimise signal significance in particle physics

We construct a surrogate loss to directly optimise the significance metric used in particle physics. We evaluate our loss function for a simple event classification task using a linear model and show ...

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This article presents a novel approach to directly optimize the significance metric in particle physics. The development of a surrogate loss function that improves signal efficiency is both innovative and relevant, as it could enhance the analytical techniques used in collider experiments. The empirical evaluation adds robustness to the claims. The focus on practical applicability makes it a potentially impactful contribution to the field.

A novel ML-fuzzy control system for optimizing PHEV fuel efficiency and extending electric range under diverse driving conditions

Aiming for a greener transportation future, this study introduces an innovative control system for plug-in hybrid electric vehicles (PHEVs) that utilizes machine learning (ML) techniques to forecast e...

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This article presents a novel application of machine learning integrated with fuzzy logic to optimize the fuel efficiency and electric range of plug-in hybrid electric vehicles (PHEVs), showcasing methodological rigor and real-world implications. The significant improvements in efficiency metrics indicate impactful advancements in sustainable vehicle technology, which is crucial for future research focused on eco-friendly transportation solutions.