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Sharp Bounds for Neighborhood degree based indices of Graphs

In this paper, we will construct formulas and bounds for Neighborhood Degree-based indices of graphs and describe graphs that attain the bounds. Furthermore, we will establish a lower bound for the sp...

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The paper presents novel findings regarding neighborhood degree-based indices and spectral radius, which could contribute significantly to theoretical graph theory. The construction of sharp bounds and formulas suggests methodological rigor, creating potential avenues for further research and applications in related fields. However, the specific applications beyond theoretical boundaries may need further elaboration in the paper itself.

Veryl: A New Hardware Description Language as an Altarnative to SystemVerilog

Veryl, a hardware description language based on SystemVerilog, offers optimized syntax tailored for logic design, ensuring synthesizability and simplifying common constructs. It prioritizes interopera...

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The introduction of Veryl as an alternative to SystemVerilog highlights its potential to enhance logic design through improved syntax and productivity tools. The focus on synthesizability and interoperability is particularly valuable, addressing common issues in hardware description languages. This could significantly influence future research and development in hardware design methodologies.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance

Decision-making in robotics using denoising diffusion processes has increasingly become a hot research topic, but end-to-end policies perform poorly in tasks with rich contact and have limited control...

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The article presents a novel approach to robotics trajectory generation using a hierarchical structure within a diffusion policy that effectively tackles challenges associated with contact-rich tasks. Its methodological rigor, robust benchmarking against state-of-the-art techniques, and emphasis on improved interpretability and controllability are significant factors that endorse its potential impact. Furthermore, the novel technical contributions provide pathways for future research in similar domains, enhancing both theoretical understanding and practical applications.

GazeGaussian: High-Fidelity Gaze Redirection with 3D Gaussian Splatting

Gaze estimation encounters generalization challenges when dealing with out-of-distribution data. To address this problem, recent methods use neural radiance fields (NeRF) to generate augmented data. H...

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The article presents a novel approach to gaze redirection using 3D Gaussian Splatting, addressing significant limitations of previous methods. It demonstrates methodological rigor through comprehensive experiments, shows clear advancements in speed and accuracy, and discusses implications for existing gaze estimation methods. Its contribution is notable in enhancing generalization across datasets, which is critical for practical applications. However, it still operates within a niche area that may limit its broader impact.

The unification in an $\widehat {\mathfrak{s}\mathfrak{u}}(8)_{ k_U = 1}$ affine Lie algebra

A flavor-unified theory based on the simple Lie algebra of ${\mathfrak{s}\mathfrak{u}}(8)$ was previously proposed to generate the observed Standard Model quark/lepton mass hierarchies and the...

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This article presents a novel approach to unifying gauge couplings using the framework of affine Lie algebras, specifically $ ext{su}(8)$. The integration of non-universal symmetry properties in a flavor-unified theory is innovative and could greatly influence future theories addressing mass hierarchies in particle physics. Its methodological rigor, especially in the treatment of supersymmetry and gauge couplings, adds to its robustness.

Oblivious Algorithms for Maximum Directed Cut: New Upper and Lower Bounds

In the maximum directed cut problem, the input is a directed graph $G=(V,E)$ , and the goal is to pick a partition $V = S \cup (V \setminus S)$ of the vertices such that as many edges a...

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This article presents novel advancements in the bounds for oblivious algorithms applied to the maximum directed cut problem, a key issue in algorithmic graph theory. The improvement in approximation ratios and the methodological rigor involving principled parameterizations and computer searches signifies a substantial contribution to the field. Its applicability in graph streaming models adds further utility and relevance, especially combined with the ongoing developments in algorithms for related problems.

Nano-confinement induced nucleation of ice-VII at room temperature

The hydrogen bond (HB) network of water under confinement has been predicted to have distinct structures from that of bulk water. However, direct measurement of the structure has not been achieved. He...

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This article presents a novel experimental observation in the field of water science, particularly focusing on the confinement effects on ice formation and its hydrogen bond network. The use of advanced techniques such as tip-enhanced Raman spectroscopy (TERS) enhances methodological rigor, while the findings have broad implications for understanding water behavior under extreme conditions, thus providing a strong basis for future research.

Data driven learning to enhance a kinetic model of distressed crowd dynamics

The mathematical modeling of crowds is complicated by the fact that crowds possess the behavioral ability to develop and adapt moving strategies in response to the context. For example, in emergency s...

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This article presents a novel approach to modeling crowd dynamics by incorporating stress as a variable, addressing a significant gap in traditional crowd modeling that often overlooks the psychological factors influencing movement. The methodology of using data-driven approaches to enhance the kinetic model adds a layer of sophistication and adaptability, making it relevant for real-world applications. Preliminary results indicate a robust application of the theoretical framework, but further validation with real data would bolster its impact.

Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations

This paper introduces a \textit{Process-Guided Learning (Pril)} framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolved oxygen (DO) conce...

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The article presents a novel integration of physical modeling with recurrent neural networks to improve predictions of lake DO concentrations, which is crucial for water quality and ecosystem health. The introduction of the 'April' model provides a significant advancement in addressing numerical stability issues during important periods, demonstrating methodological rigor. Given its applicability across multiple disciplines, the potential interdisciplinary impact further strengthens its relevance.

A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction

Urban spatio-temporal flow prediction, encompassing traffic flows and crowd flows, is crucial for optimizing city infrastructure and managing traffic and emergency responses. Traditional approaches ha...

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The article presents a novel foundational model, UniFlow, which is highly impactful due to its ability to unify previously separated approaches in urban flow prediction. The introduction of a multi-view spatio-temporal patching mechanism and the SpatioTemporal Memory Retrieval Augmentation (ST-MRA) demonstrates methodological rigor and offers significant advancements over existing models. Its superior performance, particularly in data-scarce scenarios, indicates a high applicability for real-world governmental and commercial use, which is crucial for urban planning and emergency management.

Averages of determinants of Laplacians over moduli spaces for large genus

Let $\mathcal{M}_g$ be the moduli space of hyperbolic surfaces of genus $g$ endowed with the Weil-Petersson metric. We view the regularized determinant $\log \det(Δ_{X})$ of La...

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The article presents significant findings regarding the determinants of Laplacians in moduli spaces of hyperbolic surfaces, with a focus on asymptotic behavior as genus increases. This work contributes to the understanding of geometric analysis and involves rigorous mathematical techniques. Its novel insights into scaling limits and relations to metrics may inspire further research in related areas, such as algebraic geometry and number theory.

Validation of Tumbling Robot Dynamics with Posture Manipulation for Closed-Loop Heading Angle Control

Navigating rugged terrain and steep slopes is a challenge for mobile robots. Conventional legged and wheeled systems struggle with these environments due to limited traction and stability. Northeaster...

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The article presents a novel approach to mobile robotics, addressing limitations in navigating difficult terrains through the introduction of a new multi-modal robot design. The comparison between the model and high-fidelity simulations adds methodological rigor, enhancing the credibility of the findings. Moreover, the focus on dynamic posture manipulation represents a significant advancement in closed-loop heading control for mobile robots, indicating broad applicability within the field of robotics.

Bardeen Spacetime with Charged Dirac Field

In this article, we investigate soliton solutions in a system involving a charged Dirac field minimally coupled to Einstein gravity and the Bardeen field. We analyze the impact of two key parameters o...

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This article addresses soliton solutions involving a charged Dirac field and Bardeen spacetime, which integrates notable concepts in both gravitational theories and quantum fields. The exploration of the impact of electric and magnetic charges on soliton properties showcases novel interactions between classical and quantum regimes, which is a crucial area of expanding research. The work's rigorous mathematical analysis and the implications of discovering new frozen star solutions enhance its relevance. However, the specific applicability outside theoretical physics may be limited, slightly reducing its score.

Quadratic Programming Optimization for Bio-Inspired Thruster-Assisted Bipedal Locomotion on Inclined Slopes

Our work aims to make significant strides in understanding unexplored locomotion control paradigms based on the integration of posture manipulation and thrust vectoring. These techniques are commonly ...

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The article presents a novel approach to locomotion control by integrating biological principles with advanced mathematical optimization techniques. This combination showcases high methodological rigor and expands the current understanding of bipedal locomotion technologies, especially in challenging environments. The use of quadratic programming with posture manipulation and thrust vectoring is innovative, marking a significant step forward in bio-inspired robotic designs.

Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue

Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban environments. We pre...

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The article introduces a novel framework for UAV path optimization in search and rescue, addressing significant real-world challenges like limited visibility and time constraints. Its methodological rigor is evident through the combined usage of both 3D and 2D simulators and advanced algorithms, which not only validates the proposed approach but also contributes to existing methodologies in UAV operations. The demonstrated performance improvements highlight its practical applicability, promising to enhance operational efficiency in critical scenarios.

Internal stresses in low-Reynolds-number fractal aggregates

We present a numerical model of fractal-structured aggregates in low-Reynolds-number flows. Assuming that aggregates are made of cubic particles, we first use a boundary integral method to compute the...

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The article presents a robust numerical model that offers new insights into the internal stress distributions in fractal aggregates under low-Reynolds-number flows, which is a relatively underexplored area. The focus on both settling and shear flow scenarios adds depth and specificity, making the findings particularly relevant for both theoretical understanding and practical applications in fluid dynamics. The rigorous approach and the potential implications for modeling disaggregation processes highlight its importance and applicability to future research.

On adaptivity and minimax optimality of two-sided nearest neighbors

Nearest neighbor (NN) algorithms have been extensively used for missing data problems in recommender systems and sequential decision-making systems. Prior theoretical analysis has established favorabl...

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This article presents a novel approach to nearest neighbor algorithms by analyzing them under conditions of non-smooth non-linear functions and high rates of missing data. Its methodological rigor in theoretical guarantees and empirical validation through numerical simulations adds substantial credibility. The findings are particularly relevant for modern applications in machine learning, especially in recommender systems and sequential decision-making, given the increasing complexity of data and missingness scenarios.

Real-Time Energy-Optimal Path Planning for Electric Vehicles

The rapid adoption of electric vehicles (EVs) in modern transport systems has made energy-aware routing a critical task in their successful integration, especially within large-scale networks. In case...

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The article presents a significant advancement in the field of energy-aware routing for electric vehicles, addressing a critical issue with real-world applications. Its emphasis on incorporating vehicle dynamics into energy modeling showcases its novelty. The methodological rigor is underscored by extensive experimentation on real-world transport networks, solidifying its applicability to current EV integration challenges. The real-time aspects of pathfinding are particularly relevant given the rise of EV adoption, making this research not only timely but also impactful for future developments in transportation planning.

Probabilistic Dynamic Line Rating Forecasting with Line Graph Convolutional LSTM

Dynamic line rating (DLR) is a promising solution to increase the utilization of transmission lines by adjusting ratings based on real-time weather conditions. Accurate DLR forecast at the scheduling ...

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The article presents a novel probabilistic forecasting approach leveraging line graph convolutional LSTM, which is significant in addressing the challenges of Dynamic Line Rating (DLR) due to weather uncertainty. The methodological approach incorporates temporal and spatial correlations, representing an advancement in predictive performance over existing models. The empirical results underscore reliability and efficiency, indicating the model's potential utility in real-world applications. This contributes both to immediate power grid operational improvements and lays groundwork for future research in energy management systems.

Measuring the dipole component of possible Galaxy-binary alignment in the mHz band

We discuss the usability of the gravitational wave detector LISA for studying the orientational distribution of compact white dwarf binaries in the Galactic bulge. We pay special attention to measurin...

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The article presents a novel approach for utilizing LISA to measure the distribution of white dwarf binaries, which is important for understanding galactic evolution and population dynamics. It provides a new theoretical framework that may enhance the precision of gravitational wave astronomy and inspire further studies of binary systems in both galactic and extra-galactic contexts. The methodology appears robust, and the implications for future research in gravitational wave detection are significant.