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Analytic Roofline Modeling and Energy Analysis of LULESH Proxy Application on Multi-Core Clusters

We present a thorough performance and energy consumption analysis of the LULESH proxy application in its OpenMP and MPI variants on two different clusters based on Intel Ice Lake (ICL) and Sapphire Ra...

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This article is quite relevant and impactful for performance analysis and energy efficiency in multi-core computing systems. It employs a robust experimental methodology, utilizing a comprehensive Roofline model combined with real hardware measurements, which enhances the validity of its findings. The focus on a widely used proxy application (LULESH) ensures that the implications of the analysis can have practical applications across various computational workloads, making it attractive for both academia and industry.

On uniformly minimal and 'uniformly complete' exponential systems

A.Olevskii and A.Ulanovskii obtained a scale of density results, which correspond to how well an exponential system approximates a uniformly minimal system over a compact set. We extend their result i...

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The article advances the understanding of exponential systems and their approximations through its extension of previous findings to broader contexts, including sets of positive finite measure and new definitions like 'uniformly complete systems'. This introduces novel concepts that can inspire further research in functional analysis and approximation theory.

Magnetic Fields in Massive Star-forming Regions (MagMaR). V. The Magnetic Field at the Onset of High-mass Star Formation

A complete understanding of the initial conditions of high-mass star formation and what processes determine multiplicity require the study of the magnetic field (B-field) in young, massive cores. Usin...

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This article presents novel insights into the role of magnetic fields in the initial conditions of high-mass star formation, challenging existing models such as the core-accretion theory. The use of high-resolution ALMA observations and thorough energy analysis enhances methodological rigor, showcasing its applicability in understanding star formation processes. The findings have broad implications for theories of star formation and may inspire further studies in magnetic field dynamics and high-mass star multiplicity.

Wrinkles in graphene suspended on flat substrates: structure and collapse under hydrostatic pressure

The method of molecular dynamics and molecular mechanics has been used to numerically simulate the formation of wrinkle systems during compression of a graphene sheet lying on a flat solid substrate. ...

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The article presents a detailed numerical simulation of wrinkle formation in graphene under compression, which is a novel exploration of such phenomena. The methodological approach using molecular dynamics is robust, and the findings contribute significantly to understanding material behavior under mechanical stress, which is crucial for advancing applications in nanotechnology and materials science. However, while the focus is narrow, it offers valuable insights that could inspire further research in related areas.

Delta Vectors Unify the Computation for Linear Model Treatment Effects

The science of cause and effect is extremely sophisticated and extremely hard to scale. Using a controlled experiment, scientists get rich insights by analyzing global effects, effects in different se...

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This article presents a novel approach to unify the computation of treatment effects in causal inference, which is essential for both empirical research and practical applications in various fields. The methodology introduces baseline and delta vectors to streamline the analytical processes, demonstrating methodological rigor and innovative engineering aspects that could significantly enhance performance and scalability in experimentation. Its implications for analytics across different sectors underscore its potential impact.

Extreme low submergence in deep-canopy flows

This letter investigates converged statistics in three-dimensional deep-canopy-dominated flows under two low relative submergence conditions: $h/k=1.5$ and $h/k=1.2$ . Using a multi-pla...

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The study presents novel insights into the hydrodynamic behavior of deep-canopy flows, specifically under extreme low submergence conditions. The use of advanced PIV techniques and the identification of flow modifications such as seiching show methodological rigor and provide a strong basis for understanding flow dynamics in canopy environments. The findings can influence future research on canopy hydrodynamics and related environmental phenomena.

An assessment of Alberta's strategy for controlling mountain pine beetle outbreaks

The Canadian province of Alberta spent over 500 million dollars on controlling mountain pine beetle populations, but did it work? Using a statistical modeling framework coupled with long-term field da...

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This article presents a detailed statistical analysis of control measures against mountain pine beetles, combining rigorous modeling with substantial field data. The novel insight into synergistic effects of control measures and severe winters offers a fresh perspective on pest management strategies, making it highly relevant for both practical applications and theoretical frameworks in forestry management.

Dynamical evolution of massless particles in star clusters with NBODY6++GPU-MASSLESS: I. Free-floating MLPs

Context. Low-mass bodies, such as comets, asteroids, planetesimals, and free-floating planets, are continuously injected into the intra-cluster environment after expulsion from their host planetary sy...

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The article presents a novel computational approach (NBODY6++GPU-MASSLESS) to study the dynamics of massless particles in star clusters, a topic that has been relatively overlooked. Its focus on low-mass bodies' dynamics provides new insights into star cluster evolution and potential implications for the understanding of celestial mechanics. The methodology is rigorous, leveraging advanced simulation techniques, and results show insightful trends in the behavior of massless particles. However, the claims would benefit from further validation in diverse scenarios.

Dynamical Tits alternative for groups of almost automorphisms of trees

We prove a dynamical variant of the Tits alternative for the group of almost automorphisms of a locally finite tree $\mathcal{T}$ : a group of almost automorphisms of $\mathcal{T}$ eith...

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The article presents a significant theoretical advancement in the understanding of groups of almost automorphisms of trees, specifically in relation to the Tits alternative. This is important as it generalizes previous results and possesses potential implications for both group theory and dynamical systems. The methodological rigor, evidenced by a hopefully simpler proof for a complex result, further enhances its impact within mathematical research. The work is likely to inspire further studies in related areas due to its theoretical implications and generalization capabilities.

Advancing Operational Efficiency: Airspace Users' Perspective on Trajectory-Based Operations

This work explores the evolution of the Flight Operations Center (FOC) and flight trajectory exchange tools within Trajectory-Based Operations (TBO), emphasizing the benefits of the ICAO's Flight ...

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This article tackles the critical issue of operational efficiency in aviation through the lens of Trajectory-Based Operations (TBO), which is a novel and evolving framework that holds significant promise for improving flight management systems. Its focus on the integration of advanced technologies like PBN and SWIM adds to both the methodological rigor and the applicability of the findings. The inclusion of a live flight case study further strengthens the real-world relevance of the research, making it impactful for industry stakeholders. Overall, the prospective benefits for safety, efficiency, and sustainability position this work as a major contributor to advancements in the field.

Finite groups with dense solitary subgroups

A group $G$ is said to have dense solitary subgroups if each non-empty open interval in its subgroup lattice $L(G)$ contains a solitary subgroup. In this short note, we find all finite...

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The concept of dense solitary subgroups is a novel approach that encourages exploration within the framework of group theory. Identifying all finite groups with this property proposes new avenues for research in subgroup structure and properties, which adds to its academic relevance. However, the short nature of the note may limit comprehensive data on broader implications.

Generative Modeling with Explicit Memory

Recent studies indicate that the denoising process in deep generative diffusion models implicitly learns and memorizes semantic information from the data distribution. These findings suggest that capt...

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The article presents a highly innovative approach to enhancing deep generative diffusion models through the integration of explicit memory, addressing significant computational bottlenecks. The robustness of the methodology is demonstrated by substantial empirical improvements in training and inference efficiency, as well as generation quality, indicating great potential for practical applications. The implications of this work are not only relevant for theoretical advancements in generative modeling but also for real-world applications requiring efficiency and high-quality outputs, making it a pivotal contribution to the field.

Reducing Popularity Influence by Addressing Position Bias

Position bias poses a persistent challenge in recommender systems, with much of the existing research focusing on refining ranking relevance and driving user engagement. However, in practical applicat...

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The article presents a novel perspective on addressing position bias in recommender systems, emphasizing practicality and real-world applications. The methodology is robust, utilizing both offline and online experiments on a large-scale platform, which enhances the credibility of the findings. The implications for fairness in recommender systems and impacts on business partnerships mark it as a significant contribution to the field.

Probabilistic Tits alternative for circle diffeomorphisms

Let $μ_1, μ_2$ be finitely supported probability measures on $\mathrm{Diff}^1_+(S^1)$ such that their supports genererate groups acting proximally on $S^1$ . Let $f^n_ω, f^n...

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The article presents a novel approach to establishing a probabilistic version of the Tits alternative specifically for circle diffeomorphisms, which is a significant contribution to the field of dynamical systems and mathematical analysis. The adaptation of proofs from linear groups suggests strong methodological rigor, while the exploration of measures with varying support levels indicates applicability across different scenarios. Its implications on understanding the structure of groups acting on circles can inspire further research in both theory and applications.

Mountain pine beetle struggles with jack pine: A mechanistic explanation for slowed range expansion in Alberta

Following widespread outbreaks across western North America, mountain pine beetle recently expanded its range from British Columbia into Alberta. However, mountain pine beetle's eastward expansion...

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This article presents a novel mechanistic insight into the range expansion dynamics of the mountain pine beetle (MPB), incorporating a multifaceted methodological approach that enhances its credibility and applicability. The identification of jack pine's phenotypic characteristics as a limiting factor for MPB's successful attacks adds significant depth to our understanding of host-pest interactions. The findings are particularly important for ecological forecasting and forest management, considering the potential implications for biodiversity and forest health amidst climate change. However, the caution regarding the limitations of ecological forecasting suggests some methodological uncertainty.

Reward-based Blockchain Infrastructure for 3D IC Supply Chain Provenance

In response to the growing demand for enhanced performance and power efficiency, the semiconductor industry has witnessed a paradigm shift toward heterogeneous integration, giving rise to 2.5D/3D chip...

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This article presents a novel dual-layer blockchain framework that addresses significant trust and security challenges in the semiconductor supply chain, particularly for 3D ICs. The innovative approach of integrating multiple blockchain consortiums and implementing a reputation mechanism enhances the potential for secure and traceable transactions across the diverse participants. The combination of a theoretical basis and practical implications makes this work highly relevant and potentially transformative for the semiconductor industry.

Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modeling

Accurate predictions and uncertainty quantification (UQ) are essential for decision-making in risk-sensitive fields such as system safety modeling. Deep ensembles (DEs) are efficient and scalable meth...

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The article presents a novel integration of Bayesian optimization with deep ensembles, significantly improving uncertainty quantification in deep neural networks within a critical area—system safety modeling. The approach demonstrates not only methodological rigor but also substantial practical implications, evidenced by significant performance improvements in predicting eddy viscosity in thermal stratification modeling. It addresses a key limitation in existing ensemble methods and introduces a robust solution, thus representing a meaningful advancement in the field of artificial intelligence and its application in safety-critical domains.

Direct measurement of Tan's contact in a one-dimensional Lieb-Liniger gas

The Tan contact has emerged as a pivotal quantity in characterizing many-body quantum systems, bridging microscopic short-range correlations to thermodynamic behavior. It is defined as the weight of u...

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The article presents a novel and direct measurement of the Tan contact within a strongly-correlated Lieb-Liniger gas, addressing a significant challenge previously faced in the field. Its methodological rigor, particularly the innovative two-stage expansion scheme, enhances the reliability of the findings. Furthermore, the results corroborate theoretical predictions, indicating a strong interplay between experimental and theoretical aspects in quantum many-body physics. This work not only contributes to foundational understanding but also opens avenues for future research in ultracold gases and long-range correlations.

ProtoOcc: Accurate, Efficient 3D Occupancy Prediction Using Dual Branch Encoder-Prototype Query Decoder

In this paper, we introduce ProtoOcc, a novel 3D occupancy prediction model designed to predict the occupancy states and semantic classes of 3D voxels through a deep semantic understanding of scenes. ...

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ProtoOcc presents a significant advancement in 3D occupancy prediction by combining innovative components like Dual Branch Encoder and Prototype Query Decoder. Its methodological rigor is evident in the system's state-of-the-art performance on benchmarks. The real-time inference speed also suggests practical applicability in real-world scenarios, enhancing its relevance.

Do $Λ_{CC}$ and $G$ run?

No. In this brief pedagogic note, I describe why the cosmological constant and Newton's constant are not running parameters in physical reactions.

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This article provides a specific pedagogical insight into the nature of the cosmological constant and Newton's constant as non-running parameters. However, the topic is relatively niche and may not offer substantial novel findings or methodological advancements that could influence broader research fields. Its impact is limited to theoretical frameworks rather than experimental or practical applications.