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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!

Natural extensions of embeddable semigroup actions

Continuous actions of semigroups over a topological space are discussed. We focus on semigroups that can be embedded into a group, and study the problem of defining a "natural extension," th...

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This article addresses a niche and complex area of semigroup and dynamical systems theory, which is notable for its potential to bridge gaps between semigroup actions and group representations. The exploration of natural extensions adds a novel perspective on how algebraic properties influence topological actions, suggesting new routes for future theoretical explorations. The methodology seems rigorous, and the results could have implications for both theoretical advancements and practical applications in dynamic systems.

On Fair Ordering and Differential Privacy

In blockchain systems, fair transaction ordering is crucial for a trusted and regulation-compliant economic ecosystem. Unlike traditional State Machine Replication (SMR) systems, which focus solely on...

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This paper addresses a critical gap in blockchain research by integrating fairness with Differential Privacy, which is an innovative approach that could transform transaction ordering protocols. Its novelty lies in establishing a connection between two significant concepts in distributed systems, thus enhancing the theoretical foundation while offering practical implications for regulatory compliance.

OmniJet- ${α_{ C}}$ : Learning point cloud calorimeter simulations using generative transformers

We show the first use of generative transformers for generating calorimeter showers as point clouds in a high-granularity calorimeter. Using the tokenizer and generative part of the OmniJet-$α$...

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The application of generative transformers in simulating calorimeter showers is a novel approach that leverages advanced machine learning techniques in particle physics. The method's ability to represent data as variable-length sequences and point clouds marks a significant innovation over traditional voxel grid methods, allowing for a more accurate and flexible modeling of physical phenomena. This could improve simulation accuracy and efficiency, impacting both experimental and theoretical physics communities.

Propensity score matching in semaglutide retrospective studies

Propensity Score Matching (PSM) is a causal inference technique that is used as a substitution for experimental methods when it is not possible to implement them due to logistical and ethical concerns...

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This paper provides a detailed examination of various feature selection and matching techniques within the context of propensity score matching (PSM), which is vital for ensuring robust causal inference in non-experimental studies. Its focus on methodological rigor and efficiency comparisons enriches the existing body of knowledge and encourages improved practices in this area, thus potentially influencing future research methodologies significantly.

To jump or not to jump: Adhesion and viscous dissipation dictate the detachment of coalescing wall-attached bubbles

Bubble coalescence can promote bubble departure at much smaller sizes compared to buoyancy. This can critically enhance the efficiency of gas-evolving electrochemical processes, such as water electrol...

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The article presents a novel investigation into the dynamics of bubble coalescence and detachment, which is crucial for improving gas-evolving electrochemical processes. The integration of high-speed imaging with numerical simulations enhances methodological rigor and provides significant insights into the role of adhesion forces and viscous dissipation. The findings introduce new criteria for bubble detachment, promoting further exploration in the field. Its implications for improving the efficiency of processes like water electrolysis make it highly relevant for both fundamental research and practical applications.

Primordial Black Hole Hot Spots and Nucleosynthesis

Upon their evaporation via Hawking radiation, primordial black holes (PBHs) may deposit energy in the ambient plasma on scales smaller than the typical distance between two black holes, leading to the...

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The article presents a novel perspective on primordial black holes (PBHs) and their interaction with the cosmic background, specifically focusing on the implications for Big-Bang nucleosynthesis. It addresses a relatively unexplored area of PBH physics and combines theoretical modeling with astrophysical implications, suggesting significant impacts on nucleosynthesis processes. The methodological rigor in exploring temperature profiles and photon flux also strengthens its contributions. However, the reliance on specific parameters may limit broader applicability unless further validated by observational data.

Outlyingness Scores with Cluster Catch Digraphs

This paper introduces two novel, outlyingness scores (OSs) based on Cluster Catch Digraphs (CCDs): Outbound Outlyingness Score (OOS) and Inbound Outlyingness Score (IOS). These scores enhance the inte...

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The introduction of two novel outlyingness scores (OOS and IOS) based on Cluster Catch Digraphs presents a significant advancement in the field of outlier detection, especially given the increasing complexity associated with high-dimensional data. The rigorous validation through Monte Carlo simulations and comparison with established methods showcases methodological robustness, solidifying their utility. Additionally, the applicability of these scores to real-world datasets enhances their relevance for researchers in data science. Overall, this paper's innovative approach and empirical validation suggest a strong potential for future studies that will build on these concepts in outlier detection.

Accelerating Computation of Stable Merge Tree Edit Distances using Parameterized Heuristics

In this paper, we present a novel heuristic algorithm for the stable but NP-complete deformation-based edit distance on merge trees. Our key contribution is the introduction of a user-controlled look-...

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The article presents a novel algorithm that addresses a significant computational challenge in the realm of merge trees, an area of increasing importance in data analysis and topology. The introduction of a user-controlled parameter is particularly innovative, allowing for flexibility between accuracy and efficiency. The empirical results bolster the claims of effectiveness. However, while the methodology is robust, the NP-completeness of the problem raises concerns about its general applicability beyond specific cases, which would benefit from further exploration.

Randomized Rank-Structured Matrix Compression by Tagging

In this work, we present novel randomized compression algorithms for flat rank-structured matrices with shared bases, known as uniform Block Low-Rank (BLR) matrices. Our main contribution is a techniq...

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The article presents a novel algorithm, "tagging," which enhances the efficiency of compressing flat rank-structured matrices. Its mathematical novelty, including connections to projective varieties, coupled with strong empirical validation of its superiority over existing methods, indicates high potential for advancing computational techniques in scientific computing. The significant reduction in computational costs without sacrificing accuracy adds to its applicability and relevance in real-world scenarios.

Deterministic Fault-Tolerant State Preparation for Near-Term Quantum Error Correction: Automatic Synthesis Using Boolean Satisfiability

To ensure resilience against the unavoidable noise in quantum computers, quantum information needs to be encoded using an error-correcting code, and circuits must have a particular structure to be fau...

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This article presents a novel approach to automated synthesis of fault-tolerant quantum circuits, leveraging classical techniques such as satisfiability solving. The emphasis on determinism and applicability to near-term quantum hardware addresses a significant gap in current research and practical implementations of quantum error correction. The methodological rigor is bolstered by the provision of open-source tools, which aids further exploration and development in the field.

Introducing new resonant soft x-ray scattering capability in SSRL

Resonant soft X-ray scattering (RSXS) is a powerful technique for probing both spatial and electronic structures within solid-state systems.We present a newly developed RSXS capability at beamline 13-...

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The article presents a significant advancement in resonant soft X-ray scattering technology, which is crucial for researchers studying solid-state systems. Its novelty lies in the low temperature capability and comprehensive angular motion, enhancing the exploration of reciprocal space, thereby likely influencing future experimental designs. Methodological rigor is demonstrated through extensive testing and validation of the system in diverse material studies, highlighting its broad applicability. The focus on advanced materials such as superlattices and superconductors positions this research as pivotal for ongoing investigations in these areas.

MECASA: Motor Execution Classification using Additive Self-Attention for Hybrid EEG-fNIRS Data

Motor execution, a fundamental aspect of human behavior, has been extensively studied using BCI technologies. EEG and fNIRS have been utilized to provide valuable insights, but their individual limita...

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This article presents a novel approach to combining EEG and fNIRS data for motor execution classification, which has significant implications for BCI research. It addresses the limitations of individual modalities and proposes a hybrid deep learning architecture that shows superior performance across multiple evaluation metrics. The methodological rigor is confirmed by ablation studies, and the potential applications in clinical and rehabilitation settings enhance its relevance.

A Starter Kit for Diversity-Oriented Communities for Undergraduates: Near-Peer Mentorship Programs

This mentoring resource is a guide to establishing and running near-peer mentorship programs. It is based on the working knowledge and best practices developed by the Access Network, a collection of n...

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The article provides a practical and resourceful guide for establishing near-peer mentorship programs aimed at enhancing diversity and inclusion in STEM. Its relevance is highlighted by the applied context and the synthesis of best practices derived from extensive experience across multiple communities. The emphasis on inclusivity and adaptability to various demographics and disciplines further supports its potential impact in educational settings.

On finite dimensional regular gradings

Let $A$ be an associative algebra over an algebraically closed field $K$ of characteristic 0. A decomposition $A=A_1\oplus\cdots \oplus A_r$ of $A$ into a direct sum of...

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The article addresses a specialized topic in the structure of associative algebras and introduces the concept of regular decompositions and their characterization via gradings. The exploration of minimal regular decompositions and their implications on the PI exponent is both novel and methodologically rigorous, potentially leading to new insights and developments in algebraic structures. Its applicability to finite dimensional algebras and relations to bicharacters make it relevant for deeper investigations in algebra theory.

Limit theorems for the generator of a symmetric Levy process with the delta potential

We consider a one-dimensional symmetric Levy process that has local time. In the first part, we construct a self-adjoint extension of the generator of the process so that the constructed operator corr...

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The article presents significant advancements in the theoretical understanding of symmetric Lévy processes and their generators. The construction of a self-adjoint extension and the extension of the Feynman-Kac formula to include delta potentials are noteworthy contributions that enhance existing methodologies in stochastic processes. The rigorous proof of limit theorems adds methodological rigor, which could inspire further research in probabilistic and analytic contexts. The overall novelty and specificity of the results make it a valuable resource for future studies in this area.

The $\texttt{MSG}$ model for cloudy substellar atmospheres: A grid of self-consistent substellar atmosphere models with microphysical cloud formation

State-of-the-art JWST observations are unveiling unprecedented views into the atmospheres of substellar objects in the infrared, further highlighting the importance of clouds. Current forward models s...

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The article presents a novel approach to modeling substellar atmospheres by integrating cloud formation processes in a self-consistent manner, which is crucial for interpreting JWST observations. Its methodological rigor in coupling existing model types highlights its importance. However, the failure to reproduce key observed features suggests limitations that could affect its immediate applicability in observational astrophysics.

Track reconstruction as a service for collider physics

Optimizing charged-particle track reconstruction algorithms is crucial for efficient event reconstruction in Large Hadron Collider (LHC) experiments due to their significant computational demands. Exi...

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This article presents a novel approach by applying inference-as-a-service to optimize particle track reconstruction, which addresses a critical bottleneck in LHC experiments. The utilization of modern computing resources (GPUs) in a scalable manner demonstrates methodological rigor, while the comparative evaluation of two algorithms provides robust evidence for its efficiency. The solution presented is highly applicable to ongoing and future experiments at the LHC and potentially other particle physics experiments, making it very impactful for the field.

The MeerKAT Fornax Survey IV. A close look at the cluster physics through the densest rotation measure grid

Using the Square Kilometre Array (SKA) mid precursor MeerKAT, we acquired broadband spectro-polarimetric data in the context of the MeerKAT Fornax Survey to study the Fornax cluster's magnetic fie...

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The article presents a novel and detailed analysis of the Fornax cluster's magnetic fields using the densest rotation measure grid to date, achieved through advanced spectro-polarimetric techniques with the MeerKAT telescope. The work showcases significant methodological rigor, as it not only deepens the understanding of the Fornax cluster but also establishes new benchmarks in polarized source detection. The findings, including the increment in polarized source counts at low flux densities, may influence future studies on cluster physics and magnetic field configurations in cosmological contexts.

A multi-frequency study of sub-parsec jets with the Event Horizon Telescope

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of uniqu...

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The study presents significant advancements in the understanding of black holes and AGN jets by leveraging a large data set from the EHT. Its methodological rigor in analyzing VLBI images at multiple frequencies and the comparison to theoretical models provide a robust basis for its conclusions. The discoveries of deviations from established models have the potential to inspire new theoretical frameworks, making the research highly impactful.

The 2D pressure structure of the HD 163296 protoplanetary disk as probed by multi-molecular kinematics

[Abridged] Gas kinematics is a new, unique way to study planet-forming environments by an accurate characterization of disk velocity fields. High angular resolution ALMA observations allow deep kinema...

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The article presents a novel approach to study protoplanetary disks using multi-molecular kinematics, offering important insights into the dynamics and structure of such environments. The integration of high-resolution ALMA observations with a multi-species analysis is methodologically rigorous and opens up avenues for enhanced understanding of planet formation processes. Additionally, the findings on how thermal stratification affects gas kinematics could have significant implications for both observational and theoretical studies of disks.