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Connectivity for AI enabled cities -- A field survey based study of emerging economies

The impact of Artificial Intelligence (AI) is transforming various aspects of urban life, including, governance, policy and planning, healthcare, sustainability, economics, entrepreneurship, etc. Alth...

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This article addresses a critical topic at the intersection of AI, urban studies, and telecommunications, highlighting the challenges faced in emerging economies. Its investigative approach on connectivity and its implications for AI applications is novel and necessary, particularly in addressing the digital divide. The case study in Kathmandu serves as a concrete example that adds empirical weight to its claims, making it relevant for practitioners and researchers alike.

Detecting Many-Body Scars from Fisher Zeros

The far-from-equilibrium dynamics of interacting quantum systems still defy precise understanding. One example is the so-called quantum many-body scars (QMBSs), where a set of energy eigenstates evade...

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This article presents a novel approach for detecting quantum many-body scars (QMBS) using Fisher zeros, advancing the understanding of non-equilibrium dynamics in quantum systems. The introduction of Fisher zeros as diagnostic tools is innovative and could lead to significant theoretical developments in identifying and analyzing QMBS. The methodology appears robust, as it is validated against known models, and its broader applicability in quantum statistical mechanics could stimulate further research in both QMBS and ergodicity breaking.

From Complexification to Self-Similarity: New Aspects of Quantum Criticality

Quantum phase transitions are a fascinating area of condensed matter physics. The extension through complexification not only broadens the scope of this field but also offers a new framework for under...

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The article presents a novel approach to understanding quantum criticality through the lens of complexification, which may redefine existing theories in condensed matter physics. The emphasis on self-similar phenomena and the connection to finite temperature transitions broadens the applications of quantum phase transition models. However, the impact may depend on further empirical validation of the proposed frameworks and concepts.

Void spin distribution as a powerful probe of $σ_{8}$

We present a numerical proof of the concept that the void spin distributions can in principle provide a tight constraint on the amplitude of matter density fluctuation on the scale of $8\,h^{-1}{\...

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This article presents a novel method for probing $σ_{8}$ through void spin distributions, which appears to be a unique and rigorous approach given the challenges related to other cosmic density parameters. The methodological rigor is demonstrated through extensive simulations, and the results suggest a strong and exclusive dependence on $σ_{8}$. This could potentially fill a gap in the current understanding of cosmic structures and inflationary parameters, making it pivotal for future cosmic research. Additionally, the insights on observational feasibility extend its applicability to real-world data, further enhancing its relevance.

Guided Debugging of Auto-Translated Code Using Differential Testing

Large Language Models (LLMs) hold great promise in the task of code translation. However, the lack of explainability complicates the identification of the inevitable translation errors. In this paper,...

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This article presents a novel debugging tool that directly addresses a pressing problem in the integration of large language models for code translation. The use of fuzzing and a heuristic algorithm for error identification showcases methodological rigor and innovativeness. The promising experimental results demonstrate practical applicability and potential for widespread adoption, making it a substantial contribution to the field.

Geometry and symmetries of Hermitian-Einstein and instanton connection moduli spaces

We investigate the geometry of the moduli spaces $\mathscr{M}_{HE}^*(M^{2n})$ of Hermitian-Einstein irreducible connections on a vector bundle $E$ over a Kähler with torsion (KT) manif...

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This article presents a robust analysis of the geometric structures of Hermitian-Einstein and instanton connection moduli spaces, offering significant theoretical advancements and potential practical applications in fields like algebraic geometry and mathematical physics. The focus on Kähler manifolds and their properties is particularly relevant for ongoing research due to the evident connection to string theory and gauge theory contexts. The methods employed are rigorous, and the results could inspire further studies on symmetry actions in these complex geometrical settings.

An Asymptotic Analysis of Bivalent Monoclonal Antibody-Antigen Binding

Ligand-receptor interactions are fundamental to many biological processes. For example in antibody-based immunotherapies, the dynamics of an antibody binding with its target antigen directly influence...

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The article presents a novel mathematical approach to understanding the complex dynamics of bivalent monoclonal antibody-antigen interactions, which is critical for the advancement of antibody-based therapies. The methodological rigor is evident through the combination of asymptotic analysis with numerical simulations. This research could significantly influence future studies by providing a deeper understanding of how these interactions relate to therapeutic efficacy in cancer treatments.

Very soft microgels at the interface behave and flow as Hertzian-like spheres

Soft pair potentials predict a reentrant liquid phase for high concentrations, a behavior not observed experimentally. Here, very soft microgels confined at an oil-water interface are used as a model ...

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This article presents novel findings on very soft microgels at the oil-water interface, exploring flow behavior and elastic properties that are relatively underexplored areas. The use of molecular dynamics simulations to support experimental results demonstrates methodological rigor while revealing a potentially significant mechanism related to fluid-phase behavior not previously observed. Its implications on soft matter physics and material science make it impactful for future research, despite needing further exploration into application contexts.

Justification logics with counterfactual and relevant conditionals

The purpose of this paper is to introduce justification logics based on conditional logics. We introduce a new family of logics, called conditional justification logics, which incorporates a counterfa...

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This article presents a novel approach by introducing conditional justification logics that combine counterfactuals with justification logics, showcasing methodological rigor through the development of relational models and their application to significant philosophical problems like Nozick's conditions and Gettier cases. The depth of analysis and connections made to existing philosophical literature highlight both its relevance and potential to inspire future research in formal epistemology and logic.

Control and its applications in additive combinatorics

We prove new quantitative bounds on the additive structure of sets obeying an $L^3$ 'control' assumption, which arises naturally in several questions within additive combinatorics. Thi...

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The article addresses a significant problem in additive combinatorics and provides new quantitative bounds that are expected to advance methodologies in the field. The improvements in known results apply to several prominent theorems and problems, thereby suggesting a strong potential for influencing future research directions. The mathematical rigor and clear applications bolster its impact.

Predicting Air Temperature from Volumetric Urban Morphology with Machine Learning

In this study, we firstly introduce a method that converts CityGML data into voxels which works efficiently and fast in high resolution for large scale datasets such as cities but by sacrificing some ...

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This article addresses a significant gap in urban planning by innovatively integrating volumetric urban morphology with machine learning to predict air temperature. The methodological rigor is evidenced by the novel voxelization approach and the use of advanced evaluation metrics beyond MSE, enhancing the credibility of the predictions. Its applicability in real-world urban planning scenarios further enhances its relevance.

Sensorimotor Control Strategies for Tactile Robotics

How are robots becoming smarter at interacting with their surroundings? Recent advances have reshaped how robots use tactile sensing to perceive and engage with the world. Tactile sensing is a game-ch...

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This article provides a comprehensive review of recent advances in tactile sensing for robotics, which is both novel and timely given the rise of AI and robotic applications in various fields. The emphasis on sensorimotor control strategies is particularly relevant as it addresses practical implementation challenges—a crucial aspect for both researchers and practitioners. The methodological rigor appears sound, given the review nature of the article, and it offers a structured perspective that could inspire future studies. However, specifics on experimental validation could strengthen the contributions further.

Optimal taxes and subsidies to incentivize modal shift for inner-city freight transport

With increasing freight demands for inner-city transport, shifting freight from road to scheduled line services such as buses, metros, trams, and barges is a sustainable solution. Public authorities t...

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The paper addresses a pressing issue in urban mobility and sustainability by exploring tax and subsidy structures to incentivize modal shifts. Its methodological rigor, especially the bi-level modeling approach and the computational techniques employed, adds robustness to the findings. The study is particularly relevant due to its applicability in real-world urban settings, as demonstrated by the Berlin case study, showcasing potential real impact on freight transport policies. However, while the study presents innovative solutions, its specific context may limit broader applicability without further validation across different urban environments.

DEFOM-Stereo: Depth Foundation Model Based Stereo Matching

Stereo matching is a key technique for metric depth estimation in computer vision and robotics. Real-world challenges like occlusion and non-texture hinder accurate disparity estimation from binocular...

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The study introduces DEFOM-Stereo, a novel framework that effectively integrates monocular depth estimation with stereo matching, addressing real-world challenges in computer vision. Its robust performance on multiple benchmark datasets indicates strong methodological rigor and significant advancements in stereo matching techniques. The model's ability to achieve state-of-the-art results, particularly its zero-shot generalization, suggests a high potential for broad applicability and future research explorations.

RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection

Object detection plays a crucial role in smart video analysis, with applications ranging from autonomous driving and security to smart cities. However, achieving real-time object detection on edge dev...

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The proposed framework addresses critical challenges in real-time object detection on edge devices, utilizing innovative reinforcement learning techniques to optimize the performance of DNNs in resource-constrained environments. Its focus on accuracy-latency trade-offs and experimentation demonstrates methodological rigor and practical applicability, which are crucial for advancing the field.

Pruning for Sparse Diffusion Models based on Gradient Flow

Diffusion Models (DMs) have impressive capabilities among generation models, but are limited to slower inference speeds and higher computational costs. Previous works utilize one-shot structure prunin...

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The article presents a novel iterative pruning method that aims to improve the efficiency of Diffusion Models while maintaining generation quality. This method addresses a significant limitation of existing one-shot pruning techniques by introducing a progressive and gradient-aware strategy. The methodological rigor demonstrated through extensive experimentation, as well as the practical implications in generating models more efficiently, indicate strong potential for impact in the field. However, the established framework of DMs somewhat limits the overall novelty of the approach.

Some properties of the simple nonlinear recursion $y(\ell + 1) = [1-y(\ell)]^p$ with $p$ an arbitrary positive integer

It is shown that the behavior of the solutions of the nonlinear recursion $y(\ell + 1) = [1-y(\ell)]^p$ -- where the dependent variable $y(l)$ is a real number, $\ell= 0; 1; 2...&#...

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The article presents a study of a simple nonlinear recursion that has implications in various areas of mathematical analysis and possibly applied fields such as chaos theory and dynamical systems. The novelty lies in the exploration of properties regarding its behavior for all positive integers of p, which could inspire further research into similar recursive dynamics. However, the scope appears somewhat limited as it does not explore broader applications or more complex scenarios.

Exclusive quarkonium photoproduction: predictions with the Balitsky-Kovchegov equation including the full impact-parameter dependence

The ongoing Run 3 at the Large Hadron Collider (LHC) is substantially increasing the luminosity delivered to the experiments during Run 1 and Run 2. The advent of the high-luminosity upgrade of the LH...

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This article presents novel predictions for exclusive quarkonium photoproduction using an advanced theoretical framework (the Balitsky-Kovchegov equation) that incorporates full impact-parameter dependence. It addresses significant gaps in data arising from past experiments and demonstrates methodological rigor in its predictive approach, making it timely and relevant given the LHC's increasing luminosity. Its findings may refine experimental techniques and enhance theoretical interpretation, suggesting its utility in ongoing and future research.

Unpolarized prethermal discrete time crystal

Prethermal discrete time crystals (DTCs) are a class of nonequilibrium phases of matter that exhibit robust subharmonic responses to periodic driving without requiring disorder. Prior realizations of ...

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The article introduces a novel class of discrete time crystals that does not require polarization, which significantly expands the understanding of non-equilibrium phases of matter. The focus on quantum fluctuations as a stabilizing mechanism provides a fresh perspective and paves the way for future experiments and theoretical developments. Methodologically rigorous, the study offers robust theoretical insights and suggests practical experimental protocols, enhancing its applicability.

Teaching Wav2Vec2 the Language of the Brain

The decoding of continuously spoken speech from neuronal activity has the potential to become an important clinical solution for paralyzed patients. Deep Learning Brain Computer Interfaces (BCIs) have...

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This article presents innovative work in transferring deep learning methodologies from audio speech recognition to decoding neuronal activity, addressing a significant barrier in brain-computer interfaces (BCIs) for communication. The study’s robust methodological approach and positive results regarding characterization and performance (CER scores) highlight a promising avenue for future research in neurotechnology and clinical applications, creating high potential for impact in both research and practical fields.