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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!

Ukrainian contribution to particle physics: historical perspective and prospects

Many world-known scientists and engineers like G. Breit, G. Budker, G. Charpak, G. Gamow, M. Goldhaber, A. Ioffe, S. Korolyov, E. Lifshitz, M. Ostrogradsky, S. Timoshenko, V. Veksler were born in Ukra...

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The article highlights a significant historical perspective while simultaneously addressing contemporary issues faced by Ukrainian scientists in the field of particle physics, especially in the context of war. This dual focus offers valuable insights into both the legacy and ongoing contributions of Ukrainian scientists, promoting future investment in this area. The synthesis of historical and modern aspects, along with the implications for motivation in the scientific community, adds to its relevance.

Stochastically Constrained Best Arm Identification with Thompson Sampling

We consider the problem of the best arm identification in the presence of stochastic constraints, where there is a finite number of arms associated with multiple performance measures. The goal is to i...

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This article presents a novel application of Thompson Sampling to a complex problem involving stochastic constraints, which is significant as it expands existing methodologies. The methodological rigor, with established asymptotic optimality, and application to an important problem in decision-making processes underscore its relevance. The numerical examples enhance the credibility of the findings and demonstrate practical applicability.

Computational Astrophysics, Data Science & AI/ML in Astronomy: A Perspective from Indian Community

In contemporary astronomy and astrophysics (A&A), the integration of high-performance computing (HPC), big data analytics, and artificial intelligence/machine learning (AI/ML) has become essential...

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The article discusses the critical need for integrating advanced computational techniques into the field of astrophysics, emphasizing the role of AI/ML and HPC in driving research advancements. Its focus on the Indian community sheds light on a regional aspect of a global issue, thereby addressing both localized and global impacts. The call for a national framework also indicates forward-thinking and potential policy influence, which is significant for resource-limited settings. The methodological approach suggests a strong empirical basis for the proposed framework, enhancing its practical applicability.

ZDySS -- Zero-Shot Dynamic Scene Stylization using Gaussian Splatting

Stylizing a dynamic scene based on an exemplar image is critical for various real-world applications, including gaming, filmmaking, and augmented and virtual reality. However, achieving consistent sty...

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The article addresses a significant gap in the application of stylization techniques to dynamic scenes, presenting a novel framework that not only enhances spatio-temporal consistency but also operates in a zero-shot manner. This represents a substantial advancement over traditional methods constrained by optimization requirements. The methodological rigor is evident through the incorporation of Gaussian splatting, which may also inspire further explorations in other domains of computer graphics and machine learning.

Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Media

Tracking and acquiring simultaneous optical images of randomly moving targets obscured by scattering media remains a challenging problem of importance to many applications that require precise object ...

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The article presents a novel solution to a longstanding problem in optical imaging through a combination of neuromorphic engineering and deep learning. Its innovative approach can significantly advance fields like optical imaging and computer vision, and its emphasis on computational efficiency and low power consumption increases its appeal for practical applications. The methodological rigor demonstrated through benchtop experiments adds to its relevance, indicating a strong potential for influence in future research and applications.

Functors from the infinitary model theory of modules and the Auslander-Gruson-Jensen 2-functor

We define the notion of a $λ$ -definable category, a generalisation of the notion of definable category from the model theory of modules. Let ${\cal C}$ be a $λ$ -accessible addi...

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This article presents a novel generalization of definable categories in the context of model theory for modules. It extends existing results in an impactful way by accommodating any infinite regular cardinal, which enhances the theoretical foundation for studying categories in algebra. The methodological rigor is evident through the detailed characterization of functors and the linkage to relevant concepts in model theory and additive category theory.

Effective textures from a $[SU(3)]^3$ flavored scalar sector

Current constraints on flavor-changing neutral currents (FCNCs) strongly indicate that any new physics emerging at the 1-10 TeV scale must adhere to the Minimal Flavor Violation (MFV) principle, where...

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The article presents a novel model that incorporates a gauged SU(3) flavor symmetry to address significant challenges in flavor physics, particularly relating to neutrino masses and mixing. Its methodology is robust, including effective operators and considerations of phenomenological viability, enhancing its relevance. The predicted implications for neutrino mass hierarchies and flavor-changing neutral currents (FCNCs) provide meaningful contributions to theoretical understanding, potentially influencing experimental approaches. Overall, its innovative nature and relevance to pressing questions in particle physics warrant a high score.

Parameterized Complexity of Segment Routing

Segment Routing is a recent network technology that helps optimizing network throughput by providing finer control over the routing paths. Instead of routing directly from a source to a target, packet...

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The article addresses a significant and emerging field of network technology through the lens of parameterized complexity, shedding light on the NP-hard nature of segment routing. This can impact real-world applications in optimization of network procedures. The investigation into leveraging specific structures of real-world networks enhances its relevance and applicability. The contributions regarding both hardness results and polynomial-time special cases are robust, indicating methodological rigor and potential for future research.

Add Noise, Tasks, or Layers? MaiNLP at the VarDial 2025 Shared Task on Norwegian Dialectal Slot and Intent Detection

Slot and intent detection (SID) is a classic natural language understanding task. Despite this, research has only more recently begun focusing on SID for dialectal and colloquial varieties. Many appro...

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This article presents a novel analysis of slot and intent detection for dialectal variants, addressing a significant gap in the literature regarding low-resource scenarios. The comparative approach using different training data, along with innovative methods like character-level noise injection and Layer Swapping, demonstrates methodological rigor. The high accuracy rates achieved suggest practical applicability, making it highly relevant to the field.

Cubic fourfolds with a symplectic automorphism of prime order

We classify the algebraic and transcendental lattices of a general cubic fourfold with a symplectic automorphism of prime order. We prove the rationality of two families of cubic fourfolds, and that t...

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The article presents a significant advancement in the understanding of cubic fourfolds and their symplectic automorphisms, which is a relatively niche area within algebraic geometry. Its rigorous classification of lattices and the proof of rationality introduce novel insights that could stimulate further exploration into symplectic geometry and the properties of algebraic varieties.

Surface-dependent Majorana vortex phases in topological crystalline insulators

The topological crystalline insulator SnTe exhibits two types of surface Dirac cones: one located at non-time-reversal-invariant momenta on the (001) and (110) surfaces, and the other at time-reversal...

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This article presents a thorough exploration of Majorana vortex phases in SnTe and details novel findings regarding surface Dirac cones and their topological implications. Its methodological rigor, which includes advanced lattice model calculations and the use of symmetries, coupled with the relevance of the findings to cutting-edge topics in condensed matter physics, significantly enhances its impact potential. The findings can encourage further research into topological phases and their applications in quantum computing and spintronics.

On gelation for the Smoluchowski equation

Motivated by the recent results of Andreis-Iyer-Magnanini (2023), we provide a short proof, revisiting the one of Escobedo-Mischler-Perthame (2002), that for a large class of coagulation kernels, any ...

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This article addresses a crucial aspect of the Smoluchowski equation and gelation, providing a refined proof that could advance theoretical understanding in the field. Its connection to recent work highlights its relevance and timeliness. The article demonstrates methodological rigor and presents findings applicable to further studies in coagulation theory and related phenomena.

Disjointly non-singular operators and various topologies on Banach lattices

We continue the study of dispersed subspaces and disjointly non-singular (DNS) operators on Banach lattices using topological methods. In particular, we provide a simple proof of the fact that in an o...

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The article presents a significant advancement in the understanding of disjointly non-singular operators within the context of Banach lattices. It offers a simple proof connecting various mathematical properties, which enhances methodological rigor. The connection to phase retrieval is especially novel, as it integrates classical functional analysis with contemporary applications, making it relevant for future research. Overall, its contributions towards theoretical foundations and potential practical applications are impactful.

Truthful mechanisms for linear bandit games with private contexts

The contextual bandit problem, where agents arrive sequentially with personal contexts and the system adapts its arm allocation decisions accordingly, has recently garnered increasing attention for en...

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This article presents a significant advancement in the understanding of truthful mechanisms in contextual bandit problems, addressing an important gap related to private contexts. The methodological approach, particularly the use of linear programming for achieving truthfulness while controlling regret, is innovative and potentially groundbreaking. The applicability to healthcare contexts, where truthful reporting can significantly impact outcomes, adds a layer of relevance and importance.

Temporal regularity for the stochastic heat equation with rough dependence in space

Consider the nonlinear stochastic heat equation \frac{\partial u (t,x)}{\partial t}=\frac{\partial^2 u (t,x)}{\partial x^2}+ σ(u (t,x))\dot{W}(t,x),\quad t> 0,\, x\in \mathbb{R}, ...

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This paper introduces a significant exploration of the asymptotic properties of the stochastic heat equation, particularly under the influence of rough spatial dependence. Its contribution to understanding temporal gradients and applying results such as Khintchine's law of iterated logarithm presents novel findings that could lead to further investigations into stochastic partial differential equations (SPDEs). The methodical approach and rigorous mathematical groundwork enhance its reliability and applicability.

Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case Study

Reliable slot and intent detection (SID) is crucial in natural language understanding for applications like digital assistants. Encoder-only transformer models fine-tuned on high-resource languages ge...

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This article presents a novel approach to slot and intent detection in the context of dialectal variations, which is a relatively under-explored area within natural language processing (NLP). The use of auxiliary tasks and the introduction of a new dataset add significant value to the field. The findings demonstrate methodological rigor and tangible performance improvements, suggesting practical implications for applications in low-resource dialects.

Rendezfood: A Design Case Study of a Conversational Location-based Approach in Restaurants

The restaurant industry is currently facing a challenging socio-economic situation caused by the rise of delivery services, inflation, and typically low margins. Often, technological opportunities for...

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This article presents a novel approach to enhancing customer engagement in the restaurant industry through the integration of location-based services and conversational agents. The methodological rigor displayed in both the design and evaluation phases strengthens its impact. Moreover, the socio-economic context adds to its relevance, highlighting contemporary challenges in the industry. The exploration of broader implications for the metaverse also positions it as forward-thinking. The key limitations may stem from the specific focus on a single case study, which could limit generalizability, but the insights gained are valuable for practitioners and researchers alike.

First In-Beam Demonstration of a hybrid LaBr3/CeBr3/BGO array to measure radiative capture resonance energies in an extended gas target using a novel time of flight technique

We have deployed a new hybrid array of LaBr3, CeBr3, and BGO scintillators for detecting $γ$ rays at the DRAGON recoil separator at TRIUMF. The array was developed to improve $γ$ -ray t...

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The article presents a novel hybrid scintillator array that significantly improves timing resolution for measuring radiative capture resonance energies. Its methodological innovation and successful first in-beam demonstration indicate a high potential for influencing future experimental setups in nuclear physics. The ability to achieve better precision and lower statistical uncertainties suggests advancement in the accuracy of reactions studied, which is critical for both theoretical and experimental nuclear astrophysics.

Red blood cells aggregates transport for finite concentration

Red blood cells (RBCs) are responsible for transporting oxygen and various metabolites to tissues and organs, as well as removing waste. Several cardiovascular diseases can impair these functions. For...

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The study presents novel insights into the dual role of red blood cell adhesion in both health and disease, supported by rigorous numerical simulations. The findings significantly enhance the understanding of how varying adhesion energy impacts RBC transport and oxygen delivery, which can influence future cardiovascular research and therapeutic approaches.

A Synergistic Framework for Learning Shape Estimation and Shape-Aware Whole-Body Control Policy for Continuum Robots

In this paper, we present a novel synergistic framework for learning shape estimation and a shape-aware whole-body control policy for tendon-driven continuum robots. Our approach leverages the interac...

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This article introduces a novel framework that not only advances shape estimation in continuum robots but also provides a comprehensive control strategy rooted in theoretical principles. Its integration of Cosserat rod theory for shape estimation showcases high methodological rigor and addresses significant challenges faced in the field, indicating it's a promising direction for future research and applications in robotics. Moreover, the extensive evaluations in real-world scenarios underscore the practical applicability of the approach.