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Domain-Specific Machine Translation to Translate Medicine Brochures in English to Sorani Kurdish

Access to Kurdish medicine brochures is limited, depriving Kurdish-speaking communities of critical health information. To address this problem, we developed a specialized Machine Translation (MT) mod...

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The article presents a novel application of machine translation in the medical field, specifically targeting the translation of health information for a linguistically underserved population. The methodological rigor is supported by the use of a comprehensive parallel corpus and multiple evaluation methods, highlighting its significance in improving access to healthcare information. Its findings have a direct impact on public health and healthcare delivery in Kurdish-speaking communities, encouraging further developments in domain-specific MT applications.

AirTOWN: A Privacy-Preserving Mobile App for Real-time Pollution-Aware POI Suggestion

This demo paper presents \airtown, a privacy-preserving mobile application that provides real-time, pollution-aware recommendations for points of interest (POIs) in urban environments. By combining re...

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The article presents a novel approach to integrating air quality data with personalized location recommendations, addressing important contemporary concerns such as pollution and privacy. The use of federated learning enhances user privacy, which is increasingly critical in mobile applications. Methodologically, the application of collaborative filtering in conjunction with real-time data processing and interpolation techniques demonstrates robust innovation and relevance in urban health crises. Overall, the paper's interdisciplinary approach and adaptability position it as an impactful contribution to multiple fields.

Optimal Multi-Objective Best Arm Identification with Fixed Confidence

We consider a multi-armed bandit setting with finitely many arms, in which each arm yields an $M$ -dimensional vector reward upon selection. We assume that the reward of each dimension (a.k.a. ...

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This article presents a significant advancement in the multi-objective multi-armed bandit problem by introducing a novel algorithm that effectively identifies the best arm in a fixed-confidence setting. The methodological rigor showcased through theoretical validation and empirical studies enhances its credibility. Its focus on optimizing the stopping time while overcoming computational challenges marks a notable contribution. This relevance is bolstered by filling a recognized gap in the literature, which indicates high novelty and applicability.

Two Step SOVA-Based Decoding Algorithm for Tailbiting Codes

In this work we propose a novel decoding algorithm for tailbiting convolutional codes and evaluate its performance over different channels. The proposed method consists on a fixed two-step Viterbi dec...

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The article presents a novel decoding algorithm for tailbiting convolutional codes, which is significant for communication systems. The two-step SOVA-Based technique enhances decoding performance, which is critical in fields requiring efficient data transmission. The method’s close performance to maximum-likelihood decoding suggests a strong methodological foundation that could lead to improvements in existing systems.

FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences

Federated Learning (FL) is a distributed machine learning strategy, developed for settings where training data is owned by distributed devices and cannot be shared. FL circumvents this constraint by c...

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The article addresses a timely and highly relevant challenge in Federated Learning (FL) by introducing a novel algorithm (FedPref) tailored to manage preference heterogeneity among clients. The methodological rigor is demonstrated through comprehensive experimental analyses, showcasing improved performance over existing algorithms. Its focus on personalization in FL broadens the applicability of FL to more complex and varied real-world scenarios, enhancing both privacy and model effectiveness, making it impactful for future research and applications.

Redshift leverage for the search of GRB neutrinos affected by quantum properties of spacetime

Some previous studies based on IceCube neutrinos had found intriguing preliminary evidence that some of them might be GRB neutrinos with travel times affected by quantum properties of spacetime delayi...

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The study provides a novel approach to analyzing GRB neutrinos by focusing on known redshifts and incorporating neutrino energy dependence into the time window analysis. This methodological rigor could lead to more accurate interpretations of previous findings. The implications of quantum properties affecting neutrinos open avenues for high-energy astrophysics and quantum gravity research, although the inconclusiveness of results holds back the overall impact. Still, the synergy proposed between redshift knowledge and dispersion effects is a significant contribution to the field.

Verifying Graph Algorithms in Separation Logic: A Case for an Algebraic Approach (Extended Version)

Specifying and verifying graph-manipulating programs is a well-known and persistent challenge in separation logic. We show that the obstacles in dealing with graphs are removed if one represents graph...

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The article introduces an innovative algebraic approach to verifying graph algorithms using separation logic, which addresses longstanding challenges in the field. The application of partial commutative monoids and morphisms adds novelty and enhances the methodological rigor, suggesting a robust framework for future research. The concise proof of the Schorr-Waite algorithm serves as a valuable case study and could inspire further advancements in graph manipulation verification.

Room temperature observation of the anomalous in-plane Hall effect in epitaxial thin films of a Weyl ferromagnet

Topologically nontrivial electronic states can give rise to novel anomalous Hall effects. The potential appearance of these effects at room temperature holds promise for their application in magnetic ...

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This article presents a significant advancement in the understanding of anomalous Hall effects in topologically nontrivial materials, particularly achieving observation at room temperature, which opens up new avenues for practical applications. The methodological rigor involving both experimental and theoretical approaches strengthens the findings. The novelty lies in the combination of kagome lattice structures and Weyl points, which could have broad implications for materials science and spintronics.

Forecasted Detection Limits on the (Dark) Matter Density in Supermassive Black Hole Binaries for LISA

Supermassive black hole binaries (SMBHBs) are among the most powerful known sources of gravitational waves (GWs). Accordingly, these systems could dominate GW emission in the micro- and millihertz fre...

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The paper provides a novel approach to understanding the interaction between dark matter and supermassive black hole binaries, which is a crucial topic in astrophysics and cosmology. The rigorous simulation of gravitational wave signals offers a promising methodology for future observations with LISA. The emphasis on micro- to millihertz frequencies and the potential to distinguish different scenarios positions this work as highly relevant and impactful for future research in both dark matter studies and gravitational wave astronomy.

Stable cylinders and fine structures for hyperbolic groups and curve graphs

In 1995, Rips and Sela asked if torsionfree hyperbolic groups admit globally stable cylinders. We establish this property for all residually finite hyperbolic groups and curve graphs of finite-type su...

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This article addresses a key question in the field of geometric group theory regarding the structure of hyperbolic groups, which is vital for understanding the properties of torsion-free hyperbolic groups. The introduction of globally stable cylinders and the proof of quasiisometric embeddings in curve graphs are both novel contributions. Furthermore, the use of advanced constructions like Sageev's approach showcases strong methodological rigor, making the findings applicable across various contexts in group theory and geometry.

Learning under Commission and Omission Event Outliers

Event stream is an important data format in real life. The events are usually expected to follow some regular patterns over time. However, the patterns could be contaminated by unexpected absences or ...

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The paper presents a novel approach within the temporal point process framework, addressing a previously under-explored area of event stream data by managing both types of outliers. This dual handling is significant, as most existing methods focus on one type, highlighting the methodological rigor and innovativeness of the proposed solution. The theoretical backing bolsters credibility and potential applicability across various classification problems. Additionally, the method's adaptability for various statistical merits adds to its utility. However, its applicability might still be limited to certain types of data scenarios.

A Transformer-based Autoregressive Decoder Architecture for Hierarchical Text Classification

Recent approaches in hierarchical text classification (HTC) rely on the capabilities of a pre-trained transformer model and exploit the label semantics and a graph encoder for the label hierarchy. In ...

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The article introduces RADAr, a novel approach to hierarchical text classification using a transformer-based autoregressive decoder that streamlines the model requirements while achieving competitive performance. Its simplicity and efficiency in both training and inference add practical relevance, making it an appealing alternative to existing methods. The ability to exchange encoders with emerging models enhances its utility for future research. However, while innovative, its reliance on a traditional architecture might limit its adaptability across different contexts, hence the score is just shy of a full 9.

New Oracles and Labeling Schemes for Vertex Cut Queries

We study succinct representations of vertex cuts by centralized oracles and labeling schemes. For an undirected $n$ -vertex graph $G = (V,E)$ and integer parameter $f \geq 1$ , t...

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The article offers significant advancements in theoretical computer science, particularly in graph theory and data structures, by proposing new oracle and labeling schemes for vertex cut queries which have previously received limited attention. Its focus on succinct representation is both novel and methodologically rigorous, given the reported improvements in space and query time for vertex cut queries, particularly for connected graphs. This relevance is further amplified by targeting underexplored areas in the field, promising to inspire future research and applications in related areas.

Survey of image processing settings used for mammography systems in the United Kingdom: how variable is it?

The aim was to undertake a national survey of the setup of mammography imaging systems in the UK, we were particularly interested in image processing and software version. We created a program that ca...

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This study provides a comprehensive overview of variability in mammography systems' image processing settings, highlighting critical impacts on AI performance and diagnostic effectiveness. The large dataset and focus on real-world applications enhance its relevance and applicability to clinical practices.

Text-to-SQL based on Large Language Models and Database Keyword Search

Text-to-SQL prompt strategies based on Large Language Models (LLMs) achieve remarkable performance on well-known benchmarks. However, when applied to real-world databases, their performance is signifi...

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The paper presents a novel approach to enhance Text-to-SQL translation by integrating a dynamic few-shot strategy and keyword search, addressing a significant gap in existing models when applied to complex real-world scenarios. The methodological rigor is reflected in the experimental validation against real databases, confirming the proposed approach's effectiveness. This directly contributes to the practicality of existing LLM frameworks, thereby promoting further research in this space.

Experimental Realizations of Information Engines: Beyond Proof of Concept

Gathering information about a system enables greater control over it. This principle lies at the core of information engines, which use measurement-based feedback to rectify thermal noise and convert ...

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This article presents a thorough examination of the experimental realizations of information engines, which is a cutting-edge area in thermodynamics and statistical mechanics. Its focus on recent experimental validations and novel applications showcases the article's relevance and contribution to advancing theoretical and practical understanding. The discussion of intertwined technological advances and potential applications to diverse systems enhances its interdisciplinary appeal, indicating potential to inspire future research ventures.

WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control

The wind farm control problem is challenging, since conventional model-based control strategies require tractable models of complex aerodynamical interactions between the turbines and suffer from the ...

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This article presents WFCRL, a novel and significant contribution to the field of renewable energy, particularly in wind farm optimization. The focus on cooperative multi-agent reinforcement learning (MARL) enhances existing methodologies and addresses complex aerodynamics through an innovative approach. The provision of a benchmark suite for enabling further research in this area is highly valuable. The rigorous integration with state-of-the-art simulators adds methodological strength and practical applicability.

Analysis of Resolution as a Function of Microparticle Size in Virtual Super-Resolution Optical Imaging for 2D System

The work shows that visualization using microparticles allows one to distinguish objects that are inaccessible during direct observation. This analysis is based on a full two-dimensional simulation of...

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This article presents a novel approach to enhancing optical imaging through the use of microparticles, which could significantly improve resolution in 2D systems. The rigorous examination of resolution as a function of microparticle size, complemented by comprehensive simulations, demonstrates methodological rigor. The findings are directly applicable to fields such as imaging technology and optical engineering, potentially leading to advancements in microscopic imaging techniques.

Maps from Grassmannians of 2-planes to projective spaces

Using quaternions and octonions, we construct some maps from the Grassmannian of 2-dimensional planes of $\mathbb{R}^n$ , $\mathrm{Gr}_2(\mathbb{R}^n)$ , to the projective space $\ma...

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The article presents a novel approach using quaternions and octonions to explore the mappings from Grassmannians to projective spaces, which could potentially lead to new insights in algebraic topology and geometric analysis. The isomorphism at fundamental groups and the classification of maps as submersions add significant methodological rigor, providing a strong theoretical contribution. The application regarding the Lusternik-Schnirelmann category increases the paper's relevance, suggesting practical implications in topological studies.

Overview and Roadmap of Team Automata

Team Automata is a formalism for interacting component-based systems proposed in 1997, whereby multiple sending and receiving actions from concurrent automata can synchronise. During the past 25+ year...

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The article provides a comprehensive overview of a formalism, team automata, that has significant historical relevance and ongoing applicability in component-based systems. It identifies current research trends and outlines potential future directions, making it a valuable resource for researchers in this domain. The systematic comparison with related models adds to its methodological rigor, enhancing its impact.