Thesis Tide - Find relevant and interesting papers

Collaborating in a competitive world: Heterogeneous Multi-Agent Decision Making in Symbiotic Supply Chain Environments

Supply networks require collaboration in a competitive environment. To achieve this, nodes in the network often form symbiotic relationships as they can be adversely effected by the closure of compani...

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The article presents a novel approach to addressing a significant challenge in supply chain management, specifically the dynamics of heterogeneous multi-agent systems in competitive environments. The exploration of reward sharing without profit disclosure adds an innovative angle that could enhance practical applications. The rigorous testing of both homogeneous and heterogeneous agent behaviors, along with the comparative analysis across varying demand environments, demonstrates strong methodological robustness. The implications of mitigating the bullwhip effect in supply chains could have wide-ranging impacts on operational efficiency and decision-making.

Developing a Fair Online Recruitment Framework Based on Job-seekers' Fairness Concerns

The susceptibility to biases and discrimination is a pressing issue in today's labor markets. Though digital recruitment systems play an increasingly significant role in human resources management...

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The article addresses a critical and contemporary issue in labor markets—bias and discrimination in digital recruitment. Its focus on job-seekers' fairness concerns through qualitative analysis presents a novel human-centered perspective, which enhances its applicability and potential impact. The proposed framework is substantial, as it aims to guide algorithm and interface design in a way that ensures fairness, thus holding methodological rigor and innovative contributions. However, further empirical testing of the framework is necessary for validation.

Electromagnetic Follow-up to Gravitational Wave Events with the UltraViolet EXplorer (UVEX)

The Ultraviolet Explorer (UVEX) is expected to fly in 2030 and will have the opportunity -- and the rapid near/far ultraviolet (UV) capabilities -- to glean unprecedented insight into the bright UV em...

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This article presents a novel observational strategy with the UVEX spacecraft to capitalize on the groundbreaking discoveries associated with gravitational wave events. The methodological rigor in simulations, combined with addressing real-world observational challenges, positions this study as highly impactful. Its focus on maximizing data collection from rare events like kilonovae is particularly valuable for astrophysics and multi-messenger astronomy, potentially influencing how future missions will be structured and executed.

Well-Posedness of the R13 Equations Using Tensor-Valued Korn Inequalities

In this paper, we finally catch up with proving the well-posedness of the linearized R13 moment model, which describes, e.g., rarefied gas flows. As an extension of the classical fluid equations, mome...

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The article addresses a significant gap in the understanding of the well-posedness of the R13 moment model, which is an important advancement for the field of kinetic theory and fluid dynamics. The novelty of applying tensor-valued Korn inequalities and the rigorous mathematical framework enhances its methodological rigor. This research not only contributes directly to theoretical advances but also has practical implications for rarefied gas flows, thus inspiring future research in applied mathematics and physics.

EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems

Parameter estimation and trajectory reconstruction for data-driven dynamical systems governed by ordinary differential equations (ODEs) are essential tasks in fields such as biology, engineering, and ...

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The article presents a novel algorithm, EFiGP, that significantly improves parameter estimation and trajectory reconstruction by integrating multiple advanced mathematical concepts. Its methodological approach addresses critical issues encountered in dynamical systems, such as noise and nonlinearity, indicating strong potential for real-world applications. The validation on benchmark examples further reinforces its robustness and applicability.

Mixed Motives

A mixed Weil cohomology with values in an abelian rigid tensor category is a cohomological functor on Voevodsky's category of motives which is satisfying Künneth formula and such that its restrict...

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The article presents a novel cohomological functor linked to mixed Weil cohomology and addresses the theory of mixed motives, significantly contributing to the understanding of cohomological properties in algebraic geometry. Its methodological rigor, as it demonstrates the existence of a universal mixed Weil cohomology, highlights the potential for further advancement and discussion within the field.

MedSlice: Fine-Tuned Large Language Models for Secure Clinical Note Sectioning

Extracting sections from clinical notes is crucial for downstream analysis but is challenging due to variability in formatting and labor-intensive nature of manual sectioning. While proprietary large ...

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This article presents a novel approach to automating clinical note sectioning using open-source large language models, addressing a critical need in the healthcare field for efficient data processing while prioritizing privacy concerns. The methodological rigor demonstrated through comparative analyses against proprietary models enhances its validity, and the high performance metrics bolster the implications for future use in clinical settings.

Quantum enhanced beam tracking surpassing the Heisenberg uncertainty limit

Determining a beam's full trajectory requires tracking both its position and momentum (angular) information. However, the product of position and momentum uncertainty in a simultaneous measurement...

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This article presents a novel method for tracking beams that leverages quantum entanglement to surpass traditional physical limits imposed by quantum mechanics, specifically the Heisenberg Uncertainty Limit. The methodological rigor demonstrated with the proof-of-principle using SPDC marks a significant advancement in quantum sensing. Its implications for achieving near-real-time tracking at the single-photon level showcases high applicability across various domains, augmenting our ability to exceed conventional measurement capabilities. Furthermore, the resilience to background influences indicates robust potential for practical applications.

Personalized Interpolation: An Efficient Method to Tame Flexible Optimization Window Estimation

In the realm of online advertising, optimizing conversions is crucial for delivering relevant products to users and enhancing business outcomes. Predicting conversion events is challenging due to vari...

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The article presents a novel approach to a significant challenge in online advertising, focusing on the crucial aspect of conversion optimization. Its methodological advancements, particularly the Personalized Interpolation method, demonstrate rigor and practical applicability. The emphasis on flexible optimization windows caters to the nuanced demands of advertisers, enhancing the article's relevance in an evolving market. The robust experimental validation also supports its findings, promising substantial improvements in advertising strategies.

5G LDPC Linear Transformer for Channel Decoding

This work introduces a novel, fully differentiable linear-time complexity transformer decoder and a transformer decoder to correct 5G New Radio (NR) LDPC. We propose a scalable approach to decode line...

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This article presents a novel approach to channel decoding using a transformer architecture tailored for 5G applications, addressing a significant limitation in current methods by optimizing computational complexity. The application of a differentiable decoder that employs linear-time complexity makes it particularly impactful, as it could lead to broader adoption of transformer architectures in real-time scenarios. The high relevance is further underlined by rigorous performance comparisons with established algorithms, which enhances its credibility.

StreamingRAG: Real-time Contextual Retrieval and Generation Framework

Extracting real-time insights from multi-modal data streams from various domains such as healthcare, intelligent transportation, and satellite remote sensing remains a challenge. High computational de...

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The article presents a novel framework, StreamingRAG, that significantly enhances real-time analysis of multi-modal data streams, which is increasingly crucial across various domains. Its innovative approach to constructing evolving knowledge graphs and using lightweight models addresses key limitations in existing systems. The improvements in throughput, contextual accuracy, and resource efficiency mark a substantial contribution to the field, making it highly relevant for advancing research and application in real-time data processing.

JWST 1.5 μm and 4.8 μm Photometry of Y Dwarfs

Brown dwarfs lack nuclear fusion and cool with time; the coldest known have an effective temperature below 500 K, and are known as Y dwarfs. We present a James Webb Space Telescope (JWST) photometric ...

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This article provides valuable new photometric data on an under-explored class of astronomical objects, Y dwarfs, using cutting-edge JWST technology. The novelty of confirming cold Y dwarfs and identifying unique spectral features can significantly influence ongoing and future studies in brown dwarf classification and atmospheric studies. The methodological rigor is evident through the collection of detailed photometric data and astrometric measurements, which adds credibility to its findings.

The Perceived Danger (PD) Scale: Development and Validation

There are currently no psychometrically valid tools to measure the perceived danger of robots. To fill this gap, we provided a definition of perceived danger and developed and validated a 12-item bifa...

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The article addresses a significant gap in the assessment of perceived danger associated with robots, offering a well-structured methodology and validation for a new scale. Its development of a bifactor model and the successful comparison with existing scales show both novelty and methodological rigor. The implications for human-robot interaction research are substantial, particularly given the increasing integration of robots in various settings.

Exploring User Perspectives on Data Collection, Data Sharing Preferences, and Privacy Concerns with Remote Healthcare Technology

Remote healthcare technology can help tackle societal issues by improving access to quality healthcare services and enhancing diagnoses through in-place monitoring. These services can be implemented t...

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This article presents a pertinent exploration of user perspectives on an increasingly relevant issue in the contemporary landscape of healthcare technology. The novelty lies in its comprehensive focus on varying age demographics and diverse scenarios related to remote healthcare, which could yield valuable insights for designers and policymakers. The methodological rigor, indicated by the substantial sample size, enhances the reliability of the findings. However, the specific regional focus on Canada may limit broader applicability without further research in varied contexts.

Assessing treatment efficacy for interval-censored endpoints using multistate semi-Markov models fit to multiple data streams

We introduce a computationally efficient and general approach for utilizing multiple, possibly interval-censored, data streams to study complex biomedical endpoints using multistate semi-Markov models...

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This article presents a novel approach for modeling complex biomedical data using multistate semi-Markov models, specifically addressing interval-censored data, which is often challenging in clinical research. The methodological rigor, demonstrated through its application to a significant topic such as the REGEN-COV trial against COVID-19, highlights its relevance and practical impact. The use of advanced computational techniques like MCEM further enhances its applicability in future research, making it a strong candidate for influencing new methodologies in epidemiological studies.

Social dynamics can delay or prevent climate tipping points by speeding the adoption of climate change mitigation

Social behaviour models are increasingly integrated into climate change studies, and the significance of climate tipping points for `runaway' climate change is well recognised. However, there...

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The article presents a novel integration of social behaviour models with climate change models, highlighting the important interplay between social dynamics and climate tipping points. This interdisciplinary approach enhances the understanding of how social factors can influence environmental outcomes, potentially leading to impactful strategies for climate mitigation. The methodological rigor in modeling, alongside the implication of social norms and learning rates, suggests a robust framework that could inspire future research. Additionally, the findings have significant implications for policy-making in climate change mitigation strategies.

Improved subsample-and-aggregate via the private modified winsorized mean

We develop a univariate, differentially private mean estimator, called the private modified winsorized mean designed to be used as the aggregator in subsample-and-aggregate. We demonstrate, via real d...

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The article presents a novel estimator for differentially private mean calculation, which is a significant contribution to the field of data privacy and statistics. The work addresses limitations in existing multivariate estimators and provides theoretical guarantees of its performance across various distributions. Additionally, the insights regarding the optimal choice of subsamples and convergence rates further enhance its relevance, suggesting practical applications in data analysis contexts where data privacy is paramount.

Datasheets for AI and medical datasets (DAIMS): a data validation and documentation framework before machine learning analysis in medical research

Despite progresses in data engineering, there are areas with limited consistencies across data validation and documentation procedures causing confusions and technical problems in research involving m...

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The proposed framework addresses a significant gap in the preparation and documentation of medical datasets for machine learning applications, which is crucial for reproducibility and reliability in medical research. Its novelty lies in the specific focus on medical datasets and a structured approach to standardization and validation, which enhances its applicability and usability in various research scenarios. The methodology appears rigorous, combining checklists, automated tools, and flowcharts, which could greatly streamline the data preparation process in the medical field.

An Approach to Use Depletion Charges for Modifying Band Profiles for Field-Effect Transistors

We present the study of using depletion charges for tailoring lateral band profiles and applying it to the promising gate-all-around field-effect transistors (GAAFET). Specifically, we introduce heavi...

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The article presents a novel approach for modifying the band profiles in GAAFETs, a crucial area in the miniaturization and enhancement of transistor performance. The use of depletion charges introduces a new mechanism for controlling electrical properties, which could have significant implications for future transistor design. The methodological approach, particularly the application of the finite difference method for simulations, showcases rigor and provides a clear analytical framework. Overall, the innovative techniques proposed in this study have substantial potential to influence both theoretical and applied research in semiconductor technology and integrated circuit design.

Capital and CHI: Technological Capture and How It Structures CHI Research

This paper advances a theoretical argument about the role capital plays in structuring CHI research. We introduce the concept of technological capture to theorize the mechanism by which this happens. ...

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The article offers a novel theoretical framework by introducing "technological capture" as a lens to analyze how capital influences CHI research. This fresh perspective helps to understand the structural dynamics within the field, making it highly relevant for researchers aiming to address socio-economic factors in technology design. The methodological rigor appears strong, although further empirical evidence would bolster its claims.