Viral News

CODES: Benchmarking Coupled ODE Surrogates
21 Nov

CODES: Benchmarking Coupled ODE Surrogates

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[Submitted on 28 Oct 2024 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled CODES: Benchmarking Coupled ODE Surrogates, by Robin Janssen and 2 other authors View PDF HTML (experimental) Abstract:We introduce CODES, a benchmark for comprehensive evaluation of surrogate architectures for coupled ODE systems. Besides standard metrics like mean squared error (MSE) and inference time, CODES provides insights into surrogate behaviour across multiple dimensions like interpolation, extrapolation, sparse data, uncertainty quantification and gradient correlation. The benchmark emphasizes usability through features such as integrated parallel training, a web-based configuration generator, and pre-implemented baseline…
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AtomThink: A Slow Thinking Framework for Multimodal Mathematical Reasoning
21 Nov

AtomThink: A Slow Thinking Framework for Multimodal Mathematical Reasoning

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text
21 Nov

SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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mDAE : modified Denoising AutoEncoder for missing data imputation
21 Nov

mDAE : modified Denoising AutoEncoder for missing data imputation

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Automatic Classification of General Movements in Newborns
21 Nov

Automatic Classification of General Movements in Newborns

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[Submitted on 14 Nov 2024 (v1), last revised 19 Nov 2024 (this version, v2)] View a PDF of the paper titled Automatic Classification of General Movements in Newborns, by Daphn'e Chopard and 6 other authors View PDF HTML (experimental) Abstract:General movements (GMs) are spontaneous, coordinated body movements in infants that offer valuable insights into the developing nervous system. Assessed through the Prechtl GM Assessment (GMA), GMs are reliable predictors for neurodevelopmental disorders. However, GMA requires specifically trained clinicians, who are limited in number. To scale up newborn screening, there is a need for an algorithm that can automatically classify GMs…
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Suicide Risk Assessment on Social Media with Semi-Supervised Learning
21 Nov

Suicide Risk Assessment on Social Media with Semi-Supervised Learning

stp2y0 CommentsViral News
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification
21 Nov

Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification

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arXiv:2411.12853v1 Announce Type: new Abstract: Protein structures represent the key to deciphering biological functions. The more detailed form of similarity among these proteins is sometimes overlooked by the conventional structural comparison methods. In contrast, further advanced methods, such as Triangular Spatial Relationship (TSR), have been demonstrated to make finer differentiations. Still, the classical implementation of TSR does not provide for the integration of secondary structure information, which is important for a more detailed understanding of the folding pattern of a protein. To overcome these limitations, we developed the SSE-TSR approach. The proposed method integrates secondary structure elements (SSEs) into TSR-based…
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FLAME: Frozen Large Language Models Enable Data-Efficient Language-Image Pre-training
21 Nov

FLAME: Frozen Large Language Models Enable Data-Efficient Language-Image Pre-training

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arXiv:2411.11927v1 Announce Type: new Abstract: Language-image pre-training faces significant challenges due to limited data in specific formats and the constrained capacities of text encoders. While prevailing methods attempt to address these issues through data augmentation and architecture modifications, they continue to struggle with processing long-form text inputs, and the inherent limitations of traditional CLIP text encoders lead to suboptimal downstream generalization. In this paper, we propose FLAME (Frozen Large lAnguage Models Enable data-efficient language-image pre-training) that leverages frozen large language models as text encoders, naturally processing long text inputs and demonstrating impressive multilingual generalization. FLAME comprises two key components: 1)…
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Probing the Capacity of Language Model Agents to Operationalize Disparate Experiential Context Despite Distraction
21 Nov

Probing the Capacity of Language Model Agents to Operationalize Disparate Experiential Context Despite Distraction

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arXiv:2411.12828v1 Announce Type: new Abstract: Large language model (LLM) agents show promise in an increasing number of domains. In many proposed applications, it is expected that the agent reasons over accumulated experience presented in an input prompt. We propose the OEDD (Operationalize Experience Despite Distraction) corpus, a human-annotator-validated body of scenarios with pre-scripted agent histories where the agent must make a decision based on disparate experiential information in the presence of a distractor. We evaluate three state-of-the-art LLMs (GPT-3.5 Turbo, GPT-4o, and Gemini 1.5 Pro) using a minimal chain-of-thought prompting strategy and observe that when (1) the input context contains…
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CDI: Copyrighted Data Identification in Diffusion Models
21 Nov

CDI: Copyrighted Data Identification in Diffusion Models

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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