arXiv:2511.19260v1 Announce Type: new 
Abstract: Hunger and satiety dynamics shape dietary behaviors and metabolic health, yet remain difficult to capture in everyday settings. We present a Nutrition Photoplethysmography Language Model (NPLM), integrating continuous photoplethysmography (PPG) from wearables with meal descriptions. NPLM projects PPG into embeddings interpretable by language models, enabling joint reasoning over physiology and meal context. Trained on 19,340 participants and 1.1 million meal-PPG pairs, the model improved daily caloric intake prediction by 11% over text-only baselines, with accuracy maintained when 80% of meal text was removed. In an independent validation study (n=140) with controlled dining and detailed meal information, the model replicated these findings. These results demonstrate the value of integrating physiological measurements from consumer wearables with meal information for noninvasive dietary monitoring at scale.

تم تطوير نموذج لغة جديد للضوء الضوئي الغذائي (NPLM) يدمج بيانات الضوء الضوئي المستمر (PPG) من الأجهزة القابلة للارتداء مع أوصاف الوجبات لتحسين مراقبة النظام الغذائي. تم تدريب النموذج على مجموعة بيانات كبيرة تضم 19,340 مشاركًا و1.1 مليون زوج من الوجبات وPPG، وحقق تحسينًا بنسبة 11% في توقع استهلاك السعرات الحرارية اليومية مقارنة بالطرق التقليدية المعتمدة فقط على النص.

Se ha desarrollado un nuevo Modelo de Lenguaje de Fotopletismografía Nutricional (NPLM), que integra datos de fotopletismografía (PPG) continuos de dispositivos portátiles con descripciones de comidas para mejorar el monitoreo dietético. El modelo, entrenado con un conjunto de datos de 19,340 participantes y 1.1 millones de pares de comida-PPG, logró una mejora del 11% en la predicción de la ingesta calórica diaria en comparación con los métodos tradicionales basados solo en texto.

Un nouveau modèle de langage en photopléthysmographie nutritionnelle (NPLM) a été développé, intégrant des données de photopléthysmographie (PPG) continues provenant de dispositifs portables avec des descriptions de repas pour améliorer le suivi diététique. Le modèle, formé sur un ensemble de données substantiel de 19 340 participants et 1,1 million de paires repas-PPG, a atteint une amélioration de 11 % dans la prédiction de l'apport calorique quotidien par rapport aux méthodes traditionnelles basées uniquement sur le texte.

A new Nutrition Photoplethysmography Language Model (NPLM) has been developed, integrating continuous photoplethysmography (PPG) data from wearables with meal descriptions to enhance dietary monitoring. The model, trained on a substantial dataset of 19,340 participants and 1.1 million meal-PPG pairs, achieved an 11% improvement in predicting daily caloric intake compared to traditional text-only methods.

A Nutrition Multimodal Photoplethysmography Language Model

arXiv:2511.09118v2 Announce Type: replace 
Abstract: Generative models are increasingly central to scientific workflows, yet their systematic use and interpretation require a proper understanding of their limitations through rigorous validation. Classic approaches struggle with scalability, statistical power, or interpretability when applied to high-dimensional data, making it difficult to certify the reliability of these models in realistic, high-dimensional scientific settings. Here, we propose the use of the New Physics Learning Machine (NPLM), a learning-based approach to goodness-of-fit testing inspired by the Neyman--Pearson construction, to test generative networks trained on high-dimensional scientific data. We demonstrate the performance of NPLM for validation in two benchmark cases: generative models trained on mixtures of Gaussian models with increasing dimensionality, and a public end-to-end model, known as FlowSim, developed to generate high-energy physics collision events. We demonstrate that the NPLM can serve as a powerful validation method while also providing a means to diagnose sub-optimally modeled regions of the data.

تم اقتراح نهج جديد للتحقق من صحة النماذج التوليدية، يُعرف باسم آلة التعلم الفيزيائية الجديدة (NPLM)، لمعالجة قيود الأساليب التقليدية في البيانات العلمية عالية الأبعاد. يستلهم هذا الأسلوب من بناء نيمان-بيرسون ويهدف إلى تعزيز موثوقية الشبكات التوليدية، خاصة في سير العمل العلمي المعقد.

Se ha propuesto un nuevo enfoque para validar modelos generativos, denominado New Physics Learning Machine (NPLM), para abordar las limitaciones de los métodos tradicionales en datos científicos de alta dimensión. Este método, inspirado en la construcción de Neyman-Pearson, tiene como objetivo mejorar la fiabilidad de las redes generativas, especialmente en flujos de trabajo científicos complejos.

Une nouvelle approche pour valider les modèles génératifs, appelée New Physics Learning Machine (NPLM), a été proposée pour surmonter les limitations des méthodes traditionnelles dans les données scientifiques de haute dimension. Cette méthode, inspirée de la construction de Neyman-Pearson, vise à améliorer la fiabilité des réseaux génératifs, en particulier dans des flux de travail scientifiques complexes.

A new approach to validating generative models, called the New Physics Learning Machine (NPLM), has been proposed to address the limitations of traditional methods in high-dimensional scientific data. This method is inspired by the Neyman-Pearson construction and aims to enhance the reliability of generative networks, particularly in complex scientific workflows.

Learning to Validate Generative Models: a Goodness-of-Fit Approach

arXiv:2511.18829v1 Announce Type: new 
Abstract: Heart rate estimation from photoplethysmography (PPG) signals generated by wearable devices such as smartwatches and fitness trackers has significant implications for the health and well-being of individuals. Although prior work has demonstrated deep learning models with strong performance in the heart rate estimation task, in order to deploy these models on wearable devices, these models must also adhere to strict memory and latency constraints. In this work, we explore and characterize how large pre-trained PPG models may be distilled to smaller models appropriate for real-time inference on the edge. We evaluate four distillation strategies through comprehensive sweeps of teacher and student model capacities: (1) hard distillation, (2) soft distillation, (3) decoupled knowledge distillation (DKD), and (4) feature distillation. We present a characterization of the resulting scaling laws describing the relationship between model size and performance. This early investigation lays the groundwork for practical and predictable methods for building edge-deployable models for physiological sensing.

استكشف الباحثون تقطير نماذج التصوير الضوئي (PPG) الكبيرة المدربة مسبقًا إلى نماذج أصغر مناسبة لتقدير معدل ضربات القلب في الوقت الفعلي على الأجهزة القابلة للارتداء مثل الساعات الذكية وأجهزة تتبع اللياقة البدنية. تقيم الدراسة أربع استراتيجيات للتقطير: التقطير الصارم، التقطير اللين، التقطير المعرفي المنفصل، وتقطير الميزات، بهدف تلبية قيود الذاكرة والكمون لأجهزة الحافة.

Investigadores han explorado la destilación de grandes modelos preentrenados de fotopletismografía (PPG) en modelos más pequeños adecuados para la estimación en tiempo real de la frecuencia cardíaca en dispositivos portátiles como relojes inteligentes y rastreadores de fitness. El estudio evalúa cuatro estrategias de destilación: destilación dura, destilación suave, destilación de conocimiento desacoplada y destilación de características, con el objetivo de cumplir con las restricciones de memoria y latencia de los dispositivos de borde.

Des chercheurs ont exploré la distillation de grands modèles pré-entraînés de photopléthysmographie (PPG) en modèles plus petits adaptés à l'estimation en temps réel de la fréquence cardiaque sur des dispositifs portables tels que les montres intelligentes et les trackers de fitness. L'étude évalue quatre stratégies de distillation : distillation dure, distillation douce, distillation de connaissances découplée et distillation de caractéristiques, visant à respecter les contraintes de mémoire et de latence des dispositifs en périphérie.

Researchers have explored the distillation of large pre-trained photoplethysmography (PPG) models into smaller models suitable for real-time heart rate estimation on wearable devices like smartwatches and fitness trackers. The study evaluates four distillation strategies: hard distillation, soft distillation, decoupled knowledge distillation, and feature distillation, aiming to meet the memory and latency constraints of edge devices.

A Nutrition Multimodal Photoplethysmography Language Model

Was this article worth reading? Share it

OpenL Translator

Hypertune

GPTHuman