arXiv subjects

Javier Perez

Publications and source records attributed to Javier Perez.

16 recordsLinked to original sources

arXiv2025

Quditto: Emulating and Orchestrating Distributed QKD Network Deployments

Quantum Key Distribution (QKD) offers information-theoretic security by leveraging quantum mechanics, yet the cost and complexity of dedicated hardware and fiber infrastructure have so far limited large-scale deployment and experimentation. In this paper, we introduce Quditto, an automated open-access emulation platform that combines high-fidelity quantum-channel modeling with a standardized key-delivery API, enabling users to interact with the emulated network exactly as they would with real QKD hardware. Quditto modular design supports pluggable protocol implementations, complex key management schemes and detailed channel models, including variable attenuation and decoherence. We validate Quditto by deploying networks of various sizes and demonstrate its flexibility through two proof-of-concept scenarios featuring eavesdropper attacks and heterogeneous channel conditions.

quant-ph↗

arXiv2025

A Principle-based Framework for the Development and Evaluation of Large Language Models for Health and Wellness

The incorporation of generative artificial intelligence into personal health applications presents a transformative opportunity for personalized, data-driven health and fitness guidance, yet also poses challenges related to user safety, model accuracy, and personal privacy. To address these challenges, a novel, principle-based framework was developed and validated for the systematic evaluation of LLMs applied to personal health and wellness. First, the development of the Fitbit Insights explorer, a large language model (LLM)-powered system designed to help users interpret their personal health data, is described. Subsequently, the safety, helpfulness, accuracy, relevance, and personalization (SHARP) principle-based framework is introduced as an end-to-end operational methodology that integrates comprehensive evaluation techniques including human evaluation by generalists and clinical specialists, autorater assessments, and adversarial testing, into an iterative development lifecycle. Through the application of this framework to the Fitbit Insights explorer in a staged deployment involving over 13,000 consented users, challenges not apparent during initial testing were systematically identified. This process guided targeted improvements to the system and demonstrated the necessity of combining isolated technical evaluations with real-world user feedback. Finally, a comprehensive, actionable approach is established for the responsible development and deployment of LLM-powered health applications, providing a standardized methodology to foster innovation while ensuring emerging technologies are safe, effective, and trustworthy for users.

cs.HC↗

arXiv2025

The Anatomy of a Personal Health Agent

Health is a fundamental pillar of human wellness, and the rapid advancements in large language models (LLMs) have driven the development of a new generation of health agents. However, the application of health agents to fulfill the diverse needs of individuals in daily non-clinical settings is underexplored. In this work, we aim to build a comprehensive personal health agent that is able to reason about multimodal data from everyday consumer wellness devices and common personal health records, and provide personalized health recommendations. To understand end-users' needs when interacting with such an assistant, we conducted an in-depth analysis of web search and health forum queries, alongside qualitative insights from users and health experts gathered through a user-centered design process. Based on these findings, we identified three major categories of consumer health needs, each of which is supported by a specialist sub-agent: (1) a data science agent that analyzes personal time-series wearable and health record data, (2) a health domain expert agent that integrates users' health and contextual data to generate accurate, personalized insights, and (3) a health coach agent that synthesizes data insights, guiding users using a specified psychological strategy and tracking users' progress. Furthermore, we propose and develop the Personal Health Agent (PHA), a multi-agent framework that enables dynamic, personalized interactions to address individual health needs. To evaluate each sub-agent and the multi-agent system, we conducted automated and human evaluations across 10 benchmark tasks, involving more than 7,000 annotations and 1,100 hours of effort from health experts and end-users. Our work represents the most comprehensive evaluation of a health agent to date and establishes a strong foundation towards the futuristic vision of a personal health agent accessible to everyone.

cs.AI↗

arXiv2025

Performance of Objective Speech Quality Metrics on Languages Beyond Validation Data: A Study of Turkish and Korean

Objective speech quality measures are widely used to assess the performance of video conferencing platforms and telecommunication systems. They predict human-rated speech quality and are crucial for assessing the systems quality of experience. Despite the widespread use, the quality measures are developed on a limited set of languages. This can be problematic since the performance on unseen languages is consequently not guaranteed or even studied. Here we raise awareness to this issue by investigating the performance of two objective speech quality measures (PESQ and ViSQOL) on Turkish and Korean. Using English as baseline, we show that Turkish samples have significantly higher ViSQOL scores and that for Turkish male speakers the correlation between PESQ and ViSQOL is highest. These results highlight the need to explore biases across metrics and to develop a labeled speech quality dataset with a variety of languages.

eess.AS↗

arXiv2024

Transforming Wearable Data into Personal Health Insights using Large Language Model Agents

Deriving personalized insights from popular wearable trackers requires complex numerical reasoning that challenges standard LLMs, necessitating tool-based approaches like code generation. Large language model (LLM) agents present a promising yet largely untapped solution for this analysis at scale. We introduce the Personal Health Insights Agent (PHIA), a system leveraging multistep reasoning with code generation and information retrieval to analyze and interpret behavioral health data. To test its capabilities, we create and share two benchmark datasets with over 4000 health insights questions. A 650-hour human expert evaluation shows that PHIA significantly outperforms a strong code generation baseline, achieving 84% accuracy on objective, numerical questions and, for open-ended ones, earning 83% favorable ratings while being twice as likely to achieve the highest quality rating. This work can advance behavioral health by empowering individuals to understand their data, enabling a new era of accessible, personalized, and data-driven wellness for the wider population.

cs.AI↗

arXiv2024

Towards a Personal Health Large Language Model

In health, most large language model (LLM) research has focused on clinical tasks. However, mobile and wearable devices, which are rarely integrated into such tasks, provide rich, longitudinal data for personal health monitoring. Here we present Personal Health Large Language Model (PH-LLM), fine-tuned from Gemini for understanding and reasoning over numerical time-series personal health data. We created and curated three datasets that test 1) production of personalized insights and recommendations from sleep patterns, physical activity, and physiological responses, 2) expert domain knowledge, and 3) prediction of self-reported sleep outcomes. For the first task we designed 857 case studies in collaboration with domain experts to assess real-world scenarios in sleep and fitness. Through comprehensive evaluation of domain-specific rubrics, we observed that Gemini Ultra 1.0 and PH-LLM are not statistically different from expert performance in fitness and, while experts remain superior for sleep, fine-tuning PH-LLM provided significant improvements in using relevant domain knowledge and personalizing information for sleep insights. We evaluated PH-LLM domain knowledge using multiple choice sleep medicine and fitness examinations. PH-LLM achieved 79% on sleep and 88% on fitness, exceeding average scores from a sample of human experts. Finally, we trained PH-LLM to predict self-reported sleep quality outcomes from textual and multimodal encoding representations of wearable data, and demonstrate that multimodal encoding is required to match performance of specialized discriminative models. Although further development and evaluation are necessary in the safety-critical personal health domain, these results demonstrate both the broad knowledge and capabilities of Gemini models and the benefit of contextualizing physiological data for personal health applications as done with PH-LLM.

cs.AI↗

arXiv2023

In situ digestion of canola protein gel observed by synchrotron X-Ray Scattering

We address the issue of structure changes of a canola protein gel (as a solid food model) during gastrointestinal digestion. We present a method for synchrotron Small-Angle X-ray Scattering analysis of the digestion of a gel in a capillary. Scanning the capillary allows tracking the digestion under diffusion of enzymatic juices. The fitting parameters characterizing the sizes, scattering intensities and structures allow to distinguish the compact, unfolded or aggregated states of proteins. The evolutions of these parameters enable to detail the complex changes of proteins during gel digestion, involving back-and-forth evolutions with proteins unfolding (1 st and 3 rd steps), re-compaction (2 nd step) due to gastrointestinal pH and enzyme actions, before final protein scissions (4 th step) resulting in small peptides. This complexity is related to the wide ranges of successive pH and enzyme activity acting on large and charged protein assemblies. Digestion is therefore impacted by the conditions of food preparation.

q-bio.QM↗

arXiv2023

Self-Assembly of Rhamnolipids Bioamphiphiles: Understanding Structure-Properties Relationship using Small-Angle X-Ray Scattering

The structure-properties relationship of rhamnolipids, RLs, well known microbial bioamphiphiles (biosurfactants), is exlored in detail by coupling cryogenic transmission electron microscopy (cryo-TEM) and both ex situ and in situ small angle X-ray scattering (SAXS). The self-assembly of three RLs with reasoned variation of their molecular structure (RhaC10, RhaC10C10 and RhaRhaC10C10) and a rhamnose-free C10C10 fatty acid is studied in water as a function of pH. It is found that RhaC10 and RhaRhaC10C10 form micelles in a broad pH range and RhaC10C10 undergoes a micelle-to-vesicle transition from basic to acid pH occurring at pH 6.5. Modelling coupled to fitting SAXS data allows a good estimation of the hydrophobic core radius (or length), the hydrophilic shell thickness, the aggregation number and the surface area per RL. The essentially micellar morphology found for RhaC10 and

cond-mat.mtrl-sci↗

arXiv2023

Deformation mechanisms in PBT at elevated temperatures

The plastic deformation of Polybutylene terephthalate (PBT) is investigated at three temperatures (120$^\circ$C, 150$^\circ$C, 180$^\circ$C) representative of the span between glass transition, $T_g \approx 55^\circ$C and melting $T_m \approx 225^\circ$C temperatures. The mechanisms of tensile deformation are analyzed as a function of the true strain using a combination of temperature-controlled tensile experiments coupled with 2D digital image correlation and synchrotron sourced wide angle (WAXS) and small angle (SAXS) X-ray scattering the amorphous and crystalline regions causing the transition between polymorphs, respectively from the isotropic amorphous phase to a smectic phase and from the $α$ crystalline phase to the $β$ phase.

cond-mat.mtrl-sci↗

arXiv2022

Topological connection between vesicles and nanotubes in single-molecule lipid membranes driven by head-tail interactions

Lipid nanotube-vesicle networks are important channels for intercellular communication and transport of matter. Experimentally observed in neighboring mammalian cells, but also reproduced in model membrane systems, a broad consensus exists on their formation and stability. Lipid membranes must be composed of at least two molecular components, each stabilizing low (generally a phospholipid) and high curvatures. Strong anisotropy or enhanced conical shape of the second amphiphile is crucial for the formation of nanotunnels. Anisotropic driving forces generally favor nanotube protrusions from vesicles. In the present work, we report the unique case of topologically-connected nanotubes-vesicles obtained in the absence of directional forces, in single-molecule membranes, composed of an anisotropic bolaform glucolipid, above its melting temperature, Tm. Cryo-TEM and fluorescence confocal microscopy show the interconnection between vesicles and nanotubes in a single-phase region, between 60{\textdegree} and 90{\textdegree}C under diluted conditions. Solid-state NMR demonstrates that the glucolipid can assume two distinct configurations, head-head and head-tail. These arrangements, seemingly of comparable energy above the Tm, could explain the existence and stability of the topologically-connected vesicles and nanotubes, which are generally not observed for classical single-molecule phospholipid-based membranes above their Tm.

cond-mat.soft↗

arXiv2021

Palmitic Acid Sophorolipid Biosurfactant: From Self-Assembled Fibrillar Network (SAFiN) To Hydrogels with Fast Recovery

Nanofibers are an interesting phase into which amphiphilic molecules can self-assemble. Described for a large number of synthetic lipids, they were seldom reported for natural lipids like microbial amphiphiles, known as biosurfactants. In this work, we show that the palmitic acid congener of sophorolipids (SLC16:0), one of the most studied families of biosurfactants, spontaneously forms a self-assembled fiber network (SAFiN) at pH below 6 through a pH jump process. pH-resolved in-situ Small Angle X-ray Scattering (SAXS) shows a continuous micelle-to-fiber transition, characterized by an enhanced core-shell contrast between pH 9 and pH 7 and micellar fusion into flat membrane between pH 7 and pH 6, approximately. Below pH 6, homogeneous, infinitely long nanofibers form by peeling off the membranes. Eventually, the nanofiber network spontaneously forms a thixotropic hydrogel with fast recovery rates after applying an oscillatory strain amplitude out of the linear viscoelastic regime (LVER): after being submitted to strain amplitudes during 5 min, the hydrogel recovers about 80% and 100% of its initial elastic modulus after, respectively, 20 s and 10 min. Finally, the strength of the hydrogel depends on the medium's final pH, with an elastic modulus fivefold higher at pH 3 than at pH 6.

cond-mat.soft↗

arXiv2021

B-BOP, the SPICA Imaging Polarimeter

We present the B-BOP instrument, a polarimetric camera on board the future ESA-JAXA SPICA far-infrared space observatory. B-BOP will allow the study of the magnetic field in various astrophysical environments thanks to its unprecedented ability to measure the linear polarization of the submillimeter light. The maps produced by B-BOP will contain not only information on total power, but also on the degree and the angle of polarization, simultaneously in three spectral bands (70, 200 and 350 microns). The B-BOP detectors are ultra-sensitive silicon bolometers that are intrinsically sensitive to polarization. Their NEP is close to 10E-18 W/sqrt(Hz). We will present the optical and thermal architectures of the instrument, we will detail the bolometer design and we will show the expected performances of the instrument based on preliminary lab work.

astro-ph.IM↗

arXiv2020

Synthesis of multilamellar walls vesicles polyelectrolyte-surfactant complexes from pH-stimulated phase transition using microbial biosurfactants

Multilamellar wall vesicles (MLWV) are an interesting class of polyelectrolyte-surfactant complexes (PESCs) for wide applications ranging from house-care to biomedical products. If MLWV are generally obtained by a polyelectrolyte-driven vesicle agglutination under pseudo-equilibrium conditions, the resulting phase is often a mixture of more than one structure. In this work, we show that MLWV can be massively and reproductively prepared from a recently developed method involving a pH-stimulated phase transition from a complex coacervate phase (Co). We employ a biobased pH-sensitive microbial glucolipid biosurfactant in the presence of a natural, or synthetic, polyamine (chitosan, poly-L-Lysine, polyethylene imine, polyallylamine). In situ small angle X-ray scattering (SAXS) and cryogenic transmission electron microscopy (cryo-TEM) show a systematic isostructural and isodimensional transition from the Co to the MLWV phase, while optical microscopy under polarized light experiments and cryo-TEM reveal a massive, virtually quantitative, presence of MLWV. Finally, the multilamellar wall structure is not perturbed by filtration and sonication, two typical methods employed to control size distribution in vesicles. In summary, this work highlights a new, robust, non-equilibrium phase-change method to develop biobased multilamellar wall vesicles, promising soft colloids with applications in the field of personal care, cosmetics and pharmaceutics among many others.

cond-mat.soft↗

arXiv2020

Monitoring food structure during digestion using small-angle scattering and imaging techniques

Various studies have shown that food structure has an impact on digestion kinetics. We focus here on the effects of gastric and intestinal enzymes (in-vitro digestion) on two canola seed storage proteins, napin and cruciferin. To monitor structure effect we conducted experiments on gels of these proteins at different pHs, yielding different structures and elastic modulus. What is new is to get information on the mechanisms at the lowest scales, using imaging and radiation scattering at large facilities: Synchrotron fluorescence microscopy, X-Ray scattering, at SOLEIL synchrotron, and Small-Angle Neutron Scattering, at Laboratoire L{é}on Brillouin reactor. We can identify the mechanisms at each step and in two distinct scale ranges, observed simultaneously, the one of the individual protein scale and the one of the structure connectivity:-during gelation individual canola proteins are not deeply modified in comparison with their state in solution ; larger scale gel heterogeneity appears due to connectivity or aggregation-in the gastric step (up to 40 min): o at short scale (large q) we see that the proteins disintegration is much slowed down in gels than in solutions, particularly in the gastric phase; o at larger scales (low q), we see that the gel structure is also self-resistant to the action of the enzyme (pepsin).-in the intestinal step, such kinetics differences hold until major disintegration after no more than 15 min.

cond-mat.soft↗

arXiv2018

A note on eigenvector error bounds for polynomial eigenvalue problems

The standard approach for finding eigenvalues and eigenvectors of matrix polynomials starts by embedding the coefficients of the polynomial into a matrix pencil, known as linearization. Building on the pioneering work of Nakatsukasa and Tisseur, we present error bounds for the computed eigenvectors of matrix polynomials. Our error bounds are applicable to any linearization satisfying two properties. First, eigenvectors of the original matrix polynomial can be recovered from those of the linearization without any arithmetic computation. Second, the linearization presents one-sided factorizations, which relate the residual for the linearization with the residual for the polynomial. Linearizations satisfying these two properties include the family of block Kronecker linearizations. The error bounds imply that an eigenvector has been computed accurately when the residual norm is small, provided that the computed associated eigenvalue is well-separated from the rest of the spectrum of the linearization. The theory is illustrated by numerical examples.

math.NA↗

arXiv2016

Symmetric and skew-symmetric block-Kronecker linearizations

Many applications give rise to structured matrix polynomials. The problem of constructing structure-preserving strong linearizations of structured matrix polynomials is revisited in this work and in the forthcoming ones \cite{PartII,PartIII}. With the purpose of providing a much simpler framework for structure-preserving linearizations for symmetric and skew-symmetric matrix polynomial than the one based on Fiedler pencils with repetition, we introduce in this work the families of (modified) symmetric and skew-symmetric block Kronecker pencils. These families provide a large arena of structure-preserving strong linearizations of symmetric and skew-symmetric matrix polynomials. When the matrix polynomial has degree odd, these linearizations are strong regardless of whether the matrix polynomial is regular or singular, and many of them give rise to structure-preserving companion forms. When some generic nonsingularity conditions are satisfied, they are also strong linearizations for even-degree regular matrix polynomials. Many examples of structure-preserving linearizations obtained from Fiedler pencils with repetitions found in the literature are shown to belong (modulo permutations) to these families of linearizations. In particular, this is shown to be true for the well-known block-tridiagonal symmetric and skew-symmetric companion forms. Since the families of symmetric and skew-symmetric block Kronecker pencils belong to the recently introduced set of minimal bases pencils \cite{Fiedler-like}, they inherit all its desirable properties for numerical applications. In particular, it is shown that eigenvectors, minimal indices, and minimal bases of matrix polynomials are easily recovered from those of any of the linearizations constructed in this work.

math.NA↗