arXiv subjects

Laurent Simon

Publications and source records attributed to Laurent Simon.

18 recordsLinked to original sources

arXiv2026

An iterative method bridging DFT, disorder averaging, and experiment in intercalated materials: application to Au-intercalated graphene

Intercalation can strongly modify the electronic dispersion of a host material, as directly revealed by angle-resolved photoemission spectroscopy (ARPES). We develop a general iterative method combining density functional theory (DFT), tight-binding (TB), disorder averaging within the self-consistent T-matrix approximation (SCTMA), and experiment, to construct an effective model of the intercalated system. DFT identifies the relevant microscopic degrees of freedom and constrains selected model parameters, while comparison of SCTMA calculations with experiment guides their further refinement. We apply this method to graphene intercalated with Au clusters and show that it reproduces the main ARPES signatures of the Au-cluster phase, including the broadening of the V12an Hove singularity and the emergence of kink-like features in the dispersion. The essential microscopic ingredients identified by the analysis are the hybridization between selected intercalant orbitals and the graphene states, together with an intercalation-induced local scattering potential.

cond-mat.mes-hall↗

arXiv2026

Effects of gold cluster intercalation in graphene: stationary waves and modified QPI features

Gold intercalation beneath epitaxial graphene on SiC produces a cluster phase with unusual standing waves and quasiparticle-interference (QPI) features concentrated near the graphene M points. We show that this can be explained by Au intercalation below graphene hollow sites, which induces a local scattering potential on the six surrounding carbon atoms. Within a T-matrix treatment, this ring-like scatterer produces elliptical QPI structures centered near M, in agreement with the experimental FT-STS measurements. We further show that these QPI features naturally generate the nearly stationary standing-wave patterns observed in real space. Finally, we compute the local-density-of-states contrast on and off a small cluster and show that its sign and magnitude are strongly energy dependent, consistent with the experimental observations.

cond-mat.mes-hall↗

arXiv2026

GDM AI Control Roadmap

AI agents are rapidly accelerating work at frontier AI companies, helping with AI R&D, cyber-defence, and advancing scientific discoveries. As these agents become more tightly integrated into our systems, unlocking their full potential requires rethinking how we do security. We should not assume that AI agents are always perfectly aligned, but should instead build in multiple layers of defence. We present the GDM AI Control Roadmap (v0.1) -- a first-of-its-kind blueprint for internal security against potentially misaligned AI. This report provides: * Threat modelling: We adopt a conservative approach to threat modelling and assume a hypothetical AI adversary pursuing undesirable goals in internal deployment. We introduce TRAIT&R, a taxonomy of tactics and techniques available to such a hypothetical AI adversary, building on the established security framework MITRE ATT&CK. * Capability-based mitigation: Because controlling more capable models requires more costly interventions, we link specific defensive measures to evolving model capabilities (such as the ability to reason opaquely or execute complex cyberattacks). As models get more powerful, our defences should escalate accordingly. We outline four Detection tiers (D1-D4) and three Prevention and Response tiers (R1-R3). * A portfolio of practical defences: We suggest 15 concrete, tiered mitigations. These range from low-cost interventions for current models (e.g., chain-of-thought monitoring, asynchronous alerts) to advanced safeguards for future models (e.g., real-time access control, system-level anomaly detection, internal activations monitoring, and shutdown infrastructure). AI control is a nascent field, and implementing these mitigations requires navigating difficult trade-offs between security and developer velocity. We expect the roadmap to evolve as we gain more experience and as the field in turn evolves.

cs.CR↗

arXiv2026

Alterations in Conformations of Poly(3-hexylthiophene) on Au(111) Induced by Annealing

Employing high-vacuum electrospray deposition and scanning tunneling microscopy, we investigated how individual poly(3-hexylthiophene) (P3HT) chains navigated on the periodic energy landscape of a reconstructed Au(111) surface. The resulting polymer conformations were governed by the interplay between the periodically corrugated substrate, in particular the depth and regularity of the modulated surface potential, and thermal energy. On a regularly reconstructed surface, annealing at {\deg}C provided sufficient energy for chain segments to overcome energy barriers of the corrugated surface potential landscape, allowing monomers along the chain to experience a strong thermodynamic driving force toward the low-energy valleys on the surface. The adsorbed polymers adopted a state where the polymer conformations were replicating the herringbone pattern. By contrast, on an irregularly reconstructed surface, the correspondingly disordered potential landscape yielded a diverse mix of coiled polymer chains performing a two-dimensional random walk and collapsed chains located in troughs of the energy landscape. Intriguingly, annealing at {\deg}C forced polymers to form clusters of many chains. Our results establish that thermal energy and substrate topography represent control parameters for altering polymer conformations, providing a mechanistic framework for rationally designing polymer nanostructures at the molecular level.

cond-mat.soft↗

arXiv2025

PickleBall: Secure Deserialization of Pickle-based Machine Learning Models (Extended Report)

Machine learning model repositories such as the Hugging Face Model Hub facilitate model exchanges. However, bad actors can deliver malware through compromised models. Existing defenses such as safer model formats, restrictive (but inflexible) loading policies, and model scanners have shortcomings: 44.9% of popular models on Hugging Face still use the insecure pickle format, 15% of these cannot be loaded by restrictive loading policies, and model scanners have both false positives and false negatives. Pickle remains the de facto standard for model exchange, and the ML community lacks a tool that offers transparent safe loading. We present PickleBall to help machine learning engineers load pickle-based models safely. PickleBall statically analyzes the source code of a given machine learning library and computes a custom policy that specifies a safe load-time behavior for benign models. PickleBall then dynamically enforces the policy during load time as a drop-in replacement for the pickle module. PickleBall generates policies that correctly load 79.8% of benign pickle-based models in our dataset, while rejecting all (100%) malicious examples in our dataset. In comparison, evaluated model scanners fail to identify known malicious models, and the state-of-art loader loads 22% fewer benign models than PickleBall. PickleBall removes the threat of arbitrary function invocation from malicious pickle-based models, raising the bar for attackers to depend on code reuse techniques.

cs.CR↗

arXiv2025

Machine Learning Models Have a Supply Chain Problem

Powerful machine learning (ML) models are now readily available online, which creates exciting possibilities for users who lack the deep technical expertise or substantial computing resources needed to develop them. On the other hand, this type of open ecosystem comes with many risks. In this paper, we argue that the current ecosystem for open ML models contains significant supply-chain risks, some of which have been exploited already in real attacks. These include an attacker replacing a model with something malicious (e.g., malware), or a model being trained using a vulnerable version of a framework or on restricted or poisoned data. We then explore how Sigstore, a solution designed to bring transparency to open-source software supply chains, can be used to bring transparency to open ML models, in terms of enabling model publishers to sign their models and prove properties about the datasets they use.

cs.LG↗

arXiv2025

An Explainable Diagnostic Framework for Neurodegenerative Dementias via Reinforcement-Optimized LLM Reasoning

The differential diagnosis of neurodegenerative dementias is a challenging clinical task, mainly because of the overlap in symptom presentation and the similarity of patterns observed in structural neuroimaging. To improve diagnostic efficiency and accuracy, deep learning-based methods such as Convolutional Neural Networks and Vision Transformers have been proposed for the automatic classification of brain MRIs. However, despite their strong predictive performance, these models find limited clinical utility due to their opaque decision making. In this work, we propose a framework that integrates two core components to enhance diagnostic transparency. First, we introduce a modular pipeline for converting 3D T1-weighted brain MRIs into textual radiology reports. Second, we explore the potential of modern Large Language Models (LLMs) to assist clinicians in the differential diagnosis between Frontotemporal dementia subtypes, Alzheimer's disease, and normal aging based on the generated reports. To bridge the gap between predictive accuracy and explainability, we employ reinforcement learning to incentivize diagnostic reasoning in LLMs. Without requiring supervised reasoning traces or distillation from larger models, our approach enables the emergence of structured diagnostic rationales grounded in neuroimaging findings. Unlike post-hoc explainability methods that retrospectively justify model decisions, our framework generates diagnostic rationales as part of the inference process-producing causally grounded explanations that inform and guide the model's decision-making process. In doing so, our framework matches the diagnostic performance of existing deep learning methods while offering rationales that support its diagnostic conclusions.

cs.LG↗

arXiv2025

Evaluation of Deep Audio Representations for Hearables

Effectively steering hearable devices requires understanding the acoustic environment around the user. In the computational analysis of sound scenes, foundation models have emerged as the state of the art to produce high-performance, robust, multi-purpose audio representations. We introduce and release Deep Evaluation of Audio Representations (DEAR), the first dataset and benchmark to evaluate the efficacy of foundation models in capturing essential acoustic properties for hearables. The dataset includes 1,158 audio tracks, each 30 seconds long, created by spatially mixing proprietary monologues with commercial, high-quality recordings of everyday acoustic scenes. Our benchmark encompasses eight tasks that assess the general context, speech sources, and technical acoustic properties of the audio scenes. Through our evaluation of four general-purpose audio representation models, we demonstrate that the BEATs model significantly surpasses its counterparts. This superiority underscores the advantage of models trained on diverse audio collections, confirming their applicability to a wide array of auditory tasks, including encoding the environment properties necessary for hearable steering. The DEAR dataset and associated code are available at https://dear-dataset.github.io.

cs.SD↗

arXiv2024

Drift Detection: Introducing Gaussian Split Detector

Recent research yielded a wide array of drift detectors. However, in order to achieve remarkable performance, the true class labels must be available during the drift detection phase. This paper targets at detecting drift when the ground truth is unknown during the detection phase. To that end, we introduce Gaussian Split Detector (GSD) a novel drift detector that works in batch mode. GSD is designed to work when the data follow a normal distribution and makes use of Gaussian mixture models to monitor changes in the decision boundary. The algorithm is designed to handle multi-dimension data streams and to work without the ground truth labels during the inference phase making it pertinent for real world use. In an extensive experimental study on real and synthetic datasets, we evaluate our detector against the state of the art. We show that our detector outperforms the state of the art in detecting real drift and in ignoring virtual drift which is key to avoid false alarms.

cs.DC↗

arXiv2023

ZTD$_{JAVA}$: Mitigating Software Supply Chain Vulnerabilities via Zero-Trust Dependencies

Third-party libraries like Log4j accelerate software application development but introduce substantial risk. Vulnerabilities in these libraries have led to Software Supply Chain (SSC) attacks that compromised resources within the host system. These attacks benefit from current application permissions approaches: thirdparty libraries are implicitly trusted in the application runtime. An application runtime designed with Zero-Trust Architecture (ZTA) principles secure access to resources, continuous monitoring, and least-privilege enforcement could mitigate SSC attacks, as it would give zero implicit trust to these libraries. However, no individual security defense incorporates these principles at a low runtime cost. This paper proposes Zero-Trust Dependencies to mitigate SSC vulnerabilities: we apply the NIST ZTA to software applications. First, we assess the expected effectiveness and configuration cost of Zero-Trust Dependencies using a study of third-party software libraries and their vulnerabilities. Then, we present a system design, ZTD$_{SYS}$, that enables the application of Zero-Trust Dependencies to software applications and a prototype, ZTD$_{JAVA}$, for Java applications. Finally, with evaluations on recreated vulnerabilities and realistic applications, we show that ZTD$_{JAVA}$ can defend against prevalent vulnerability classes, introduces negligible cost, and is easy to configure and use.

cs.CR↗

arXiv2023

Self-Assembly of Soot Nanoparticles on the Surface of Resistively Heated Carbon Microtubes in Near-Hexagonal Arrays of Micropyramids

Almost regular hexagonal arrays of a few micrometers tall and wide micropyramids consisting of soot nano-particles are formed on the surface of graphitized hollow filaments, which are resistively heated to ~1800$^\circ$C-2400$^\circ$C in an Ar atmosphere containing trace amounts of oxygen (~300 p.p.m.). At the higher temperatures (T>2300$^\circ$C, approximately) the soot particles are represented mainly by multi-shell carbon nano-onions. The height and the width of the pyramids is strongly dependent on the temperature of the resistive heating, diminishing from 5-10mkm at T=1800$^\circ$C to 1mkm at 2300-2400$^\circ$C. Quasi-hexagonal arrays of the micropyramids are organized in the convex ``craters'' on the surface of the microtubes, which grow with the time of the thermal treatment. The pyramids are pointing always normally to the surface of the craters, except at the boundaries between the craters, where the normal direction is not well defined. The pyramids are soft and can be easy destroyed by touching them, but can be hardened by heating them in the oxygen-free atmosphere. The pyramids are observed only on the exterior surface of the microtubes, but not on their inner surface. This suggests that the thermophoretic force generated by a strong temperature gradient near the external surface of the tubes may be the cause of the micropyramids formation. Electrostatic charging of the soot nanoparticles due to thermionic emission may also be relevant to this phenomenon. The micropyramids can function as field emission point sources, as demonstrated with the use of a micro-nanoprobing station, mounted in a scanning electron microscope.

cond-mat.mtrl-sci↗

arXiv2020

SAT Heritage: a community-driven effort for archiving, building and running more than thousand SAT solvers

SAT research has a long history of source code and binary releases, thanks to competitions organized every year. However, since every cycle of competitions has its own set of rules and an adhoc way of publishing source code and binaries, compiling or even running any solver may be harder than what it seems. Moreover, there has been more than a thousand solvers published so far, some of them released in the early 90's. If the SAT community wants to archive and be able to keep track of all the solvers that made its history, it urgently needs to deploy an important effort. We propose to initiate a community-driven effort to archive and to allow easy compilation and running of all SAT solvers that have been released so far. We rely on the best tools for archiving and building binaries (thanks to Docker, GitHub and Zenodo) and provide a consistent and easy way for this. Thanks to our tool, building (or running) a solver from its source (or from its binary) can be done in one line.

cs.AI↗

arXiv2019

Community Structure in Industrial SAT Instances

Modern SAT solvers have experienced a remarkable progress on solving industrial instances. Most of the techniques have been developed after an intensive experimental process. It is believed that these techniques exploit the underlying structure of industrial instances. However, there are few works trying to exactly characterize the main features of this structure. The research community on complex networks has developed techniques of analysis and algorithms to study real-world graphs that can be used by the SAT community. Recently, there have been some attempts to analyze the structure of industrial SAT instances in terms of complex networks, with the aim of explaining the success of SAT solving techniques, and possibly improving them. In this paper, inspired by the results on complex networks, we study the community structure, or modularity, of industrial SAT instances. In a graph with clear community structure, or high modularity, we can find a partition of its nodes into communities such that most edges connect variables of the same community. In our analysis, we represent SAT instances as graphs, and we show that most application benchmarks are characterized by a high modularity. On the contrary, random SAT instances are closer to the classical Erdös-Rényi random graph model, where no structure can be observed. We also analyze how this structure evolves by the effects of the execution of a CDCL SAT solver. In particular, we use the community structure to detect that new clauses learned by the solver during the search contribute to destroy the original structure of the formula. This is, learned clauses tend to contain variables of distinct communities.

cs.AI↗

arXiv2019

Hearing your touch: A new acoustic side channel on smartphones

We present the first acoustic side-channel attack that recovers what users type on the virtual keyboard of their touch-screen smartphone or tablet. When a user taps the screen with a finger, the tap generates a sound wave that propagates on the screen surface and in the air. We found the device's microphone(s) can recover this wave and "hear" the finger's touch, and the wave's distortions are characteristic of the tap's location on the screen. Hence, by recording audio through the built-in microphone(s), a malicious app can infer text as the user enters it on their device. We evaluate the effectiveness of the attack with 45 participants in a real-world environment on an Android tablet and an Android smartphone. For the tablet, we recover 61% of 200 4-digit PIN-codes within 20 attempts, even if the model is not trained with the victim's data. For the smartphone, we recover 9 words of size 7--13 letters with 50 attempts in a common side-channel attack benchmark. Our results suggest that it not always sufficient to rely on isolation mechanisms such as TrustZone to protect user input. We propose and discuss hardware, operating-system and application-level mechanisms to block this attack more effectively. Mobile devices may need a richer capability model, a more user-friendly notification system for sensor usage and a more thorough evaluation of the information leaked by the underlying hardware.

cs.CR↗

arXiv2016

Systemization of Pluggable Transports for Censorship Resistance

An increasing number of countries implement Internet censorship at different scales and for a variety of reasons. In particular, the link between the censored client and entry point to the uncensored network is a frequent target of censorship due to the ease with which a nation-state censor can control it. A number of censorship resistance systems have been developed thus far to help circumvent blocking on this link, which we refer to as link circumvention systems (LCs). The variety and profusion of attack vectors available to a censor has led to an arms race, leading to a dramatic speed of evolution of LCs. Despite their inherent complexity and the breadth of work in this area, there is no systematic way to evaluate link circumvention systems and compare them against each other. In this paper, we (i) sketch an attack model to comprehensively explore a censor's capabilities, (ii) present an abstract model of a LC, a system that helps a censored client communicate with a server over the Internet while resisting censorship, (iii) describe an evaluation stack that underscores a layered approach to evaluate LCs, and (iv) systemize and evaluate existing censorship resistance systems that provide link circumvention. We highlight open challenges in the evaluation and development of LCs and discuss possible mitigations.

cs.CR↗

arXiv2014

Sonic interaction with a virtual orchestra of factory machinery

This paper presents an immersive application where users receive sound and visual feedbacks on their interactions with a virtual environment. In this application, the users play the part of conductors of an orchestra of factory machines since each of their actions on interaction devices triggers a pair of visual and audio responses. Audio stimuli were spatialized around the listener. The application was exhibited during the 2013 Science and Music day and designed to be used in a large immersive system with head tracking, shutter glasses and a 10.2 loudspeaker configuration.

cs.MM↗

arXiv2010

Dirac point metamorphosis from third-neighbor couplings in graphene

We study the band structure and the density of states of graphene in the presence of a next-to-nearest-neighbor coupling (N2) and a third-nearest-neighbor coupling (N3). We show that for values of N3 larger or equal to 1/3 of the value of the nearest-neighbor hopping (NN), extra Dirac points appear in the spectrum. If N3 is exactly equal to 1/3 NN, the new Dirac points are localized at the M points of the Brillouin zone and are hybrid: the electrons have a linear dispersion along the GammaM direction and a quadratic dispersion along the perpendicular direction MK. For larger values of N3 the new points have a linear dispersion, and are situated along the MK line. For a value of N3 equal to 1/2 NN, these points merge with the Dirac cones at the K points, yielding a gapless quadratic dispersion around K, while for larger values each quadratic point at K splits again into four Dirac points. The effects of changing the N2 coupling are not so dramatic. We calculate the density of states and we show that increasing the N3 coupling lowers the energy of the Van Hove singularities, and when N3 is larger than 1/3 NN the Van Hove singularities split in two, giving rise to extra singularities at low energies.

cond-mat.mes-hall↗

arXiv2009

Laser Doppler Velocimetry for Joint Measurements of Acoustic and Mean Flow Velocities : LMS-based Algorithm and CRB Calculation

This paper presents a least mean square (LMS) algorithm for the joint estimation of acoustic and mean flow velocities from laser doppler velocimetry (LDV) measurements. The usual algorithms used for measuring with LDV purely acoustic velocity or mean flow velocity may not be used when the acoustic field is disturbed by a mean flow component. The LMS-based algorithm allows accurate estimations of both acoustic and mean flow velocities. The Cramér-Rao bound (CRB) of the associated problem is determined. The variance of the estimators of both acoustic and mean flow velocities is also given. Simulation results of this algorithm are compared with the CRB and the comparison leads to validate this estimator.

physics.class-ph↗