Ahmad Bitar

Tapered-shrinkage covariance matrix estimation and its application to hyperspectral target detection

Yousef Zaitoun, Ahmad Bitar, Alexandre Baussard

Hyperspectral target detection mainly relies on an accurate covariance matrix estimation, particularly in high-dimensional settings. Tapering-based covariance estimators have improved such estimation in many applications. In this paper, we investigate the impact of different tapering weights within the TApered or BAnded Shrinkage …

Linear Regression for Currency European Call Option Pricing in Incomplete Markets

Ahmad Bitar

Abstract The least squares method is the traditional regression technique for pricing European options in incomplete markets by constructing a self-financing hedging portfolio that does not perfectly replicate the call option. However, the least squares method is quite sensitive to even a …

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Unsupervised Anomaly Detection via Brownian Feature Trajectories and Stochastic Geometry

Redwane Ait-Ouammi, Ahmad Bitar, Hichem Snoussi, Alain Staron

Embedded sensor systems operating in heterogeneous and evolving environments face critical challenges in detecting rare or novel anomalies under strict power, communication, and supervision constraints. To address these limitations, we propose a dual-model framework that combines stochastic modeling and geometric analysis for …

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Brownian Feature Trajectories with Geometric Decision Boundaries for Edge Anomaly Detection

Redwane Ait-Ouammi, Ahmad Bitar, Alain Staron, Hichem Snoussi

Embedded sensor systems deployed in heterogeneous and evolving environments face strong constraints in power, supervision, and connectivity, which limit the applicability of classical anomaly detection methods. This paper introduces a lightweight framework for unsupervised anomaly detection based on Brownian feature trajectories. Each …

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Brownian-Inspired Neural Networks for Explainable Edge Vibration Anomaly Detection

Redwane Ait-Ouammi, Ahmad Bitar, Hichem Snoussi, Alain Staron

Embedded vibration sensors enable real-time anomaly detection in remote and resource-constrained environments, yet existing methods often lack adaptability and interpretability. We propose a physics-informed Brownian-inspired neural network that models cumulative vibration features as stochastic trajectories and introduces Stochastic Brownian activation (SABA). Unlike …

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Résolution du Rubik's Cube par Apprentissage par Renforcement

Romain Goldenchtein, Simon Gelbart, Ahmad Bitar

Ce rapport technique a pour objectif principal la création d'un algorithme intelligent d'apprentissage par renforcement pour résoudre le Rubik's Cube. Pour cela, il faut commencer par comprendre comment marche le cube, sa structure et comment le résoudre. Ensuite, il faudra recenser les …

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Robust European Call Option Pricing via Linear Regression

Ahmad Bitar

The one-period trinomial option pricing model is well known in the literature as it considers three possible movement directions of the asset price. However, by equating the price of the option with the self-financing hedging portfolio at maturity, this yields a linear …

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Detection of subpixel targets with low fill-fraction: An application to hyperspectral imagery

Ahmad Bitar

In hyperspectral imagery, each subpixel target is well known as the target of interest that occupies only a fraction of the pixel area. The remaining part of the pixel is then filled with the background (at the same spatial location). In this …

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Descriptive epidemiology of 399 histologically confirmed newly diagnosed meningeal solitary fibrous tumours and haemangiopericytomas in France: 2006–2015

Charles Champeaux-Depond, Sonia Zouaoui, Amélie Darlix, Valérie Rigau et autres

fr, re (code pays fourni par la source)

Descriptive epidemiology of 30,223 histopathologically confirmed meningiomas in France: 2006–2015

Charles Champeaux-Depond, Sonia Zouaoui, Amélie Darlix, Valérie Rigau et autres

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Covariance matrix estimation for robust portfolio allocation

Ahmad Bitar, Nathan De Carvalho, Valentin Gatignol

In this technical report , we aim to combine different protfolio allocation techniques with covariance matrix estimators to meet two types of clients' requirements: client A who wants to invest money wisely, not taking too much risk, and not willing to pay …

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A Novel Method to Increase Diffusion and Confusion in AES Algorithm

Zakria Mahrousa, Ahmad Bitar, Yahia Fareed

The Advanced Encryption Standard (AES) is the most famous amongst symmetric block cipher algorithms, and the most used one in many different applications, because its encryption strength and its resistance to many attacks.In this paper, a novel approach will be introduced to …

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