The ArDM project: a Dark Matter Direct Detection Experiment based on Liquid Argon

The Dark Matter part of the universe presumably consists of WIMPs (Weakly Interacting Massive Particles). The ArDM project aims at measuring signals induced by WIMPs in a liquid argon detector. A 1-ton prototype is currently developed with the goal of demonstrating the feasibility of such a direct detection experiment with large target mass. The technical design of the detector aims at ind ependently measuring the scintillation light and the ionization charge originating from an interaction of a WIMP with an argon nucleus. The principle of the experiment and the conceptual design of the detector are described.Comment: 4 pages, 1 figure, Invited talk at 2nd Workshop On TeV Particle Astrophysics, 28-31 August 2006, Madison, WI, US

Ce que peuvent et ne peuvent pas faire les astuces de doublement pour les bandits multi-bras

An online reinforcement learning algorithm is anytime if it does not need to know in advance the horizon T of the experiment. A well-known technique to obtain an anytime algorithm from any non-anytime algorithm is the "Doubling Trick". In the context of adversarial or stochastic multi-armed bandits, the performance of an algorithm is measured by its regret, and we study two families of sequences of growing horizons (geometric and exponential) to generalize previously known results that certain doubling tricks can be used to conserve certain regret bounds. In a broad setting, we prove that a geometric doubling trick can be used to conserve (minimax) bounds in $R_T = O(\sqrt{T})$ but cannot conserve (distribution-dependent) bounds in $R_T = O(\log T)$. We give insights as to why exponential doubling tricks may be better, as they conserve bounds in $R_T = O(\log T)$, and are close to conserving bounds in $R_T = O(\sqrt{T})$.Un algorithme en ligne d'apprentissage par renforcement est dit "à tout moment" (anytime) s'il n'a pas besoin de connaître à l'avance l'horizon T de l'expérience. Une technique bien connue pour obtenir un algorithme à tout moment à partir d'un algorithme qui ne l'est pas est "l'astuce de doublement" (Doubling Trick). Dans le contexte des bandits multi-bras adverses ou stochastiques, la performance d'un algorithme est mesurée par son regret, et nous étudions deux familles de séquences d'horizons croissants (géométrique et exponentielle), pour généraliser des résultats précédemment connus que certaines astuces de doublement peuvent être utilisées pour conserver certaines limites de regret. Dans un cadre très générique, nous prouvons qu'une astuce géométrique de doublement peut être utilisée pour conserver les bornes (minimax) en $R_T = O(\sqrt{T})$ mais ne peut pas conserver les bornes (dépendantes de la distribution) en $R_T = O(\log T)$. Nous donnons un aperçu des raisons pour lesquelles les astuces de doublage exponentiel peuvent être meilleures, car elles conservent les bornes en $R_T = O(\log T)$, et sont proches de conserver les bornes en $R_T = O(\sqrt{T}$)

Modèles de Bandits Multi-Joueurs Revisités

International audienceMulti-player Multi-Armed Bandits (MAB) have been extensively studied in the literature, motivated by applications to Cognitive Radio systems. Driven by such applications as well, we motivate the introduction of several levels of feedback for multi-player MAB algorithms. Most existing work assume that sensing information is available to the algorithm. Under this assumption, we improve the state-of-the-art lower bound for the regret of any decentralized algorithms and introduce two algorithms, RandTopM and MCTopM, that are shown to empirically outperform existing algorithms. Moreover, we provide strong theoretical guarantees for these algorithms, including a notion of asymptotic optimality in terms of the number of selections of bad arms. We then introduce a promising heuristic, called Selfish, that can operate without sensing information, which is crucial for emerging applications to Internet of Things networks. We investigate the empirical performance of this algorithm and provide some first theoretical elements for the understanding of its behavior.Les bandits multi-joueurs multiarmes (MAB) ont fait l'objet d'études approfondies dans la littérature, motivés par des applications aux systèmes de radio intelligente. De telles applications motivent l'introduction de plusieurs niveaux d'informations pour les algorithmes MAB multi-joueurs. La plupart des travaux récents supposent que l'algorithme dispose d'informations de détection (sensing). Dans cette hypothèse, nous améliorons la meilleure borne inférieure connue pour le regret de tout algorithme décentralisé, et introduisons deux algorithmes, RandTopM et MCTopM, qui sont empiriquement meilleurs par rapport aux algorithmes existants. De plus, nous fournissons de solides garanties théoriques pour ces algorithmes, y compris une notion d'optimalité asymptotique en termes de nombre de sélections des mauvais bras. Nous introduisons ensuite une heuristique prometteuse, appelée Selfish, qui peut fonctionner sans utiliser le sensing, ce qui est crucial pour les applications émergentes aux réseaux de type Internet des Objets. Nous étudions les performances empiriques de cet algorithme et fournissons quelques premiers éléments théoriques pour la compréhension de son comportement

Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits

International audienceWe introduce GLR-klUCB, a novel algorithm for the piecewise iid non-stationary bandit problem with bounded rewards. This algorithm combines an efficient bandit algorithm, kl-UCB, with an efficient, parameter-free, changepoint detector, the Bernoulli Generalized Likelihood Ratio Test, for which we provide new theoretical guarantees of independent interest. Unlike previous non-stationary bandit algorithms using a change-point detector, GLR-klUCB does not need to be calibrated based on prior knowledge on the arms' means. We prove that this algorithm can attain a $O(\sqrt{TA \Upsilon_T\log(T)})$ regret in $T$ rounds on some ``easy'' instances, where A is the number of arms and $\Upsilon_T$ the number of change-points, without prior knowledge of $\Upsilon_T$. In contrast with recently proposed algorithms that are agnostic to $\Upsilon_T$, we perform a numerical study showing that GLR-klUCB is also very efficient in practice, beyond easy instances

CCNE1 and survival of patients with tubo-ovarian high-grade serous carcinoma: An Ovarian Tumor Tissue Analysis consortium study

BACKGROUND: Cyclin E1 (CCNE1) is a potential predictive marker and therapeutic target in tubo-ovarian high-grade serous carcinoma (HGSC). Smaller studies have revealed unfavorable associations for CCNE1 amplification and CCNE1 overexpression with survival, but to date no large-scale, histotype-specific validation has been performed. The hypothesis was that high-level amplification of CCNE1 and CCNE1 overexpression, as well as a combination of the two, are linked to shorter overall survival in HGSC. METHODS: Within the Ovarian Tumor Tissue Analysis consortium, amplification status and protein level in 3029 HGSC cases and mRNA expression in 2419 samples were investigated. RESULTS: High-level amplification (>8 copies by chromogenic in situ hybridization) was found in 8.6% of HGSC and overexpression (>60% with at least 5% demonstrating strong intensity by immunohistochemistry) was found in 22.4%. CCNE1 high-level amplification and overexpression both were linked to shorter overall survival in multivariate survival analysis adjusted for age and stage, with hazard stratification by study (hazard ratio [HR], 1.26; 95% CI, 1.08-1.47, p = .034, and HR, 1.18; 95% CI, 1.05-1.32, p = .015, respectively). This was also true for cases with combined high-level amplification/overexpression (HR, 1.26; 95% CI, 1.09-1.47, p = .033). CCNE1 mRNA expression was not associated with overall survival (HR, 1.00 per 1-SD increase; 95% CI, 0.94-1.06; p = .58). CCNE1 high-level amplification is mutually exclusive with the presence of germline BRCA1/2 pathogenic variants and shows an inverse association to RB1 loss. CONCLUSION: This study provides large-scale validation that CCNE1 high-level amplification is associated with shorter survival, supporting its utility as a prognostic biomarker in HGSC