The Stochastic Shortest Path Problem : A polyhedral combinatorics perspective

In this paper, we give a new framework for the stochastic shortest path problem in finite state and action spaces. Our framework generalizes both the frameworks proposed by Bertsekas and Tsitsikli and by Bertsekas and Yu. We prove that the problem is well-defined and (weakly) polynomial when (i) there is a way to reach the target state from any initial state and (ii) there is no transition cycle of negative costs (a generalization of negative cost cycles). These assumptions generalize the standard assumptions for the deterministic shortest path problem and our framework encapsulates the latter problem (in contrast with prior works). In this new setting, we can show that (a) one can restrict to deterministic and stationary policies, (b) the problem is still (weakly) polynomial through linear programming, (c) Value Iteration and Policy Iteration converge, and (d) we can extend Dijkstra's algorithm

Correcting for ascertainment bias in the inference of population structure

Background: The ascertainment process of molecular markers amounts to disregard loci carrying alleles with low frequencies. This can result in strong biases in inferences under population genetics models if not properly taken into account by the inference algorithm. Attempting to model this censoring process in view of making inference of population structure (i.e.identifying clusters of individuals) brings up challenging numerical difficulties. Method: These difficulties are related to the presence of intractable normalizing constants in Metropolis-Hastings acceptance ratios. This can be solved via an Markov chain Monte Carlo (MCMC) algorithm known as single variable exchange algorithm (SVEA). Result: We show how this general solution can be implemented for a class of clustering models of broad interest in population genetics that includes the models underlying the computer programs STRUCTURE, GENELAND and GESTE. We also implement the method proposed for a simple example and show that it allows us to reduce the bias substantially. Availability: Further details and a computer program implementing the method are available from http://folk.uio.no/gillesg/AscB/ Contact: [email protected]

Enhancing PGA Tour Performance: Leveraging ShotlinkTM Data for Optimization and Prediction

In this study, we demonstrate how data from the PGA Tour, combined with stochastic shortest path models (MDPs), can be employed to refine the strategies of professional golfers and predict future performances. We present a comprehensive methodology for this objective, proving its computational feasibility. This sets the stage for more in-depth exploration into leveraging data available to professional and amateurs for strategic optimization and forecasting performance in golf. For the replicability of our results, and to adapt and extend the methodology and prototype solution, we provide access to all our codes and analyses (R and C++)

Modeling crowd dynamics through coarse-grained data analysis

Understanding and predicting the collective behaviour of crowds is essential to improve the efficiency of pedestrian flows in urban areas and minimize the risks of accidents at mass events. We advocate for the development of crowd traffic management systems, whereby observations of crowds can be coupled to fast and reliable models to produce rapid predictions of the crowd movement and eventually help crowd managers choose between tailored optimization strategies. Here, we propose a Bi-directional Macroscopic (BM) model as the core of such a system. Its key input is the fundamental diagram for bi-directional flows, i.e. the relation between the pedestrian fluxes and densities. We design and run a laboratory experiments involving a total of 119 participants walking in opposite directions in a circular corridor and show that the model is able to accurately capture the experimental data in a typical crowd forecasting situation. Finally, we propose a simple segregation strategy for enhancing the traffic efficiency, and use the BM model to determine the conditions under which this strategy would be beneficial. The BM model, therefore, could serve as a building block to develop on the fly prediction of crowd movements and help deploying real-time crowd optimization strategies

In vitro and in vivo characterization of noso-502, a novel inhibitor of bacterial translation

Antibacterial activity screening of a collection of Xenorhabdus strains led to the discovery of the odilorhabdins, a new antibiotic class with broad-spectrum activity against Gram-positive and Gram-negative pathogens. Odilorhabdins inhibit bacterial translation by a new mechanism of action on ribosomes. A lead optimization program identified NOSO-502 as a promising candidate. NOSO-502 has MIC values ranging from 0.5 to 4 μg/ml against standard Enterobacteriaceae strains and carbapenem- resistant Enterobacteriaceae (CRE) isolates that produce KPC, AmpC, or OXA enzymes and metallo-β-lactamases. In addition, this compound overcomes multiple chromosome-encoded or plasmid-mediated resistance mechanisms of acquired resistance to colistin. It is effective in mouse systemic infection models against Escherichia coli EN122 (extended-spectrum β-lactamase [ESBL]) or E. coli ATCC BAA-2469 (NDM-1), achieving a 50% effective dose (ED50) of 3.5 mg/kg of body weight and 1-, 2-, and 3-log reductions in blood burden at 2.6, 3.8, and 5.9 mg/kg, respectively, in the first model and 100% survival in the second, starting with a dose as low as 4 mg/kg. In a urinary tract infection (UTI) model with E. coli UTI89, urine, bladder, and kidney burdens were reduced by 2.39, 1.96, and 1.36 log10 CFU/ml, respectively, after injection of 24 mg/kg. There was no cytotoxicity against HepG2, HK-2, or human renal proximal tubular epithelial cells (HRPTEpiC), no inhibition of hERG-CHO or Nav 1.5-HEK current, and no increase of micronuclei at 512 μM. NOSO-502, a compound with a new mechanism of action, is active against Enterobacteriaceae, including all classes of CRE, has a low potential for resistance development, shows efficacy in several mouse models, and has a favorable in vitro safety profile

Rapid response to the M_w 4.9 earthquake of November 11, 2019 in Le Teil, Lower Rhône Valley, France

On November 11, 2019, a Mw 4.9 earthquake hit the region close to Montelimar (lower Rhône Valley, France), on the eastern margin of the Massif Central close to the external part of the Alps. Occuring in a moderate seismicity area, this earthquake is remarkable for its very shallow focal depth (between 1 and 3 km), its magnitude, and the moderate to large damages it produced in several villages. InSAR interferograms indicated a shallow rupture about 4 km long reaching the surface and the reactivation of the ancient NE-SW La Rouviere normal fault in reverse faulting in agreement with the present-day E-W compressional tectonics. The peculiarity of this earthquake together with a poor coverage of the epicentral region by permanent seismological and geodetic stations triggered the mobilisation of the French post-seismic unit and the broad French scientific community from various institutions, with the deployment of geophysical instruments (seismological and geodesic stations), geological field surveys, and field evaluation of the intensity of the earthquake. Within 7 days after the mainshock, 47 seismological stations were deployed in the epicentral area to improve the Le Teil aftershocks locations relative to the French permanent seismological network (RESIF), monitor the temporal and spatial evolution of microearthquakes close to the fault plane and temporal evolution of the seismic response of 3 damaged historical buildings, and to study suspected site effects and their influence in the distribution of seismic damage. This seismological dataset, completed by data owned by different institutions, was integrated in a homogeneous archive and distributed through FDSN web services by the RESIF data center. This dataset, together with observations of surface rupture evidences, geologic, geodetic and satellite data, will help to unravel the causes and rupture mechanism of this earthquake, and contribute to account in seismic hazard assessment for earthquakes along the major regional Cévenne fault system in a context of present-day compressional tectonics

Le problème du plus court chemin stochastique et ses variantes : fondements et applications à l'optimisation de stratégie dans le sport

A golf course consists of eighteen holes. On each hole, the golfer has to move the ball from the tee to the flag in a minimum number of shots. Under some assumptions, the golfer's problem can be modeled as a stochastic shortest path problem (SSP). SSP problem is a special case of Markov Decision Processes in which an agent evolves dynamically in a finite set of states. In each state, the agent chooses an action that leads him to another state following a known probability distribution. This action induces a cost. There exists a `sink node' in which the agent, once in it, stays with probability one and a cost zero. The goal of the agent is to reach the sink node with a minimum expected cost. In the first chapter, we study the SSP problem theoretically. We define a new framework in which the assumptions needed for the existence of an optimal policy are weakened. We prove that the most famous algorithm still converge in this setting. We also define a new algorithm to solve exactly the problem based on the primal-dual algorithm. In the second chapter we detail the golfer's problem model as a SSP. Thanks to the Shotlink database, we create `numerical clones' of players and simulate theses clones on different golf course in order to predict professional golfer's scores. We apply our model on two competitions: the master of Augusta in 2017 and the Ryder Cup in 2018. In the third chapter, we study the 2-player natural extension of SSP problem: the stochastic shortest path games. We study two special cases, and in particular linear programming formulation of these games.Un parcours de golf est composé de dix-huit trous. Sur chaque trou, le problème du golfeur est de déplacer la balle d'un point de départ prédéfini jusqu'au drapeau en un minimum de coups. Sous certaines hypothèses, ce problème peut se modéliser comme un problème de plus court chemin stochastique (PCCS). Le problème du PCCS est un processus de Markov particulier dans lequel un agent évolue dynamiquement dans un ensemble fini d'états. En chaque état, l'agent choisis une action, induisant un coût, qui le mène en un autre état en suivant une distribution de probabilité connue. Il existe également un état `puits' particulier dans lequel, une fois atteint, on reste avec une probabilité un et un coût de zéro. Le but de l'agent est, depuis un état initial, d'atteindre l'état puits en un coût moyen minimal. Dans un premier chapitre, nous étudions de manière théorique le problème du PCCS. Après avoir redéfini un cadre d'étude dans lequel nous avons affaibli les hypothèses d'existence d'une solution optimale, nous avons prouvé que les algorithmes classiques de résolution convergent dans ce nouveau cadre. Nous avons également défini un nouvel algorithme de résolution basé sur l'algorithme primal-dual. Dans le deuxième chapitre, nous détaillons la modélisation du problème d'optimisation de stratégies au golf en un problème de PCCS. Grâce à la base de données Shotlink, nous définissons des `clones numériques' de joueurs que nous pouvons faire jouer artificiellement sur différents parcours de golf afin de prédire les scores des joueurs. Nous avons appliqué ce modèle à deux compétitions : le master d'Augusta en 2017 et la Ryder Cup en 2018. Dans un troisième et dernier chapitre, nous étudions l'extension naturelle à deux joueurs du problème du PCCS : les jeux de plus courts chemins stochastiques. Nous étudions particulièrement les formulations programmation linéaire de ces jeux et de deux cas particuliers de ceux-ci

The stochastic shortest path problem and its variations : foundations and applications to sport strategy optimization

Un parcours de golf est composé de dix-huit trous. Sur chaque trou, le problème du golfeur est de déplacer la balle d'un point de départ prédéfini jusqu'au drapeau en un minimum de coups. Sous certaines hypothèses, ce problème peut se modéliser comme un problème de plus court chemin stochastique (PCCS). Le problème du PCCS est un processus de Markov particulier dans lequel un agent évolue dynamiquement dans un ensemble fini d'états. En chaque état, l'agent choisis une action, induisant un coût, qui le mène en un autre état en suivant une distribution de probabilité connue. Il existe également un état `puits' particulier dans lequel, une fois atteint, on reste avec une probabilité un et un coût de zéro. Le but de l'agent est, depuis un état initial, d'atteindre l'état puits en un coût moyen minimal. Dans un premier chapitre, nous étudions de manière théorique le problème du PCCS. Après avoir redéfini un cadre d'étude dans lequel nous avons affaibli les hypothèses d'existence d'une solution optimale, nous avons prouvé que les algorithmes classiques de résolution convergent dans ce nouveau cadre. Nous avons également défini un nouvel algorithme de résolution basé sur l'algorithme primal-dual. Dans le deuxième chapitre, nous détaillons la modélisation du problème d'optimisation de stratégies au golf en un problème de PCCS. Grâce à la base de données Shotlink, nous définissons des `clones numériques' de joueurs que nous pouvons faire jouer artificiellement sur différents parcours de golf afin de prédire les scores des joueurs. Nous avons appliqué ce modèle à deux compétitions : le master d'Augusta en 2017 et la Ryder Cup en 2018. Dans un troisième et dernier chapitre, nous étudions l'extension naturelle à deux joueurs du problème du PCCS : les jeux de plus courts chemins stochastiques. Nous étudions particulièrement les formulations programmation linéaire de ces jeux et de deux cas particuliers de ceux-ci.A golf course consists of eighteen holes. On each hole, the golfer has to move the ball from the tee to the flag in a minimum number of shots. Under some assumptions, the golfer's problem can be modeled as a stochastic shortest path problem (SSP). SSP problem is a special case of Markov Decision Processes in which an agent evolves dynamically in a finite set of states. In each state, the agent chooses an action that leads him to another state following a known probability distribution. This action induces a cost. There exists a `sink node' in which the agent, once in it, stays with probability one and a cost zero. The goal of the agent is to reach the sink node with a minimum expected cost. In the first chapter, we study the SSP problem theoretically. We define a new framework in which the assumptions needed for the existence of an optimal policy are weakened. We prove that the most famous algorithm still converge in this setting. We also define a new algorithm to solve exactly the problem based on the primal-dual algorithm. In the second chapter we detail the golfer's problem model as a SSP. Thanks to the Shotlink database, we create `numerical clones' of players and simulate theses clones on different golf course in order to predict professional golfer's scores. We apply our model on two competitions: the master of Augusta in 2017 and the Ryder Cup in 2018. In the third chapter, we study the 2-player natural extension of SSP problem: the stochastic shortest path games. We study two special cases, and in particular linear programming formulation of these games

Correcting for ascertainment bias in the inference of population structure

Background: The ascertainment process of molecular markers amounts to disregard loci carrying alleles with low frequencies. This can result in strong biases in inferences under population genetics models if not properly taken into account by the inference algorithm. Attempting to model this censoring process in view of making inference of population structure (i.e. identifying clusters of individuals) brings up challenging numerical difficulties. Method: These difficulties are related to the presence of intractable normalizing constants in Metropolis-Hastings acceptance ratios. This can be solved via an Markov chain Monte Carlo (MCMC) algorithm known as single variable exchange algorithm (SVEA). Result: We show how this general solution can be implemented for a class of clustering models of broad interest in population genetics that includes the models underlying the computer programs STRUCTURE, GENELAND and GESTE. We also implement the method proposed for a simple example and show that it allows us to reduce the bias substantially