Learning Lexicographic Preference Trees From Positive Examples

This paper considers the task of learning the preferences of users on a combinatorial set of alternatives, as it can be the case for example with online configurators. In many settings, what is available to the learner is a set of positive examples of alternatives that have been selected during past interactions. We propose to learn a model of the users' preferences that ranks previously chosen alternatives as high as possible. In this paper, we study the particular task of learning conditional lexicographic preferences. We present an algorithm to learn several classes of lexicographic preference trees, prove convergence properties of the algorithm, and experiment on both synthetic data and on a real-world bench in the domain of recommendation in interactive configuration

The complexity of unsupervised learning of lexicographic preferences

International audienceThis paper considers the task of learning users' preferences on a combinatorial set of alternatives, as generally used by online configurators, for example. In many settings, only a set of selected alternatives during past interactions is available to the learner. Fargier et al. [2018] propose an approach to learn, in such a setting, a model of the users' preferences that ranks previously chosen alternatives as high as possible; and an algorithm to learn, in this setting, a particular model of preferences: lexicographic preferences trees (LP-trees). In this paper, we study complexity-theoretical problems related to this approach. We give an upper bound on the sample complexity of learning an LP-tree, which is logarithmic in the number of attributes. We also prove that computing the LP tree that minimises the empirical risk can be done in polynomial time when restricted to the class of linear LP-trees

Establishment and analysis of a reference transcriptome for Spodoptera frugiperda

International audienceBackground Spodoptera frugiperda (Noctuidae) is a major agricultural pest throughout the American continent. The highly polyphagous larvae are frequently devastating crops of importance such as corn, sorghum, cotton and grass. In addition, the Sf9 cell line, widely used in biochemistry for in vitro protein production, is derived from S. frugiperda tissues. Many research groups are using S. frugiperda as a model organism to investigate questions such as plant adaptation, pest behavior or resistance to pesticides.ResultsIn this study, we constructed a reference transcriptome assembly (Sf_TR2012b) of RNA sequences obtained from more than 35 S. frugiperda developmental time-points and tissue samples. We assessed the quality of this reference transcriptome by annotating a ubiquitous gene family - ribosomal proteins - as well as gene families that have a more constrained spatio-temporal expression and are involved in development, immunity and olfaction. We also provide a time-course of expression that we used to characterize the transcriptional regulation of the gene families studied.ConclusionWe conclude that the Sf_TR2012b transcriptome is a valid reference transcriptome. While its reliability decreases for the detection and annotation of genes under strong transcriptional constraint we still recover a fair percentage of tissue-specific transcripts. That allowed us to explore the spatial and temporal expression of genes and to observe that some olfactory receptors are expressed in antennae and palps but also in other non related tissues such as fat bodies. Similarly, we observed an interesting interplay of gene families involved in immunity between fat bodies and antennae

Gout and pseudo-gout-related crystals promote GLUT1-mediated glycolysis that governs NLRP3 and interleukin-1β activation on macrophages

Objective Macrophage activation by monosodium urate (MSU) and calcium pyrophosphate (CPP) crystals mediates an interleukin (IL)-1β-dependent inflammation during gout and pseudo-gout flare, respectively. Since metabolic reprogramming of macrophages goes along with inflammatory responses dependently on stimuli and tissue environment, we aimed to decipher the role of glycolysis and oxidative phosphorylation in the IL-1β-induced microcrystal response. Methods Briefly, an in vitro study (metabolomics and real-time extracellular flux analysis) on MSU and CPP crystal-stimulated macrophages was performed to demonstrate the metabolic phenotype of macrophages. Then, the role of aerobic glycolysis in IL-1β production was evaluated, as well in vitro as in vivo using 18F-fluorodeoxyglucose positron emission tomography imaging and glucose uptake assay, and molecular approach of glucose transporter 1 (GLUT1) inhibition. Results We observed that MSU and CPP crystals led to a metabolic rewiring toward the aerobic glycolysis pathway explained by an increase in GLUT1 plasma membrane expression and glucose uptake on macrophages. Also, neutrophils isolated from human synovial fluid during gout flare expressed GLUT1 at their plasma membrane more frequently than neutrophils isolated from bloodstream. Both glucose deprivation and treatment with either 2-deoxyglucose or GLUT1 inhibitor suppressed crystal-induced NLRP3 activation and IL-1β production, and microcrystal inflammation in vivo. Conclusion In conclusion, we demonstrated that GLUT1-mediated glucose uptake is instrumental during the inflammatory IL-1β response induced by MSU and CPP crystals. These findings open new therapeutic paths to modulate crystal-related inflammation

The Warburg Effect Is Genetically Determined in Inherited Pheochromocytomas

The Warburg effect describes how cancer cells down-regulate their aerobic respiration and preferentially use glycolysis to generate energy. To evaluate the link between hypoxia and Warburg effect, we studied mitochondrial electron transport, angiogenesis and glycolysis in pheochromocytomas induced by germ-line mutations in VHL, RET, NF1 and SDH genes. SDH and VHL gene mutations have been shown to lead to the activation of hypoxic response, even in normoxic conditions, a process now referred to as pseudohypoxia. We observed a decrease in electron transport protein expression and activity, associated with increased angiogenesis in SDH- and VHL-related, pseudohypoxic tumors, while stimulation of glycolysis was solely observed in VHL tumors. Moreover, microarray analyses revealed that expression of genes involved in these metabolic pathways is an efficient tool for classification of pheochromocytomas in accordance with the predisposition gene mutated. Our data suggest an unexpected association between pseudohypoxia and loss of p53, which leads to a distinct Warburg effect in VHL-related pheochromocytomas

Short and long term outcome of bilateral pallidal stimulation in chorea-acanthocytosis

BACKGROUND: Chorea-acanthocytosis (ChAc) is a neuroacanthocytosis syndrome presenting with severe movement disorders poorly responsive to drug therapy. Case reports suggest that bilateral deep brain stimulation (DBS) of the ventro-postero-lateral internal globus pallidus (GPi) may benefit these patients. To explore this issue, the present multicentre (n=12) retrospective study collected the short and long term outcome of 15 patients who underwent DBS. METHODS: Data were collected in a standardized way 2-6 months preoperatively, 1-5 months (early) and 6 months or more (late) after surgery at the last follow-up visit (mean follow-up: 29.5 months). RESULTS: Motor severity, assessed by the Unified Huntington's Disease Rating Scale-Motor Score, UHDRS-MS), was significantly reduced at both early and late post-surgery time points (mean improvement 54.3% and 44.1%, respectively). Functional capacity (UHDRS-Functional Capacity Score) was also significantly improved at both post-surgery time points (mean 75.5% and 73.3%, respectively), whereas incapacity (UHDRS-Independence Score) improvement reached significance at early post-surgery only (mean 37.3%). Long term significant improvement of motor symptom severity (≥ 20 % from baseline) was observed in 61.5 % of the patients. Chorea and dystonia improved, whereas effects on dysarthria and swallowing were variable. Parkinsonism did not improve. Linear regression analysis showed that preoperative motor severity predicted motor improvement at both post-surgery time points. The most serious adverse event was device infection and cerebral abscess, and one patient died suddenly of unclear cause, 4 years after surgery. CONCLUSION: This study shows that bilateral DBS of the GPi effectively reduces the severity of drug-resistant hyperkinetic movement disorders such as present in ChAc

Preferences learning in combinatorial spaces and application to recommandation in interactive configuration

L'analyse et l'exploitation des préférences interviennent dans de nombreux domaines, comme l'économie, les sciences sociales ou encore la psychologie. Depuis quelques années, c'est l'e-commerce qui s'intéresse au sujet dans un contexte de personnalisation toujours plus poussée. Notre étude s'est portée sur la représentation et l'apprentissage de préférences sur des objets décrits par un ensemble d'attributs. Ces espaces combinatoires sont immenses, ce qui rend impossible en pratique la représentation in extenso d'un ordre de préférences sur leurs objets. C'est pour cette raison que furent construits des langages permettant de représenter de manière compacte des préférences sur ces espaces combinatoires. Notre objectif a été d'étudier plusieurs langages de représentation de préférences et l'apprentissage de préférences. Nous avons développé deux axes de recherche. Le premier axe est l'algorithme DRC, un algorithme d'inférence dans les réseaux bayésiens. Alors que les autres méthodes d'inférence utilisent le réseau bayésien comme unique source d'information, DRC exploite le fait qu'un réseau bayésien est souvent appris à partir d'un ensemble d'objets qui ont été choisis ou observés. Ces exemples sont une source d'information supplémentaire qui peut être utilisée lors de l'inférence. L'algorithme DRC, de ce fait, n'utilise que la structure du réseau bayésien, qui capture des indépendances conditionnelles entre attributs et estime les probabilités conditionnelles directement à partir du jeu de données. DRC est particulièrement adapté à une utilisation dans un contexte où les lois de probabilité évoluent mais où les indépendances conditionnelles ne changent pas. Le second axe de recherche est l'apprentissage de k-LP-trees à partir d'exemples d'objets vendus. Nous avons défini formellement ce problème et introduit un score et une distance adaptés. Nous avons obtenu des résultats théoriques intéressants, notamment un algorithme d'apprentissage de k-LP-trees qui converge avec assez d'exemples vers le modèle cible, un algorithme d'apprentissage de LP-tree linéaire optimal au sens où il minimise notre score, ainsi qu'un résultat sur le nombre d'exemples suffisants pour apprendre un " bon " LP-tree linéaire : il suffit d'avoir un nombre d'exemples qui dépend logarithmiquement du nombre d'attributs du problème. Enfin, une contribution expérimentale évalue différents langages dont nous apprenons des modèles à partir d'historiques de voitures vendues. Les modèles appris sont utilisés pour la recommandation de valeur en configuration interactive de voitures Renault. La configuration interactive est un processus de construction de produit où l'utilisateur choisit successivement une valeur pour chaque attribut. Nous évaluons la précision de la recommandation, c'est-à-dire la proportion des recommandations qui auraient été acceptées, et le temps de recommandation ; de plus, nous examinons les différents paramètres qui peuvent influer sur la qualité de la recommandation. Nos résultats sont concluants : les méthodes que nous avons évaluées, qu'elles proviennent de la littérature ou de nos contributions théoriques, sont bien assez rapides pour être utilisées en ligne et ont une précision très élevée, proche du maximum théorique.The analysis and the exploitation of preferences occur in multiple domains, such as economics, humanities and psychology. E-commerce got interested in the subject a few years ago with the surge of product personalisation. Our study deals with the representation and the learning of preferences on objects described by a set of attributes. These combinatorial spaces are huge, which makes the representation of an ordering in extenso intractable. That's why preference representation languages have been built: they can represent preferences compactly on these huge spaces. In this dissertation, we study preference representation languages and preference learning.Our work focuses on two approaches. Our first approach led us to propose the DRC algorithm for inference in Bayesian networks. While other inference algorithms use the sole Bayesian network as a source of information, DRC makes use of the fact that Bayesian networks are often learnt from a set of examples either chosen or observed. Such examples are a valuable source of information that can be used during the inference. Based on this observation, DRC uses not only the Bayesian network structure that captures the conditional independences between attributes, but also the set of examples, by estimating the probabilities directly from it. DRC is particularly adapted to problems with a dynamic probability distribution but static conditional independences. Our second approach focuses on the learning of k-LP-trees from sold items examples. We formally define the problem and introduce a score and a distance adapted to it. Our theoretical results include a learning algorithm of k-LP-trees with a convergence property, a linear LP-tree algorithm minimising the score we defined and a sample complexity result: a number of examples logarithmic in the number of attributes is enough to learn a "good" linear LP-tree. We finally present an experimental contribution that evaluates different languages whose models are learnt from a car sales history. The models learnt are used to recommend values in interactive configuration of Renault cars. The interactive configuration is a process in which the user chooses a value, one attribute at a time. The recommendation precision (the proportion of recommendations that would have been accepted by the user) and the recommendation time are measured. Besides, the parameters that influence the recommendation quality are investigated. Our results are promising: these methods, described either in the literature or in our contributions, are fast enough for an on-line use and their success rate is high, even close to the theoretical maximum

La recommandation en configuration interactive

Encadré par Hélène Fargier et Jérôme Mengin (IRIT)Online congurators are getting more and more common because they are at a privileged position between the customer and the company. In fact, most customers choose the car they want with an online configurator before actually purchasing it. Recommendation for configurators is an emerging issue. This research report deals with new recommendation methods for maximizing customer's satisfaction or for finding a compromise between a customer and the company. The algorithms presented in this rep ort are efficient: they are suitable for online configuration and have a near-optimal success-rate.Les configurateurs de produit en ligne se multiplient sur les plate-formes d'e-commerce et prennent de plus en plus d'importance. Ils deviennent pour les entreprises des interfaces privilégiées avec le client : une grande majorité des clients qui souhaitent acheter une voiture ont utilisé un configurateur afin de la choisir. La recommandation dans le cadre des configurateurs constitue donc un enjeu majeur mais n'est pas encore très explorée par la recherche. Ce mémoire traite de nouvelles méthodes qui recommandent efficacement un utilisateur en maximisant son taux de satisfaction ou en recherchant un compromis entre les désirs de l'utilisateur et de l'entreprise. Les algorithmes de ce mémoire sont efficaces : ils sont aptes à une utilisation en ligne et possèdent un taux de réussite proche du maximum théorique

Apprentissage de préférences en espace combinatoire et application à la recommandation en configuration interactive

The analysis and the exploitation of preferences occur in multiple domains, such as economics, humanities and psychology. E-commerce got interested in the subject a few years ago with the surge of product personalisation. Our study deals with the representation and the learning of preferences on objects described by a set of attributes. These combinatorial spaces are huge, which makes the representation of an ordering in extenso intractable. That's why preference representation languages have been built: they can represent preferences compactly on these huge spaces. In this dissertation, we study preference representation languages and preference learning.Our work focuses on two approaches. Our first approach led us to propose the DRC algorithm for inference in Bayesian networks. While other inference algorithms use the sole Bayesian network as a source of information, DRC makes use of the fact that Bayesian networks are often learnt from a set of examples either chosen or observed. Such examples are a valuable source of information that can be used during the inference. Based on this observation, DRC uses not only the Bayesian network structure that captures the conditional independences between attributes, but also the set of examples, by estimating the probabilities directly from it. DRC is particularly adapted to problems with a dynamic probability distribution but static conditional independences. Our second approach focuses on the learning of k-LP-trees from sold items examples. We formally define the problem and introduce a score and a distance adapted to it. Our theoretical results include a learning algorithm of k-LP-trees with a convergence property, a linear LP-tree algorithm minimising the score we defined and a sample complexity result: a number of examples logarithmic in the number of attributes is enough to learn a "good" linear LP-tree. We finally present an experimental contribution that evaluates different languages whose models are learnt from a car sales history. The models learnt are used to recommend values in interactive configuration of Renault cars. The interactive configuration is a process in which the user chooses a value, one attribute at a time. The recommendation precision (the proportion of recommendations that would have been accepted by the user) and the recommendation time are measured. Besides, the parameters that influence the recommendation quality are investigated. Our results are promising: these methods, described either in the literature or in our contributions, are fast enough for an on-line use and their success rate is high, even close to the theoretical maximum.L'analyse et l'exploitation des préférences interviennent dans de nombreux domaines, comme l'économie, les sciences sociales ou encore la psychologie. Depuis quelques années, c'est l'e-commerce qui s'intéresse au sujet dans un contexte de personnalisation toujours plus poussée. Notre étude s'est portée sur la représentation et l'apprentissage de préférences sur des objets décrits par un ensemble d'attributs. Ces espaces combinatoires sont immenses, ce qui rend impossible en pratique la représentation in extenso d'un ordre de préférences sur leurs objets. C'est pour cette raison que furent construits des langages permettant de représenter de manière compacte des préférences sur ces espaces combinatoires. Notre objectif a été d'étudier plusieurs langages de représentation de préférences et l'apprentissage de préférences. Nous avons développé deux axes de recherche. Le premier axe est l'algorithme DRC, un algorithme d'inférence dans les réseaux bayésiens. Alors que les autres méthodes d'inférence utilisent le réseau bayésien comme unique source d'information, DRC exploite le fait qu'un réseau bayésien est souvent appris à partir d'un ensemble d'objets qui ont été choisis ou observés. Ces exemples sont une source d'information supplémentaire qui peut être utilisée lors de l'inférence. L'algorithme DRC, de ce fait, n'utilise que la structure du réseau bayésien, qui capture des indépendances conditionnelles entre attributs et estime les probabilités conditionnelles directement à partir du jeu de données. DRC est particulièrement adapté à une utilisation dans un contexte où les lois de probabilité évoluent mais où les indépendances conditionnelles ne changent pas. Le second axe de recherche est l'apprentissage de k-LP-trees à partir d'exemples d'objets vendus. Nous avons défini formellement ce problème et introduit un score et une distance adaptés. Nous avons obtenu des résultats théoriques intéressants, notamment un algorithme d'apprentissage de k-LP-trees qui converge avec assez d'exemples vers le modèle cible, un algorithme d'apprentissage de LP-tree linéaire optimal au sens où il minimise notre score, ainsi qu'un résultat sur le nombre d'exemples suffisants pour apprendre un " bon " LP-tree linéaire : il suffit d'avoir un nombre d'exemples qui dépend logarithmiquement du nombre d'attributs du problème. Enfin, une contribution expérimentale évalue différents langages dont nous apprenons des modèles à partir d'historiques de voitures vendues. Les modèles appris sont utilisés pour la recommandation de valeur en configuration interactive de voitures Renault. La configuration interactive est un processus de construction de produit où l'utilisateur choisit successivement une valeur pour chaque attribut. Nous évaluons la précision de la recommandation, c'est-à-dire la proportion des recommandations qui auraient été acceptées, et le temps de recommandation ; de plus, nous examinons les différents paramètres qui peuvent influer sur la qualité de la recommandation. Nos résultats sont concluants : les méthodes que nous avons évaluées, qu'elles proviennent de la littérature ou de nos contributions théoriques, sont bien assez rapides pour être utilisées en ligne et ont une précision très élevée, proche du maximum théorique

Recommendation using Bayesian inference for product configuration

International audienc