Parallel Chip Firing Game associated with n-cube orientations

We study the cycles generated by the chip firing game associated with n-cube orientations. We show the existence of the cycles generated by parallel evolutions of even lengths from 2 to $2^n$ on $H_n$ (n >= 1), and of odd lengths different from 3 and ranging from 1 to $2^{n-1}-1$ on $H_n$ (n >= 4)

Efficient Estimation with Many Weak Instruments Using Regularization Techniques

The problem of weak instruments is due to a very small concentration parameter. To boost the concentration parameter, we propose to increase the number of instruments to a large number or even up to a continuum. However, in finite samples, the inclusion of an excessive number of moments may be harmful. To address this issue, we use regularization techniques as in Carrasco (2012) and Carrasco and Tchuente (2014). We show that normalized regularized two-stage least squares (2SLS) and limited maximum likelihood (LIML) are consistent and asymptotically normally distributed. Moreover, our estimators are asymptotically more efficient than most competing estimators. Our simulations show that the leading regularized estimators (LF and T of LIML) work very well (are nearly median unbiased) even in the case of relatively weak instruments. An application to the effect of institutions on output growth completes the article

Armed Conflict and Early Human Capital Accumulation: Evidence from Cameroon's Anglophone Conflict

This paper examines the impact of the Anglophone Conflict in Cameroon on human capital accumulation. Using high-quality individual-level data on test scores and information on conflict-related violent events, a difference-in-differences design is employed to estimate the conflict's causal effects. The results show that an increase in violent events and conflict-related deaths causes a significant decline in test scores in reading and mathematics. The conflict also leads to higher rates of teacher absenteeism and reduced access to electricity in schools. These findings highlight the adverse consequences of conflict-related violence on human capital accumulation, particularly within the Anglophone subsystem. The study emphasizes the disproportionate burden faced by Anglophone pupils due to language-rooted tensions and segregated educational systems

Religious Competition, Culture and Domestic Violence: Evidence from Colombia

This paper studies how religious competition, as measured by the emergence of religious organizations with innovative worship styles and cultural practices, impacts domestic violence. Using data from Colombia, the study estimates a two-way fixed-effects model and reveals that the establishment of the first non-Catholic church in a predominantly Catholic municipality leads to a significant decrease in reported cases of domestic violence. This effect persists in the long run, indicating that religious competition introduces values and practices that discourage domestic violence, such as household stability and reduced male dominance. Additionally, the effect is more pronounced in municipalities with less clustered social networks, suggesting the diffusion of these values and practices through social connections. This research contributes to the understanding of how culture influences domestic violence, emphasizing the role of religious competition as a catalyst for cultural change

High school human capital portfolio and college outcomes

This paper assesses the relationship between courses taken in high school and college major choice. It considers individuals as holding a portfolio of relative human capital rates that may either be similar to those in their major - specialized - or different from those in their major - diversified. Using High School and Beyond survey data, I find a U-shaped relationship between the diversification of high school courses portfolio, measured by the differences from the typical student in the major, and college performance. The underlying relation linking high school to college is assessed by estimating a structural model of high school human capital acquisition and college major choice. Policy experiments suggest that taking an additional quantitative course in high school increases the probability that a college student chooses a science, technology, engineering, or math major by four percentage points with little effect on college performance

Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States

This study develops an economic model for a social planner who prioritizes health over shortterm wealth accumulation during a pandemic. Agents are connected through a weighted undirected network of contacts, and the planner’s objective is to determine the policy that contains the spread of infection below a tolerable incidence level, and that maximizes the present discounted value of real income, in that order of priority. The optimal unique policy depends both on the configuration of the contact network and the tolerable infection incidence. Comparative statics analyses are conducted: (i) they reveal the tradeoff between the economic cost of the pandemic and the infection incidence allowed; and (ii) they suggest a correlation between different measures of network centrality and individual lockdown probability with the correlation increasing with the tolerable infection incidence level. Using unique data on the networks of nursing and long-term homes in the U.S., we calibrate our model at the state level and estimate the tolerable COVID-19 infection incidence level. We find that laissez-faire (more tolerance to the virus spread) pandemic policy is associated with an increased number of deaths in nursing homes and higher state GDP growth. In terms of the death count, laissez-faire is more harmful to nursing homes than more peripheral in the networks, those located in deprived counties, and those who work for a profit. We also find that U.S. states with a Republican governor have a higher level of tolerable incidence, but policies tend to converge with high death count

Evaluation of the in vivo activity of different concentrations of Clerodendrum umbellatum poir against Schistosoma mansoni infection in mice

Clerodendrum umbellatum Poir (Verbenaceae) is traditionally used in Cameroon for the treatment of many diseases including intestinal helminthiasis. This study was undertaken to assess the in vivo antischistosomal activity of its leaves aqueous extract on a Schistosoma mansoni mice model and to determine the most effective dose of this extract. Mice showing a patent infection of S. mansoni were daily treated with C. umbellatum leaves aqueous extract at the doses of 40, 80 or 160 mg/kg body weight for 14 days. Seven days after administration of the extract, schistosomicidal activity was evaluated on the liver and spleen weights, faecal eggs releasing, liver egg count and worm burden. Treatment using C. umbellatum leaves aqueous extract resulted in an important reduction in faecal egg output by 75.49 % and 85.14 % for 80 mg/kg and 160 mg/kg of the extract respectively. These reduction rates did not differ significantly from the 100 % obtained in the group of infected mice treated with 100 mg/kg of praziquantel. C. umbellatum leaves aqueous extract was lethal to S. mansoni worm. A 100 % reduction rate was recorded in the group of infected mice treated with 160 mg/kg of the extract, as well as in praziquantel-treated mice. An amelioration of the hepatosplenomegaly was noticed in both the extract-treated mice and the praziquantel-treated mice. From these results, we can conclude that C.umbellatum leaves aqueous extract demonstrated schistosomicidal properties in S. mansoni model at doses of at least 80 mg/kg body weight.Key words: Clerodendrum umbellatum, Schistosoma mansoni, faecal egg output, worm burden, mic

Modélisation et dérivation de profils utilisateurs à partir de réseaux sociaux : approche à partir de communautés de réseaux k-égocentriques

Dans la plupart des systèmes nécessitant la modélisation de l'utilisateur pour adapter l'information à ses besoins spécifiques, l'utilisateur est représenté avec un profil généralement composé de ses centres d'intérêts. Les centres d'intérêts de l'utilisateur sont construits et enrichis au fil du temps à partir de ses interactions avec le système. De par cette nature évolutive des centres d'intérêts de l'utilisateur, le profil de l'utilisateur ne peut en aucun moment être considéré comme entièrement connu par un système. Cette connaissance partielle du profil de l'utilisateur à tout instant t a pour effet de réduire considérablement les performances des mécanismes d'adaptation de l'information à l'utilisateur lorsque le profil de l'utilisateur ne contient pas (ou contient très peu) les informations nécessaires à leur fonctionnement. Cet inconvénient est particulièrement plus récurrent chez les nouveaux utilisateurs d'un système (instant t=0, problème du démarrage à froid) et chez les utilisateurs peu actifs. Pour répondre à cette problématique, plusieurs travaux ont exploré des sources de données autres que celles produites par l'utilisateur dans le système : utilisateurs au comportement similaire (utilisé dans le filtrage collaboratif) ou données produites par l'utilisateur dans d'autres systèmes (conception de profil utilisateur multi-application et gestion des identités multiples des utilisateurs). Très récemment, avec l'avènement du Web social et l'explosion des réseaux sociaux en ligne, ces derniers sont de plus en plus étudiés comme source externe de données pouvant servir à l'enrichissement du profil de l'utilisateur. Ceci a donné naissance à de nouveaux mécanismes de filtrage social de l'information : systèmes de recherche d'information sociale, systèmes de recommandation sociaux, etc. Les travaux actuels portant sur les mécanismes de filtrage social de l'information démontrent que ce nouveau champ de recherche est très prometteur. Une étude sur les travaux existants nous permet tout de même de noter particulièrement deux faiblesses : d'une part, chacune des approches proposées dans ces travaux reste très spécifique à son domaine d'application (et au mécanisme associé), et d'autre part, ces approches exploitent de manière unilatérale les profils des individus autour de l'utilisateur dans le réseau social. Pour pallier ces deux faiblesses, nos travaux de recherche proposent une démarche méthodique permettant de définir d'une part un modèle social générique de profil de l'utilisateur réutilisable dans plusieurs domaines d'application et par différents mécanismes de filtrage social de l'information, et à proposer d'autre part, une technique permettant de dériver de manière optimale des informations du profil de l'utilisateur à partir de son réseau social. Nous nous appuyons sur des travaux existants en sciences sociales pour proposer une approche d'usage des communautés (plutôt que des individus) autour de l'utilisateur. La portion significative de son réseau social est constituée des individus situés à une distance maximum k de l'utilisateur et des relations entre ces individus (réseau k-égocentrique). A partir de deux évaluations de l'approche proposée, l'une dans le réseau social numérique Facebook, et l'autre dans le réseau de co-auteurs DBLP, nous avons pu démontrer la pertinence de notre approche par rapport aux approches existantes ainsi que l'impact de mesures telles que la centralité de communautés (degré ou proximité par exemple) ou la densité des réseaux k-égocentriques sur la qualité des résultats obtenus. Notre approche ouvre de nombreuses perspectives aux travaux s'intéressant au filtrage social de l'information dans de multiples domaines d'application aussi bien sur le Web (personnalisation de moteurs de recherche, systèmes de recommandation dans le e-commerce, systèmes adaptatifs dans les environnements e-Learning, etc.) que dans les intranets d'entreprise (systèmes d'analyses comportementales dans les réseaux d'abonnés de clients télécoms, détection de comportements anormaux/frauduleux dans les réseaux de clients bancaires, etc.).In most systems that require user modeling to adapt information to each user's specific need, a user is usually represented by a user profile in the form of his interests. These interests are learnt and enriched over time from users interactions with the system. By the evolving nature of user's interests, the user's profile can never be considered fully known by a system. This partial knowledge of the user profile at any time t significantly reduces the performance of adaptive systems, when the user's profile contains no or only some information. This drawback is particularly most recurrent for new users in a system (time t = 0, also called cold start problem) and for less active users. To address this problem, several studies have explored data sources other than those produced by the user in the system: activities of users with similar behavior (e.g. collaborative filtering techniques) or data generated by the user in other systems (e.g., multi-application user's profiles, multiple identities management systems). By the recent advent of Social Web and the explosion of online social networks sites, social networks are more and more studied as an external data source that can be used to enrich users' profiles. This has led to the emergence of new social information filtering techniques (e.g. social information retrieval, social recommender systems). Current studies on social information filtering show that this new research field is very promising. However, much remains to be done to complement and enhance these studies. We particularly address two drawbacks: (i) each existing social information filtering approach is specific in its field scope (and associated mechanisms), (ii) these approaches unilaterally use profiles of individuals around the user in the social network to improve traditional information filtering systems. To overcome these drawbacks in this thesis, we aim at defining a generic social model of users' profiles that can be reusable in many application domains and for several social information filtering mechanisms, and proposing optimal techniques for enriching user's profile from the user's social network. We rely on existing studies in social sciences to propose a communities (rather than individuals) based approach for using individuals around the user in a specific part of his social network, to derive his social profile (profile that contains user's interest derived from his social network). The significant part of the user's social network used in our studies is composed of individuals located at a maximum distance k (in the entire social network) from the user, and relationships between these individuals (k-egocentric network). Two evaluations of the proposed approach based on communities in k-egocentric networks have been conducted in the online social network Facebook and the co-authors network DBLP. They allow us to demonstrate the relevance of the proposal with respect to existing individual based approaches, and the impact of structural measures such as the centrality of communities (degree or proximity) or user's k-egocentric network density, on the quality of results. Our approach opens up many opportunities for future studies in social information filtering and many application domains as well as on the Web (e.g. personalization of search engines, recommender systems in e-commerce, adaptive systems in e-Learning environment) or in Intranets business systems (e.g. behavioral analysis in networks of subscribers telecom customers, detection of abnormal behavior network bank customers, etc.)