A simple proof of existence of equilibrium in a one sector growth modelp with bounded or unbounded returns from below

We analyze a Ramsey economy when net investment is constrained to be non negative. We prove existence of a competitive equilibrium when utility need not be bounded from below and the Inada-type conditions need not hold. The analysis is carried out by means of a direct and technically standard strategy. This direct strategy (a) allows us to obtain detailed results concerning properties of competitive equilibria, and (b) is amenable to be easily adapted for the analysis of analogous models often found in macroeconomics.Ramsey model; one sector growth model; non negative net investment; competitive equilibrium

False Analog Data Injection Attack Towards Topology Errors: Formulation and Feasibility Analysis

In this paper, we propose a class of false analog data injection attack that can misguide the system as if topology errors had occurred. By utilizing the measurement redundancy with respect to the state variables, the adversary who knows the system configuration is shown to be capable of computing the corresponding measurement value with the intentionally misguided topology. The attack is designed such that the state as well as residue distribution after state estimation will converge to those in the system with a topology error. It is shown that the attack can be launched even if the attacker is constrained to some specific meters. The attack is detrimental to the system since manipulation of analog data will lead to a forged digital topology status, and the state after the error is identified and modified will be significantly biased with the intended wrong topology. The feasibility of the proposed attack is demonstrated with an IEEE 14-bus system.Comment: 5 pages, 7 figures, Proc. of 2018 IEEE Power and Energy Society General Meetin

News Session-Based Recommendations using Deep Neural Networks

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift. Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent Neural Networks. In this paper, it is proposed an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News Recommender Systems. This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks. The recommendation task addressed in this work is next-item prediction for users sessions: "what is the next most likely article a user might read in a session?" Users sessions context is leveraged by the architecture to provide additional information in such extreme cold-start scenario of news recommendation. Users' behavior and item features are both merged in an hybrid recommendation approach. A temporal offline evaluation method is also proposed as a complementary contribution, for a more realistic evaluation of such task, considering dynamic factors that affect global readership interests like popularity, recency, and seasonality. Experiments with an extensive number of session-based recommendation methods were performed and the proposed instantiation of CHAMELEON meta-architecture obtained a significant relative improvement in top-n accuracy and ranking metrics (10% on Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada. https://recsys.acm.org/recsys18/dlrs

Le renforcement des capacités des organisations paysannes et rurales : enseignements de l'expérience de la Banque Mondiale

La présente étude s'efforce de tirer les premiers enseignements de l'expérience de la Banque Mondiale en matière de renforcement des capacités des organisations paysannes et rurales (OPR)1. A partir de 1997, la Banque Mondiale et l'Aide française ont apporté leur soutien à l'élaboration et à la mise en oeuvre, dans des pays en développement, de nouveaux projets d'appui aux services agricoles et aux organisations paysannes et rurales qui visent à promouvoir des services régis par la demande des producteurs. Dans leur conception, ces projets constituent des innovations institutionnelles pour trois raisons principales: - Ils visent la construction d'un partenariat institutionnalisé entre les organisations paysannes et rurales et les organismes de recherche et de vulgarisation qui doit permettre aux producteurs organisés (i) d'exprimer une demande collective d'appui à l'innovation technique et économique et (ii) d'influer sur les orientations et sur la prise de décision au sein des institutions de service concernées. - La Banque Mondiale apporte son appui aux institutions (publiques ou para-publiques) chargées des services agricoles mais aussi aux organisations paysannes et rurales afin de renforcer leurs capacités et de créer des conditions d'un dialogue équilibré entre les différents acteurs engagés dans le partenariat. - Ces projets s'accompagnent de réformes institutionnelles qui s'effectuent à un rythme variable selon les pays mais qui visent à rendre les institutions de recherche et de vulgarisation responsables vis à vis des producteurs. Les premiers projets ont concerné le Sénégal, la Guinée, le Mali et le Burkina Faso; la démarche a ensuite été étendue progressivement à d'autres pays d'Afrique sub-saharienne, à l'Afrique du Nord (Tunisie, Maroc) et la demande pour des projets de ce type s'accroît en Asie (Indonésie, Inde). Le présent rapport est organisé comme suit: - La première partie présente une brève analyse de la relation entre les institutions de recherche et de vulgarisation et les organisations de producteurs et des évolutions que cette relation a connues sur une longue période; elle met notamment l'accent (i) sur les facteurs qui expliquent l'importance croissante du rôle joué par les OPR dans les processus d'innovation et (ii) sur les actions soutenues par les différents bailleurs de fonds pour renforcer les capacités des OPR. - La deuxième partie est centrée sur les résultats des études de cas réalisées dans quatre pays; après une brève description des programmes soutenus par la Banque Mondiale, sont mis en évidence les points communs et les principales différences identifiés. - La troisième partie du rapport présente les principaux enseignements qui peuvent être tirés de l'expérience de la Banque Mondiale en ce qui concerne le renforcement des capacités des OPR et formule des recommandations. - La quatrième partie regroupe les quatre études de cas réalisées au Sénégal, au Burkina Faso, au Ghana et en Ouganda. - La cinquième partie présente les résultats de l'analyse relative à la place accordée aux organisations paysannes et rurales et au renforcement de leurs capacités dans les projets de la Banque Mondiale, dans le secteur agricole. (Résumé d'auteur

Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE Trans Med Imag; added copyright notic

LIPN: Introducing a new Geographical Context Similarity Measure and a Statistical Similarity Measure based on the Bhattacharyya coefficient

International audienceThis paper describes the system used by the LIPN team in the task 10, Multilingual Semantic Textual Similarity, at SemEval 2014, in both the English and Spanish sub-tasks. The system uses a support vector regression model, combining different text similarity measures as features. With respect to our 2013 participation, we included a new feature to take into account the geographical context and a new semantic distance based on the Bhattacharyya distance calculated on co-occurrence distributions derived from the Spanish Google Books n-grams dataset