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

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

Large time off-equilibrium dynamics of a manifold in a random potential

We study the out of equilibrium dynamics of an elastic manifold in a random potential using mean-field theory. We find two asymptotic time regimes: (i) stationary dynamics, (ii) slow aging dynamics with violation of equilibrium theorems. We obtain an analytical solution valid for all large times with universal scalings of two-time quantities with space. A non-analytic scaling function crosses over to ultrametricity when the correlations become long-range. We propose procedures to test numerically or experimentally the extent to which this scenario holds for a given system.Comment: 12 page

Recertification of 25-hydroxyvitamin D standards by Isotope Pattern Deconvolution (IPD)

Vitamin D (VTD) is an important prohormone widely known since its deficiency is directly related to development of rickets in children and osteoporosis in adults. Furthermore, recent studies have demonstrated that vitamin D has also an important role in non-skeletal conditions such as autoimmune diseases, cardiovascular diseases and cancer, among others. This vitamin can be found in two main forms: vitamin D2 and vitamin D3. The metabolism of both forms of vitamin D are subjected to a first hydroxylation in the liver to form 25-hydroxyvitamin D (25(OH)D) and then to a second one in the kidney to form 1,25-dihydroxyvitamin D (1,25(OH)2D), the active form of vitamin D. Nevertheless, the measurement of 25(OH)D in serum samples is preferred test for the assessment of vitamin D status over the 1,25(OH)2D. There are two main reasons for this choice: the longer lifetime (3 weeks versus 4 h) and its higher concentration levels (ng/mL versus pg/mL)[2]. Over the last years, a dramatic rise in vitamin D testing (as 25(OH)D) has been observed, due to two main reasons: the increased number of patients with VTD deficiency and the increase of the role of VTD as a biomarker related to several diseases. In this work we propose a recertification approach for 25(OH)D2/D3 solvent standards based on Isotope Pattern Deconvolution (IPD) using NIST SRM 2972 as reference material. This approach could help to meet the requirements for external standardization criteria using in-house calibration curves