Formal Connections for families of Star Products

We define the notion of a formal connection for a smooth family of star products with fixed underlying symplectic structure. Such a formal connection allows one to relate star products at different points in the family. This generalizes the formal Hitchin connection introduced by the first author. We establish a necessary and sufficient condition that guarantees the existence of a formal connection, and we describe the space of formal connections for a family as an affine space modelled by the derivations of the star products. Moreover we show that if the parameter space has trivial first cohomology group any two flat formal connections are related by an automorphism of the family of star products.Comment: 26 pages, some typos fixed, references improved, to appear in Commun. Math. Phy

An automatic method for the lexical disambiguation of names

Este artículo presenta un método completamente automático que resuelve la desambiguación léxica de nombres calculando la densidad conceptual de cada uno de los sentidos del nombre a desambiguar. La evaluación del método se ha realizado sobre el corpus SemCor con un contexto de sólo dos nombres, obteniendo una precisión de 81.5% y un recall de 60.25%.Palabras clave: desambiguación léxica de nombres, densidad conceptual.This article presents a completely automatic method that solves the lexical disambiguation of names by calculating the conceptual density of each of the senses of the name to be disambiguated. The evaluation of the method has been carried out on the SemCor corpus with a context of only two names, obtaining an accuracy of 81.5% and a recall of 60.25%. Keywords: lexical disambiguation of names, conceptual density

Un método automático para la desambiguación léxica de nombres

Este artículo presenta un método completamente automático que resuelve la desambiguación léxica de nombres calculando la densidad conceptual de cada uno de los sentidos del nombre a desambiguar. La evaluación del método se ha realizado sobre el corpus SemCor con un contexto de sólo dos nombres, obteniendo una precisión de 81.5% y un recall de 60.25%.Palabras clave: desambiguación léxica de nombres, densidad conceptual

SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder

In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a small set of labelled data samples. Features are extracted using topic modelling based on latent Dirichlet allocation, and then a comprehensive data model is created using a Stacked Denoising Autoencoder (SDA). Topic modelling summarises the data providing ease of use and high interpretability by visualising the topics using word clouds. Given that the SMS messages can be regarded as either spam (unwanted) or ham (wanted), the SDA is able to model the messages and accurately discriminate between the two classes without the need for a pre-labelled training set. The results are compared against the state-of-the-art spam detection algorithms with our proposed approach achieving over 97 % accuracy which compares favourably to the best reported algorithms presented in the literature

Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning

Göpfert C, Paaßen B, Hammer B. Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 510-517.Large margin nearest neighbor classification (LMNN) is a popular technique to learn a metric that improves the accuracy of a simple k-nearest neighbor classifier via a convex optimization scheme. However, the optimization problem is convex only under the assumption that the nearest neighbors within classes remain constant. In this contribution we show that an iterated LMNN scheme (multi-pass LMNN) is a valid optimization technique for the original LMNN cost function without this assumption. We further provide an empirical evaluation of multi-pass LMNN, demonstrating that multi-pass LMNN can lead to notable improvements in classification accuracy for some datasets and does not necessarily show strong overfitting tendencies as reported before

Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning

Göpfert C, Paaßen B, Hammer B. Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 510-517.Large margin nearest neighbor classification (LMNN) is a popular technique to learn a metric that improves the accuracy of a simple k-nearest neighbor classifier via a convex optimization scheme. However, the optimization problem is convex only under the assumption that the nearest neighbors within classes remain constant. In this contribution we show that an iterated LMNN scheme (multi-pass LMNN) is a valid optimization technique for the original LMNN cost function without this assumption. We further provide an empirical evaluation of multi-pass LMNN, demonstrating that multi-pass LMNN can lead to notable improvements in classification accuracy for some datasets and does not necessarily show strong overfitting tendencies as reported before

Non-Negative Kernel Sparse Coding for the Analysis of Motion Data

Hosseini B, Hülsmann F, Botsch M, Hammer B. Non-Negative Kernel Sparse Coding for the Analysis of Motion Data. In: E.P. Villa A, Masulli P, Javier Pons Rivero A, eds. Artificial Neural Networks and Machine Learning – ICANN 2016. Lecture Notes in Computer Science. Vol 9887. Cham: Springer; 2016: 506-514.We are interested in the decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time warping (DTW), which offers particularly successful pairwise motion data comparison, and sparse coding (SC), which enables an automatic decomposition of vectorial data into a sparse linear combination of base vectors. We enhance SC as follows: an efficient kernelization which extends its application domain to general similarity data such as offered by DTW, and its restriction to non-negative linear representations of signals and base vectors in order to guarantee a meaningful dictionary. Empirical evaluations on motion capture benchmarks show the effectiveness of our framework regarding interpretation and discrimination concerns

Role of continuous glucose monitoring in diabetic patients at high cardiovascular risk. an expert-based multidisciplinary delphi consensus

Background: Continuous glucose monitoring (CGM) shows in more detail the glycaemic pattern of diabetic subjects and provides several new parameters (“glucometrics”) to assess patients’ glycaemia and consensually guide treatment. A better control of glucose levels might result in improvement of clinical outcome and reduce disease complications. This study aimed to gather an expert consensus on the clinical and prognostic use of CGM in diabetic patients at high cardiovascular risk or with heart disease. Methods: A list of 22 statements concerning type of patients who can benefit from CGM, prognostic impact of CGM in diabetic patients with heart disease, CGM use during acute cardiovascular events and educational issues of CGM were developed. Using a two-round Delphi methodology, the survey was distributed online to 42 Italian experts (21 diabetologists and 21 cardiologists) who rated their level of agreement with each statement on a 5-point Likert scale. Consensus was predefined as more than 66% of the panel agreeing/disagreeing with any given statement. Results: Forty experts (95%) answered the survey. Every statement achieved a positive consensus. In particular, the panel expressed the feeling that CGM can be prognostically relevant for every diabetic patient (70%) and that is clinically useful also in the management of those with type 2 diabetes not treated with insulin (87.5%). The assessment of time in range (TIR), glycaemic variability (GV) and hypoglycaemic/hyperglycaemic episodes were considered relevant in the management of diabetic patients with heart disease (92.5% for TIR, 95% for GV, 97.5% for time spent in hypoglycaemia) and can improve the prognosis of those with ischaemic heart disease (100% for hypoglycaemia, 90% for hyperglycaemia) or with heart failure (87.5% for hypoglycaemia, 85% for TIR, 87.5% for GV). The experts retained that CGM can be used and can impact the short- and long-term prognosis during an acute cardiovascular event. Lastly, CGM has a recognized educational role for diabetic subjects. Conclusions: According to this Delphi consensus, the clinical and prognostic use of CGM in diabetic patients at high cardiovascular risk is promising and deserves dedicated studies to confirm the experts’ feeling

Serum Uric Acid Predicts All-Cause and Cardiovascular Mortality Independently of Hypertriglyceridemia in Cardiometabolic Patients without Established CV Disease: A Sub-Analysis of the URic acid Right for heArt Health (URRAH) Study

High serum uric acid (SUA) and triglyceride (TG) levels might promote high-cardiovascular risk phenotypes across the cardiometabolic spectrum. However, SUA predictive power in the presence of normal and high TG levels has never been investigated. We included 8124 patients from the URic acid Right for heArt Health (URRAH) study cohort who were followed for over 20 years and had no established cardiovascular disease or uncontrolled metabolic disease. All-cause mortality (ACM) and cardiovascular mortality (CVM) were explored by the Kaplan-Meier estimator and Cox multivariable regression, adopting recently defined SUA cut-offs for ACM (>= 4.7 mg/dL) and CVM (>= 5.6 mg/dL). Exploratory analysis across cardiometabolic subgroups and a sensitivity analysis using SUA/serum creatinine were performed as validation. SUA predicted ACM (HR 1.25 [1.12-1.40], p < 0.001) and CVM (1.31 [1.11-1.74], p < 0.001) in the whole study population, and according to TG strata: ACM in normotriglyceridemia (HR 1.26 [1.12-1.43], p < 0.001) and hypertriglyceridemia (1.31 [1.02-1.68], p = 0.033), and CVM in normotriglyceridemia (HR 1.46 [1.23-1.73], p < 0.001) and hypertriglyceridemia (HR 1.31 [0.99-1.64], p = 0.060). Exploratory and sensitivity analyses confirmed our findings, suggesting a substantial role of SUA in normotriglyceridemia and hypertriglyceridemia. In conclusion, we report that SUA can predict ACM and CVM in cardiometabolic patients without established cardiovascular disease, independent of TG levels