Análisis de las sociedades gestoras de fondo de inversión en España

En el presente artículo se realiza un análisis del sector de las Sociedades Gestoras de Instituciones de Inversión Colectiva en España, no desde el punto de vista del inversor, sino desde la perspectiva de los propios gestores de las instituciones. Se lleva a cabo un análisis sectorial basado en su evolución histórica durante la década de los 90, y se estudian los extraordinarios niveles de rentabilidad que el negocio parece ofrecer, estableciendo matizaciones al respecto. Se realiza también un análisis de las características determinantes de las relaciones entre las sociedades gestoras y sus correspondientes empresas matrices, tanto en lo que se refiere al funcionamiento interno como a los intereses en las empresas participadas por sus fondos.In this paper an analysis o f the Mutual Funds Management sector in Spain is done, not from the point of view of the final investor, but from the managers one. The historical evolution of this sector during the 90’s is resumed, and the uncommon rentability rates are analized, offering a wide range of refinements about this topic. Also, we analize the main characteristics of the relationship between the managers and the main companies, according to the rules of the internal performance and the interest of the funds in the companies wich are being invested

Synchronization centrality and explosive synchronization in complex networks

Synchronization of networked oscillators is known to depend fundamentally on the interplay between the dynamics of the graph's units and the microscopic arrangement of the network's structure. For non identical elements, the lack of quantitative tools has hampered so far a systematic study of the mechanisms behind such a collective behavior. We here propose an effective network whose topological properties reflect the interplay between the topology and dynamics of the original network. On that basis, we are able to introduce the "synchronization centrality", a measure which quantifies the role and importance of each network's node in the synchronization process. In particular, we use such a measure to assess the propensity of a graph to synchronize explosively, thus indicating a unified framework for most of the different models proposed so far for such an irreversible transition. Taking advantage of the predicting power of this measure, we furthermore discuss a strategy to induce the explosive behavior in a generic network, by acting only upon a small fraction of its nodes

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

The development of low-power wearable systems requires specialized techniques to accommodate their unique requirements and constraints. While significant advancements have been made in the inference phase of artificial intelligence, the training phase remains a challenge, particularly for biomedical wearable systems. Traditional training algorithms might not be suitable for these applications due to the substantial memory requirements and high computational costs associated with processing the large number of bits involved in neural network operations. In this paper, we introduce a novel learning procedure specifically designed for low-power wearable systems, dubbed Bio-BPfree (deep neural network training without backpropagation for low-power wearable systems). Using a two-class classification task, Bio-BPfree replaces conventional forward and backward backpropagation passes with four forward passes, two for data of the positive class and two for data of the negative class. Each layer is equipped with a unique objective function aimed at minimizing the distance between data points within the same class while maximizing the distance between data points from different classes. Our experimental results, which were obtained by conducting rigorous evaluations on the MIT-BIH dataset that features electrocardiogram (ECG) signals, effectively demonstrate the superior performance and suitability of Bio-BPfree for two-class classification tasks, particularly within the challenging environment of low-power wearable systems designed for continuous health monitoring and assessment.RYC2021-032853-

Efficient Hardware Design Of Iterative Stencil Loops

A large number of algorithms for multidimensional signals processing and scientific computation come in the form of iterative stencil loops (ISLs), whose data dependencies span across multiple iterations. Because of their complex inner structure, automatic hardware acceleration of such algorithms is traditionally considered as a difficult task. In this paper, we introduce an automatic design flow that identifies, in a wide family of bidimensional data processing algorithms, sub-portions that exhibit a kind of parallelism close to that of ISLs; these are mapped onto a space of highly optimized ad-hoc architectures, which is efficiently explored to identify the best implementations with respect to both area and throughput. Experimental results show that the proposed methodology generates circuits whose performance is comparable to that of manually-optimized solutions, and orders of magnitude higher than those generated by commercial HLS tools

Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains a global model by aggregating local models from hospitals. Existing FL techniques adopt a central server-based network topology, where the server assembles the local models trained in each hospital to create a global model. However, the server could be a point of failure, and models trained in FL usually have worse performance than those trained in the centralized learning manner when the patient's data are not independent and identically distributed (Non-IID) in the hospitals. This paper presents a decentralized FL framework, including training with adaptive ensemble learning and a deployment phase using knowledge distillation. The adaptive ensemble learning step in the training phase leads to the acquisition of a specific model for each hospital that is the optimal combination of local models and models from other available hospitals. This step solves the non-IID challenges in each hospital. The deployment phase adjusts the model's complexity to meet the resource constraints of wearable systems. We evaluated the performance of our approach on edge computing platforms using EPILEPSIAE and TUSZ databases, which are public epilepsy datasets.RYC2021-032853-

Law, Liberty and the Rule of Law (in a Constitutional Democracy)

In the hunt for a better--and more substantial--awareness of the “law,” The author intends to analyze the different notions related to the “rule of law” and to criticize the conceptions that equate it either to the sum of “law” and “rule” or to the formal assertion that “law rules,” regardless of its relationship to certain principles, including both “negative” and “positive” liberties. Instead, he pretends to scrutinize the principles of the “rule of law,” in general, and in a “constitutional democracy,” in particular, to conclude that the tendency to reduce the “democratic principle” to the “majority rule” (or “majority principle”), i.e. to whatever pleases the majority, as part of the “positive liberty,” is contrary both to the “negative liberty” and to the “rule of law” itself

CATÁLOGO DE LOS LÍQUENES EPIFÍTICOS DE LA SIERRA DE CORBERA (VALENCIA; ESPANA): COMENTARIOS COROLÓGICOS

In this paper the epiphytic lichen flora of ((Sierra de Corbera)) is studied. 65 species are recognized. Scoliciosporum umbrinum (Ach.) Arnold, Bacidia fuscorrubella (Ach.) Bausch, Bacidia subchlorotica (Nyl.) Flagey, Catillaria melanobola (Nyl.) B. de Lesd., Caloplaca pulchrevirens (Anzi.) Jatta, Caloplaca sarcopisioides (Korber) Zahlbr., Physcia vitii Nádv., Pertusaria leucostoma (Bernh.) Massal., Arthonia fuliginosa (Turn. et Bon.) Flot., Melaspilea bagliettoana Zahlbr., Arthopyrenia platypirenia (Nyl.) Arnold, Polyblastiopsis lactea (IVlassal.) Zahlbr. are new records for the spanish flora. A description of the most important species is given. Ecology and chorology of the most recognized taxa is given. Finally chorologyc conclusions are presented.En el presente trabajo se estudia la flora liquénica epifítica de la Sierra de Corbera. Se citan 65 especies de las que se comenta brevemente su ecología. Se describen de manera concisa las más interesantes de las cuales creemos son taxones nuevos para España: Scoliciosporum umbrinum (Ach.) Arnold, Bacidia fuscorrubella (Ach.) Bausch, Bacidia subchlorotica (Nyl.) Flagey, Catillaria melanobola (Nyl.) B. de Lesd., Caloplaca pulchrevirens (Anzi.) Jatta, Caloplaca sarcopisioides (Korber) Zahlbr., Physcia vitii Nádv., Pertusaria leucostoma (Bernh.) Massal., Arthonia fuliginosa (Turn. et Bon.) Flot., Melaspilea bagliettoana Zahlbr., Arthopyrenia platypirenia (Nyl.) Arnold, Polyblastiopsis lactea (Massal.) Zahlbr. Finalmente se realiza una aproximación corológica de la flora del territorio

Prediction-for-CompAction: navigation in social environments using generalized cognitive maps

The ultimate navigation efficiency of mobile robots in human environments will depend on how we will appraise them: merely as impersonal machines or as human-like agents. In the latter case, an agent may take advantage of the cooperative collision avoidance, given that it possesses recursive cognition, i.e.,the agent's decisions depend on the decisions made by humans that in turn depend on the agent's decisions. To deal with this high-level cognitive skill, we propose a neural network architecture implementing Prediction-for-CompAction paradigm. The network predicts possible human-agent collisions and compacts the time dimension by projecting a given dynamic situation into a static map. Thereby emerging compact cognitive map can be readily used as a "dynamic GPS" for planning actions or mental evaluation of the convenience of cooperation in a given context. We provide numerical evidence that cooperation yields additional room for more efficient navigation in cluttered pedestrian flows, and the agent can choose path to the target significantly shorter than a robot treated by humans as a functional machine. Moreover, the navigation safety, i.e., the chances to avoid accidental collisions, increases under cooperation. Remarkably, these benefits yield no additional load to the mean society effort. Thus, the proposed strategy is socially compliant, and the humanoid agent can behave as "one of us"