1,720,983 research outputs found
Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models
No full textThe Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years, it has shown significant utility across various climate-related tasks, ranging from forecasting and climate change research to renewable energy prediction, resource management, air quality risk assessment, and the forecasting of rare events, among others. Unfortunately, the availability of CERRA is lagging 2 years behind the current date, due to constraints in acquiring the requisite external data and the intensive computational demands inherent in its generation. As a solution, this paper introduces a novel method using diffusion models to approximate CERRA downscaling in a data-driven manner, without additional informations. By leveraging the lower resolution ERA5 dataset, which provides boundary conditions for CERRA, we approach this as a super-resolution task. Focusing on wind speed around Italy, our model, trained on existing CERRA data, shows promising results, closely mirroring the original CERRA. Validation with in-situ observations further confirms the model’s accuracy in approximating ground measurements
Enhancing variational generation through self-decomposition
Get PDFIn this article we introduce the notion of Split Variational Autoencoder (SVAE), whose output x^ is obtained as a weighted sum σ⊙x1^+(1−σ)⊙x2^ of two generated images x1^,x2^ , and σ is a learned compositional map. The composing images x1^,x2^ , as well as the σ -map are automatically synthesized by the model. The network is trained as a usual Variational Autoencoder with a negative loglikelihood loss between training and reconstructed images. No additional loss is required for x1^,x2^ or σ , neither any form of human tuning. The decomposition is nondeterministic, but follows two main schemes, that we may roughly categorize as either “syntactic” or “semantic.” In the first case, the map tends to exploit the strong correlation between adjacent pixels, splitting the image in two complementary high frequency sub-images. In the second case, the map typically focuses on the contours of objects, splitting the image in interesting variations of its content, with more marked and distinctive features. In this case, according to empirical observations, the Fréchet Inception Distance (FID) of x1^ and x2^ is usually lower (hence better) than that of x^ , that clearly suffers from being the average of the former. In a sense, a SVAE forces the Variational Autoencoder to make choices, in contrast with its intrinsic tendency to average between alternatives with the aim to minimize the reconstruction loss towards a specific sample. According to the FID metric, our technique, tested on typical datasets such as Mnist, Cifar10 and CelebA, allows us to outperform all previous purely variational architectures (not relying on normalization flows)
A Categorial Model for Logic Programs: Indexed Monoidal Categories
No full textWe propose a simple notion of model for Logic Programs based on indexed monoidal categories. On the one hand our proposal is consistent with well-known techniques for providing a categorical semantics for logical systems. On the other hand, it allows us to keep the effectiveness of the Horn Clause Logic fragment of first order logic. This is shown by providing an effective construction of the initial model of a program, obtained through the application of a general methodology aimed at defining a categorical semantics for structured transition systems. Thus the declarative view (as logical theory) and the operational view (as structured transition system) of a logic program are reconciled in a highly formal framework, which provides interesting hints to possible generalizations of the logic programming paradigm
HELM and the Semantic Math-Web
No full textThe eXtensible Markup Language (XML) opens the possibility to start anew, on a solid technological ground, the ambitious goal of developing a suitable technology for the creation and maintenance of a virtual, distributed, hypertextual library of formal mathematical knowledge. In particular, XML provides a central technology for storing, retrieving and processing mathematical documents, comprising sophisticated web-publishing mechanisms (stylesheets) covering notational and stylistic issues. By the application of XML technology to the large repositories of structured, content oriented information offered by Logical Frameworks we meet the ultimate goal of the Semantic Web, that is to allow machines the sharing and exploitation of knowledge in the Web way, i.e. without central authority, with few basic rules, in a scalable, adaptable, extensible manner
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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