scholarly journals Intrinsic Complexity of Uniform Learning

2003 ◽  
pp. 39-53
Author(s):  
Sandra Zilles
Keyword(s):  
Intrinsic Complexity

Abstract Recursion and Intrinsic Complexity

2018 ◽  
Author(s):  
Yiannis N. Moschovakis
Keyword(s):  
Intrinsic Complexity

scholarly journals Microsurgical Anatomy of the Anterior Circulation of the Brain Adjusted to the Neurosurgeon’s Daily Practice

2021 ◽  
Vol 11 (4) ◽  
pp. 519
Author(s):  
Tomas Poblete ◽  
Daniel Casanova ◽  
Miguel Soto ◽  
Alvaro Campero ◽  
Jorge Mura
Keyword(s):  
Daily Practice ◽  
Computed Tomographic Angiography ◽  
Poor Quality ◽  
Surgical Anatomy ◽  
Microsurgical Anatomy ◽  
Anterior Circulation ◽  
Computed Tomographic ◽  
Intrinsic Complexity ◽  
Tomographic Angiography

The study of cerebrovascular anatomy can be difficult and may take time due to its intrinsic complexity. However, it can also be difficult for the following reasons: the excessive description of neuroanatomy making articles hard to read, the unclear clinical application of what is written, the use of simplified or intricate schematic drawings that are not always appropriate for effective teaching, the poor quality of neuroanatomy dissections and the use of unusual views of figures that are not strictly related to the most frequent neuroimages to be interpreted in daily practice. Because of this, we designed an article that incorporates original and accurate anatomical dissections in an attempt to improve its comprehensibility. Five formalin-fixed adult cadaveric heads, whose vessels were injected with a colored silicone mixture (red for arteries and blue for veins), were dissected and examined under a microscope with magnifications from 3× to 40×. Special emphasis has been placed on correlating topographic anatomy with routine neuroimaging studies from computed tomographic angiography (CTA) and digital subtraction angiography (DSA). The essential surgical anatomy in a neurosurgeon’s daily practice is also described. The cadaveric dissections included in this study contribute to the understanding of the cerebrovascular anatomy necessary for the neurosurgeon’s daily practice.

scholarly journals Modelling the European Union Sustainability Transition: A Soft-Linking Approach

2021 ◽  
Vol 13 (11) ◽  
pp. 6303
Author(s):  
Andrea M. Bassi ◽  
Valeria Costantini ◽  
Elena Paglialunga
Keyword(s):  
European Union ◽  
Computable General Equilibrium ◽  
Methodological Approach ◽  
Strong Support ◽  
Policy Instruments ◽  
Evaluation Process ◽  
Clean Energy ◽  
Spillover Effects ◽  
The European Union ◽  
Intrinsic Complexity

The European Green Deal (EGD) is the most ambitious decarbonisation strategy currently envisaged, with a complex mix of different instruments aiming at improving the sustainability of the development patterns of the European Union in the next 30 years. The intrinsic complexity brings key open questions on the cost and effectiveness of the strategy. In this paper we propose a novel methodological approach to soft-linking two modelling tools, a systems thinking (ST) and a computable general equilibrium (CGE) model, in order to provide a broader ex-ante policy evaluation process. We use ST to highlight the main economic feedback loops the EGD strategy might trigger. We then quantify these loops with a scenario analysis developed in a dynamic CGE framework. Our main finding is that such a soft-linking approach allows discovery of multiple channels and spillover effects across policy instruments that might help improve the policy mix design. Specifically, positive spillovers arise from the adoption of a revenue recycling mechanism that ensures strong support for the development and diffusion of clean energy technologies. Such spillover effects benefit not only the European Union (EU) market but also non-EU countries via trade-based technology transfer, with a net positive effect in terms of global emissions reduction.

scholarly journals Low-Carb and Ketogenic Diets in Type 1 and Type 2 Diabetes

Nutrients ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 962 ◽  
Author(s):  
Bolla ◽  
Caretto ◽  
Laurenzi ◽  
Scavini ◽  
Piemonti
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Dietary Interventions ◽  
Ketogenic Diets ◽  
Patients With Diabetes ◽  
Intrinsic Complexity ◽  
Few Data ◽  
Reducing Carbohydrates

Low-carb and ketogenic diets are popular among clinicians and patients, but the appropriateness of reducing carbohydrates intake in obese patients and in patients with diabetes is still debated. Studies in the literature are indeed controversial, possibly because these diets are generally poorly defined; this, together with the intrinsic complexity of dietary interventions, makes it difficult to compare results from different studies. Despite the evidence that reducing carbohydrates intake lowers body weight and, in patients with type 2 diabetes, improves glucose control, few data are available about sustainability, safety and efficacy in the long-term. In this review we explored the possible role of low-carb and ketogenic diets in the pathogenesis and management of type 2 diabetes and obesity. Furthermore, we also reviewed evidence of carbohydrates restriction in both pathogenesis of type 1 diabetes, through gut microbiota modification, and treatment of type 1 diabetes, addressing the legitimate concerns about the use of such diets in patients who are ketosis-prone and often have not completed their growth.

scholarly journals The intrinsic combinatorial organization and information theoretic content of a sequence are correlated to the DNA encoded nucleosome organization of eukaryotic genomes

2015 ◽  
Vol 32 (6) ◽  
pp. 835-842 ◽  
Author(s):  
Filippo Utro ◽  
Valeria Di Benedetto ◽  
Davide F.V. Corona ◽  
Raffaele Giancarlo
Keyword(s):  
Closed Form ◽  
Dna Sequence ◽  
Chemical Properties ◽  
Supplementary Information ◽  
Information Theoretic ◽  
Nucleosome Organization ◽  
A Genome ◽  
Intrinsic Complexity ◽  
Mathematical Formulas ◽  
Eukaryotic Genomes

Abstract Motivation: Thanks to research spanning nearly 30 years, two major models have emerged that account for nucleosome organization in chromatin: statistical and sequence specific. The first is based on elegant, easy to compute, closed-form mathematical formulas that make no assumptions of the physical and chemical properties of the underlying DNA sequence. Moreover, they need no training on the data for their computation. The latter is based on some sequence regularities but, as opposed to the statistical model, it lacks the same type of closed-form formulas that, in this case, should be based on the DNA sequence only. Results: We contribute to close this important methodological gap between the two models by providing three very simple formulas for the sequence specific one. They are all based on well-known formulas in Computer Science and Bioinformatics, and they give different quantifications of how complex a sequence is. In view of how remarkably well they perform, it is very surprising that measures of sequence complexity have not even been considered as candidates to close the mentioned gap. We provide experimental evidence that the intrinsic level of combinatorial organization and information-theoretic content of subsequences within a genome are strongly correlated to the level of DNA encoded nucleosome organization discovered by Kaplan et al. Our results establish an important connection between the intrinsic complexity of subsequences in a genome and the intrinsic, i.e. DNA encoded, nucleosome organization of eukaryotic genomes. It is a first step towards a mathematical characterization of this latter ‘encoding’. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: [email protected].

scholarly journals Elementary Formal Systems, Intrinsic Complexity, and Procrastination

1997 ◽  
Vol 132 (1) ◽  
pp. 65-84 ◽  
Author(s):  
Sanjay Jain ◽  
Arun Sharma
Keyword(s):  
Formal Systems ◽  
Intrinsic Complexity

scholarly journals A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carlo Donadio ◽  
Massimo Brescia ◽  
Alessia Riccardo ◽  
Giuseppe Angora ◽  
Michele Delli Veneri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Morphological Variations ◽  
Control Factors ◽  
Novel Approach ◽  
Drainage Networks ◽  
Key Characteristics ◽  
Intrinsic Complexity ◽  
Solar System Bodies ◽  
Manual Extraction

AbstractSeveral approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.

On the parallels between cosmology and astrobiology: a transdisciplinary approach to the search for extraterrestrial life

2016 ◽  
Vol 15 (4) ◽  
pp. 251-260 ◽  
Author(s):  
Charles Morphy D. Santos ◽  
Leticia P. Alabi ◽  
Amâncio C. S. Friaça ◽  
Douglas Galante
Keyword(s):  
Extraterrestrial Life ◽  
Transdisciplinary Approach ◽  
Research Areas ◽  
Copernican Revolution ◽  
Scientific Status ◽  
Intrinsic Complexity ◽  
Experimental Approaches ◽  
The Universe

AbstractThe establishment of cosmology as a science provides a parallel to the building-up of the scientific status of astrobiology. The rise of astrobiological studies is explicitly based on a transdisciplinary approach that reminds of the Copernican Revolution, which eroded the basis of a closed Aristotelian worldview and reinforced the notion that the frontiers between disciplines are artificial. Given the intrinsic complexity of the astrobiological studies, with its multifactorial evidences and theoretical/experimental approaches, multi- and interdisciplinary perspectives are mandatory. Insulated expertise cannot grasp the vastness of the astrobiological issues. This need for integration among disciplines and research areas is antagonistic to excessive specialization and compartmentalization, allowing astrobiology to be qualified as a truly transdisciplinary enterprise. The present paper discusses the scientific status of astrobiological studies, based on the view that every kind of life, Earth-based or not, should be considered in a cosmic context. A confluence between ‘astro’ and ‘bio’ seeks the understanding of life as an emerging phenomenon in the universe. Thus, a new epistemological niche is opened, pointing to the development of a pluralistic vision for the philosophy of astrobiology.

scholarly journals FORECASTING PRICE INCREMENTS USING AN ARTIFICIAL NEURAL NETWORK

2001 ◽  
Vol 04 (01) ◽  
pp. 45-56 ◽  
Author(s):  
FILIPPO CASTIGLIONE
Keyword(s):  
Financial Time Series ◽  
Black Box ◽  
Neural Nets ◽  
Neural Net ◽  
Forecast Method ◽  
Time Series Forecast ◽  
Simplified Approach ◽  
Financial Time ◽  
System A ◽  
Intrinsic Complexity

Financial forecasting is a difficult task due to the intrinsic complexity of the financial system. A simplified approach in forecasting is given by "black box" methods like neural networks that assume little about the structure of the economy. In the present paper we relate our experience using neural nets as financial time series forecast method. In particular we show that a neural net able to forecast the sign of the price increments with a success rate slightly above 50% can be found. Target series are the daily closing price of different assets and indexes during the period from about January 1990 to February 2000.

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