Search results for " Complexity"
showing 10 items of 623 documents
Development of an RNA-based kit for easy generation of TCR-engineered lymphocytes to control T-cell assay performance.
2018
Cell-based assays to monitor antigen-specific T-cell responses are characterized by their high complexity and should be conducted under controlled conditions to lower multiple possible sources of assay variation. However, the lack of standard reagents makes it difficult to directly compare results generated in one lab over time and across institutions. Therefore TCR-engineered reference samples (TERS) that contain a defined number of antigen-specific T cells and continuously deliver stable results are urgently needed. We successfully established a simple and robust TERS technology that constitutes a useful tool to overcome this issue for commonly used T-cell immuno-assays. To enable users t…
A Novel Tsetlin Automata Scheme to Forecast Dengue Outbreaks in the Philippines
2018
Being capable of online learning in unknown stochastic environments, Tsetlin Automata (TA) have gained considerable interest. As a model of biological systems, teams of TA have been used for solving complex problems in a decentralized manner, with low computational complexity. For many domains, decentralized problem solving is an advantage, however, also may lead to coordination difficulties and unstable learning. To combat this negative effect, this paper proposes a novel TA coordination scheme designed for learning problems with continuous input and output. By saving and updating the best solution that has been chosen so far, we can avoid having the overall system being led astray by spur…
An Integrative Framework for the Construction of Big Functional Networks
2018
We present a methodology for biological data integration, aiming at building and analysing large functional networks which model complex genotype-phenotype associations. A functional network is a graph where nodes represent cellular components (e.g., genes, proteins, mRNA, etc.) and edges represent associations among such molecules. Different types of components may cohesist in the same network, and associations may be related to physical[biochemical interactions or functional/phenotipic relationships. Due to both the large amount of involved information and the computational complexity typical of the problems in this domain, the proposed framework is based on big data technologies (Spark a…
Generation of TCR-engineered reference cell samples to control T-cell assay performance
2020
In vitro cellular assays analyzing antigen-specific T cells are characterized by their high complexity and require controlled conditions to lower experimental variations. Without standard cellular reagents, it is difficult to compare results over time and across institutions. To overcome this problem, a simple and robust technology was developed to generate TCR-engineered reference samples (TERS) containing defined numbers of antigen-specific T cells. Utilization of TERS enables performance control of three main T-cell assays: MHC-peptide multimer staining, IFN-gamma ELISpot and cytokine flow cytometry. TERS continuously deliver stable results and can be stored for longer periods of time. H…
DNA combinatorial messages and Epigenomics: The case of chromatin organization and nucleosome occupancy in eukaryotic genomes
2019
Abstract Epigenomics is the study of modifications on the genetic material of a cell that do not depend on changes in the DNA sequence, since those latter involve specific proteins around which DNA wraps. The end result is that Epigenomic changes have a fundamental role in the proper working of each cell in Eukaryotic organisms. A particularly important part of Epigenomics concentrates on the study of chromatin, that is, a fiber composed of a DNA-protein complex and very characterizing of Eukaryotes. Understanding how chromatin is assembled and how it changes is fundamental for Biology. In more than thirty years of research in this area, Mathematics and Theoretical Computer Science have gai…
Deep neural attention-based model for the evaluation of italian sentences complexity
2020
In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems.
Multi-class Text Complexity Evaluation via Deep Neural Networks
2019
Automatic Text Complexity Evaluation (ATE) is a natural language processing task which aims to assess texts difficulty taking into account many facets related to complexity. A large number of papers tackle the problem of ATE by means of machine learning algorithms in order to classify texts into complex or simple classes. In this paper, we try to go beyond the methodologies presented so far by introducing a preliminary system based on a deep neural network model whose objective is to classify sentences into more of two classes. Experiments have been carried out on a manually annotated corpus which has been preprocessed in order to make it suitable for the scope of the paper. The results sho…
The Chinese Postman Problem with Load-Dependent Costs
2018
[EN] We introduce an interesting variant of the well-known Chinese postman problem (CPP). While in the CPP the cost of traversing an edge is a constant (equal to its length), in the variant we present here the cost of traversing an edge depends on its length and on the weight of the vehicle at the moment it is traversed. This problem is inspired by the perspective of minimizing pollution in transportation, since the amount of pollution emitted by a vehicle not only depends on the travel distance but also on its load, among other factors. We define the problem, study its computational complexity, provide two mathematical programming formulations, and propose two metaheuristics for its soluti…
Interpretability of Recurrent Neural Networks in Remote Sensing
2020
In this work we propose the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for multivariate time series of satellite data for crop yield estimation. Recurrent nets allow exploiting the temporal dimension efficiently, but interpretability is hampered by the typically overparameterized models. The focus of the study is to understand LSTM models by looking at the hidden units distribution, the impact of increasing network complexity, and the relative importance of the input covariates. We extracted time series of three variables describing the soil-vegetation status in agroe-cosystems -soil moisture, VOD and EVI- from optical and microwave satellites, as well as available in si…
Neutralidad y honorabilidad del árbitro: : De la ética a la diligencia
2021
One of the main principles of arbitration is that the arbitral tribunal must be independent, impartial, neutral and diligent in carrying out its duties throughout the arbitration proceedings. However, the sophistication and complexity of arbitration as an extra-judicial dispute resolution procedure can undermine confidence in the efficiency of the system. In this regard, the professional (arbitral) ethics and the honourability of the arbitrator are of major importance. This article aims to highlight the importance of the neutrality of the arbitrator, the integrity of the arbitration and the reputation of this procedure