Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units

Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

A spatiotemporal Data Envelopment Analysis (S-T DEA) approach:the need to assess evolving units

One of the major challenges in measuring efficiency in terms of resources and outcomes is the assessment of the evolution of units over time. Although Data Envelopment Analysis (DEA) has been applied for time series datasets, DEA models, by construction, form the reference set for inefficient units (lambda values) based on their distance from the efficient frontier, that is, in a spatial manner. However, when dealing with temporal datasets, the proximity in time between units should also be taken into account, since it reflects the structural resemblance among time periods of a unit that evolves. In this paper, we propose a two-stage spatiotemporal DEA approach, which captures both the spatial and temporal dimension through a multi-objective programming model. In the first stage, DEA is solved iteratively extracting for each unit only previous DMUs as peers in its reference set. In the second stage, the lambda values derived from the first stage are fed to a Multiobjective Mixed Integer Linear Programming model, which filters peers in the reference set based on weights assigned to the spatial and temporal dimension. The approach is demonstrated on a real-world example drawn from software development.

Units of F5kD10

2000 Mathematics Subject Classification: 20C05, 16U60, 16S84, 15A33.

Automated Generating of Test Units for Evaluation of Cognitive and Language Skills

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016

Heterogenitás és technikai hatékonyság - a magyar specializált szántóföldi növénytermesztő üzemek esete = Heterogeneity and technical efficiency - the case of Hungary's specialized arable crop production units

A cikkben paneladatok segítségével a magyar gabonatermesztő üzemek 2001 és 2009 közötti technikai hatékonyságát vizsgáljuk. A technikai hatékonyság szintjének becslésére egy hagyományos sztochasztikus határok modell (SFA) mellett a látens csoportok modelljét (LCM) használjuk, amely figyelembe veszi a technológiai különbségeket is. Eredményeink arra utalnak, hogy a technológiai heterogenitás fontos lehet egy olyan ágazatban is, mint a szántóföldi növénytermesztés, ahol viszonylag homogén technológiát alkalmaznak. A hagyományos, azonos technológiát feltételező és a látens osztályok modelljeinek összehasonlítása azt mutatja, hogy a gabonatermesztő üzemek technikai hatékonyságát a hagyományos modellek alábecsülhetik. _____ The article sets out to analyse the technical efficiency of Hungarian crop farms between 2001 and 2009, using panel data and employing both standard stochastic frontier analysis and the latent class model (LCM) to estimate technical efficiency. The findings suggest that technological heterogeneity plays an important role in the crop sector, though it is traditionally assumed to employ homogenous technology. A comparison of standard SFA models that assumes the technology is common to all farms and LCM estimates highlights the way the efficiency of crop farms can be underestimated using traditional SFA models.

Modeling the Impacts of Climate Change on Phytogeographical Units. A Case Study of the Moesz Line

Regional climate models (RCMs) provide reliable climatic predictions for the next 90 years with high horizontal and temporal resolution. In the 21st century northward latitudinal and upward altitudinal shift of the distribution of plant species and phytogeographical units is expected. It is discussed how the modeling of phytogeographical unit can be reduced to modeling plant distributions. Predicted shift of the Moesz line is studied as case study (with three different modeling approaches) using 36 parameters of REMO regional climate data-set, ArcGIS geographic information software, and periods of 1961-1990 (reference period), 2011-2040, and 2041-2070. The disadvantages of this relatively simple climate envelope modeling (CEM) approach are then discussed and several ways of model improvement are suggested. Some statistical and artificial intelligence (AI) methods (logistic regression, cluster analysis and other clustering methods, decision tree, evolutionary algorithm, artificial neural network) are able to provide development of the model. Among them artificial neural networks (ANN) seems to be the most suitable algorithm for this purpose, which provides a black box method for distribution modeling.

Factors affecting emergency department length of stay of patients admitted to telemetry and critical care units

The purpose of this study was to determine the emergency department (ED) length of stay (LOS) of patients admitted to inpatient telemetry and critical care units and to identify the factors that contribute to a prolonged ED LOS. It also examined whether there was a difference in ED LOS between clients evaluated by an ED physician, an Advanced Registered Nurse Practitioner (ARNP) or a Physician's Assistant (PA).^ A data collection tool was devised and used to record data obtained by retrospectively reviewing 110 charts of patients from this sample. The mean ED LOS was 286.75 minutes. Multiple factors were recorded as affecting the ED LOS of this sample, including: age, diagnosis, consultations, multiple radiographs, pending admission orders, nurse unable to call report/busy, relatives at bedside, observation or stabilization necessary, bed not ready and infusion in progress. No significant difference in ED LOS was noted between subjects initially evaluated by a physician, an ARNP or a PA. ^

Renormalization group flows between non-unitary conformal models

Recentemente sono stati valutati come fisicamente consistenti diversi modelli non-hermitiani sia in meccanica quantistica che in teoria dei campi. La classe dei modelli pseudo-hermitiani, infatti, si adatta ad essere usata per la descrizione di sistemi fisici dal momento che, attraverso un opportuno operatore metrico, risulta possibile ristabilire una struttura hermitiana ed unitaria. I sistemi PT-simmetrici, poi, sono una categoria particolarmente studiata in letteratura. Gli esempi riportati sembrano suggerire che anche le cosiddette teorie conformi non-unitarie appartengano alla categoria dei modelli PT-simmetrici, e possano pertanto adattarsi alla descrizione di fenomeni fisici. In particolare, si tenta qui la costruzione di determinate lagrangiane Ginzburg-Landau per alcuni modelli minimali non-unitari, sulla base delle identificazioni esistenti per quanto riguarda i modelli minimali unitari. Infine, si suggerisce di estendere il dominio del noto teorema c alla classe delle teorie di campo PT-simmetriche, e si propongono alcune linee per una possibile dimostrazione dell'ipotizzato teorema c_{eff}.