Data on the saturation behaviour of the 63-90 µm quartz from the Carpathian Basin.

This dataset offers valuable insights into the luminescence saturation behaviour of 63-90 µm quartz grains sourced from the Carpathian Basin, as examined under controlled laboratory conditions. Its significance lies not only in shedding light on the luminescence properties specific to this region but also in facilitating comparative analyses with quartz samples from other geographic areas. Moreover, the dataset contributes novel findings to the ongoing investigations concerning the upper dating limit of quartz grains, which holds implications for refining luminescence dating methodologies.

Dataset of dust mass accumulation rates for the loess-palaeosol sequences from the Carpathian Basin.

In this article, a dataset of age-depth modelling data, sedimentation rates and dust mass accumulation rates (MAR) from four loess-palaeosol sequences from the Carpathian Basin is presented. The dataset is related to the article "Detailed luminescence dating of dust mass accumulation rates over the last two glacial-interglacial cycles from the Irig loess-palaeosol sequence, Carpathian Basin", published in the journal Global and Planetary Change by Perić et al. [1].

Speaker Recognition Using Constrained Convolutional Neural Networks in Emotional Speech.

Speaker recognition is an important classification task, which can be solved using several approaches. Although building a speaker recognition model on a closed set of speakers under neutral speaking conditions is a well-researched task and there are solutions that provide excellent performance, the classification accuracy of developed models significantly decreases when applying them to emotional speech or in the presence of interference. Furthermore, deep models may require a large number of parameters, so constrained solutions are desirable in order to implement them on edge devices in the Internet of Things systems for real-time detection.

Whether the Support Region of Three-Bit Uniform Quantizer Has a Strong Impact on Post-Training Quantization for MNIST Dataset?

Driven by the need for the compression of weights in neural networks (NNs), which is especially beneficial for edge devices with a constrained resource, and by the need to utilize the simplest possible quantization model, in this paper, we study the performance of three-bit post-training uniform quantization. The goal is to put various choices of the key parameter of the quantizer in question (support region threshold) in one place and provide a detailed overview of this choice's impact on the performance of post-training quantization for the MNIST dataset. Specifically, we analyze whether it is possible to preserve the accuracy of the two NN models (MLP and CNN) to a great extent with the very simple three-bit uniform quantizer, regardless of the choice of the key parameter.

Design of a 2-Bit Neural Network Quantizer for Laplacian Source.

Achieving real-time inference is one of the major issues in contemporary neural network applications, as complex algorithms are frequently being deployed to mobile devices that have constrained storage and computing power. Moving from a full-precision neural network model to a lower representation by applying quantization techniques is a popular approach to facilitate this issue. Here, we analyze in detail and design a 2-bit uniform quantization model for Laplacian source due to its significance in terms of implementation simplicity, which further leads to a shorter processing time and faster inference.

Speech Technology Progress Based on New Machine Learning Paradigm.

Speech technologies have been developed for decades as a typical signal processing area, while the last decade has brought a huge progress based on new machine learning paradigms. Owing not only to their intrinsic complexity but also to their relation with cognitive sciences, speech technologies are now viewed as a prime example of interdisciplinary knowledge area. This review article on speech signal analysis and processing, corresponding machine learning algorithms, and applied computational intelligence aims to give an insight into several fields, covering speech production and auditory perception, cognitive aspects of speech communication and language understanding, both speech recognition and text-to-speech synthesis in more details, and consequently the main directions in development of spoken dialogue systems.

Outcome prediction of initial lamotrigine monotherapy in adult patients with newly diagnosed localization related epilepsies.

Objective: To develop and test model to predict outcome of treatment with initial lamotrigine monotherapy in adult patients with newly diagnosed localization - related epilepsy, using data available at the time of diagnosis.

Methods: Prospective longitudinal study included consecutive series of adult patients with newly diagnosed localization - related epilepsy started of lamotrigine monotherapy. Logistic regression analysis using backward procedure was performed with treatment failure as the outcome variable.

Artificial neural networks based early clinical prediction of mortality after spontaneous intracerebral hemorrhage.

Numerous outcome prediction models have been developed for mortality and functional outcome after spontaneous intracerebral haemorrhage (ICH). However, no outcome prediction model for ICH has considered the impact of care restriction. To develop and compare results of the artificial neural networks (ANN) and logistic regression (LR) models, based on initial clinical parameters, for prediction of mortality after spontaneous ICH.

[Syringomyelia in demyelinating disease of the central nervous system: report of two cases].

Introduction: Syringomyelia is a cavitary extension inside the spinal cord which can be either symptomatic or congenitally-idiopathic. Syringomyelia during the course of the disease in patients presenting with clinically definite multiple sclerosis was described earlier. Syringomyelia in patients presenting with a clinically isolated syndrome suggestive of multiple sclerosis is unusual.

A case of complex arteriovenous malformation with skull destruction presented in sixth decade of life.

Intracranial AVMs are typically diagnosed before the patient has reached the age of 40 years, and a few cases have been reported of AVM with skull destruction. We described a rare case of a complex cerebral AVM with skull destruction, presented de novo in 52-year-old woman with epileptic seizures. Neuroimaging investigations revealed complex AVM in right hemisphere as well as extracranially, with signs of skull destructions, likely caused by significant involvement of feeders from external carotid artery.

[Influence of low-intensity laser therapy on spatial perception threshold and electroneurographic finding in patients with diabetic polyneuropathy].

Introduction: Low-intensity laser therapy (LILT) can be applied in cases when patients with diabetic polyneuropathy (DPN) suffer from chronic severe neuropathic pain.

Objective: We wanted to analyse influence of LILT on spatial perception threshold (SPT) and electroneurographic (ENG) parameters in patients with painful DPN.

Method: We analysed 45 patients (25 males), average age 54.

Sensory-motor index is useful parameter in electroneurographical diagnosis of carpal tunnel syndrome.

It was performed electroneurographic (ENG) studies with surface electrodes and examined nervus medianus (NM) in 60 patients (38 females), average age of 50,28 years (X+/-SD=50,28+/-11), with clinical diagnosis of carpal tunnel syndrome (CTS) and at least one border or discrete abnormal value of conventional electrophysiological tests. It was also examined 57 healthy individuals (33 females) as control group, average age of 45,65 years (X+/-SD=45,65+/-9,68). The sensitivity and specificity of sensory-motor index (SMI), terminal latency index (TLI) and residual latency (RL) were calculated and compared.