Search

In this paper we address the challenges of documenting early digital artifacts in collections built to offer historical context for future generations. Through insights from active community members (N=20), we examine current archival needs and obstacles. We assess the potential of the CIDOC Conceptual Reference Model (CRM) for categorizing fragmented digital data. Despite its complexity, CIDOC-CRM proves logical, human-readable, and adaptable, enabling archivists to select minimal yet effective building blocks set to empower community-led heritage projects., Comment: 8 pages, 1 table, 2 figures

The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of ways. Possible support for the therapists can be estimation of the time at which subject goes into deep relaxation. This paper presents the results of the investigations of a cohort of 26 students exposed to Guided Imagery relaxation technique and mental task workloads conducted with the use of dense array electroencephalographic amplifier. The research reported herein aimed at verification whether it is possible to detect differences between those two states and to classify them using deep learning methods and recurrent neural networks such as EEGNet, Long Short-Term Memory-based classifier, 1D Convolutional Neural Network and hybrid model of 1D Convolutional Neural Network and Long Short-Term Memory. The data processing pipeline was presented from the data acquisition, through the initial data cleaning, preprocessing and postprocessing. The classification was based on two datasets: one of them using 26 so-called cognitive electrodes and the other one using signal collected from 256 channels. So far there have not been such comparisons in the application being discussed. The classification results are presented by the validation metrics such as: accuracy, recall, precision, F1-score and loss for each case. It turned out that it is not necessary to collect signals from all electrodes as classification of the cognitive ones gives the results similar to those obtained for the full signal and extending input to 256 channels does not add much value. In Disscussion there were proposed an optimal classifier as well as some suggestions concerning the prospective development of the project., Comment: In review

This paper introduces a novel approach to enhance the performance of pre-trained neural networks in medical image segmentation using Neural Architecture Search (NAS) methods, specifically Differentiable Architecture Search (DARTS). We present the concept of Implantable Adaptive Cell (IAC), small but powerful modules identified through Partially-Connected DARTS, designed to be injected into the skip connections of an existing and already trained U-shaped model. Our strategy allows for the seamless integration of the IAC into the pre-existing architecture, thereby enhancing its performance without necessitating a complete retraining from scratch. The empirical studies, focusing on medical image segmentation tasks, demonstrate the efficacy of this method. The integration of specialized IAC cells into various configurations of the U-Net model increases segmentation accuracy by almost 2\% points on average for the validation dataset and over 3\% points for the training dataset. The findings of this study not only offer a cost-effective alternative to the complete overhaul of complex models for performance upgrades but also indicate the potential applicability of our method to other architectures and problem domains.

Trust is the foundation of every area of life. Without it, it is difficult to build lasting relationships. Unfortunately, in recent years, trust has been severely damaged by the spread of fake news and disinformation, which has become a serious social problem. In addition to trust, the factor influencing interpersonal relationships is perceived attractiveness, which is currently created to a large extent by digital media. Understanding the principles of judging others can be helpful in fighting prejudice and rebuilding trust in society. One way to learn about people's choices is to record their brain activity as they make choices. The article presents an experiment in which the faces of different people were presented, and the participants' task was to assess how much they can trust a given person and how attractive they are. During the study, the EEG signal was recorded, which was used to build models of logistic regression classifiers. In addition, the most active areas of the brain that participate in the assessment of trust and attractiveness of the face were indicated.

A user-friendly fibre-coupled single-photon source operating at telecom wavelengths is a key component of photonic quantum networks providing long-haul ultra-secure data exchange. To take full advantage of quantum-mechanical data protection and to maximize the transmission rate and distance, a true quantum source providing single-photons on demand is highly desirable. We tackle this great challenge by developing a ready to use semiconductor quantum dot (QD)-based device that launches single photons at a wavelength of 1.3 um directly into a single-mode optical fibre. In our approach the QD is deterministically integrated into a nanophotonic structure to ensure efficient on-chip coupling into a fibre. The whole arrangement is integrated into a 19" compatible housing to enable stand-alone operation by cooling via a compact Stirling cryocooler. The realized source delivers single photons with multiphoton events probability as low as 0.15 and single-photon emission rate up to 73 kHz into a standard telecom single-mode fibre., Comment: 20 pages, 3 figures

It was previously reported, that temperature may significantly influence neural dynamics on different levels of brain modelling. Due to this fact, while creating the model in computational neuroscience we would like to make it scalable for wide-range of various brain temperatures. However currently, because of a lack of experimental data and an absence of analytical model describing temperature influence on synapses, it is not possible to include temperature effects on multi-neuron modelling level. In this paper, we propose first step to deal with this problem: new analytical model of AMPA-type synaptic conductance, which is able to include temperature effects in low-frequency stimulations. It was constructed on basis of Markov model description of AMPA receptor kinetics and few simplifications motivated both experimentally and from Monte Carlo simulation of synaptic transmission. The model may be used for efficient and accurate implementation of temperature effects on AMPA receptor conductance in large scale neural network simulations. This in fact, opens wide-range of new possibilities for researching an influence of temperature on brain functioning., Comment: 14 pages, 11 figures