Miracle-dz/UniFAR-FTUs huggingface dataset:
Architektura świadomości. Część II: Struktura molekularna i biofizyka pamięci

Cześc II pracy objaśnia molekularne podloze pamieci w oparciu o zalozenia Neuro-Elektro-Dynamiki postulowanej przez Aura i Yoga. Wskazano na biofizyczne mechanizmy generacji wspomnien, refleksji i odczuc dzieki efatycznym sprzezeniom synaptycznym, spelniającym wymagania indukowania impresjonow Vadakkana. Wykazano, ze dzialanie mechanizmu selekcyjnego WTA moze byc dobrym modelem opisującym funkcje przelączania uwagi. Wskazano na relacje miedzy pamiecią roboczą, krotkoterminową i trwalą i ich spojnośc z molekularnymi procesami zapamietywania i rozpoznawania wzorcow. Wskazano, ze trwala pamiec epizodyczna wymaga konwersji sekwencji czasowych sygnalow bottom-up do rozkladu przestrzennego tworzącego impresjony dynamiczne. Odwrotna transformacja tych impresjonow umozliwia przypominanie epizodow z przeszlości. Omowiono znaczenie wrodzonych struktur sieci neuronowych zapewniających dziedziczenie zachowan instynktownych. Struktury te nie wykluczają zachowania plastyczności umyslu umozliwiającej efektywne uczenie.
Computational Simulation for Square Diaphragms of a Piezoresistive Pressure Sensor

This paper presents a methodology for analytical computation and computational simulation using the finite element method - FEM for a piezoresistive sensor element in flexible polymer substrate A4 paper type. The computational simulation aims to find the region of greatest mechanical stress of the clamped square diaphragm set in the four sides, when a pressure is applied. The steps for the simulation are the definition of the geometry, generation of the mesh, inclusion of the properties of the material and execution of the simulation. The mathematical modeling of maximum mechanical stress is described analytically and computationally using ANSYS software. The analytical calculations were compared with the computational simulation and presented a relative percentage error of 13.61% for the maximum mechanical stress. In this work, it is verified that the piezoresistor should be positioned in the middle of the diaphragm edges a / b = 1 to take advantage of the maximum mechanical stress curves. The result is extremely important and will define the best location for deposition of graphite films in future fabrications of sensor elements.
Indoor Multi-Camera-Based Testbed for 3-D Tracking and Control of UAVs

Flight testbeds with multiple unmanned aerial vehicles (UAVs) are especially important to support research on multi-vehicle-related algorithms. The existing platforms usually lack a generic and complete solution allowing for software and hardware design. For such a purpose, this paper presents the design and implementation of a comprehensive multi-camera-based testbed for 3-D tracking and control of UAVs. First, the testbed software consists of a multi-camera system and a ground control system, which performs image processing, camera calibration, 3-D reconstruction, pose estimation, and motion control. In the multi-camera system, the positions and orientations of UAVs are first reconstructed by using epipolar geometric constraints and triangulation methods and then filtered by an extended Kalman filter (EKF). In the ground control system, a classical proportional–derivative (PD) controller is designed to receive the navigation data from the multi-camera system and then generates control commands to the target vehicles. Then, the testbed hardware employs smart cameras with field-programmable gate array (FPGA) modules to allow for real-time computation at a frame rate of 100 Hz. Lightweight quadcopter Parrot Bebop drones are chosen as the target UAVs, which does not require any modification to the hardware. Artificial infrared reflective markers are asymmetrically mounted on target vehicles and observed by multiple infrared cameras located around the flight region. Finally, extensive experiments are performed to demonstrate that the proposed testbed is a comprehensive and complete platform with good scalability applicable for research on a variety of advanced guidance, navigation, and control algorithms.
The Visual Representations of Territorial Dynamics: Retrospective and Input from New Computing Environments

Nowadays, a diverse number of cartographical products are attempting to integrate the notion of time in their map views, through animated cartography and geovisualization platforms. These questions about representing time and timescales are not new. Many visual solutions have been proposed to carry out reasoning on both space and time in a very efficient and effective way. These representations of spatial dynamics constitute a heritage for modern visualizations. They can be classified in three families of practice: chronography (time oriented), cartography (space oriented) and statistics (theme oriented). This article deals with a review of past and current spatio-temporal visualizations. We first proposed a review of past approaches in spatio-temporal visualizations. We tried to determine how this whole heritage constitutes the foundations of modern cartographical exploration. Second, we analyzed a set of 42 dynamic cartographical visualizations on the Web on the basis of 13 criteria, which covered proposed functional features, semiological innovations and services rendered. This analysis revealed common associations of parameters and trends among current webmaps showing dynamics, but also a low diversity of certain dimensions of webmaps. We finally positioned them into MacEachren's " map-use cube " (1994), which is a frame of reference for geovisualizations analysis. Two new categories were intergrated in the cube, which superseded others in the webmaps case: maps for data contextualization and maps for data presentation.
Nocturnal Convection Initiation during PECAN 2015

Nocturnal convection initiation (NCI) is more difficult to anticipate and forecast than daytime convection initiation (CI). A major component of the Plains Elevated Convection at Night (PECAN) field campaign in the U.S. Great Plains was to intensively sample NCI and its near environment. In this article, we summarize NCI types observed during PECAN: 1 June–16 July 2015. These NCI types, classified using PECAN radar composites, are associated with 1) frontal overrunning, 2) the low-level jet (LLJ), 3) a preexisting mesoscale convective system (MCS), 4) a bore or density current, and 5) a nocturnal atmosphere lacking a clearly observed forcing mechanism (pristine). An example and description of each of these different types of PECAN NCI events are presented. The University of Oklahoma real-time 4-km Weather Research and Forecasting (WRF) Model ensemble forecast runs illustrate that the above categories having larger-scale organization (e.g., NCI associated with frontal overrunning and NCI near a preexisting MCS) were better forecasted than pristine. Based on current knowledge and data from PECAN, conceptual models summarizing key environmental features are presented and physical processes underlying the development of each of these different types of NCI events are discussed.
Finite-Time Distributed Linear Equation Solver for Solutions With Minimum $l_1$-Norm

This paper proposes a continuous-time distributed algorithm for multiagent networks to achieve a solution with the minimum <inline-formula><tex-math notation="LaTeX">$l_1$</tex-math></inline-formula>-norm to underdetermined linear equations. The proposed algorithm comes from a combination of the Filippov set-valued map with the projection-consensus flow. Given the underlying network is undirected and fixed, it is shown that the proposed algorithm drives all agents’ individual states to converge in finite time to a common value, which is the minimum <inline-formula><tex-math notation="LaTeX">$l_1$</tex-math></inline-formula>-norm solution.
Magnetic profile of proximity-coupled 
(Dy,Bi)2Te3/(Cr,Sb)2Te3
 topological insulator heterostructures

Magnetic topological insulators (TIs) are an ideal playground for the study of novel quantum phenomena building on time-reversal symmetry broken topological surface states. By combining different magnetic TIs in a heterostructure, their magnetic and electronic properties can be precisely tuned. Recently, we have combined high-moment Dy:Bi2Te3with high transition temperature Cr:Sb2Te3 in a superlattice, and found, using x-ray magnetic circular dichroism (XMCD), that long-range magnetic order can be introduced in the Dy:Bi2Te3 layers. Accompanying first-principles calculations indicated that the origin of the long-range magnetic order is a strong antiferromagnetic coupling between Dy and Cr magnetic moments at the interface extending over several layers. However, based on XMCD alone, which is either averaging over the entire thin film stack or is surface sensitive, this coupling scenario could not be fully confirmed. Here we use polarized neutron reflectometry (PNR), which is ideally suited for the detailed study of superlattices, to retrieve the magnetization in a layer- and interface-resolved way. We find that the magnetization is, in contrast to similar recent studies, homogeneous throughout the individual layers, with no apparent interfacial effects. This finding demonstrates that heterostructure engineering is a powerful way of controlling the magnetic properties of entire layers, with the effects of coupling reaching beyond the interface region.
Two to Tango: Co-evolution of Hominid Natural Killer Cell Receptors and MHC

Natural killer (NK) cells have diverse roles in hominid immunity and reproduction. Modulating these functions are the interactions between major histocompatibility complex (MHC) class I molecules that are ligands for two NK cell surface receptor types. Diverse killer cell immunoglobulin-like receptors (KIR) bind specific motifs encoded within the polymorphic MHC class I cell surface glycoproteins, while, in more conserved interactions, CD94:NKG2A receptors recognize MHC-E with bound peptides derived from MHC class I leader sequences. The hominid lineage presents a choreographed co-evolution of KIR with their MHC class I ligands. MHC-A, -B, and -C are present in all great apes with species-specific haplotypic variation in gene content. The Bw4 epitope recognized by lineage II KIR is restricted to MHC-B but also present on some gorilla and human MHC-A. Common to great apes, but rare in humans, are MHC-B possessing a C1 epitope recognized by lineage III KIR. MHC-C arose from duplication of MHC-B and is fixed in all great apes except orangutan, where it exists on approximately 50% of haplotypes and all allotypes are C1-bearing. Recent study showed that gorillas possess yet another intermediate MHC organization compared to humans. Like orangutans, but unlike the Pan-Homo species, duplication of MHC-B occurred. However, MHC-C is fixed, and the MHC-C C2 epitope (absent in orangutans) emerges. The evolution of MHC-C drove expansion of its cognate lineage III KIR. Recently, position −21 of the MHC-B leader sequence has been shown to be critical in determining NK cell educational outcome. In humans, methionine (−21M) results in CD94:NKG2A-focused education whereas threonine (−21T) produces KIR-focused education. This is another dynamic position among hominids. Orangutans have exclusively −21M, consistent with their intermediate stage in lineage III KIR-focused evolution. Gorillas have both −21M and −21T, like humans, but they are unequally encoded by their duplicated B genes. Chimpanzees have near-fixed −21T, indicative of KIR-focused NK education. Harmonious with this observation, chimpanzee KIR exhibit strong binding and, compared to humans, smaller differences between binding levels of activating and inhibitory KIR. Consistent between these MHC-NK cell receptor systems over the course of hominid evolution is the evolution of polymorphism favoring the more novel and dynamic KIR system.
Bayesian variable selection using spike‐and‐slab priors with application to high dimensional electroencephalography data by local modelling

Because of the immense technological advances, very often we encounter data in high dimensions. Any set of measurements taken at multiple time points for multiple subjects leads to data of more than two dimensions (a matrix of covariates for each subject). We present a Bayesian variable‐selection method to identify the active regions in the brain as a response to a certain stimulus. We build binary classification models of subject level responses by using binary regression with Gaussian models on the latent variables. We also study the scaled normal priors on the latent variables, as they cover a large family of distributions. We use continuous spike‐and‐slab priors to incorporate variable selection within the modelling. Because of the computational complexity, we build many local (at different time points) models and make predictions by utilizing the temporal structure between the local models. We perform two‐stage variable selection for each of these local models. We demonstrate the effectiveness of such modelling through the results of a simulation study. We then present the performance of these models on multisubject neuroimaging (electroencephalography) data to study the effects on the functional states of the frontal cortex and parietal lobe for chronic exposure of alcohol.
Nuclear magnetic resonance studies of translational diffusion in thermotropic ionic liquid crystals

ABSTRACT The NMR methodologies employed for investigating translational diffusion in anisotropic fluids and the results of their applications to ionic liquid crystals are reviewed. Experiments on ionic liquid crystals are preferably performed using oriented samples and require magnetic field gradients in orthogonal directions. Diffusion experiments in anisotropic systems with broad NMR lines are performed using line narrowing techniques and by application of strong static or pulsed field gradients for efficient gradient encoding/decoding of the spatial locations of molecules. Self-diffusion studies on various thermotropic ion-conductive materials exhibiting smectic, cubic, and columnar phases have been reported. Diffusion rates and anisotropy characterise the translational dynamics of ions in nanostructures and reflect the molecular ordering and ion pairing/dissociation processes. Distinct diffusion behaviours were observed for cations and anions. The knowledge of molecular mobility in ionic liquid crystals is important for the understanding their dynamic properties and is, therefore, valuable for the development of anisotropic soft materials for ion transport. Graphical abstract
Identifying Key Nodes in Social Network with Improved PageRank Algorithm

: [ Objective ] This paper modifies the PageRank algorithm for signed network, aiming to identify the key nodes in social network. [ Methods ] Based on the theory of signed network, we proposed the KeyRank algorithm, which combined the PageRank algorithm with node centrality. We examined the new algorithm with user data from the Slashdot website to obtain every user’s ranking. [ Results ] The rankings of PageRank algorithm, in-degree and M-PR algorithm had significant medium level positive correlation with the rankings obtained with the KeyRank algorithm. [ Limitations ] The KeyRank algorithm ignored the interactions between the positive and negative links in each iteration. [ Conclusions ] There is difference between the rankings of nodes by traditional and KeyRank algorithms. The signed links poses important impacts on the rankings, which shows the improved algorithm’s theoretical and practical significance.
A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction

The diagnosis of bankruptcy companies becomes extremely important for business owners, banks, governments, securities investors, and economic stakeholders to optimize the profitability as well as to minimize risks of investments. Many studies have been developed for bankruptcy prediction utilizing different machine learning approaches on various datasets around the world. Due to the class imbalance problem occurring in the bankruptcy datasets, several special techniques would be used to improve the prediction performance. Oversampling technique and cost-sensitive learning framework are two common methods for dealing with class imbalance problem. Using oversampling techniques and cost-sensitive learning framework independently also improves predictability. However, for datasets with very small balancing ratios, combining two above techniques will produce the better results. Therefore, this study develops a hybrid approach using oversampling technique and cost-sensitive learning, namely, HAOC for bankruptcy prediction on the Korean Bankruptcy dataset. The first module of HAOC is oversampling module with an optimal balancing ratio found in the first experiment that will give the best overall performance for the validation set. Then, the second module uses the cost-sensitive learning model, namely, CBoost algorithm to bankruptcy prediction. The experimental results show that HAOC will give the best performance value for bankruptcy prediction compared with the existing approaches.
Kinematic Optimization of a Reconfigurable Spherical Parallel Mechanism for Robotic-Assisted Craniotomy

The craniotomy is a surgical task that is required to allow access to the patient's brain. It consists of using neurosurgical drills to open a path through the skull. The high risk resulting from human dexterous limit justifies the use of an accurate robotic system to perform craniotomy. The present work introduces the kinematic design of a mechanism for a robotic manipulator dedicated to craniotomy. Motion capture experiments have been carried out to measure the motion of a surgical drill during the execution of craniotomy on human cadavers. The results of the experiments are discussed. As this medical application requires a remote center of motion (RCM), a new type of 3-RRR spherical parallel mechanism (SPM) is proposed to manipulate the surgical drill. The novelty of this mechanism is the integration of a reconfigurable base that re-orients the first revolute joint of the RRR legs. A mechanical architecture concept is introduced to implement this reconfiguration. It is made of three pantographic linkages that manipulate the base of the SPM. The kinematics of the new mechanism is analyzed. The influence of this reconfigurable parameter is studied on two different aspects: the mechanism workspace and kinematic performances. Based on these kinematic data, the optimization of a mechanism is performed. The drill motion trajectories are used to evaluate the behavior of the optimized mechanism. It is finally compared to the classical SPM with a trihedral base, showing the contribution of the new reconfiguration variable on the mechanism dexterity.
Appearance model update based on online learning and soft-biometrics traits for people re-identification in multi-camera environments

Intelligent surveillance systems in multi-camera environments pose a hard-open problem for computer vision. The way the people look changes inside and also among cameras, so people re-identification task can be largely improved collecting data about people already identified and take advantage of it as time advances in surveillance video. Furthermore, a camera change or a slight change in the objective traits may require the complete re-formulation of the appearance models. In this paper, we propose several heuristics for updating the appearance model in a multi-camera surveillance environment. Through these heuristics, the subject's appearance model is updated across different time and environmental conditions. The update process is carried out primarily in three different aspects: 1) based on time lapses, 2) based on the change of camera, and 3) based on the automatic selection of the most representative samples selected through decision functions of the classifier. The proposed system focuses on video surveillance environments, that is, the objective is to identify an individual across the set of cameras in the surveillance area, the comparison considers only those people that share time and space. We used four public benchmarks to test our claims; the results confirm the importance of continuous appearance model's updating.
Polarization of Gravitational Waves from Binary Systems

Gravitational waves (GWs) are one of the key predictions of Einstein’s theory of general relativity (GR). Scientists have been looking for evidence of GWs since their prediction by Einstein in 1916. The first direct detection was achieved by LIGO in 2015, 100 years after their prediction, from merging of two black holes in a binary into one. According to GR gravitational waves have two independent polarization states. In this paper, we study the power radiated and the strain of GW along the two polarization states from a compact binary system with its general orbital properties.
Planeamento Global de Trajetórias com Desvio de Obstáculos Híbrido para Embarcações Autónomas

De modo a tornar o metodo de transporte maritimo mais eficiente, rentavel e seguro foi desenvolvido e implementado um algoritmo de planeamento de trajetorias com desvio de obstaculos em tempo real para ser futuramente utilizado pelas embarcacoes.Com multiplos pontos de destino definidos, este algoritmo permite em primeiro lugar o planeamento completo da missao tendo em consideracao todos os obstaculos estaticos conhecidos no momento e de seguida a execucao desta, atuando de forma reativa na ocorrencia de obstaculos dinâmicos. Considerando a informacao obtida por um sistema de percecao, principalmente composto por LIDAR, GPS e uma câmera estereoscopica, este metodo permite a estimacao de uma velocidade livre de colisao ao ser atuada no ASV.
CRAB: an Application for Distributed Scientific Analysis in Grid Projects

Starting from 2008 the CMS experiment will produce several Petabytes of data each year, to be distributed over many computing centers located in many different countries. The CMS computing model defines how the data has to be distributed in a way that CMS physicists can efficiently run their analysis over the data. CRAB (CMS Remote Analysis Builder) is the tool, designed and developed by the CMS collaboration, that facilitates the access to the distributed data in a totally transparent way. The tool's main feature is the possibility to distribute and and to parallelize the local CMS batch data analysis processes over different Grid environments without any specific knowledge of the underlying computational infrastructures. More specifically CRAB interacts with both the local user environment, with CMS Data Management services and with the Grid middleware.
Periodic Steady State Identification for use in Modelica based AC electrical system simulation

Analysis of dynamic systems is often carried out at steady state condition. For cyclic systems like rotating machinery, it is not possible to detect this condition by simply monitoring the change rate of their variables, due to their periodicity. This paper focuses on methods for stationary periodic steady state identification of AC electrical systems. An overview of relevant methods is given and mappings of periodic variables to equivalent stationary variables are discussed. Two new periodic steady state monitors based on Short Time Fourier Transformation are proposed. The study was motivated by the need to identify the steady state condition of an aircraft electrical network for power quality checks. An implementation with Modelica tools is demonstrated.