Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors influencing speed and precision of cursor positioning using a mouse

Influencing factors on speed and accuracy of cursor positioning using a mouse and the learning progress of subjects were analysed in experimental studies. 64 subjects (44 of whom were female) with an average age of about 24 years had to conduct 1200 cursor positioning trials over a period of two days. The results show that the c:d-ratio, that is, the mouse movement distance necessary per distance unit of the cursor movement, did not influence the positioning time. Rather, the target size (the necessary accuracy), the mouse movement distance, and (to a lesser extent) the display size are the decisive factors. In order to shorten the mouse movement distance for longer target distances, a speed-dependent variable gearing ratio is often implemented. This increases the distance the cursor is moved for the faster mouse movements, but it also violates the proportionality of mouse and cursor movement distances in the interest of a shorter mouse movement distance. For the target sizes used in this study, the speed.
Examining the factor structure of the WISC-IV in clinical and non-clinical samples: a multiple-group confirmatory factor analysis

How to cite this article: Celik C, Yigit İ, Guzey Yigit M, Erden G. Examining the factor structure of the WISC-IV in clinical and non-clinical samples: a multiple-group confirmatory factor analysis. Dusunen Adam The Journal of Psychiatry and Neurological Sciences 2020;33:296-309. Examining the factor structure of the WISC-IV in clinical and non-clinical samples: a multiple-group confirmatory factor analysis
Sleep modifications in a Drosophila melanogaster model of Fragile X syndrome

Fragile X syndrome (FXS) is a neurodevelopmental disorder characterized by intellectual disabilities, disruptions in sleep, and autism in humans. Mutations in Fragile X Mental Retardation gene 1 (FMR1), which codes for a protein that modifies the expression of many target proteins, are primarily responsible for this disorder. Genetic modifications of FMR1 can increase or decrease the overall amount of sleep in humans. A potential pharmaceutical target of FXS is dopamine, a critical neurotransmitter in the regulation of sleep and wakefulness. In fruit flies (Drosophila melanogaster) dopamine has been shown to alter sleep. The mushroom body, a structure in the Drosophila brain that regulates sleep and memory, is innervated by dopaminergic neurons and is heavily impacted by low expression of dFMR1. In this study, a Drosophila Activity Monitoring system (DAMs) was used to measure sleep and locomotor activity in Drosophila models of FXS. We found that fruit flies with underexpression of dFMR1 slept more than their control but in shorter periods, which may lead to impaired memory consolidation. Furthermore, the activity of these flies when they are awake is reduced, leaving them with fewer chances to feed, court, and mate. In addition, the mushroom body specific overexpression of dFMR1 in a null background was able to rescue sleep deficits. Further studies on dFMR1 in Drosophila will help us understand how sleep is disrupted and how these changes lead to modifications in behavior.
Convolutional Neural Networks for the Classiﬁcation of 3D Medical Images

Medical imaging is a fundamental screening and diagnostic tool. Healthcare professionals can nowadays rely on various types of image modalities of the human body, including three-dimensional images such as magnetic resonance images. However, with increasingly more information and image complexity, the pressure that radiologists are subjected to is ever increasing, and the resulting fatigue can lead to diagnostic errors. Machine learning mechanisms have been proposed for the analysis of medical images, although most previous work has dealt with inputs involving two dimensions. This work proposes an approach based on convolutional neural networks, combined with recurrent neural networks, for the classiﬁcation of three-dimensional medical images. The proposed architecture aims to extract features from the individual slices of the three-dimensional image, using a convolutional network, and correlate them with the three-dimensional nature of the original images using a recurrent neuronal network. Experiments were carried out with different architectures to classify three-dimensional images of the knee, leveraging a publicly available data-set. The results show that the main model presented in this work, based on the ResNet architecture and LSTM units, is efﬁcient for the classiﬁcation of this type of images, despite being relatively simple.
Vasoplegic shock after pheocromocytoma resection: the role of methylene blue Shock vasopléjico luego de la resección de un fecromocitoma: el rol del azul de metileno

The perioperative management of pheochromocytoma is a challenge for the perioperative medical team. From preoperative optimization to management of adrenergic crises in the operating room, the role of the anesthesiologist is crucial in order to obtain good results. in particular, hypotension after surgical isolation of the tumor can be a problem given its refractoriness to commonly used vasoactive agents. We will describe the use of methylene blue in a case of vasoplegic shock refractory to norepinephrine. Case presentation
Content Based Image Retrieval using Convolutional neural network and SVM

One of the most interesting and rapidly growing search fields in the last decade has been content-based image retrieval. Image retrieval is the process of obtaining and describing images from a huge database. Images are extracted and then matched to obtain the image. Many feature descriptors have been developed, however their effectiveness is influenced by a variety of factors. This study proposes a new technique for CBIR based on deep learning-based feature extraction and support vector machine-based base similarity matching. In the proposed method, a pre-train convolutional neural network (CNN) is utilised to extract robust features from images, and SVM is used for similarity matching. Publically available Corel dataset is used for the evaluation of performance of the proposed method and experimental analysis of the proposed approach is simulated on MATLAB2017a toolbox which comprises the various functions to simulate it. Propose method gives 97.27 % accuracy, which is significance improvement with compare to existing methods.
COVID-19 in the Republic of Belarus: pandemic features and the interim safety and efficacy assessment of the Gam-COVID-Vac vaccine

Objective. To study the COVID-19 pandemic features among the population of the Republic of Belarus from February 2020 to September 2021 and assess the safety (tolerance) and epidemiological efficacy of the Gam-COVID-Vac vaccine (Sputnik V). Materials and methods. A retrospective analysis of COVID-19 cases in the Republic of Belarus from the beginning of registration (February 28, 2020) to September 12, 2021 was performed. To assess the COVID-19 case detection dynamics, official registration data available on the website of the Ministry of Health of the Republic of Belarus were used. Vaccine safety (tolerance) and efficacy were assessed in an observational study. Safety (tolerance) was assessed by presence/absence of adverse reactions: general and local ones. The efficacy rate (E) and the epidemiological efficacy index (K) was calculated according to the formula: E(%)=100*(b-a)/b, K=b/a. Results. Our data show that The COVID-19 pandemic in the Republic of Belarus is characterized by successive development stages: the first is the absence of COVID-19 cases in the country; the second is the registration of individual infection cases that came from abroad followed by local pathogen spread among the country's population; the third is a local spread of COVID-19 among individuals who had contact with infected people; the fourth is the detection of cases where patients had no history of exposure to COVID-19 patients. As of calendar week 26, 2021 Delta variant of SARS-CoV-2 has become the prevalent in the country. Follow-up results in January-August 2021 showed that the Sputnik V vaccine was well tolerated, with 80,832 adverse reactions reported (2.99% (95% CI 2.9-3.0) of the total number of vaccine doses administered). In terms of severity, adverse reactions were mild (91.4% (95% CI 91.2-91.6)) and moderate (8.6% (95% CI 8.6-8.8)). The epidemiological efficacy rate was 96.3%, the epidemiological efficacy index was 26.7. Thus, the results obtained testify to high prophylactic efficacy of the Sputnik V vaccine. Conclusions. The COVID-19 pandemic in the Republic of Belarus is characterized by successive development stages: from no cases in early 2020 to detected cases where most individuals had no history of contact with COVID-19 patients; periods of rising and falling incidence. The Sputnik V vaccine has demonstrated a high safety profile and epidemiological efficacy throughout mass vaccination in the Republic of Belarus.
An Empirical Study of Finding Similar Exercises

Education artificial intelligence aims to profit tasks in the education domain such as intelligent test paper generation and consolidation exercises where the main technique behind is how to match the exercises, known as the finding similar exercises(FSE) problem. Most of these approaches emphasized their model abilities to represent the exercise, unfortunately there are still many challenges such as the scarcity of data, insufficient understanding of exercises and high label noises. We release a Chinese education pre-trained language model BERT$_{Edu}$ for the label-scarce dataset and introduce the exercise normalization to overcome the diversity of mathematical formulas and terms in exercise. We discover new auxiliary tasks in an innovative way depends on problem-solving ideas and propose a very effective MoE enhanced multi-task model for FSE task to attain better understanding of exercises. In addition, confidence learning was utilized to prune train-set and overcome high noises in labeling data. Experiments show that these methods proposed in this paper are very effective.
Learning Intrinsic Images for Clothing

Reconstruction of human clothing is an important task and often relies on intrinsic image decomposition. With a lack of domain-specific data and coarse evaluation metrics, existing models failed to produce satisfying results for graphics applications. In this paper, we focus on intrinsic image decomposition for clothing images and have comprehensive improvements. We collected CloIntrinsics, a clothing intrinsic image dataset, including a synthetic training set and a real-world testing set. A more interpretable edge-aware metric and an annotation scheme is designed for the testing set, which allows diagnostic evaluation for intrinsic models. Finally, we propose ClothInNet model with carefully designed loss terms and an adversarial module. It utilizes easy-to-acquire labels to learn from real-world shading, significantly improves performance with only minor additional annotation effort. We show that our proposed model significantly reduce texture-copying artifacts while retaining surprisingly tiny details, outperforming existing state-of-the-art methods.
Which CNNs and Training Settings to Choose for Action Unit Detection? A Study Based on a Large-Scale Dataset

In this paper we explore the influence of some frequently used Convolutional Neural Networks (CNNs), training settings, and training set structures, on Action Unit (AU) detection. Specifically, we first compare 10 different shallow and deep CNNs in AU detection. Second, we investigate how the different training settings (i.e. centering/normalizing the inputs, using different augmentation severities, and balancing the data) impact the performance in AU detection. Third, we explore the effect of increasing the number of labelled subjects and frames in the training set on the AU detection performance. These comparisons provide the research community with useful tips about the choice of different CNNs and training settings in AU detection. In our analysis, we use a large-scale naturalistic dataset, consisting of ~55K videos captured in the wild. To the best of our knowledge, there is no work that had investigated the impact of such settings on a large-scale AU dataset.
Self-supervised High-fidelity and Re-renderable 3D Facial Reconstruction from a Single Image

—Reconstructing high-ﬁdelity 3D facial texture from a single image is a challenging task since the lack of complete face information and the domain gap between the 3D face and 2D image. The most recent works tackle facial texture reconstruction problem by applying either generation-based or reconstruction-based methods. Although each method has its own advantage, none of them is capable of recovering a high-ﬁdelity and re-renderable facial texture, where the term ’re-renderable’ demands the facial texture to be spatially complete and disentangled with environmental illumination. In this paper, we propose a novel self-supervised learning framework for reconstructing high-quality 3D faces from single-view images in-the-wild. Our main idea is to ﬁrst utilize the prior generation module to produce a prior albedo, then leverage the detail reﬁnement module to obtain detailed albedo. To further make facial textures disentangled with illumination, we present a novel detailed illumination representation which is reconstructed with the detailed albedo together. We also design several regularization loss functions on both the albedo side and illumination side to facilitate the disentanglement of these two factors. Finally, thanks to the differentiable rendering technique, our neural network can be efﬁciently trained in a self-supervised manner. Extensive experiments on challenging datasets demonstrate that our framework substantially outperforms state-of-the-art approaches in both qualitative and quantitative comparisons.
Two-dimensional deep regression for early yield prediction of winter wheat

Crop yield prediction is one of the tasks of Precision Agriculture that can be automated based on multi-source periodic observations of the fields. We tackle the yield prediction problem using a Convolutional Neural Network (CNN) trained on data that combines radar satellite imagery and on-ground information. We present a CNN architecture called Hyper3DNetReg that takes in a multi-channel input image and outputs a two-dimensional raster, where each pixel represents the predicted yield value of the corresponding input pixel. We utilize radar data acquired from the Sentinel-1 satellites, while the on-ground data correspond to a set of six raster features: nitrogen rate applied, precipitation, slope, elevation, topographic position index (TPI), and aspect. We use data collected during the early stage of the winter wheat growing season (March) to predict yield values during the harvest season (August). We present experiments over four fields of winter wheat and show that our proposed methodology yields better results than five compared methods, including multiple linear regression, an ensemble of feedforward networks using AdaBoost, a stacked autoencoder, and two other CNN architectures.
Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation

Abstract This article develops a sparsity-inducing version of Bayesian Causal Forests, a recently proposed nonparametric causal regression model that employs Bayesian Additive Regression Trees and is specifically designed to estimate heterogeneous treatment effects using observational data. The sparsity-inducing component we introduce is motivated by empirical studies where not all the available covariates are relevant, leading to different degrees of sparsity underlying the surfaces of interest in the estimation of individual treatment effects. The extended version presented in this work, which we name Shrinkage Bayesian Causal Forest, is equipped with an additional pair of priors allowing the model to adjust the weight of each covariate through the corresponding number of splits in the tree ensemble. These priors improve the model’s adaptability to sparse data generating processes and allow to perform fully Bayesian feature shrinkage in a framework for treatment effects estimation, and thus to uncover the moderating factors driving heterogeneity. In addition, the method allows prior knowledge about the relevant confounding covariates and the relative magnitude of their impact on the outcome to be incorporated in the model. We illustrate the performance of our method in simulated studies, in comparison to Bayesian Causal Forest and other state-of-the-art models, to demonstrate how it scales up with an increasing number of covariates and how it handles strongly confounded scenarios. Finally, we also provide an example of application using real-world data. Supplementary materials for this article are available online.
Green's functions of the Boltzmann transport equation with the full scattering matrix for phonon nanoscale transport beyond the relaxation-time approximation

The phonon Boltzmann transport equation (BTE) has been widely utilized to study thermal transport in solids. While for a number of materials the exact solution to the BTE has been obtained for a uniform heat flow, problems arising in micro/nanoscale heat transport have been analyzed within the relaxation time approximation (RTA). Since the RTA breaks down at temperatures low compared to the Debye temperature, this approximation prevents the study of an important class of high Debye temperature materials such as diamond, graphite, graphene and some other 2D materials. We present a full scattering matrix formalism that goes beyond the RTA approximation and obtain a Green’s function solution for the linearized BTE, which leads to an explicit expression for the phonon distribution and temperature field produced by an arbitrary spatio-temporal distribution of heat sources in an unbounded medium. The presented formalism is capable of describing a wide range of phenomena, from heat dissipation by nanoscale hot spots to the propagation of second sound waves. We provide numerical results for graphene for a spatially
Switching Recurrent Kalman Networks

Forecasting driving behavior or other sensor measurements is an essential component of autonomous driving systems. Often real-world multivariate time series data is hard to model because the underlying dynamics are nonlinear and the observations are noisy. In addition, driving data can often be multimodal in distribution, meaning that there are distinct predictions that are likely, but averaging can hurt model performance. To address this, we propose the Switching Recurrent Kalman Network (SRKN) for efficient inference and prediction on nonlinear and multi-modal time-series data. The model switches among several Kalman filters that model different aspects of the dynamics in a factorized latent state. We empirically test the resulting scalable and interpretable deep state-space model on toy data sets and real driving data from taxis in Porto. In all cases, the model can capture the multimodal nature of the dynamics in the data.
Improving Learning from Demonstrations by Learning from Experience

How to make imitation learning more general when demonstrations are relatively limited has been a persistent problem in reinforcement learning (RL). Poor demonstrations lead to narrow and biased date distribution, non-Markovian human expert demonstration makes it difficult for the agent to learn, and over-reliance on sub-optimal trajectories can make it hard for the agent to improve its performance. To solve these problems we propose a new algorithm named TD3fG that can smoothly transition from learning from experts to learning from experience. Our algorithm achieves good performance in the MUJOCO environment with limited and sub-optimal demonstrations. We use behavior cloning to train the network as a reference action generator and utilize it in terms of both loss function and exploration noise. This innovation can help agents extract a priori knowledge from demonstrations while reducing the detrimental effects of the poor Markovian properties of the demonstrations. It has a better performance compared to the BC+ fine-tuning and DDPGfD approach, especially when the demonstrations are relatively limited. We call our method TD3fG meaning TD3 from a generator.
GÖFI - Ein Neurofeedback-System für die Kinder- und Jugendpsychiatrie

Eine interessante Anwendung von EEG-Biofeedback (Neurofeedback, NF) stellt das 'Brain Computer Interface' dar: Ein Cursor oder ein beliebiges anderes Objekt auf einem Bildschirm kann über einen aus dem EEG extrahierten Parameter bzw. über die Veränderung dieses Parameters gezielt gesteuert werden [1,2]. Auf diese Weise können z.B. Patienten mit amyotrophischer Lateralsklerose, die eine geeignete Strategie zur Manipulation erlernt haben, mit ihrer Umwelt kommunizieren [3], Neurofeedback ist aber nicht nur zur Kommunikation ohne Muskelkraft geeignet, sondern kann bei bestimmten neurologischen und psychiatrischen Störungen auch zur Therapie eingesetzt werden. Rockstroh et al. [4] berichten beispielsweise von beachtlichen Erfolgen bei medikamentös nicht einstellbaren Epilepsie-Patienten. Von 18 Patienten, die ein Jahr nach Durchführung des NF-Trainings nachuntersucht werden konnten, blieben sechs Patienten anfallsfrei, sieben hatten signifikant weniger Anfälle. Lubar et al. [5] führten NF-Training bei Kindern mit hyperkinetischer Störung (HKS) durch. Nach dem NFTraining konnte eine signifikante Verbesserung der Aufmerksamkeitsleistungen und eine Abnahme der psychopathologischen Auffälligkeiten (nach Elterneinschätzung) festgestellt werden. Allerdings gaben auch die Eltern der Kinder, die das NF-Training nicht erfolgreich durchführen konnten, eine Verbesserung des Verhaltens ihrer Kinder an. Tansey [6] berichtete über zwei Jungen mit Tourette-Syndrom (der schwersten Form der Tic-Störungen), die nach NF-Training keine Tics mehr zeigten. Für die Wirkungsweise des NF-Trainings bei Epilepsien konnte das folgende Erklärungsmodell erarbeitet werden [7,8]: Langsame kortikale Potentiale (SCPs, engl.: slow cortical potentials) geben das Erregbarkeitsniveau kortikaler Zellensembles wieder. Steigt die Erregbarkeit über ein bestimmtes Ausmaß (-> Negativierung), so wird normalerweise eine Gegenregulation (-> Positivierung) eingeleitet, die das betroffene Netzwerk wieder in ein 'mittleres Erregbarkeitsniveau zurückgeregelt. Beim epileptischen Krampfanfall versagt dieser Gegenregulationsmechanismus wegen mangelnder neuronaler Hemmungsfähigkeit. Durch das Erlernen der Selbstkontrolle über die SCPs mittels NF-Training können defizitär arbeitende Hemmechanismen gestärkt werden. Bei den o.g. kinderund jugendpsychiatrischen Störungsbildern bleiben hingegen noch Fragen offen. So wurden bisher keine kontrollierten Studien durchgeführt, die die Effektivität von Neurofeedback systematisch prüften. Außerdem fehlen Untersuchungen, die sich mit den zentralnervösen Wirkungsmechanismen des Neurofeedback-Trainings auf die störungsspezifischen neuronalen Funktionsmuster auseinandersetzen.
Comparative Analysis of Machine Learning Models for Predicting Travel Time

In this paper, five different deep learning models are being compared for predicting travel time. These models are autoregressive integrated moving average (ARIMA) model, recurrent neural network (RNN) model, autoregressive (AR) model, Long-short term memory (LSTM) model, and gated recurrent units (GRU) model. The aim of this study is to investigate the performance of each developed model for forecasting travel time. The dataset used in this paper consists of travel time and travel speed information from the state of Missouri. The learning rate used for building each model was varied from 0.0001-0.01. The best learning rate was found to be 0.001. The study concluded that the ARIMA model was the best model architecture for travel time prediction and forecasting.