Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical Image Compression Based on Combining Region Growing and Wavelet Transform

Medical data grows very fast and hence medical institutions need to store high volume of data about their patients. Medical images are one of the most important data types about patients. As a result, hospitals have a high volume of images that require a huge storage space and transmission bandwidth to store these images. Most of the time transmission bandwidth is not sufficient to storing and transmit all the image data with the required efficiency. Image compression is the process of encoding information using fewer bits than an un-encoded representation using specific encoding schemes. Compression is useful because it helps to reduce the consumption of expensive resources, such as storage space or transmission bandwidth (computing). In this paper, a medical image compression technique based on combining region growing and wavelets algorithms was introduced. A region growing algorithm is used to simply partitioning the image into two parts foreground and background depending on the intensity values. Then, wavelets methods applied on foreground regions including important regions. These regions are compressed lossless to keep the appearance of the image as intact while making the simplifications and the other region is lossy compressed to reduce the file size, leading to that the overall compression ratio gets better and the reconstructed image seems like the original one. To prove the capability of the proposed algorithm, different four image structures from X-Ray, Computed tomography CT and Magnetic resonance imaging MRI types are tested.
Revisiting generalization for deep learning: PAC-Bayes, flat minima, and generative models

In this work, we construct generalization bounds to understand existing learning algorithms and propose new ones. Generalization bounds relate empirical performance to future expected performance. The tightness of these bounds vary widely, and depends on the complexity of the learning task and the amount of data available, but also on how much information the bounds take into consideration. We are particularly concerned with data and algorithmdependent bounds that are quantitatively nonvacuous. We begin with an analysis of stochastic gradient descent (SGD) in supervised learning. By formalizing the notion of flat minima using PAC-Bayes generalization bounds, we obtain nonvacuous generalization bounds for stochastic classifiers based on SGD solutions. Despite strong empirical performance in many settings, SGD rapidly overfits in others. By combining nonvacuous generalization bounds and structural risk minimization, we arrive at an algorithm that trades-off accuracy and generalization guarantees. We also study generalization in the context of unsupervised learning. We propose to use a two sample test statistic for training neural network generator models and bound the gap between the population and the empirical estimate of the statistic.
Zooming Into Neural Mechanisms Of Decision Confidence Through The Lens Of EEG

Neural mechanisms responsible for feelings of certainty in reasoning and decision-making remain unclear. This thesis attempts to address this problem by examining the role of error-related EEG potentials (error-related negativity ERN, error positivity Pe) in decision confidence. The amplitude of these potentials has been shown to correlate with error detection and confidence ratings in simple perceptual decisions. In order to test whether this pattern holds in more complex decisions, we investigated activity changes in ERN and Pe in response to manipulations of decision confidence in an arithmetic reasoning task. In an EEG experiment, 49 participants had to quickly respond whether an equation (e.g. 9 * 7 = 65) is correct or incorrect and then report their decision confidence. Task difficulty and response fluency were varied as manipulations of confidence. The results indicated that ERN and Pe did not mediate the effect of task difficulty on confidence. Response fluency impacted confidence only for simple decisions, and this effect was partially mediated by ERN. These results suggest that Pe could be an index of implicit cognitive control, whereas ERN tracks decision confidence in simple decisions and is susceptible to response fluency manipulations.
An artificial intelligent platform for live cell identification and the detection of cross-contamination

Background About 30% of cell lines have been cellular cross-contaminated and misidentification, which can result in invalidated experimental results and unusable therapeutic products. Cell morphology under the microscope was observed routinely, and further DNA sequencing analysis was performed periodically to verify cell line identity, but the sequencing analysis was costly, time-consuming, and labor intensive. The purpose of this study was to construct a novel artificial intelligence (AI) technology for “cell face” recognition, in which can predict DNA-level identification labels only using cell images. Methods Seven commonly used cell lines were cultured and co-cultured in pairs (totally 8 categories) to simulated the situation of pure and cross-contaminated cells. The microscopy images were obtained and labeled of cell types by the result of short tandem repeat profiling. About 2 million patch images were used for model training and testing. AlexNet was used to demonstrate the effectiveness of convolutional neural network (CNN) in cell classification. To further improve the feasibility of detecting cross-contamination, the bilinear network for fine-grained identification was constructed. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the cell semantic segmentation was conducted by DilatedNet. Results The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. Conclusions The deep CNN model proposed in this study has the ability to recognize small differences in cell morphology, and achieved high classification accuracy.
Inference of Language Functional Network in Healthy, Cancerous and Bilingual Brains by fMRI and Network Modeling

Inference of functional language networks from functional Magnetic Resonant Imaging and network theory by Qiongge Li Adviser: Professor Hernan Makse We study the underlying mechanism by which language processing occurs in the human brain using inference methods on functional magnetic resonance imaging data. The data analyzed stems from several cohorts of subjects; a monolingual group, a bilingual group, a healthy control group and one diseased case. We applied a complex statistical inference pipeline to determine the network structure of brain components involved with language. This healthy network reveals a fully connected triangular relationship between the preSupplementary Motor Area (pre-SMA), the Broca’s Area (BA), and the ventral Pre-Motor Area (PreMA) in the left hemisphere. This “triangle” shows consistently in all the healthy subjects (100%) we analyzed regardless of their monoor multi-lingual status. In addition, we found that Wernicke’s Area (WA) on the left hemisphere connects with BA and PreMA to form a “V” shape connectivity across 75% of the monolinguals, 50% of the bilinguals speaking a second language and 100% of the bilinguals speaking their native language. By comparing the quantified link weights, we found that the strongest link is between BA and PreMA, followed by pre-SMA and PreMA, and then pre-SMA and BA. This is consistent for all healthy subjects (p < 0.05). Furthermore, we conducted a k-core analysis testing the resiliency of subnetworks in the three groups. Our results show that nodes in the three triangle areas belong mostly to the maximum shell, whereas WA populates mostly in the lower shell, consistently across the data.
Profiling of odor components and their mixtures.

Abstract Observers evaluated five odors and their 26 mixtures (two, three, four, and five components) by magnitude estimation. Estimates revealed that in mixtures there is moderate suppression of perceived intensity; a vector model proposed by Berglund (Annals of the New York Academy of Sciences, 1974, 237, 35-51) provides a good prediction of the intensity of binary mixtures, but overpredicts the intensity of three, four, and five component mixtures. Hedonic tones of mixtures were difficult to predict from hedonic tones of components. Qualities of mixtures were difficult to predict from quality of components. Intermediacy, of quality and hedonic values, was often observed.
Anonymous authentication key agreement scheme with pairing-based cryptography for home-based multi-sensor Internet of Things

Home-based multi-sensor Internet of Things, as a typical application of Internet of Things, interconnects a variety of intelligent sensor devices and appliances to provide intelligent services to individuals in a ubiquitous way. As families become more and more intelligent, complex, and technology-dependent, there is less and less need for human intervention. Recently, many security attacks have shown that Internet home-based Internet of Things have become a vulnerable target, leading to personal privacy problems. For example, eavesdroppers can acquire the identity of specific devices or sensors through public channels, which is not secure, to infer individual public life in the home area network. Authentication is the essential portion of many secure systems processing of verifying and declaring identity. Before providing confidential information, home-based-Internet of Things service authenticates users and devices. The communication and processing capabilities of intelligent devices are limited. Therefore, in home-based Internet of Things, lightweight authentication and key agreement technology are very important to resist known attacks. This article proposes an anonymous authenticated key agreement protocol using pairing-based cryptography. The protocol proposed in this article provides lightweight computation and ensures the security of communication between home-based multi-sensor Internet of Things network and Internet network.
Deep learning for automatic delineation of tumours from PET/CT images

Purpose The delineation of tumours and malignant lymph nodes in medical images is an essential part of radiotherapy. However, it is both time-consuming and prone to inter-observer variability. Automating this process is therefore beneficial as it will reduce the time effort of radiotherapy planning and the inter-observer variability. One method of automating delineation is by using neural networks. Deep learning experiments, however, requre tuning of a vast amount of parameters. Thus, a systematic methodology for conducting such experiments is vital to ensure reproducibility. This thesis will introduce the theory of deep learning and present the SciNets library, a framework for rapid model prototyping with guaranteed reproducibility. This framework was used to develop a model for automatic delineation of gross tumour volume and malignant lymph nodes in the head and neck region. Methods The SciNets library (available at https://github.com/yngvem/scinets/)is a Python library that creates and trains deep learning models parametrised by a series of JSON files containing model hyperparameters. Furthermore, an extensive visualisation suite is included to inspect the training process. This library was used to assess the applicability of neural networks for automatic tumour delineation. The dataset consisted of medical scans taken of 197 patients who recieved treatment at the Oslo University Hospital, The Radium Hospital. 18F-FDG-PET coregistered to contrast-enhanced CT scans (i.e. contrast-enhanced PET/CT scans) were available for all patients. The image dataset was split into a training set (142 patients), a validation set (15 patients) and a test set (40 patients), stratified by tumour stage. A vast parameter sweep was performed on this dataset. All tested models were based on the U-Net architecture. Both the Cross Entropy and dice loss were tested, as well as the novel F2 and F4 loss introduced herein. Channel dropping and Hounsfield windowing were used for preprocessing, with varying window centres and widths. Both Adam and SGDR+momentum
Multiscale tsallis entropy for pulmonary crackle detection

Abnormalities in the lungs can be detected from the sound produced by the lungs. Diseases that occur in the lungs or respiratory tract can produce a distinctive lung sound. One of the examples of the lung sound is the pulmonary crackle caused by pneumonia or chronic bronchitis. Various digital signal processing techniques are developed to detect pulmonary crackle sound automatically, such as the measurement of signal complexity using Tsallis entropy (TE). In this study, TE measurements were performed through several orders on the multiscale pulmonary crackle signal. The pulmonary crackle signal was decomposed using the coarse-grained procedure since the lung sound as the biological signal had a multiscale property. In this paper, we used 21 pulmonary crackle sound and 22 normal lung sound for the experiment. The results showed that the second order TE on the scale of 1-15 had the highest accuracy of 97.67%. This result was better compared to the use of multi-order TE from the previous study, which resulted in an accuracy of 95.35%.
Wind speed forecasting using First Order Markov Transition with Regime Switch and Time Duration

Due to the dynamic nature of time series, it is common to deploy varying models in predicting behavior of the inherent characteristics in time series. The growing need in nonlinear series prediction and the limitations of linear models in predicting the behaviors of such, lead to the quest of models capable of predicting both dynamic and non-linear behaviors in time series. Markov Regime switching model commonly referred to as the regime switching model is used to forecast wind speed taking into account variation of speed with time. Eviews, a statistical, econometric and economic modelling package is used for the Markov regime switch estimation, simulation and forecasting of A 24-hour period real wind speed data measurement from NREL. Values obtained from forecast accuracy evaluation are Root Mean Squared Error (RMSE) is at 0.242, the Mean Absolute Error (MAE) is 0.17 and the Mean Absolute Percentage Error (MAPE) is 31.38. The statistics showed that the forecast is closely related to the actual values.
Effect of different pre‐drying treatments on the quality of dried apple slices

The effect of pre-treatment such as osmotic dehydration (OD), honey immersion (HI), citric acid dipping (CD), steam blanched (SB), and sulfiting in sodium-metabisulfite (SU) of apple slices on physicochemical, sensory and antioxidant properties were studied. Chemical composition, total phenols, Vit (C), antioxidant activities and sensory evaluation were tested for all treatments. Results Showed that SB pre-treatment had the lowest percentage of moisture content and the highest percentage of total soluble solids (TSS). Sulfiting pretreatment had the maximum value of ascorbic acid, total phenolics content (TPC) and antioxidant activity (AOA). Sensory evaluation of dried apple slices showed that SU and HI treatments had the best value of taste and texture. Furthermore, HI had the best value of odor, while SU treatment had the best value of color. Future work could look at the possibility of enrobing the apple wedges with coatings (e.g. sodium alginate) to reduce the disadvantages of some treatment.
REGULATED AIR POLLUTANT EMISSIONS FROM HIGHER EMITTERS STATIONARY SOURCES IN BELO HORIZONTE, MINAS GERAIS, BRAZIL

Belo Horizonte, capital of Minas Gerais state, is a highly urbanized city located in the third largest metropolitan area of Brazil, being one of the most important cities in the country. There are several potential air pollutant emission sources in the studied area, such as industry and the vehicular fleet (1,669,884 vehicles in 2015). These can affect the air quality, which can have an impact on population health. Despite this critical scenario, few studies have been developed with the objective of evaluating the air quality in this city, especially regarding the identification of pollutant emission sources. Thus, the aim of this study was to identify and quantify atmospheric emissions by stationary sources of significant environmental impact in Belo Horizonte. The quantification of emissions was preferably performed based on chimneys monitoring data. However, in the absence of these data, estimates were made based on AP-42 guidelines. As a result, 75 chimneys were identified, belonging to 28 companies. Pollutant emission rates of 305, 235, 234, 224, 206, 180 and 63 t year-1 were observed for NOX, CO, TSP, PM10, PM2.5, SO2 and VOC, respectively. The results obtained can contribute to the diagnosis, modeling and management of air quality in Belo Horizonte.
Material Appearance: Ordering and Clustering

Appearance is a complex psychovisual phenomenon impacted by various objective and subjective factors that are not yet fully understood. In this work we use real objects and un-constrained conditions to study appearance perception in human subjects, allowing free interaction between objects and observers. Human observers were asked to describe resin objects from an artwork collection and to complete two visual tasks of appearance-based clustering and ordering. The process was ﬁlmed for subsequent analysis with the consent of the observers. While clustering task helps us identify attributes people use to assess appearance similarity and difference, the ordering task is used to identify potential cues to create an appearance ordering system. Finally, we generate research hypotheses about how people perceive appearance and outline future studies to validate them. Preliminary observations revealed interesting cross-individual consistency in appearance assessment, while personal background of the observer might be affecting deviation from the general appearance assessment trends. On the other hand, no appearance ordering system stood out from the rest that might be explained with the sparse sampling of our dataset.
Developing Algorithms to Detect Incidents on Freeways From Loop Detector and Vehicle Re-Identification Data

DEVELOPING ALGORITHMS TO DETECT INCIDENTS ON FREEWAYS FROM LOOP DETECTOR AND VEHICLE RE-IDENTIFICATION DATA BirajAdhikari Old Dominion University, 2019 Director: Dr. Mecit Cetin A new approach for testing incident detection algorithms has been developed and is presented in this thesis. Two new algorithms were developed and tested taking California #7, which is the most widely used algorithm to date, and SVM (Support Vector Machine), which is considered one of the best performing classifiers, as the baseline for comparisons. Algorithm #B in this study uses data from Vehicle Re-Identification whereas the other three algorithms (California #7, SVM and Algorithm #A) use data from a double loop detector for detection of an incident. A microscopic traffic simulator is used for modeling three types of incident scenarios and generating the input data. Two incident scenarios are generated by closing either one lane or two lanes of a four-lane highway. The third scenario involves bottleneck blocking two lanes of the freeway with an incident occurring in the upstream of the bottleneck. The highway network is five miles long and simulated in VISSIM. Traffic parameters like occupancy, speed, flow and number of vehicles passing through the loop detector are collected to assess the traffic condition between the sensors or detectors. The proposed performance test inspects whether the algorithms thus tested were able to detect any occurrences and incidences within the first minutes in different scenarios and compares their respective detections to identify the best performing algorithm in all the contingencies. The results indicate that the implementation of this new approach not only reduces the dilemma of selecting thresholds but also checks algorithm performance in different incident scenarios so that the response time for clearing such incidences is as short as possible. Likewise, making use of Reidentification data and travel time makes the incident detection more trivial and self-evident and thus outperformed the algorithms using traditional data like occupancy speed and volume in uncongested traffic conditions. Further different SVM models were trained and tested inspecting the effects of change in location of incident concerning detectors. However, using data from loop detector performed well when the incident happened at the upstream detector while using that from re-identification encountered delays in overall detection time for the same. Copyright, 2019, by BirajAdhikari, All Rights Reserved. This thesis is dedicated to the proposition that “The secret of getting ahead is getting started” by Mark Twain.
2D axial-azimuthal particle-in-cell benchmark for low-temperature partially magnetized plasmas

The increasing need to demonstrate the correctness of computer simulations has highlighted the importance of benchmarks. We define in this paper a representative simulation case to study low-temperature partially-magnetized plasmas. Seven independently developed particle-in-cell codes have simulated this benchmark case, with the same specified conditions. The characteristics of the codes used, such as implementation details or computing times and resources, are given. First, we compare at steady-state the time-averaged axial profiles of three main discharge parameters (axial electric field, ion density and electron temperature). We show that the results obtained exhibit a very good agreement within 5% between all the codes. As E × B discharges are known to cause instabilities propagating in the direction of electron drift, an analysis of these instabilities is then performed and a similar behaviour is retrieved between all the codes. A particular attention has been paid to the numerical convergence by varying the number of macroparticles per cell and we show that the chosen benchmark case displays a good convergence. Detailed outputs are given in the supplementary data, to be used by other similar codes in the perspective of code verification.
Combined Effect of Poultry Manure and Soil Mulching on Soil Properties, Physiological Responses, Yields and Water-use Efficiencies of Sorghum Plants under Water Stress

ABSTRACT The present investigation was planned to determine the effect of deficit irrigation, mulching and poultry manure application on sorghum grain, forage yield and water-use efficiencies. Two field experiments were conducted during 2016 and 2017 growing seasons at Demo farm, Fayoum, Egypt. Eighteen treatments comprising of two rates of rice straw as soil mulching (SM; zero and 10 ton per hectare), three irrigation treatments (I100% = 100%, I85% = 85% and I70% = 70% of crop evapotranspiration (ETc) and three poultry manure (PM) levels (0, 24 and 36 ton per hectare) were studied under controlled surface irrigation system. Sorghum growth attributes (plant height, stem diameter, leave area), grain and forage yield, water-use efficiencies (G-WUE and F-WUE) were significantly (p < .05) affected by irrigation quantity and both soil mulch and poultry manure application. Poultry manure resulted in a significant decrease in soil bulk density, electrical conductivity (ECe), soil pH, hydraulic conductivity and total porosity, useful pores, and water holding pores were increased. The results showed that underwater scarcity, application of (I85 × PM36× SM10) treatment was found to be favorable to save 15% of the applied irrigation water as compared to I100, at the time produced the same sorghum yields.