Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Line Objects by Means of Gabor Wavelets and Hough Transform

This article presents a method for detecting linear objects with a defined direction based on image and lidar data. It was decided to use Gabor waves for this purpose. The Gabor wavelet is a sinusoid modulated by the Gauss function. The orientation angle of the sinusoid means that the waveform can only operate in strictly defined directions. It should, therefore, provide an appropriate solution to the problem posed by the publication. The research problem focused in the first stage on determining the approximate location of only the analysed objects, and in the next step on correct and accurate detection. The first stage was carried out using Gabor filters, the second using the Hough transform. The tests were performed for both laser data and image data. In both cases, good results were obtained for both stages: approximate location and precise detection.
Quality Improvement With Statistical Process Control In The Softwere Industry.

In this context of a worldwide market opening, economy defies with numerous challenges, is no longer enough to produce, the current principles are based on quality as a condition for achieving productivity and competitiveness. And given that the quality is no static, it is constantly being changed, and because customers are increasingly demanding, any business organization that aims to be competitive it has to innovate. in the Statistical Process Control (SPC) – a Powerful management method which enables quality improvement and waste elimination. This paper suggests the improvement of the quality of a process through the use of SPC in an enterprise of the Software industry makes a brief review of concepts related with the methodology. It was sought a way to adapt it to the reality of the company. This review systematic identified from the past studies from 2000 to 2012. The evidence of SPC application spread throughout the information Technology industry and need more research in this area.
Prevalent Sources of Enteric Fevers among Women in Mosul City

Background: Enteric fever is a serious blood stream infection caused by the bacterium Salmonella Typhi and Salmonella paratyphi. It is commonly associated with direct or indirect person-to-person transmission as a result of improper hygiene and unsafe food/water handling practices. The aims of current study were to determine if there is a source of enteric fever infection of public health concern and to stop transmission from such a source and to assess the predisposing risk factors in a hope to decrease or prevent such transmission.Methods: Ethical approval was obtained at the beginning of the study. A case-series study design was adopted to carry out the study which was conducted in private clinic in Mosul city, the center of the Nineveh governorate in the north of Iraq, over one-year period starting from1 st of April 2017, after military operation has been ended, to 1 st of April 2018. Two hundred thirtyone cases were eligible to be included in the study as they fit the criteria of case selection. Data analysis was done using computer-based Minitab 18 software. Results: Out of 4350 cases attending private clinic of gynecology and obstetrics for any cause during period of data collection, only 231 cases were eligible to be included in the study.Conclusion: the study concludes that there are many common sources of typhoid infection in Mosul community with ignorance of population about them. More health education about common sources and availability of vaccination together with health sanitation measures needed to overcome these common sources and prevent disease occurrence.
ELECTRONIC HEALTH RECORD USAGE IN SOUTHERN US STATES

The purpose of this project was to review the electronic health record technology usage in the southern US states to determine if anomalies exist by state or by year. The analysis of secondary data collected and disseminated by U.S. Department of Health and Human Services (HHS)’s Office of the National Coordinator for Health IT revealed several anomalies that warrant further investigation. The major finding was that some statistically significant differences existed, in consecutive years, for demonstrated use of electronic heath record technology by some health care professionals. The second interesting finding surrounds the demonstrated use by some segments of the healthcare community. Although no statically significant difference was discovered by health professionals in general, we did discover some differences in the nurse practitioner and health physician assistance communities of some states. We anticipate these findings will be the catalyst for future research to answer the question why the differences existed.
Deep Learning Based Underwater Object Detection and Pose Estimation

—In this paper, We present a deep learning based object pose estimation that speciﬁcally targets underwater applications. We improve the existing approach for making a dataset using a 3D CAD model for underwater object detection and pose estimation. We also present a ﬁne-resolution pose estimator for underwater objects. We show that object detection and pose estimation networks trained via our dataset present preliminary potential for deep learning based approaches in underwater. Lastly, we show that our synthetic image dataset provides meaningful performance for deep learning models in underwater environments.
Inverse Learning of Symmetries

Symmetry transformations induce invariances which are frequently described with deep latent variable models. In many complex domains, such as the chemical space, invariances can be observed, yet the corresponding symmetry transformation cannot be formulated analytically. We propose to learn the symmetry transformation with a model consisting of two latent subspaces, where the first subspace captures the target and the second subspace the remaining invariant information. Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser. Unlike previous methods, we focus on the challenging task of minimising mutual information in continuous domains. To this end, we base the calculation of mutual information on correlation matrices in combination with a bijective variable transformation. Extensive experiments demonstrate that our model outperforms state-of-the-art methods on artificial and molecular datasets.
Lipschitz standardization for multivariate learning

In this work, we focus on the use of gradient-based optimization to perform approximate inference in multivariate probabilistic models. We show that these approaches suffer from the negative transfer problem, broadly studied in the context of multi-task learning. As a result, they often infer models that fit only a subset of the observed variables, overlooking the rest. Unfortunately, as the likelihood model is fixed, adaptive solutions from the multi-task literature do not apply in the probabilistic setting. Yet, we show that likelihood functions, and specifically their local Lipschitz smoothness, may be modified through the data via data standardization. In our analysis, we show that existing data standardization techniques often ease fairer learning under common continuous likelihood functions. Based on this finding, we propose a novel data preprocessing method, Lipschitz standardization, that results in a fairer learning process in mixed continuous and discrete variable models. Our experiments on different real-world datasets show that Lipschitz standardization leads to more accurate models for missing data imputation than the ones inferred using standard data preprocessing techniques. The models and datasets employed in the experiments can be found in this https URL.
Evaluation of Deconvolution Methods

Where blind deconvolution is the recovery of a true image from a (noisy) convolved image, non-blind deconvolution is the recovery of a true image from the point spread function it is convolved with and a (noisy) convolved image. Four blind and four non-blind deconvolution methods were assessed and compared. Experimental evaluation was performed with different noise types (i.e. shotnoise, Gaussian noise and impulse noise), noise intensities, point spread function types (i.e. out-of-focus blur, Gaussian blur, linear motion blur and non-linear motion blur), point spread function sizes, and true images. The results of the said empirical evaluation were presented in this document. Correlations were found between variables (such as method, noise type, noise intensity, etc.) and reconstruction quality as well as runtime and memory usage. The in this document presented results indicated that the use case dictates which deconvolution method is appropriate.
A hypomorphic Stip1 allele reveals the requirement for chaperone networks in mouse development and aging

The chaperone machinery is well conserved from yeast to mammals, however our knowledge of their impact on mammalian physiology is lagging. Stress-inducible phosphoprotein-1 (STI1; STIP1; Hop) is a co-chaperone that simultaneously interacts with Hsp70 and Hsp90 via three tetratricopeptide repeat (TPR) domains, of which TPR1 and TPR2B may be redundant in yeast. In-depth analysis of human datasets indicated that STI1 belongs to a set of co-chaperones that is essential in humans and that the TPR1 domain is evolutionarily conserved, suggesting that in mammals it may be required for optimal STI1 activity in vivo. We generated mice with a hypomorphic Stip1 allele lacking the TPR1 domain. While these mice are viable, they presented decreased levels of Hsp90 client proteins and co-chaperones, suggesting profound dysregulation of chaperone networks. We used this hypomorphic STI1 mutant mouse line to investigate the requirement of STI1-mediated regulation of chaperone networks in mouse physiology. Embryonic cell pluripotency was severely affected by decreased STI1 activity, contributing to the abnormal development in these mice. Moreover, adult TPR1-deprived STI1 mice presented age-related hippocampal neurodegeneration, resulting in compromised memory recall. Our findings reveal a requirement for optimal regulation of chaperone networks and their clients during development and strict dependence on full STI1 activity for healthy neuronal aging. These experiments demonstrate the unique experimental power of using hypomorphic alleles to reveal how chaperone networks regulate mammalian physiology.
Efficient simulation of filament elastohydrodynamics in three dimensions

Fluid-structure simulations of slender inextensible filaments in a viscous fluid are often plagued by numerical stiffness. Recent coarse-graining studies have reduced the computational requirements of simulating such systems, though have thus far been limited to the motion of planar filaments. In this work we extend such frameworks to filament motion in three dimensions, identifying and circumventing coordinate-system singularities introduced by filament parameterisation via repeated changes of basis. The resulting methodology enables efficient and rapid study of the motion of flexible filaments in three dimensions, and is readily extensible to a wide range of problems, including filament motion in confined geometries, large-scale active matter simulations, and the motility of mammalian spermatozoa.
VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading.

Financial trading is at the forefront of time-series analysis, and has grown hand-in-hand with it. The advent of electronic trading has allowed complex machine learning solutions to enter the field of financial trading. Financial markets have both long term and short term signals and thus a good predictive model in financial trading should be able to incorporate them together. One of the most sought after forms of electronic trading is high-frequency trading (HFT), typically known for microsecond sensitive changes, which results in a tremendous amount of data. LSTMs are one of the most capable variants of the RNN family that can handle long-term dependencies, but even they are not equipped to handle such long sequences of the order of thousands of data points like in HFT. We propose very-long short term memory networks, or VLSTMs, to deal with such extreme length sequences. We explore the importance of VLSTMs in the context of HFT. We compare our model on publicly available dataset and got a 3.14\% increase in F1-score over the existing state-of-the-art time-series forecasting models. We also show that our model has great parallelization potential, which is essential for practical purposes when trading on such markets.
Oxidation von Glucose und ethoxylierten Alkoholen an geträgerten Edelmetallkatalysatoren

Im Rahmen dieser Arbeit wurden Untersuchungen zur selektiven Oxidation von Glucose und ethoxylierten Alkoholen mit Sauerstoff zu den korrespondierenden Carbonsauren an Gold- und Gold-Platin-Bimetallkatalysatoren durchgefuhrt. Es konnte erstmals gezeigt werden, dass Goldkatalysatoren hinsichtlich ihrer Langzeitstabilitat, Selektivitat und Aktivitat dem technischen Einsatz in der Glucoseoxidation gewachsen sind. Um ihre technische Anwendbarkeit zu zeigen, wurden Glucoseoxidationen innerhalb eines sehr breiten Glucosekonzentrationsbereiches (1 bis 57,5 Prozent) und Sauerstoffpartialdruckbereiches (1,5 bis 9 bar) durchgefuhrt. Auserdem wurde die Langzeitstabilitat der Goldkatalysatoren in der Glucoseoxidation unter kontinuierlichen Bedingungen untersucht. Dazu wurde ein kontinuierlicher Ruhrkesselreaktor geplant und aufgebaut, der es ermoglicht, die pulverformigen Goldkatalysatoren im Drei-Phasensystem kontinuierlich zu verwenden. Zur Abtrennung der pulverformigen Katalysatoren wurde erstmals ein Ultraschallseparator eingesetzt. Sowohl IW- als auch DP Urea-Goldkatalysatoren auf Aluminiumoxid wurden unter verschiedenen Reaktionsbedingungen untersucht. Beide Katalysatoren zeigten eine hervorragende Langzeitstabilitat von 70 bzw. 110 Tagen ohne Verlust von Aktivitat und Selektivitat. Ebenfalls konnte in dieser Arbeit erstmals das Potential von Goldkatalysatoren und Gold-Platin-Bimetallkatalysatoren in der Oxidation der primaren Alkoholfunktion von Polyethylenglycolen (PEGs) und Fettalkoholethoxylaten (Genapole) gezeigt werden. Die Selektivitat der Katalysatoren erwies sich als hervorragend. Bei den PEGs konnten Eduktkonzentrationen bis 50 Prozent und bei den Genapolen bis 20 Prozent vollstandig und selektiv mit guter Aktivitat zu den korrespondierenden Carbonsauren oxidiert werden. Gold- und Gold-Platin-Bimetallkatalysatoren stellen eine vielversprechende Option fur die Oxidation von ethoxylierten Alkoholen dar.
nPrint: Standard Packet-level Network Traffic Analysis

This paper presents nPrint, a standard, packet-based representation of network traffic. nPrint enables machine learning on network traffic without manual feature engineering. We combine nPrint with automated machine learning (AutoML) to demonstrate that nPrint can generate a standard traffic representation across a variety of machine learning tasks and models. We present the design and implementation of nPrint, describe how we integrate it with AutoML, and apply the pipeline to three common network traffic classification problems: operating system detection, device fingerprinting, and application identification. Our evaluation shows that models trained on nPrint achieve higher performance than the state-of-the-art tools for these tasks, without relying on manually engineered rules or features. nPrint's contribution is thus in its generality, as it lowers the barrier to applying machine learning techniques for a variety of network traffic analysis problems. We have implemented and released nPrint as open-source software. Our performance evaluation demonstrates that nPrint can be deployed many settings, from offline transformation of standard packet capture formats to online streaming deployments.
A model for multiscaling and clustering of volatility in financial indexes

We propose a stochastic model which matches some relevant stylized facts observed in time series of financial indexes, and that are not fully captured by the models most often used in this context. These stylized facts concern with the distribution of the log-returns (increments of the logarithm of the index). This distribution is not Gaussian, and its moments obey peculiar scaling relations (multiscaling). Moreover, absolute values of log-returns in disjoint time intervals are positively correlated (clustering of volatility): their correlation has slow (sub-exponential) decay for moderate time distances (up to few months), and have a faster decay for larger distances. The simplicity of the model allows sharp analytic results, statistical estimation of its few parameters, and low computational effort in simulations, allowing its concrete use in applications such as option pricing.
Identification and characterisation of novel cancer testis antigens in human cancer cells

Carcinogenesis is a multi-step process which involves genomic instability and abnormal cellular growth over long period of time which eventually develop tumour. Cancer testis antigens consistently reported in many types of cancer which suggest its oncogenic role. But, it’s functional role in cancer still unknown and need further investigation. Also, cancer testis antigens might be used as potential targets for cancer immunotherapy due to their main presence in normal testis cells and abnormally exist in several types of human cancer. Their aberrant expression in cancer makes them useful to target human malignancies by immunotherapy. TEX19 one of the novel CT gene was identified in this study. TEX19 show interesting expression pattern due to its confined in normal testis and expressed in many cancer tissues. This expression may indicate the oncogenic activity of TEX19 gene in somatic tissue. Thus, the possible approach to use this gene as target for cancer immunotherapy and cancer prognosis and diagnosis. In addition, TEX19 might play a role in stemness state, this was observed in TEX19 depleted cells, the stem cell markers genes such as OCT$, NANOG, SOX2 were affected after the knockdown of TEX19. However, Tex19 was not affected after differentiating NT-2 cancer cells using Retinoic acid and HMBA. We also studied the relation between TEX19 and transposable genetic elements by knockdown TEX19 in NT-2 and A2780 cancer cells. The qRT-PCR results show that human ERVK family gene expression was affected by TEX19 depletion. Moreover, our RNA sequencing data of TEX19 depleted sample and qRT-PCR analysis reveal the influence of TEX19 depletion on some differentially expressed genes. Interestingly, these genes were upregulated and down regulated after the depletion of TEX19 in four types of human cancer cell lines.
Investigation of Experiential Learning in Higher Education in Japan : Focusing on the Career Design of Pre-Service Teachers

This paper aims to investigate “experiential learning” from the perspective of the career design of students by examining five short case reports in order to assess those volunteers who have come into contact with children both within and outside the school environment. The findings of this study reveal that students are capable of overcoming the easy image of youth, and promote the volition that actualizes their perspective with experiential learning.
DIELECTRIC BEHAVIOR AND FUNCTIONALITY OF POLYMER MATRIX/CIGARETTE BUTTS COMPOSITES

Cellulose acetate powder which is extracted from the cigarette (insulator part as a filler) has been used with polymer to produce PVC Cement/cellulose acetate composite. The dielectric behavior of this composite is analyzed as a function of weight fractions (0.1, 0.2, 0.3, 0.4, and 0.5 wt%), temperature range (30 110) ◦C and frequency (120Hz -2MHz). Impedance and σac. conductivity of the composites behaviors as function of frequency and temperature have also studied. The results show that, the real permittivity, dielectric loss and loss tangent for all composites increase with increasing cellulose acetate filler content.
A REVIEW PAPER ON IDENTIFYING STUDENTS INTEREST IN E-LEARNING USING WEB USAGE MINING

--In the present era, World Wide Web has become a popular and unavoidable medium to extract useful and relevant information from the huge and scattered information particularly for education and e-learning. In every educational organization e-learning is becoming an integral part of teaching-learning process. Web mining is the use of data mining techniques to automatically discover and extract relevant and useful information from World Wide Web documents and services. The Web usage mining includes the data from the web server logs, proxy server logs, browser logs, user profiles, registration data, user sessions or transactions, cookies, user profiles, registration data and any other data as the results of interactions. This paper gives the overview of various methods of e-learning and aims to experiment with pattern classification for students’ behaviour and their interest in e-learning. It then identifies the problems within e-learning that web usage mining can solve as well as present the existing approaches.
Post-Thyroidectomy Complications and Risk Factors in Tabuk, Saudi Arabia: A Retrospective Cohort Study

Background Thyroid surgery is one of the most commonly performed procedures internationally. There were no studies conducted in Tabuk, Saudi Arabia, on post-thyroidectomy complications and their risk factors. Objective The aim of this study was to assess post-thyroidectomy complications and determine the risk factors of such complications. Methods This retrospective study included all cases that underwent thyroidectomy at King Salman Armed Forces Hospital, Tabuk, Saudi Arabia, from January 2012 to December 2017. Patients with preoperative hypoparathyroidism, chronic kidney disease, or history of dysphonia were excluded. Data were collected from medical records. Results The study showed 182 patients who underwent thyroidectomy operation between January 2012 and December 2017. Temporary hypocalcemia was developed in 116 patients (63.7%) while it persisted in three (1.6%). Change of voice was reported in five patients (2.7%) while two (1.1%) lost a high-pitched voice. Seroma, hematoma, and tracheal injury were documented in 1.6%, 1.1%, and 0.5%, respectively. Multivariate analysis showed that total thyroidectomy was the most significant (four times) risk factor for the development of hypocalcemia as compared to other surgical procedures. Conclusion Hypocalcemia was the most frequent post-thyroidectomy complication, whereas voice changes, seroma, hematoma, and tracheal injury are rare complications. Additionally, total thyroidectomy has the highest risk of postoperative hypocalcemia.
Lasers for Hyperfine State Detection and Cooling of Ytterbium Ions

The purpose of this REU project was to implement a system of lasers making it possible to cool trapped ytterbium ions. The cooling cycle accessed by these lasers could also be used to read out the hyperfine state of the ion— a process which could be an important operation in quantum information processing. In this paper, the theory behind using trapped ytterbium ions as ”qubits” for quantum computing is discussed, as are some methods to approach this goal, and a description of how one of the cooling lasers was set up, locked, and stabilized. Some improvements to attain a greater degree of laser frequency stability are also discussed.