Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF SUPPLEMENTARY FEEDING ON SHEEP SKIN AND COAT CHARACTERISTICS IN HALAIEB, SHALATEEN AND ABOU RAMAD TRIANGLE

ifteen adult local ewes aged 2-4 years with an average body weight of 30 kg were employed to study the effects of grazing and supplementary feeding on wool follicles activity. Animals were randomly divided into three equal groups. The first group was kept in confinement (control, group 1) and fed on clover hay supplemented with concentrate feed mixture (1% of BW), while the other two groups (2 and 3) were grazing the natural rangelands and supplemented with the same concentrate mixture (1 or 2% of BW, respectively). Animals raised in Hederbah valley, Shalateen Research Station from July 2013 to April 2014. At the end of the experiment, skin samples were taken and used to examine the follicle activity and their fibres in response to grazing and supplementary feeding. Results revealed that there is a significant increase in various primary follicle dimensions and their fibres of animals having the supplementary feeding with the double amount of concentrate except in the wall thickness which decreased significantly in all treated animals in comparison with the control group. The increase in the internal diameter of the follicles accompanied by a decrease in the follicles wall thickness was in relation to the increased values of the fiber diameter. Medulla thickness also showed an increased value by the double supplementary feeding. Grazing process showed a significant decrease in the external diameter and wall thickness of the primary follicles and in all secondary follicle dimensions in animals having 1% from body weight concentrate mixture in comparison with the control group. On the other hand, supplementary feeding level by 2% from body weight concentrate mixture showed a slight increase in all secondary follicle dimensions and their fiber diameters in comparison with the other grazed group. Histochemically, there is an excess protein and carbohydrate content in the outer root sheath of both primary and secondary wool follicles than that in the inner root sheath. Administration of double amount of concentrate resulted in a significant increase in the protein content in both sheaths of the primary follicles. An intermediate content was observed in the grazed group having concentrate 1% from body weight. The general carbohydrates showed a reversible trend and the intermediate value was detected in the control group. In secondary follicles, free grazing revealed a non significant decrease in the general protein contents in both follicle sheaths while the general carbohydrates showed an increase in grazed animal than the control group and in the supplemented group than the other two groups. Generally, the supplementary feeding increased the activity of the primary and secondary wool follicles and consequently the wool production and reduced the effect of grazing process.
LAPPEENRANTA UNIVERSITY OF TECHNOLOGY School of Business Finance

Author: Pakarinen, Jarmo Title: The Fed Model: International Analysis Faculty: LUT, School of Business Major: Finance Year: 2010 Master’s Thesis: Lappeenranta University of Technology 58 pages, 3 figures, 18 tables Examiners: Professor Minna Martikainen Professor Eero Pätäri
RISK FACTORS FOR FEMALE INFERTILITY AT A TERTIARY HEALTH FACILITY IN AKURE, SOUTH-WEST NIGERIA

Background. The inability of couples to achieve pregnancy is a major cause of psycho-social problems in family relationship that could lead to marital disharmony. Objective. The aim of this study was to find out the possible risk factors for female infertility. Methods. A case-control design and a sample size of 400 (200 cases of infertility and 200 controls) were used in the study. Cases and controls were selected at random at the infertility and family planning clinic of the University of Medical Sciences Teaching Hospital Complex, Akure and were subjected to a predesigned interviewer administered questionnaire to collect the data. The cases were classified into primary and secondary infertility; binary and stepwise logistic regressions were used to generate the Odds ratio and 95% confidence interval of the possible risk factors and the level of significance was set at P<0.05. Results. The mean age of the women with infertility was 28.5±5.43 years and the mean age of those in the control group was 29.1±5.62 years. Among the cases, 155 (77.5%) had secondary infertility, while 45 (22.5%) had primary infertility. Significant risk factors for female infertility included presence of fibroids, having had fibroid operation, multiple sexual partners, previous abortion, polycystic ovary syndrome (PCOS), sexually transmitted infection (STI) and post abortion sepsis. Conclusion. The study showed that secondary infertility is still the most prevalent and the risk factors were multi factorial. Efforts should be intensified to reduce infertility due to preventable causes.
Function of oncological endoprosthesis in recurrent osteosarcoma of distal femur

Endoprothesis is the commonly used method of segmental reconstruction after bone tumor resection around knee. To achieve stability after extensive removal of knee structures, hinged endoprostheses are usually being preferred as a limb salvage procedure. Despite advances in diagnostics and preoperative tumor chemotherapy and radiotherapy, as well as meticulous intraoperative preparation, local tumor recurrence is still possible. We analyzed 22 patients with osteosarcoma in distal femur who underwent the knee arthroplasty with modular replacement system between 2002 and 2007. Three of them (two females aged 33 and 44 and one male aged 29) developed local recurrence 7, 9 and 12 months after operation. All of them were offered the amputation or metastatectomy, as well as further chemotherapy, and all refused. Reasons for refusal included disseminated disease, uncertain prognosis of amputation treatment and still good functional outcome despite existing tumor around the prosthesis. Two of the above mentioned patients deceased from disseminated disease 7 and 11 months later, one patient is still being followed 10 months long. Clinical examinations have shown the effective and satisfactory prosthesis functions in reported patients during the whole observation period. We have concluded that limb salvage surgery with implantation of hinged knee endoprosthesis in tumors in distal femur provide satisfactory functional outcome even when local tumor recurrence is found.
Clinical outcomes and complications following primary total elbow arthroplasty using the Latitude prosthesis

Background The Latitude total elbow arthroplasty (TEA) is an implant with limited published data on its performance and outcomes. The aim of this study was to report the short-term outcomes of the Latitude TEA as well as to describe the radiographic outcomes and complications. Methods The Latitude was implanted in 20 patients (23 elbows) in a linked configuration. Patients were recalled to clinic for the assessment of their range-of-motion and compared to preoperative values. Administration of functional outcome measures was also performed. Results Mean follow-up was 4.7 years (range, 1 to 7.5 years) with four elbows requiring revision. The flexion–extension arc improved from 86.6 to 101.3 (range, 76 to 126) postoperatively (p = 0.04). The average Disabilities of the Arm, Shoulder, and Hand score was 28.1 (range, 5.8 to 50.4) and the average Mayo Elbow Performance Score was 89.6 (range, 76 to 100), with 83% of elbows scoring in the good or excellent range. Radiolucencies were detected in 60% of patients and 31% of these lucencies progressed in size at the time of follow-up. Conclusions The Latitude prosthesis provides patients with favorable clinical outcomes with improvements in their range-of-motion and a complication rate comparable to other elbow arthroplasty implants.
1 Corporate Financial Risk , Macroeconomic Risk , and Tax Avoidance Behavior

Since global financial crisis of 2007-2008, national governments gradually encounter the critical issue on credit crisis because governments had to implement a series of policies to prevent financial institutions from repeating the mistake responsible for the crisis. However, we argue that firms with a higher level of financial risk would likely to encourage them to engage tax avoidance due to more difficulties in obtaining funds. Therefore, this paper empirically investigates the relationship between corporate financial risk and tax avoidance by employing firm-specific financial risk and macroeconomic constraints measures. We use US firm’s data over the period of 2005– 2016 and then apply regression analysis, robustness test, and sensitivity test to examine our research hypotheses. Our empirical evidences indicate that firms with higher level of firm-specific financial risk would exhibit decreasing tax avoidance, while a market with higher levels of macroeconomic constraints lead firms to engage in tax avoidance.
Determination of shoreline change along the Black Sea coast of Istanbul using remote sensing and GIS technology

This study aims to determine the shoreline changes along the Black Sea Coast of Istanbul, which has been occurred in 7 years from 2009 to 2016. For this purpose, remote sensing technology used to detect the shorelines in the study period and geographic information system was established for determining the general and specific changes along the shoreline. In this context, geometric and atmospheric corrections were applied to the data as the preprocessing of Landsat-7 Enhanced Thematic Mapper Plus and Landsat-8 Operational Land Imager images. Then after, the Black Sea Shoreline data of Istanbul was extracted by applying supervised classification using maximum likelihood classification algorithm and normalized difference water index to Landsat images. As the following stage of the study, the changes along Black Sea Shoreline were determined using the digital shoreline analysis system software based on the extracted shoreline data. As a result of the study maximum negative and positive distances were determined similarly for the outputs of both applied methodologies as approximately –438 and 466 m. The study concluded that the most significant change along the shoreline was observed at the European side of the city, at a location close to the construction area of Istanbul Airport.
Multiscale Sparsifying Transform Learning for Image Denoising

The data-driven sparse methods such as synthesis dictionary learning and sparsifying transform learning have been proven to be effective in image denoising. However, these methods are intrinsically single-scale, which ignores the multiscale nature of images. This often leads to suboptimal results. In this paper, we propose several strategies to exploit multiscale information in image denoising through the sparsifying transform learning denoising (TLD) method. To this end, we first employ a simple method of denoising each wavelet subband independently via TLD. Then, we show that this method can be greatly enhanced using wavelet subbands mixing, which is a cheap fusion technique, to combine the results of single-scale and multiscale methods. Finally, we remove the need for denoising detail subbands. This simplification leads to an efficient multiscale denoising method with competitive performance to its baseline. The effectiveness of the proposed methods are experimentally shown over two datasets: 1) classic test images corrupted with Gaussian noise, and 2) fluorescence microscopy images corrupted with real Poisson-Gaussian noise. The proposed multiscale methods improve over the single-scale baseline method by an average of about 0.2 dB (in terms of PSNR) for removing synthetic Gaussian noise form classic test images and real Poisson-Gaussian noise from microscopy images, respectively. Interestingly, the proposed multiscale methods keep their superiority over the baseline even when noise is relatively weak. More importantly, we show that the proposed methods lead to visually pleasing results, in which edges and textures are better recovered. Extensive experiments over these two different datasets show that the proposed methods offer a good trade-off between performance and complexity.
Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval

We propose an efficient pipeline for large-scale landmark image retrieval that addresses the diversity of the dataset through two-stage discriminative re-ranking. Our approach is based on embedding the images in a feature-space using a convolutional neural network trained with a cosine softmax loss. Due to the variance of the images, which include extreme viewpoint changes such as having to retrieve images of the exterior of a landmark from images of the interior, this is very challenging for approaches based exclusively on visual similarity. Our proposed re-ranking approach improves the results in two steps: in the sort-step, k-nearest neighbor search with soft-voting to sort the retrieved results based on their label similarity to the query images, and in the insert-step, we add additional samples from the dataset that were not retrieved by image-similarity. This approach allows overcoming the low visual diversity in retrieved images. In-depth experimental results show that the proposed approach significantly outperforms existing approaches on the challenging Google Landmarks Datasets. Using our methods, we achieved 1st place in the Google Landmark Retrieval 2019 challenge on Kaggle. Our code is publicly available here: https://github.com/lyakaap/Landmark2019-1st-and-3rd-Place-Solution
Commentaries on "Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception" [Science Robotics Vol 4 Issue 30 (2019) 1-10

This correspondence comments on the findings reported in a recent Science Robotics article by Mitrokhin et al. [1]. The main goal of this commentary is to expand on some of the issues touched on in that article. Our experience is that hyperdimensional computing is very different from other approaches to computation and that it can take considerable exposure to its concepts before attaining practically useful understanding. Therefore, in order to provide an overview of the area to the first time reader of [1], the commentary includes a brief historic overview as well as connects the findings of the article to a larger body of literature existing in the area.
Susceptible-Infected-Recovered (SIR) Dynamics of COVID-19 and Economic Impact

I estimate the Susceptible-Infected-Recovered (SIR) epidemic model for Coronavirus Disease 2019 (COVID-19). The transmission rate is heterogeneous across countries and far exceeds the recovery rate, which enables a fast spread. In the benchmark model, 28% of the population may be simultaneously infected at the peak, potentially overwhelming the healthcare system. The peak reduces to 6.2% under the optimal mitigation policy that controls the timing and intensity of social distancing. A stylized asset pricing model suggests that the stock price temporarily decreases by 50% in the benchmark case but shows a W-shaped, moderate but longer bear market under the optimal policy.
Carnegie Supernova Project-II: A New Method to Photometrically Identify Sub-types of Extreme Type Ia Supernovae

We present a new method to photometrically delineate between various sub-types of Type Ia supernovae (SNe Ia). Using the color-stretch parameters, sBV or sgr, and the time of i-band primary maximum relative to the B-band ( ) or g-band ( ) maximum it is demonstrated that 2003fg-like, 1991bg-like, and 2002cx-like SNe Ia can be identified readily. In the cases of these extreme SNe Ia, their primary i-band maximum occurs after the time of the B- or g-band maxima. We suggest that the timing of the i-band maximum can reveal the physical state of the SN Ia explosion as it traces: (i) the speed of the recombination front of iron group elements in the ejecta, (ii) the temperature evolution and rate of adiabatic cooling in the ejecta and, (iii) the presence of interaction with a stellar envelope. This photometric sub-typing can be used in conjunction with other SNe analysis, such as the Branch diagram, to examine the physics and diversity of SNe Ia. The results here can also be used to screen out non-Ia SNe from cosmological samples that do not have complete spectroscopic typing. Finally, as future surveys like that of the Vera C. Rubin Observatory (previously referred to as the Large Synoptic Survey Telescope) create large databases of light curves of many objects this photometric identification can be used to readily identify and study the rates and bulk properties of peculiar SNe Ia.
Data Parallelism in Training Sparse Neural Networks

Network pruning is an effective methodology to compress large neural networks, and sparse neural networks obtained by pruning can benefit from their reduced memory and computational costs at use. Notably, recent advances have found that it is possible to find a trainable sparse neural network even at random initialization prior to training; hence the obtained sparse network only needs to be trained. While this approach of pruning at initialization turned out to be highly effective, little has been studied about the training aspects of these sparse neural networks. In this work, we focus on measuring the effects of data parallelism on training sparse neural networks. As a result, we find that the data parallelism in training sparse neural networks is no worse than that in training densely parameterized neural networks, despite the general difficulty of training sparse neural networks. When training sparse networks using SGD with momentum, the breakdown of the perfect scaling regime occurs even much later than the dense at large batch sizes.
PoisHygiene: Detecting and Mitigating Poisoning Attacks in Neural Networks

The black-box nature of deep neural networks (DNNs) facilitates attackers to manipulate the behavior of DNN through data poisoning. Being able to detect and mitigate poisoning attacks, typically categorized into backdoor and adversarial poisoning (AP), is critical in enabling safe adoption of DNNs in many application domains. Although recent works demonstrate encouraging results on detection of certain backdoor attacks, they exhibit inherent limitations which may significantly constrain the applicability. Indeed, no technique exists for detecting AP attacks, which represents a harder challenge given that such attacks exhibit no common and explicit rules while backdoor attacks do (i.e., embedding backdoor triggers into poisoned data). We believe the key to detect and mitigate AP attacks is the capability of observing and leveraging essential poisoning-induced properties within an infected DNN model. In this paper, we present PoisHygiene, the first effective and robust detection and mitigation framework against AP attacks. PoisHygiene is fundamentally motivated by Dr. Ernest Rutherford's story (i.e., the 1908 Nobel Prize winner), on observing the structure of atom through random electron sampling.
Born-Again Tree Ensembles

The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives. As a consequence, research into interpretable machine learning has rapidly grown in an attempt to better control and fix possible sources of mistakes and biases. Tree ensembles offer a good prediction quality in various domains, but the concurrent use of multiple trees reduces the interpretability of the ensemble. Against this background, we study born-again tree ensembles, i.e., the process of constructing a single decision tree of minimum size that reproduces the exact same behavior as a given tree ensemble in its entire feature space. To find such a tree, we develop a dynamic-programming based algorithm that exploits sophisticated pruning and bounding rules to reduce the number of recursive calls. This algorithm generates optimal born-again trees for many datasets of practical interest, leading to classifiers which are typically simpler and more interpretable without any other form of compromise.
Chemical evolution of ultra-faint dwarf galaxies in the self-consistently calculated integrated galactic IMF theory

The galaxy-wide stellar initial mass function (gwIMF) of a galaxy in dependence on its metallicity and star formation rate can be calculated by the integrated galactic IMF (IGIMF) theory. This theory has been applied in a study of the chemical evolution of the ultra-faint dwarf (UFD) satellite galaxies, but failed to reproduce the data. Here, we find that the IGIMF theory is naturally consistent with the data. We applied the time-evolving gwIMF, which was calculated at each time step. The number of type Ia supernova explosions that forms per unit stellar mass was renormalised according to the gwIMF. The chemical evolution of Boötes I, one of the best-observed UFD, was calculated. Our calculation suggests a mildly bottom-light and top-light gwIMF for Boötes I, and that this UFD has the same gas-consumption timescale as other dwarfs, but was quenched about 0.1 Gyr after formation. This is consistent with independent estimations, and it is similar to Dragonfly 44. The recovered best-fitting input parameters in this work are not covered in previous work, creating a discrepancy between our conclusions. In addition, a detailed discussion of the uncertainties is presented to address the dependence of the chemical evolution model results on the applied assumptions. This study demonstrates the power of the IGIMF theory in understanding star formation in extreme environments and shows that UDFs are a promising pathway to constrain the variation of the low-mass stellar IMF.
A parsec-scale radio jet launched by the central intermediate-mass black hole in the dwarf galaxy SDSS J090613.77+561015.2

The population of intermediate-mass black holes (IMBHs) in nearby dwarf galaxies plays an important ‘ground truth’ role in exploring black hole formation and growth in the early Universe. In the dwarf elliptical galaxy SDSS J090613.77+561015.2 (z = 0.0465), an accreting IMBH has been revealed by optical and X-ray observations. Aiming to search for possible radio core and jet associated with the IMBH, we carried out very long baseline interferometry (VLBI) observations with the European VLBI Network at 1.66 GHz. Our imaging results show that there are two 1-mJy components with a separation of about 52 mas (projected distance 47 pc) and the more compact component is located within the 1σ error circle of the optical centroid from available Gaia astrometry. Based on their positions, elongated structures and relatively high brightness temperatures, as well as the absence of star-forming activity in the host galaxy, we argue that the radio morphology originates from the jet activity powered by the central IMBH. The existence of the large-scale jet implies that violent jet activity might occur in the early epochs of black hole growth and thus help to regulate the co-evolution of black holes and galaxies.
The number of closed ideals in $L(L_p)$

We show that there are $2^{2^{\aleph_0}}$ different closed ideals in the Banach algebra $L(L_p(0,1))$, $1<p\not= 2<\infty$. This solves a problem in A. Pietsch's 1978 book "Operator Ideals". The proof is quite different from other methods of producing closed ideals in the space of bounded operators on a Banach space; in particular, the ideals are not contained in the strictly singular operators and yet do not contain projections onto subspaces that are non Hilbertian. We give a criterion for a space with an unconditional basis to have $2^{2^{\aleph_0}}$ closed ideals in term of the existence of a single operator on the space with some special asymptotic properties. We then show that for $1<q<2$ the space ${\frak X}_q$ of Rosenthal, which is isomorphic to a complemented subspace of $L_q(0,1)$, admits such an operator.