Miracle-dz/UniFAR-FTUs huggingface dataset:
HIERARCHICAL PROBABILISTIC INFERENCE OF COSMIC SHEAR

Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxy properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the global shear inference, thereby rendering our algorithm computationally tractable for large surveys. With simple numerical examples we demonstrate the improvements in accuracy from our importance sampling approach, as well as the significance of the conditional distribution specification for the intrinsic galaxy properties when the data are generated from an unknown number of distinct galaxy populations with different morphological characteristics.
Determinants of Technology Adoption and Intensity of Adoption among Rice Farming Households in Ogun State, Nigeria.

Purpose: Production and productivity of the agricultural sector in SSA is low due to low technological adoption and techniques among others. One of the major goals of Nigerian agriculture development programs and policies is transition from low productivity subsistence agriculture to a high productivity agro-industrial economy through improved technology adoption. Methodology: A multi-stage sampling technique was used in this study to select 158 rice farming households The first stage involved the purposive selection of two Agricultural Development programme zones (Ikenne and Abeokuta zones) in the state. The second stage was purposive selection of two blocks per zone based on the concentration of rice farmers. Six farming cells were randomly selected from each block making a total of twenty-four (24) farming community, seven rice farmers were randomly selected from each farming cell giving a sample size of 168 rice farmers. A set of structured questionnaires were used to collect data. Out of the 168-questionnaire administered 158 of it were gotten and used for the study. The data were analysed using descriptive statistics, adoption index, tobit regression model. Findings: Technologies adoption was high among the young farmers than the older farmers. The household size was also a determinant factor of technology adoption. As the household increases in size, there is high probability that all the production technologies will be adopted. Access to credit facility was found to be positive and significant in all the production technologies available to the rice farmers. Recommendations: It was recommended that the young people should be encouraged to modern technologies available for rice production, the farmers should be educated on modern technologies in the production of rice. Credit facilities should be made available for the farm household.
Deep transfer learning for connection defect identification in prefabricated structures

Defect and damage identification is a crucial task in structural health monitoring (SHM) systems. Recent advances in deep neural networks (DNNs) show success in identification from data for a wide range of SHM systems. However, this approach faces challenges in terms of robustness and scalability with respect to data scarcity. Data collection for the training of DNNs from both the field and laboratory experiments is costly. To address this issue, we employ transfer learning (TL) through the use of deep convolutional neural networks (CNNs) for defect identification in the context of a sensor network’s vibration data. A deep TL (DTL) paradigm is used herein so that a pretrained CNN, primarily trained for generalized defect identification tasks where sufficient training data exist (source domain), can be re-trained partially (fine-tuned) as a later secondary process that targets this application domain (target domain) specifically. Different DTL cases are compared, and training data are enhanced with numerical simulation data. The efficacy and robustness of this method are demonstrated on defect identification for full-scale prefabricated concrete shear wall structures with different levels of data scarcity. This method utilizes dynamic responses collected using a sensor network. This is an extension of deep learning vision for non-vision tasks. Defect features are extracted from the dataset of dynamic responses using this DTL frame. Experimental results show that this approach can improve identification models on datasets with few samples.
Neural Network and Back Propagation

Humans and computers are suited to different types of tasks. Like, finding the sine of any number is difficult for humans, but easy for the computers. On the other hand, looking and identifying the image of a bird is very easy for us, as compared to the machines [1]. But nowadays, Artificial neural networks or ANNs are most commonly used in image processing. In fact, the recent deep learning algorithms have surpassed the human ability of image recognition. It is proved and stated that BPNN (Backpropagation Neural Networks) have a very high accuracy. A large part of recent success of neural network is explained on the basis of very high data availability. Thus, in B-P the network is trained with a lot of training examples so that it doesn’t compromise with the efficiency of the system. By the end of this century, it is expected that computers will be training neural networks with as many neurons as the human brain has [1][2]. Although it is difficult to predict what the true abilities of artificial intelligence will be by then, our experience with computer vision should prepare us to expect something unexpected. So, with this attempt paper tries to give the idea of how image is recognized and how back propagation works.
Quantum Strategies and Local Operations

This thesis is divided into two parts. In Part I we introduce a new formalism for quantum strategies, which specify the actions of one party in any multi-party interaction involving the exchange of multiple quantum messages among the parties. This formalism associates with each strategy a single positive semidefinite operator acting only upon the tensor product of the input and output message spaces for the strategy. We establish three fundamental properties of this new representation for quantum strategies and we list several applications, including a quantum version of von Neumann's celebrated 1928 Min-Max Theorem for zero-sum games and an efficient algorithm for computing the value of such a game. In Part II we establish several properties of a class of quantum operations that can be implemented locally with shared quantum entanglement or classical randomness. In particular, we establish the existence of a ball of local operations with shared randomness lying within the space spanned by the no-signaling operations and centred at the completely noisy channel. The existence of this ball is employed to prove that the weak membership problem for local operations with shared entanglement is strongly NP-hard. We also provide characterizations of local operations in terms of linear functionals that are positive and "completely" positive on a certain cone of Hermitian operators, under a natural notion of complete positivity appropriate to that cone. We end the thesis with a discussion of the properties of no-signaling quantum operations.
Lake Ice Will Be Less Safe for Recreation and Transportation Under Future Warming

Millions of lakes from around the world freeze during winter. These frozen surfaces provide essential ecosystem services that are vital to many northern communities. However, the availability of safe lake ice that is oftentimes required to support these services is under threat from climate change. Here we use a 100‐member ensemble of climate model simulations to investigate changes in the presence of safe lake ice for different recreation and provisioning activities across the Northern Hemisphere. Our projections suggest that the duration of safe ice for recreational purposes will shorten, on average, by 13, 17, and 24 days within a 1.5°C, 2°C, and 3°C warmer world (relative to 1900–1929), respectively. The projected change in the duration of safe ice will be greater in higher latitudes, but with the most densely populated lower‐latitude regions experiencing the greatest percent change. The use of lake ice to support critical transportation infrastructure will also be influenced by future warming through the loss of thicker ice this century. However, our projections suggest that the magnitude of change in the duration of safe ice will differ depending on the ice thickness requirements. For transportation that requires the thickest ice cover, the number of days with safe ice will decline by 90%, 95%, and 99% with 1.5°C, 2°C, and 3°C global warming, respectively. We highlight the need for the development and implementation of adaptation plans to address the imminent loss of critical wintertime transportation infrastructure across the Northern Hemisphere.
Balance dynamics and gravity waves in four-dimensional data assimilation

Balance Dynamics and Gravity Waves in Four-Dimensional Data Assimilation Lisa Neef Doctor of Philosophy Graduate Department of Physics University of Toronto 2007 This thesis examines the application of three methods of so-called four-dimensional data assimilation to dynamical models where there exists a timescale separation between vortical motion and (relatively fast) inertia-gravity waves. Using a highly simplified dynamical model which admits one nonlinear vortical mode and one inertia-gravity wave, we evaluate the relative strengths and weaknesses of linearization-based and ensemble-based sequential assimilation (i.e. two varieties of the Kalman filter), and four-dimensional variational assimilation (4DVAR). The first part of this study is concerned with balanced flow, or flow where vortical motion dominates and inertial/gravitational motion is “slaved” to the dominant flow. The goal of assimilation in this context is to recover the true balanced state, without the excitation of spurious inertia-gravity waves. It is shown that the excitation of spurious waves becomes more difficult to control as the nonlinearity of the assimilation system is increased, for example by decreasing observation frequency. If not enough components of the true state are observed or observations are infrequent relative to the nonlinearity of the model, the explicit evolution of error covariances using a tangent-linear model can easily become quite inaccurate, which results in a highly unstable assimilation cycle wherein spurious waves are excited and not controlled. Both ensemble-based and implicit variational covariance models offer improvements, but these are themselves limited by error due to sampling and non-Gaussianity of the ensemble and by the tendency to settle
Higienização das mãos em unidades de terapia intensiva neonatal, pediátrica e adulto

Objetivo: Avaliar a efetividade de um programa educativo multimodal de higienização das mãos para profissionais da saúde de UTIs Neonatal, Pediátrica e Adulto. Método: Trata-se de uma pesquisa quasi-experimental, do tipo série temporal interrompida com um grupo e, portanto, esse foi avaliado antes e após a intervenção. A coleta de dados foi realizada em três períodos: (1) período pré-intervenção, realizada em 2017, observada a adesão à higienização das mãos pelos profissionais de saúde e identificado o consumo de álcool gel; (2) período de intervenção, realizada durante o ano de 2018, no qual foi executado o programa educativo multimodal; (3) período pós-intervenção, no qual foi realizada nova investigação utilizando a mesma metodologia empregada no primeiro período desta pesquisa. Resultados: Foram observadas 640 oportunidades e 327 ações, resultando assim em uma adesão global de 51,09%. O consumo de álcool em gel em 2018 obteve uma média de 27,01 mL/paciente-dia dentro das unidades e no ano de 2017 foi de 43,13 mL/paciente-dia no mesmo período. Conclusão: Apesar do período de intervenção ter ajudado os profissionais a esclarecerem dúvidas, esse ainda se mostrou não ser suficiente para manter os índices encontrados no período pré-intervenção. Pode-se sugerir que medidas educativas que promovam o aumento da adesão à higienização das mãos sejam rotineiramente realizadas.
Phonetic stability across time: Linguistic enclaves in Switzerland

The present paper shows results of a proof-of-concept study on two historically related but geographically separated dialects of Swiss German. Between the 12th and 14th centuries, speakers of Valais German (southwestern Switzerland) emigrated to the southeastern part of the country, forming so-called Sprachinseln (enclaves) of Valais German in a Romansh-speaking area. Using an online survey, we collected responses from 300+ participants from the cantons of Valais and Grisons to examine how the two dialects have developed over time. Results suggest stability on the phonetic level, i. e. the two dialects still sound very similar despite having been geographically separated for 800 years. The morphosyntax and lexicon, however, exhibit substantial change. We discuss historic and sociodemographic factors that may explain these patterns.
Why are the BL Lac objects AO 0235+164 and PKS 0537−441 not multiply imaged by gravitational lensing?

Des galaxies ont ete recemment decouvertes en avant-plan d'objets BL Lac. L'absence d'images multiples de ces objets BL Lac par l'effet de mirage gravitationnel permet d'obtenir des contraintes sur les parametres des galaxies situees en avant-plan. Sous l'hypothese ou la distribution de masse de la galaxie peut etre decrite comme une sphere isotherme non singuliere, l'analyse fournit une limite inferieure sur le rayon du noyau de la galaxie. Les resultats sont ensuite appliques aux objets BL Lac AO 0235+164 et PKS 0537-441
A theoretical study on wave function dependency of positronium impact collision cross sections

Triple Differential cross sections (TDCS) and double differential cross sections (DDCS) for single ionization of Sodium by Positronium impact are calculated using a frozen core approach within the frame work of Coulomb Distroted Eikonal Approximation (CDEA). TDCS and DDCS are presented within energy range 25 eV to 5000 eV for two different trial wave functions. In general, the DDCS evaluated from the two wave functions are found to differ by about ten to thousand times of magnitude. Comparison of the DDCS for two different wave functions reveals one possible source of the magnitude difference, i.e., the active electron’s change of state due to the change in the orbital configuration.
Impact of Antibiotics on the Lung Microbiome and Lung Function in Children With Cystic Fibrosis 1 Year After Hospitalization for an Initial Pulmonary Exacerbation

Abstract Background Cystic fibrosis (CF) is characterized by recurrent pulmonary exacerbations (PEx) and lung function decline. PEx are frequently treated with antibiotics. However, little is known about the effects of antibiotics on the airway microbiome of persons with CF over time. The purpose of this study was to evaluate changes in the microbiome and lung function in persons with CF over 1 year following an initial study pulmonary exacerbation (iPEx). Methods Twenty children aged ≤18 years with CF were enrolled in the study, which occurred prior to the routine administration of highly effective modulator therapy. Respiratory samples and spirometry were obtained at a minimum of quarterly visits and up to 1 year after an iPEx. Metagenomic sequencing was performed, and bacterial taxa were assigned using MetaPhlAn 2.0. Paired t test, analysis of variance, and generalized least squares regression were used to compare outcome variables. Results The mean age of study participants at the time of the iPEx was 10.6 years. There were 3 ± 1.6 PEx treated with antibiotics per person during the study period. Bacterial richness was similar at 1 year compared to iPEx (40.3 vs 39.3, P = .852), whereas the mean Shannon diversity index was significantly higher at 1 year (2.84 vs 1.62, P < .001). The number of PEx treated with antibiotics was not associated with changes in microbial diversity but was associated with changes in lung function. Conclusions In our 1-year prospective study, we found that microbial diversity increased despite decreases in lung function associated with repeated PEx events requiring antibiotic therapy.
Co-Transcriptomic Analysis of the Maize–Western Corn Rootworm Interaction

The Western corn rootworm (WCR; Diabrotica virgifera virgifera) is an economically important belowground pest of maize. Belowground feeding by WCR is damaging because it weakens the roots system, diminishes nutrient uptake, and creates entry points for fungal and bacterial pathogens and increases lodging, all of which can significantly suppress maize yields. Previously, it was demonstrated that belowground herbivory can trigger plant defense responses in the roots and the shoots, thereby impacting intraplant communication. Although several aspects of maize-WCR interactions have been reported, co-transcriptomic remodeling in the plant and insect are yet to be explored. We used a maize genotype, Mp708, that is resistant to a large guild of herbivore pests to study the underlying plant defense signaling network between below and aboveground tissues. We also evaluated WCR compensatory transcriptome responses. Using RNA-seq, we profiled the transcriptome of roots and leaves that interacted with WCR infestation up to 5 days post infestation (dpi). Our results suggest that Mp708 shoots and roots had elevated constitutive and WCR-feeding induced expression of genes related to jasmonic acid and ethylene pathways, respectively, before and after WCR feeding for 1 and 5 days. Similarly, extended feeding by WCR for 5 days in Mp708 roots suppressed many genes involved in the benzoxazinoid pathway, which is a major group of indole-derived secondary metabolites that provides resistance to several insect pests in maize. Furthermore, extended feeding by WCR on Mp708 roots revealed several genes that were downregulated in WCR, which include genes related to proteolysis, neuropeptide signaling pathway, defense response, drug catabolic process, and hormone metabolic process. These findings indicate a dynamic transcriptomic dialog between WCR and WCR-infested maize plants.
Applications of Markov Decision Process Model and Deep Learning in Quantitative Portfolio Management during the COVID-19 Pandemic

Whether for institutional investors or individual investors, there is an urgent need to explore autonomous models that can adapt to the non-stationary, low-signal-to-noise markets. This research aims to explore the two unique challenges in quantitative portfolio management: (1) the difficulty of representation and (2) the complexity of environments. In this research, we suggest a Markov decision process model-based deep reinforcement learning model including deep learning methods to perform strategy optimization, called SwanTrader. To achieve better decisions of the portfolio-management process from two different perspectives, i.e., the temporal patterns analysis and robustness information capture based on market observations, we suggest an optimal deep learning network in our model that incorporates a stacked sparse denoising autoencoder (SSDAE) and a long–short-term-memory-based autoencoder (LSTM-AE). The findings in times of COVID-19 show that the suggested model using two deep learning models gives better results with an alluring performance profile in comparison with four standard machine learning models and two state-of-the-art reinforcement learning models in terms of Sharpe ratio, Calmar ratio, and beta and alpha values. Furthermore, we analyzed which deep learning models and reward functions were most effective in optimizing the agent’s management decisions. The results of our suggested model for investors can assist in reducing the risk of investment loss as well as help them to make sound decisions.
Impact of Skin Disinfection on Cutaneous Microbiota, before and after Peripheral Venous Catheter Insertion

Introduction. Patients with invasive medical devices are at high risk for infection. Skin colonization is the initial stage of these infections, leading to the recommendation of practices requiring disinfection using antiseptics. Microbial communities playing a major role in skin health could be impacted by antiseptic procedures. Aim. To characterize and compare the bacterial communities of skin samples from patients before an antisepsis procedure, and after removal of the medical device itself, according to the nature of the antiseptic molecule (povidone iodine or chlorhexidine). Methods. The study focused on alterations in bacterial communities depending on the nature of the antiseptic procedure and type of intravascular device. After amplification of 16S rDNA, libraries (n = 498 samples) were sequenced using MiSeq platform. Results. Using an in-house pipeline (QIIME2 modules), while no alteration in skin microbiota diversity was associated with antiseptic procedure or PVC type, according to culture results (p < 0.05), alterations were at times associated with restricted diversity and higher dissimilarity (p < 0.05). Antiseptic procedures and PVC types were associated with the modification of specific bacterial representations with modulation of the Bacillota/Bacteroidota (Firmicutes/Bacteroidetes) ratio (modulation of C. acnes, Prevotella, Lagierella, and Actinomyces spp.) (p < 0.05). At baseline, the microbiota shows certain bacteria that are significantly associated with future PVC colonization and/or bacteremia (p < 0.05). All of these modulations were associated with altered expression of metabolic pathways (p < 0.05). Discussion. Finally, this work highlights the need to optimize the management of patients requiring intravascular devices, possibly by modulating the skin microbiota.
Effect of Impurity Scattering on Percolation of Bosonic Islands and Superconductivity in Fe Implanted NbN Thin Films

A reentrant temperature dependence of the thermoresistivity ρxx(T) between an onset local superconducting ordering temperature Tloconset and a global superconducting transition at T=Tglooffset has been reported in disordered conventional 3-dimensional (3D) superconductors. The disorder of these superconductors is a result of either an extrinsic granularity due to grain boundaries, or of an intrinsic granularity ascribable to the electronic disorder originating from impurity dopants. Here, the effects of Fe doping on the electronic properties of sputtered NbN layers with a nominal thickness of 100 nm are studied by means of low-T/high-μ0H magnetotransport measurements. The doping of NbN is achieved via implantation of 35 keV Fe ions. In the as-grown NbN films, a local onset of superconductivity at Tloconset=15.72K is found, while the global superconducting ordering is achieved at Tglooffset=15.05K, with a normal state resistivity ρxx=22μΩ·cm. Moreover, upon Fe doping of NbN, ρxx=40μΩ·cm is estimated, while Tloconset and Tglooffset are measured to be 15.1 K and 13.5 K, respectively. In Fe:NbN, the intrinsic granularity leads to the emergence of a bosonic insulator state and the normal-metal-to-superconductor transition is accompanied by six different electronic phases characterized by a N-shaped T dependence of ρxx(T). The bosonic insulator state in a s-wave conventional superconductor doped with dilute magnetic impurities is predicted to represent a workbench for emergent phenomena, such as gapless superconductivity, triplet Cooper pairings and topological odd frequency superconductivity.