Miracle-dz/UniFAR-FTUs huggingface dataset:
Weak lensing light-cones in modified gravity simulations with and without massive neutrinos

We present a novel suite of cosmological N-body simulations called the DUSTGRAIN-pathfinder, implementing simultaneously the effects of an extension to General Relativity in the form of $f(R)$ gravity and of a non-negligible fraction of massive neutrinos. We describe the generation of simulated weak lensing and cluster counts observables within a past light-cone extracted from these simulations. The simulations have been performed by means of a combination of the MG-GADGET code and a particle-based implementation of massive neutrinos, while the light-cones have been generated using the MapSim pipeline allowing us to compute weak lensing maps through a ray-tracing algorithm for different values of the source plane redshift. The mock observables extracted from our simulations will be employed for a series of papers focussed on understanding and possibly breaking the well-known observational degeneracy between $f(R)$ gravity and massive neutrinos, i.e. the fact that some specific combinations of the characteristic parameters for these two phenomena (the $f_{R0}$ scalar amplitude and the total neutrino mass $\Sigma m_{\nu}$) may result indistinguishable from the standard $\mathrm{\Lambda CDM}$ cosmology through several standard observational probes. In particular, in the present work we show how a tomographic approach to weak lensing statistics could allow - especially for the next generation of wide-field surveys - to disentangle some of the models that appear statistically indistinguishable through standard single-redshift weak lensing probe.
Using Expenditure PPPs for Sectoral Output and Productivity Comparisons

P power parities (PPPs) have a wide range of analytical and policy applications.1 Traditionally, PPPs have been used for international comparisons of income, expenditure, and output. Most well known are the comparisons of levels of gross domestic product (GDP) per capita as published by the World Bank (e.g., World Bank 2008). Diff erences in GDP per capita across countries are mainly determined by levels of labor productivity at the aggregate economy level. Today, comparative series of output per worker and per hour worked are being produced routinely by various statistical organizations and in academia, such as the Penn World Table and the series produced by the Organisation for Economic Co-operation and Development (OECD), Conference Board, and Groningen Growth and Development Centre (GGDC) at the University of Groningen. Various useful analytical applications of productivity levels, however, are also found at the more detailed level of individual industries. Because of the large diff erences in economic structure across countries, international comparisons of output and productivity at the sector level (agriculture, manufacturing, and services) provide useful complements to comparisons of GDP by expenditure categories. Previous research has shown that a low overall level of productivity is not necessarily indicative of large gaps in all sectors. Generally, it is assumed that productivity gaps in manufacturing can be large, and that gaps in services productivity across countries are much smaller. Th is well-known fi nding is at the heart of the Harrod-Balassa-Samuelson eff ect and often invoked to explain the lower relative prices of services in low-income countries compared with high-income countries. Also, studies of convergence and divergence in the world economy are increasingly being made at the industry level. Tests of international trade theories and endogenous growth models require measures relative to the world productivity frontier by sector. By defi nition, a major part of the research in these areas requires PPPs from the production side rather than from the expenditure side—that is, PPPs should refl ect the diff erences in the output 24
How to Sim2Real with Gaussian Processes: Prior Mean versus Kernels as Priors

—Gaussian Processes (GPs) have been widely used in robotics as models, and more recently as key structures in active learning algorithms, such as Bayesian optimization. GPs consist of two main components: the mean function and the kernel. Specifying a prior mean function has been a common way to incorporate prior knowledge. When a prior mean function could not be constructed manually, the next default has been to incorporate prior (simulated) observations into a GP as ‘fake’ data. Then, this GP would be used to further learn from true data on the target (real) domain. We argue that embedding prior knowledge into GP kernels instead provides a more ﬂexible way to capture simulation-based information. We give examples of recent works that demonstrate the wide applicability of such kernel-centric treatment when using GPs as part of Bayesian optimization. We also provide discussion that helps to build intuition for why such ‘kernels as priors’ view is beneﬁcial.
Identifying Issues for Learners in Completing Online Courses on Machine Learning and Deep Learning: Five Issues Found in a Fully Automated Learning Environment for the Purpose of Scalable AI Education

Information technology is becoming increasingly sophisticated and rapidly developing. Although the demand for highly skilled technical human resources is rising, the supply is insufficient. Since this is a global problem, not only industries but also governments have become active in trying to find a solution. Online education is one method which enables training of a large number of people. However, our knowledge on how to train a large number of technical professionals in highly advanced emerging technologies such as artificial intelligence is insufficient. In this paper, we propose a fully automated online teaching method for learners who want to understand Machine Learning and Deep Learning and identify the issues that learners face in completing online courses on their own in a fully automated learning environment. This study concludes by presenting the five issues identified through the data collected from the Deep Learning and Machine Learning online courses designed by the proposed teaching method
Instructional Support for Intuitive Knowledge Acquisition When Learning with an Ecological Computer Simulation

Intuitive knowledge seems to influence human decision-making outside of consciousness and differs from deliberate cognitive and metacognitive processes. Intuitive knowledge can play an essential role in problem solving and may offer the initiation of subsequent learning processes. Scientific discovery learning with computer simulations leads to the acquisition of intuitive knowledge. To improve knowledge acquisition, particular instructional support is needed as pure discovery learning often does not lead to successful learning outcomes. Hence, the goal of this study was to determine whether two different instructional interventions for scientific discovery effectively produced intuitive knowledge acquisition when learning with computer simulations. Instructional interventions for learning with computer simulations on the topic ‘ecosystem water’ were developed and tested in the two well-known categories data interpretation and self-regulation using a sample of 117 eighth graders during science class. The results demonstrated the efficacy of these instructional interventions on learners’ intuitive knowledge acquisition. A predetermined combination of instructional support for data interpretation and for self-regulation proved to be successful for learners’ intuitive knowledge acquisition after a learning session involving the computer simulation. Furthermore, the instructional intervention describing and interpreting own simulation outcomes for data interpretation seems to be an effective method for acquiring intuitive knowledge.
Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms

We consider a new unsupervised learning task of inferring parameters of a multiobjective decision making model, based on a set of observed decisions from the human expert. This setting is important in applications (such as the task of portfolio management) where it may be difﬁcult to obtain the human expert’s intrinsic decision making model. We formulate such a learning problem as an inverse multiobjective optimization problem (IMOP) and propose its ﬁrst sophisticated model with statistical guarantees. Then, we reveal several fundamental connections between IMOP, K-means clustering, and manifold learning. Lever-aging these critical insights and connections, we propose two algorithms to solve IMOP through manifold learning and clustering. Numerical re-sults conﬁrm the effectiveness of our model and the computational efﬁcacy of algorithms.
Why Do Customers Buy Products on Social Commerce Platform? A Study from Affordance Theory

This study examines the influence mechanism of the factors of influencing live streaming shopping which is a new social commerce mode for customer purchase based on the affordance theory. Our research examines how visibility affordance, metavoicing affordance and guidance shopping affordance which are three main affordances in live streaming shopping influence the customers’ purchase intention. The results show that visibility affordance, metavoicing affordance and guidance shopping affordance will positively impact customer purchase intention. And our research is of great help in live streaming shopping research in social commerce research field. Also, our research provides some advices for social commerce operators.
UvA-DARE ( Digital Academic Repository ) Waveform relaxation methods for implicit differential equations

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Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript

Recent years have witnessed intensive research interests on training deep neural networks (DNNs) more efﬁciently by quantization-based compression methods, which facilitate DNNs training in two ways: (1) activations are quantized to shrink the memory consumption, and (2) gradients are quantized to decrease the communication cost. However, existing methods mostly use a uniform mechanism that quantizes the values evenly. Such a scheme may cause a large quantization variance and slow down the convergence in practice. In this work, we introduce T INY S CRIPT , which applies a non-uniform quantization algorithm to both activations and gradients. T INY S CRIPT models the original values by a family of Weibull distributions and searches for “quantization knobs” that minimize quantization variance. We also discuss the convergence of the non-uniform quantization algorithm on DNNs with varying depths, shedding light on the number of bits required for convergence. Experiments show that T INY S CRIPT always obtains lower quantization variance, and achieves comparable model qualities against full precision training using 1-2 bits less than the uniform-based counterpart.
Prevalence of Intestinal Parasites in Dogs from Municipality La Lisa, Havana, Cuba

Intestinal parasitic infections in dogs represent a major concern in developing countries as they are important definitive or reservoir hosts for several zoonotic parasites. The present study was conducted to determine the prevalence of intestinal parasitic infections in stray dogs and domestic dogs in the municipality of la Lisa, in La Havana, between June 2014 to March 2015. A total of 97 faecal samples from 56 stray dogs and 41 household dogs were examined by parasitological concentration techniques. The overall prevalence of gastrointestinal parasites was 44.3% (95% CI: 33.9-54.7). Ancylostoma caninum (21.6%), Trichuris vulpis (16.5) and Giardia duodenlis (7.2%) were the most prevalent intestinal parasites identified in canine faecal samples, indicating an important faecal contamination of the analyzed area. There were no significant differences between the prevalence of gastrointestinal parasites and the kind of dog analyzed. However stray dogs were associated with a higher risk of infection as well as those older than 1 year old. This information may be useful in the implementation of effective prevention and control programmes by the National Group of Zoonosis in our country. Appropriate public health education for dog’s owners is necessary to reduce the risks of zoonotic infections
WHAT ARE THE PROGENITORS OF COMPACT, MASSIVE, QUIESCENT GALAXIES AT z = 2.3? THE POPULATION OF MASSIVE GALAXIES AT z > 3 FROM NMBS AND CANDELS

Using public data from the NEWFIRM Medium-Band Survey (NMBS) and the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS), we investigate the population of massive galaxies at z > 3. The main aim of this work is to identify the potential progenitors of z ∼ 2 compact, massive, quiescent galaxies (CMQGs), furthering our understanding of the onset and evolution of massive galaxies. Our work is enabled by high-resolution images from CANDELS data and accurate photometric redshifts, stellar masses, and star formation rates (SFRs) from 37-band NMBS photometry. The total number of massive galaxies at z > 3 is consistent with the number of massive, quiescent galaxies (MQGs) at z ∼ 2, implying that the SFRs for all of these galaxies must be much lower by z ∼ 2. We discover four CMQGs at z > 3, pushing back the time for which such galaxies have been observed. However, the volume density for these galaxies is significantly less than that of galaxies at z < 2 with similar masses, SFRs, and sizes, implying that additional CMQGs must be created in the intervening ∼1 Gyr between z = 3 and z = 2. We find five star-forming galaxies at z ∼ 3 that are compact (Re < 1.4 kpc) and have stellar mass M* > 1010.6 M☉; these galaxies are likely to become members of the massive, quiescent, compact galaxy population at z ∼ 2. We evolve the stellar masses and SFRs of each individual z > 3 galaxy adopting five different star formation histories (SFHs) and studying the resulting population of massive galaxies at z = 2.3. We find that declining or truncated SFHs are necessary to match the observed number density of MQGs at z ∼ 2, whereas a constant delayed-exponential SFH would result in a number density significantly smaller than observed. All of our assumed SFHs imply number densities of CMQGs at z ∼ 2 that are consistent with the observed number density. Better agreement with the observed number density of CMQGs at z ∼ 2 is obtained if merging is included in the analysis and better still if star formation quenching is assumed to shortly follow the merging event, as implied by recent models of the formation of MQGs.
A CASE OF THE EASTERN CAPE PROVINCE, SOUTH AFRICA

The Eastern Cape province of South Africa is one of the poorest provinces in the country with vast rural areas. A telemedicine system was implemented in the province in order to improve health care services. Despite large investments from the National Department of Health, only a third of telemedicine sites in the province are operational. Technology problems, such as unreliable electricity supply and low bandwidth, was identified as barriers to the successful implementation of telemedicine in South Africa, but these issues have since been addressed. Nevertheless, the uptake of telemedicine remains poor. One of the documented barriers to the successful implementation of telemedicine is user acceptance by health care workers. This study made use of the Technology Acceptance Model (TAM) to identify the factors that influence the user acceptance of telemedicine among health care workers in a developing country such as South Africa. The study used a quantitative survey approach. A questionnaire was distributed to all the hospitals and clinics around the province where telemedicine had been implemented. The results were analyzed using SPSS 20 and the conclusions drawn from the results were presented to five experts in the field of telemedicine and Information Systems. In general, the attitude of the health care workers was positive towards telemedicine, although the present usage of the system was low. Health care workers, especially those in the rural areas, perceived the telemedicine system to be useful to improve the quality of health care services they provide. The perceived ease of use of the telemedicine system was influenced by both the educational qualification and area in which the health care workers worked. Those that did not complete high school and worked in rural areas were apprehensive about using the telemedicine system. However, the majority of the health care workers indicated that they did plan to make use of the telemedicine system in the future. Interventions focused on education should be specific for rural or urban areas. In rural areas education should focus on computer literacy skills and how to use the telemedicine system, while in urban areas the awareness of the telemedicine should be increased.
Comparative Study of Physico-Chemical Parameters and Heavy Metal Detection in Agricultural Soil Irrigated By Sewage Water and Tube Well Water in Rewari City Rural

Present Comparative study revealed that Use of sewage water for irrigation of crops in Agricultural soil change the PhysicoChemical parameters and heavy metals content of the soil as compared to agricultural soil in which the crops are irrigated by tube well water or by canal water in nearby villages of Rewari city. Use of sewage water improves the fertility status of the soil as compare to the soil irrigated by natural sources. The percentage increase of soil samples investigated and analyzed for organic carbon (+49.19%), total nitrogen (+109.09%), available potassium (+49.03%), available phosphorus (+72.08%), electrical conductivity (+58.62%) and also water holding capacity (+22.30). The sewage irrigation show remarkable increase in the concentration of heavy metals in the soil, i.e. Zn (+470.05%) and Cu (+232.27%) and Pb (+106.64%). The results observed does not show much variation in the concentration of Fe (+51.40) and Cd (+27.41) metal in sewage irrigated soil compare to tube well irrigated soil. These are in permissible limits of Indian standard and show good soil quality in experimental site analyzed soil samples irrigated by sewage water but long term use of sewage effluents in the fields will certainly increase the heavy metals content and in future it will affect the human and animals of this area if the use of untreated sewage water continuously used by the farmers for the irrigation of this locality.
Production , Purification and Characterization of Exopolysaccharide from Fomitopsis feei

10.21276/sajb.2018. 6.7. 6 Abstract: Production of exopolysaccharide from Fomitopsis feei was screened under nine different production media. Common production medium was found to be good for the best production and after isolation, purification by DEAE cellulose column chromatography and the purified exopolysaccharide was characterized using H1, C13 NMR, FTIR, GC-MS analyses and found to be a proteopolysaccharide.
C-Peptid , basale und postprandiale Insulinresistenz nach kohlenhydratreicher Testmahlzeit – Hinweis auf eine erhöhte Insulinclearance bei PCOS-Patientinnen ? C-Peptide , Baseline and Postprandial Insulin Resistance after a Carbohydrate-Rich Test Meal – Evidence for an Increased Insulin Clearance in

Introduction Known characteristics of patients with PCOS include infertility, menstrual disorders, hirsutism and also often insulin resistance. These symptoms increase with increasing body weight. In the LIPCOS study (Lifestyle Intervention for Patients with Polycystic Ovary Syndrome [PCOS]) long-term changes of the PCOS in dependence on pregnancy and parenthood were systematically assessed. In the framework of the LIPCOS study, PCOS patients were given a standardised carbohydrate-rich test meal in order to examine glucose homeostasis and insulin secretion. The results were compared with those of a eumenorrhoeic control group who all had corresponding BMI values and corresponding ages. Methods and Patients 41 PCOS patients (without diabetes) and 68 controls received a standardised carbohydrate-rich test meal (260 kcal, 62% carbohydrates, 32% fat, 6% proteins) in order to generate a submaximal insulin and glucose stimulation. The values were determined at baseline and postprandial after 60, 120 and 180 minutes. In addition, the corresponding C‐peptide levels were recorded. Results In the PCOS patients (n = 41), the insulin secretion test after a standardised test meal showed almost identical baseline and postprandial insulin levels when compared with those of the ageand BMI-matched eumenorrhoeic controls (n = 68). In the PCOS patients, the baseline and postprandial glucose levels were significantly elevated (92.88 ± 10.28 [PCOS] vs. 85.07 ± 9.42mg/dL [controls]; p < 0.001) so was C-peptide (p < 0.025). Conclusions In the present study we have shown for the first time that, after consumption of a standardised test meal, PCOS patients formally exhibit a higher fasting insulin resistance than controls. In spite of the higher stimulated C-peptide levels, the insulin levels did GebFra Science |Originalarbeit 59 Stassek J et al. C-Peptid, basale und. Geburtsh Frauenheilk 2017; 77: 59–66 Su pp le m en ta ry M at er ia l – de ut sc hs pr ac hi ge Zu sa tz in fo rm at io n! Zi ti er ba r is t au ss ch lie ß lic h de r en gl is ch sp ra ch ig e A rt ik el . not increase more strongly with increasing glucose levels than in controls which may be indicative of a higher insulin clearance in PCOS patients. Su pp le m en ta ry M at er ia l – de ut sc hs pr ac hi ge Zu sa tz in fo rm at io n! Zi ti er ba r is t au ss ch lie ß lic h de r en gl is ch sp ra ch ig e A rt ik el . GebFra Science |Originalarbeit
Massively Parallel Eigensolvers based on Unconstrained Energy Functionals Methods

This poster focuses on a preconditioned conjugate gradient based iterative eigensolver using an unconstrained energy functional minimization scheme. This scheme avoids an explicit reorthogonalization of the trial eigenvectors and becomes an attractive alternative for the solution of very large problems. The unconstrained formulation is implemented in the first-principles materials and chemistry CP2K code [1], which performs electronic structure calculations based on a density functional theory approximation to the solution of the many-body Schrödinger equation. The systems we use in our studies have a number of atoms ranging from 2,247 to 12,288. We study the convergence of the unconstrained formulation and its scaling on a Cray XC40 (a partition with 9,688 Intel KNL nodes). We show that there is a trade-off between the preconditioner that leads to fast convergence and lower cost preconditioners that lead to best time to solution. ACM Reference format: Osni Marques, Mauro Del Ben and Andrew Canning. 2019. Massively Parallel Eigensolvers based on Unconstrained Energy Functionals Methods. In Proceedings of SC19, Denver, CO, Nov 17-22, 3 pages.