Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Education Outcomes in Low- and Middle-Income Countries: Outcomes-Based Contracting and Early Grade Literacy

Fifty-three percent of Grade 4 learners cannot read for meaning in low- and middle-income countries despite an investment of between 33-49% of education expenditure on primary education. Teaching children to read in the early grades is fundamental to building resilient education systems, as the ability to read early in life is a great predictor for education success, and will minimise learning loss during education emergencies similar to COVID-19 school closures, for children who can read for meaning can carry on learning outside of school buildings. Further, the predicted financing gaps in the next few years, as a result of COVID-19, will require governments to utilise limited financial resources effectively and efficiently by implementing literacy programmes proven to be impactful, using financial instruments like outcomes-based contracting that can mobilise and coordinate non-traditional educational finance and incentivise service providers to improve results by paying for achievement of agreed outcomes.
Business Formation: A Tale of Two Recessions

The trajectory of new business applications and transitions to employer businesses differs markedly during the Great Recession and the COVID-19 recession. Both applications and transitions to employer start-ups decreased slowly but persistently in the post-Lehman crisis period of the Great Recession. In contrast, during the COVID-19 recession, new applications initially declined but have since sharply rebounded, resulting in a surge in applications during 2020. Projected transitions to employer businesses also rise, but this is dampened by a change in the composition of applications in 2020 toward applications that are more likely to be nonemployers.
Exploring the Farm Level Opportunity Cost for Protecting Environment: Evidence from Turkey

The study spatially explored the farm-level opportunity cost of protecting environment and examined the sufficiency level of government support for covering farm level of opportunity cost in TR83 region of Turkey. By using questionnaires, research data were collected from randomly selected 334 farms that participated in Environmentally Friendly Agricultural Land Protection Program (EFALP) and purposively selected 27 conventional farms. Farm-level opportunity cost of protecting environment was calculated via subtracting gross revenue calculated under condition of protecting environment from the gross revenue calculated under conventional ones. Gross revenue under conventional condition and under condition of protecting environment was elicited by using the MOTAD linear approximation of the quadratic programming. Sufficiency of government support for protecting environment was revealed through taking the difference between farm-level opportunity cost for protecting environment and total government support payment for environmental consideration to farm included in EFALP. Research results showed that government support paid to sample farms did not cover opportunity cost of farm for protecting environment in TR83 region. Considering spatially differentiation of farm-level opportunity cost for protecting environment when policy makers determine the quantity of government support may positively enhance the dissemination of EFALP programs and reduce the adverse effects of agricultural
Effect of Age on Physico-Chemical and Sensorial Quality of Buffalo Meat

3 Abstract: During the study physical, chemical and sensory properties of Longissimus dors i(LD) muscle were determined within age group of = 1.5y (group A),>1.5 to 2y (group B) and >2y (group C). pH of LD muscle among three groups were found to be non significant. Water holding capacity, cooking and drip loss varied between three groups due to age and fat content with significant results (P 0.05). In group A, B and C, sensory scores assigned by the panel of judges for color, flavor, juiciness, tenderness and overall palatability revealed non significant results (P>0.05) although different attributes were influenced by the age of the animals.
THE PREVALENCE OF HYDATIDOSIS IN RUMINANTS OF DIFFERENT IRAQI REGIONS

A retrospective study covered the period of 10-year (2000-2010) to investigate the prevalence of hydatidosis in ruminants of three provinces abattoirs in Iraq (Al Muthanna, Al Qadysiah and Al Najaf). A total of 923391 animals including 424167 sheep, 163070 goats, 264351 cow, 33133 buffalo and 38670 camel, inspected macroscopically for hydatidosis in 10-year period, out of which 9909, 2250, 6387, 740 and 414 respectively were infected with hydatidosis. The incidence of the disease in all slaughtered animals varied according to the province, but in general, Al Muthanna province recorded the highest incidence among all slaughtered species followed by Al Qadysiah then Al Najaf province. Buffalo recorded the highest incidence percentage (5.5%) in Al Muthanna and sheep (3.8%) in AlQadysiah while in AL Najaf cow showed the highest incidence (2.1%). The current results provide a baseline data for the prevalence of this parasitic disease in these three provinces and its incidence among different species of ruminants, which provide the opportunity for future monitoring and evaluation leading to control programs of hydatid cyst in Iraq.
Are Patients With Diabetes Mellitus at Increased Risk of COVID-19 Infection

Background: The aim of this study was to examine the clinical course and outcomes of patients with diabetes mellitus (DM) with coronavirus disease-2019 (COVID-19). Methods: This retrospective, single-center study included 185 adult patients diagnosed with COVID-19. All patients were separated into 4 groups. Group 1(n=79): patients with no accompanying disease, Group 2 (n=14): patients with only DM, Group 3 (n=31): patients with comorbid disease(s) including DM, Group 4 (n=61): patients with comorbid disease(s) without DM. Data about COVID-19 management and outcome were obtained from the medical records of the patients. COVID-19 was confirmed by real-time polymerase chain reaction (RT-PCR) from throat swab samples. All patients underwent chest x-rays or chest computed tomography. Results: 185 patients diagnosed with COVID-19 were evaluated. The COVID-19 prognoses of the patients were classified as good, moderate and poor. No statistically significant difference was determined between the groups in terms of COVID-19 prognosis (p>0.05). While the rate of DM patients with a good prognosis was 20.4%, the DM patient rate increased up to 40% among moderate or poor prognosis patients. A statistically significant difference was observed between blood glucose levels and mortality (p: 0.008). Mortality due to COVID-19 pneumonia developed in 15 (8.1%) patients. Mortality increase was mostly encountered in the group with DM and accompanying comorbidities. It was observed that ACEI / ARB use had no effect on mortality. Conclusions: Although the study results do not show a statistically significant effect of DM on the prognosis of COVID-19 patients, the higher rate of DM patients in the group with poor prognosis suggests that it may affect the severity of COVID-19. These results may be useful for clinicians in the management of DM patients with COVID-19.
Topiramate for Peri-Operative Neuroprotection in Infant Heart Surgery

Objective: To test feasibility and to generate preliminary data regarding dosage, safety and efficacy of topiramate for neuroprotection in neonates requiring cardiothoracic surgery. Study Design: An open pilot trial of topiramate versus no topiramate in 24 term infants requiring cardiac surgery in the first month of life. Topiramate levels, number of missed doses, possible adverse effects, plasma S100β and Bayley III scores at 9 and 18 months were analyzed. Results: Among the 12 infants who received topiramate, median number of doses prior to surgery was 4.5 and after surgery was 7. No adverse effects related to topiramate administration were noted. Mean topiramate levels were 3.7 μg/mL at 3 days and 5.5 μg/mL at 1 day after surgery. S100β levels and Bayley III scores were not significantly different between the two groups. Conclusion: Administration of topiramate in the peri-operative period for infants with congenital heart disease is feasible. The serum topiramate levels were low, suggesting that the dose was too low in this population. No adverse effects or benefits of topiramate were noted. The study was not powered to determine effect of topiramate on developmental outcomes but facilitates sample size calculations for future studies.
EFFECT OF CREDIT RISK MANAGEMENT ON FINANCIAL PERFORMANCE OF COMMERCIAL BANKS IN NEPAL

The main purpose of this study is to investigate the effect of credit risk on the financial performance of commercial banks in Nepal. The balance panel data of ten commercial banks with 160 observations for the period of 2001 to 2016 have been used for the analysis. The regression results revealed that capital adequacy ratio (CAR), non-performing loan ratio (NPLR), and management quality ratio (MQR) have significant relationship with the financial performance (ROA) of the commercial banks in Nepal. Similarly, credit to deposit ratio (CDR) and risk sensitivity (RS) have no significant impact on the financial performance of the commercial banks in Nepal. KEY WORD: return on asset, capital adequacy ratio, non-performing loan ratio, management, and quality ratio, credit to deposit ratio and risk sensitivity.
Bioresorbable Multilayer Photonic Cavities as Temporary Implants for Tether-Free Measurements of Regional Tissue Temperatures

Objective and Impact Statement. Real-time monitoring of the temperatures of regional tissue microenvironments can serve as the diagnostic basis for treating various health conditions and diseases. Introduction. Traditional thermal sensors allow measurements at surfaces or at near-surface regions of the skin or of certain body cavities. Evaluations at depth require implanted devices connected to external readout electronics via physical interfaces that lead to risks for infection and movement constraints for the patient. Also, surgical extraction procedures after a period of need can introduce additional risks and costs. Methods. Here, we report a wireless, bioresorbable class of temperature sensor that exploits multilayer photonic cavities, for continuous optical measurements of regional, deep-tissue microenvironments over a timeframe of interest followed by complete clearance via natural body processes. Results. The designs decouple the influence of detection angle from temperature on the reflection spectra, to enable high accuracy in sensing, as supported by in vitro experiments and optical simulations. Studies with devices implanted into subcutaneous tissues of both awake, freely moving and asleep animal models illustrate the applicability of this technology for in vivo measurements. Conclusion. The results demonstrate the use of bioresorbable materials in advanced photonic structures with unique capabilities in tracking of thermal signatures of tissue microenvironments, with potential relevance to human healthcare.
Inhomogeneous phases in the 2 + 1-dimensional Gross-Neveu model via mean-eld lattice eld theory

The focus of this thesis is the investigation of 2 + 1-dimensional Gross-Neveu model in the limit of in nite fermion avors (the large-Nf limit) via meaneld lattice eld theory. The phase diagram of this theory, which serves as a model for spontaneous chiral symmetry breaking, is herein studied in the μ-T plane. Of special interest is the existence of an inhomogeneous phase, where the chiral condensate depends on the spatial coordinates. Within this thesis, lattice techniques are applied to investigate the phase diagrams of QCD-inspired models with particular emphasis on the search for inhomogeneous phases. An inhomogeneous phase is obtained at nite lattice spacing. The phase, however, has a strong dependence on the lattice spacing and the used discretization. In the continuum limit the inhomogeneous phase vanishes. Indications for a degenerate ground state at T → 0, where inhomogeneous modulations of the chiral condensate are energetically equivalent to spatially constant ones, are found at low and intermediate chemical potentials. An extension of the 2 + 1-dimensional Gross-Neveu model via introduction of an additional chemical potential μI , which couples to a fermionic, isospin-like degree of freedom, is proposed. A shrinking of the inhomogeneous phase, which is present at nite lattice spacing for vanishing μI , is observed when increasing this additional chemical potential. It vanishes completely when μI exceeds a certain threshold.
Near and Mid-Infrared Spectroscopy for the Quantification of Botrytis Bunch Rot in White Wine Grapes

Botrytis bunch rot (BBR), one of the most important diseases of wine grapes, is usually quantified in the vineyard by visual estimation of percentage disease severity on individual grape bunches. This method is prone to assessor error and there is a need for a more objective quantification method that is cost-effective and practical. Near infrared (NIR; 800–2690 nm) and mid-infrared (mid-IR; 2510–25, 770 nm) spectroscopy were investigated as alternatives to visual estimation. Partial least squares (PLS) analysis of the NIR and mid-IR spectra from near-ripe grape bunches from Tasmanian vineyards was used to generate prediction models from both raw data and data pre-processed using the Savitzky–Golay derivative. The entire spectral range for each spectral region was analysed first, after which specific spectral ranges were analysed based on their influence on the initial PLS analysis. The spectral range of 1260–1370 nm with Savitzky–Golay smoothing and first derivative pre-processing produced the PLS model with the highest predictive ability in the NIR spectral region, with a ratio of standard error of prediction to standard deviation (RPD) of 2.2. The spectral range of 8760–9520 nm with Savitzky–Golay smoothing and first derivative pre-processing produced the PLS model with the highest predictive ability in the mid-IR spectral region, with a RPD of 1.7. Both methods demonstrated the potential for spectroscopic quantification of BBR. However, further calibration is required to increase the accuracy of these models, particularly at low BBR severities, if they are to be considered suitable for use in the vineyard.
Ionized nitrogen mono‐hydride bands are identified in the presolar and carbonado diamond spectra

Abstract– None of the well‐established nitrogen‐related IR absorption bands, common in synthetic and terrestrial diamonds, have been identified in the presolar diamond spectra. In the carbonado diamond spectra, only the single nitrogen impurity (C center) is identified and the assignments of the rest of the nitrogen‐related bands are still debated. It is speculated that the unidentified bands in the nitrogen absorption region are not induced by nitrogen, but rather by nitrogen‐hydrides because in the interstellar environment, nitrogen reacts with hydrogen and forms NH+; NH; NH2; NH3. Among these hydrides, the electronic configuration of NH+ is the closest to carbon. Thus, this ionized nitrogen‐mono‐hydride is the best candidate to substitute carbon in the diamond structure. The bands of the substitutional NH+ defect are deduced by redshifting the irradiation‐induced N+ bands due to the mass of the additional hydrogen. The six bands of the NH+ defects are identified in both the presolar and the carbonado diamond spectra. The new assignments identify all of the nitrogen‐related bands in the spectra, indicating that presolar and carbonado diamonds contain only single nitrogen impurities.
Models of random subtrees of a graph

Consider a connected graph $G=(E,V)$ with $N=|V|$ vertices. A subtree of $G$ with size $n$ is a tree which is a subgraph of $G$, with $n$ vertices. When $n=N$, such a subtree is called a spanning tree. The main purpose of this paper is to explore the question of uniform sampling of a subtree of $G$, or a subtree of $G$ with a fixed number of nodes $n$, for some $n\leq N$. We provide asymptotically exact simulation methods using Markov chains. We highlight the case of the uniform subtree of $\Z^2$ with $n$ nodes, containing the origin $(0,0)$ for which Schramm asked several questions. We produce pictures, statistics, and some conjectures. \par The second aim of the paper is devoted to survey other models of random subtrees of a graph, among them, we will discuss DLA models, the first passage percolation, the uniform spanning tree and the minimum spanning tree. We also provide a number of new models, some statistics, and some conjectures.
The PetscSF Scalable Communication Layer

PetscSF, the communication component of the Portable, Extensible Toolkit for Scientific Computation (PETSc), is designed to provide PETSc's communication infrastructure suitable for exascale computers that utilize GPUs and other accelerators. PetscSF provides a simple application programming interface (API) for managing common communication patterns in scientific computations by using a star-forest graph representation. PetscSF supports several implementations based on MPI and NVSHMEM, whose selection is based on the characteristics of the application or the target architecture. An efficient and portable model for network and intra-node communication is essential for implementing large-scale applications. The Message Passing Interface, which has been the de facto standard for distributed memory systems, has developed into a large complex API that does not yet provide high performance on the emerging heterogeneous CPU-GPU-based exascale systems. In this article, we discuss the design of PetscSF, how it can overcome some difficulties of working directly with MPI on GPUs, and we demonstrate its performance, scalability, and novel features.
Inspecting the Cepheid Distance Ladder: the Hubble Space Telescope Distance to the SN Ia Host Galaxy NGC 5584

The current tension between the direct and the early-universe measurements of the Hubble constant, H 0, requires detailed scrutiny of all the data and methods used in the studies on both sides of the debate. The Cepheids in the Type Ia supernova (SN Ia) host galaxy NGC 5584 played a key role in the local measurement of H 0. The SH0ES project used the observations of this galaxy to derive a relation between the Cepheids’ periods and ratios of their amplitudes in different optical bands of the Hubble Space Telescope and used these relations to analyze the light curves of the Cepheids in around half of the current sample of local SN Ia host galaxies. In this work, we present an independent detailed analysis of the Cepheids in NGC 5584. We employ different tools for our photometric analysis and a completely different method for our light-curve analysis, and we do not find a systematic difference between our period and mean magnitude measurements compared to those reported by SH0ES. By adopting a period–luminosity relation calibrated by the Cepheids in the Milky Way, we measure a distance modulus μ = 31.810 ± 0.047 (mag), which is in agreement with μ = 31.786 ± 0.046 (mag) measured by SH0ES. In addition, the relations we find between the periods and amplitude ratios of the Cepheids in NGC 5584 are significantly tighter than those of SH0ES, and their potential impact on the direct H 0 measurement will be investigated in future studies.
SparseBERT: Rethinking the Importance Analysis in Self-attention

Transformer-based models are popularly used in natural language processing (NLP). Its core component, self-attention, has aroused widespread interest. To understand the self-attention mechanism, a direct method is to visualize the attention map of a pre-trained model. Based on the patterns observed, a series of efficient Transformers with different sparse attention masks have been proposed. From a theoretical perspective, universal approximability of Transformer-based models is also recently proved. However, the above understanding and analysis of self-attention is based on a pre-trained model. To rethink the importance analysis in self-attention, we study the significance of different positions in attention matrix during pre-training. A surprising result is that diagonal elements in the attention map are the least important compared with other attention positions. We provide a proof showing that these diagonal elements can indeed be removed without deteriorating model performance. Furthermore, we propose a Differentiable Attention Mask (DAM) algorithm, which further guides the design of the SparseBERT. Extensive experiments verify our interesting findings and illustrate the effect of the proposed algorithm.
NEW HIGHER ORDER GAUSSIAN RIESZ TRANSFORMS AND NON-CENTERED MAXIMAL FUNCTION ON VARIABLE LEBESGUE SPACES WITH RESPECT TO GAUSSIAN MEASURE ESTEFANÍA DALMASSO AND ROBERTO SCOTTO

We give sufficient conditions on the exponent p : R → [1,∞) for the boundedness of the non-centered Gaussian maximal function on variable Lebesgue spaces Lp(·)(Rd, γd), as well as of the new higher order Riesz transforms associated with the Ornstein-Uhlenbeck semigroup, which are the natural extensions of the supplementary first order Gaussian Riesz transforms defined by A. Nowak and K. Stempak in [24].
Provable Compressed Sensing With Generative Priors via Langevin Dynamics

Deep generative models have emerged as a powerful class of priors for signals in various inverse problems such as compressed sensing, phase retrieval and super-resolution. In this work, we consider the compressed sensing problem and assume the unknown signal to lie in the range of some pre-trained generative model. A popular approach for signal recovery is via gradient descent in the low-dimensional latent space. While gradient descent has achieved good empirical performance, its theoretical behavior is not well understood. We introduce the use of stochastic gradient Langevin dynamics (SGLD) for compressed sensing with a generative prior. Under mild assumptions on the generative model, we prove the convergence of SGLD to the true signal. We also demonstrate competitive empirical performance to standard gradient descent.
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks

In order to safely deploy Deep Neural Networks (DNNs) within the perception pipelines of real-time decision making systems, there is a need for safeguards that can detect out-of-training-distribution (OoD) inputs both efficiently and accurately. Building on recent work leveraging the local curvature of DNNs to reason about epistemic uncertainty, we propose Sketching Curvature of OoD Detection (SCOD), an architecture-agnostic framework for equipping any trained DNN with a task-relevant epistemic uncertainty estimate. Offline, given a trained model and its training data, SCOD employs tools from matrix sketching to tractably compute a low-rank approximation of the Fisher information matrix, which characterizes which directions in the weight space are most influential on the predictions over the training data. Online, we estimate uncertainty by measuring how much perturbations orthogonal to these directions can alter predictions at a new test input. We apply SCOD to pre-trained networks of varying architectures on several tasks, ranging from regression to classification. We demonstrate that SCOD achieves comparable or better OoD detection performance with lower computational burden relative to existing baselines.
Inactivation of Aspergillus flavus Spores in Water by Ultraviolet Irradiation

Aspergillus species in drinking water are involved in the production of health risks. Since fungi spores are resistant to many water disinfectants, the efficiency of UV radiation on the inactivation of Aspergillus flavus spores was evaluated and found that a fluence of 32mJ/Cm is effective for inactivation of 2