Miracle-dz/UniFAR-FTUs huggingface dataset:
Out-Migration from Bihar: Major Reasons and Destinations

The overall development scenario in Bihar has always put the state in a spotlight in debates on regional imbalances, economic development, poverty etc. The backwardness of this eastern state can be attributed to various factors, such as very low agricultural output coupled with a high dependence on agriculture, a hugely skewed distribution of land with landlessness being widespread among peasants, lack of industrialisation, high population growth etc. The decades-long period of economic backwardness in the state has led to there being very few employment opportunities in the limited industrial sector. The scarce economic resources have also resulted in inadequate and poor educational and public heath infrastructure, which in turn have resulted in poor health and educational outcomes in the state. This can be considered as the most prominent among the many factors contributing to heavy outmigration from the state, especially in the category of low-skilled labour.
Reproduction and growth of Sphaerechinus granularis (Echinodermata: Echinoidea) in southern Brittany

Although the English Channel is the northern limit of the latitudinal range of Sphaerechinus granularis (Echinodermata: Echinoidea), this echinoid can produce locally high densities in Brittany, such as in the Glénan Archipelago (Bay of Concarneau) where a small-scale fishery has been exploited since 1985. The reproductive cycle, the growth patterns and the migration of the species in this area were studied from 1988 to 1992. Sphaerechinus granularis presents an annual reproductive cycle with a major spawning in the early spring. But inter annual variability in the time when spawning starts can be observed. A relationship is established between the date of onset of spawning and the climatic conditions during the whole period of gonadal growth. Recruitment is isolated in time and reflects the reproductive cycle.
Users’ Experiences of a Mobile Health Self-Management Approach for the Treatment of Cystic Fibrosis: Mixed Methods Study

Background Despite a large number of clinical trials aiming at evaluating the digital self-management of chronic diseases, there is little discussion about users’ experiences with digital approaches. However, a good user experience is a critical factor for technology adoption. Understanding users’ experiences can inform the design of approaches toward increased motivation for digital self-management. Objective This study aimed to evaluate the self-management of cystic fibrosis (CF) with a focus on gastrointestinal concerns and the care of young patients. Following a user-centered design approach, we developed a self-management app for patients and parents and a web tool for health care professionals (HCPs). To evaluate the proposed solutions, a 6-month clinical trial was conducted in 6 European CF competence centers. This paper analyzes the user acceptance of the technology and the benefits and disadvantages perceived by the trial participants. Methods A mixed methods approach was applied. Data were collected through 41 semistructured qualitative interviews of patients, parents, and HCPs involved in the clinical trial. In addition, data were collected through questionnaires embedded in the self-management app. Results Support for enzyme dose calculation and nutrition management was found to be particularly useful. Patients and parents rapidly strengthened their knowledge about the treatment and increased their self-efficacy. Reported benefits include reduced occurrence of symptoms and enhanced quality of life. Patients and parents had different skills, requiring follow-up by HCPs in an introductory phase. HCPs valued obtaining precise information about the patients, allowing for more personalized advice. However, the tight follow-up of several patients led to an increased workload. Over time, as patient self-efficacy increased, patient motivation for using the app decreased and the quality of the reported data was reduced. Conclusions Self-management enfolds a collaboration between patients and HCPs. To be successful, a self-management approach should be accepted by both parties. Through understanding behaviors and experiences, this study defines recommendations for a complex case—the demanding treatment of CF. We identify target patient groups and situations for which the app is most beneficial and suggest focusing on these rather than motivating for regular app usage over a long time. We also advise the personalized supervision of patients during the introduction of the approach. Finally, we propose to develop guidance for HCPs to facilitate changes in practice. As personalization and technology literacy are factors found to influence the acceptance of digital self-management of other chronic diseases, it is relevant to consider the proposed recommendations beyond the case of CF.
Spectral Clustering with Graph Neural Networks for Graph Pooling

Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph. SC can be used in Graph Neural Networks (GNNs) to implement pooling operations that aggregate nodes belonging to the same cluster. However, the eigendecomposition of the Laplacian is expensive and, since clustering results are graph-specific, pooling methods based on SC must perform a new optimization for each new sample. In this paper, we propose a graph clustering approach that addresses these limitations of SC. We formulate a continuous relaxation of the normalized minCUT problem and train a GNN to compute cluster assignments that minimize this objective. Our GNN-based implementation is differentiable, does not require to compute the spectral decomposition, and learns a clustering function that can be quickly evaluated on out-of-sample graphs. From the proposed clustering method, we design a graph pooling operator that overcomes some important limitations of state-of-the-art graph pooling techniques and achieves the best performance in several supervised and unsupervised tasks.
Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging

Computed Tomography (CT) takes X-ray measurements on the subjects to reconstruct tomographic images. As X-ray is radioactive, it is desirable to control the total amount of dose of X-ray for safety concerns. Therefore, we can only select a limited number of measurement angles and assign each of them limited amount of dose. Traditional methods such as compressed sensing usually randomly select the angles and equally distribute the allowed dose on them. In most CT reconstruction models, the emphasize is on designing effective image representations, while much less emphasize is on improving the scanning strategy. The simple scanning strategy of random angle selection and equal dose distribution performs well in general, but they may not be ideal for each individual subject. It is more desirable to design a personalized scanning strategy for each subject to obtain better reconstruction result. In this paper, we propose to use Reinforcement Learning (RL) to learn a personalized scanning policy to select the angles and the dose at each chosen angle for each individual subject. We first formulate the CT scanning process as an Markov Decision Process (MDP), and then use modern deep RL methods to solve it. The learned personalized scanning strategy not only leads to better reconstruction results, but also shows strong generalization to be combined with different reconstruction algorithms.
Avaliação da atividade antioxidante dos compostos fenólicos naturalmente presentes em subprodutos do pseudofruto de caju (Anacardium occidentale L.)

This study aimed to determine the antioxidant capacity of cashew apple pulp and raw concentrated extract. The antioxidant potential of aqueous (EAq) and alcoholic (EAlc) extracts and of free phenolic acids (FPA) and esterified (soluble - SPA and insoluble - IPA) fractions of the by-products of the cashew apple clone CCP-76 were evaluated in a b-carotene-linoleate system by the free radical [2,2-diphenyl-1-picrylhydrazyl (DPPH)] scavenging assay and the Rancimat test. In addition, the total phenolic content and phenolic acid profile were determined using Folin-Ciocateau reagent and gas chromatography, respectively. The EAq and the FPA fraction of the by-products showed the highest phenolic content. The phenolic acid fractions generally displayed stronger antioxidant activity than the extracts tested with the b-carotene-linoleate and DPPH systems. However, in the Rancimat test, the extracts showed higher antioxidant activity than the fractions and BHT. Nine phenolic acids (gallic, ferulic, caffeic, protocatechuic, quinic, cinnamic, gentisic, p-coumaric and salicylic acids), which give the fractions their antioxidant potential, were identified and quantified (both free and esterified forms). These results characterized the in vitro antioxidant capacity of the pulp and raw concentrated extract of the cashew apple clone CCP-76.
Phylogenetic study of Fraction I protein in the genus Citrus and its close related genera

The large subunits (LS) and small subunits (SS) of Fraction I protein from 57 species and cultivars of the genus Citrus, Fortunella and Poncirus were compared to analyze the phylogenetic relationships among them. C. medica only had different LS. P. trifoliata and C. reticulata had unique SS respectively. Fortunella species, C. hystrix, C. latipes, C. limon and C. grandis also had the other unique SS. We consider that C. medica and C. reticulata are the original species of the cultivated citrus of the genus Citrus, along with C. grandis presumably. Thus we show our ideas of phylogenetic relationships of Citrus and its relatives based on their soelectrofocusing pattern of Fraction I protein.
Sampling-Based Motion Planning on Manifold Sequences

We address the problem of planning robot motions in constrained configuration spaces where the constraints change throughout the motion. A novel problem formulation is introduced that describes a task as a sequence of intersecting manifolds, which the robot needs to traverse in order to solve the task. We specify a class of sequential motion planning problems that fulfill a particular property of the change in the free configuration space when transitioning between manifolds. For this problem class, a sequential motion planning algorithm SMP is developed that searches for optimal intersection points between manifolds by using RRT* in an inner loop with a novel steering strategy. We provide a theoretical analysis regarding its probabilistic completeness and demonstrate its performance on kinematic planning problems where the constraints are represented as geometric primitives. Further, we show its capabilities on solving multi-robot object transportation tasks.
REL: An Entity Linker Standing on the Shoulders of Giants

Entity linking is a standard component in modern retrieval system that is often performed by third-party toolkits. Despite the plethora of open source options, it is difficult to find a single system that has a modular architecture where certain components may be replaced, does not depend on external sources, can easily be updated to newer Wikipedia versions, and, most important of all, has state-of-the-art performance. The REL system presented in this paper aims to fill that gap. Building on state-of-the-art neural components from natural language processing research, it is provided as a Python package as well as a web API. We also report on an experimental comparison against both well-established systems and the current state-of-the-art on standard entity linking benchmarks.
Variação temporal e espacial na composição de guildas alimentares da ictiofauna em lagoas marginais do Rio Cuiabá, Pantanal Norte

O presente trabalho teve como objetivo avaliar a variacao espacial e temporal das guildas alimentares das comunidades de peixes em 10 lagoas marginais do Rio Cuiaba no Pantanal. As lagoas foram amostradas em tres periodos do ciclo hidrologico (inicio e final da seca e inicio da enchente de 2005). As guildas alimentares foram determinadas atraves da analise do conteudo estomacal das especies mais abundantes da comunidade. Foram analisadas 37 especies pertencentes a oito guildas alimentares (insetivora, herbivora, onivora, zooplanctivora, planctivora, detritivora, bentivora e iliofaga), as quais variaram espacial e temporalmente. Observamos uma mudanca na composicao das guildas troficas entre os periodos e locais analisados, porem o numero de guildas nao variou espaco-temporalmente. Nossos resultados sugerem que as mudancas espaciais e temporais na composicao das guildas podem estar relacionadas com diversos fatores, como as alteracoes na composicao da comunidade onde novas especies podem compor as diferentes guildas; a exploracao de recursos mais abundantes em determinadas epocas do ano favorecendo a presenca de algumas guildas somente em alguns periodos.
$\mathcal{L}$-invariants, $p$-adic heights and factorization of $p$-adic $L$-functions

We continue with our study of the non-critical exceptional zeros of Katz' $p$-adic $L$-functions attached to a CM field $K$, following two threads. In the first thread, we redefine the $\mathcal{L}$-invariant associated to each $\mathbb{Z}_{p}$-extension $K_{\Gamma}$ of $K$ in terms of $p$-adic height pairings and interpolate them as $K_{\Gamma}$ varies to a universal (multivariate) $\mathcal{L}$-invariant. In the second thread, we use our results to study the exceptional zeros of the Rankin--Selberg $p$-adic $L$-functions attached to the self-products of nearly ordinary CM families at weight-one points, via the factorization results we establish. The factorization theorems are the algebraic counterparts of the conjectural extensions of the results due to Gross and Dasgupta.
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs

We study properties of Graph Convolutional Networks (GCNs) by analyzing their behavior on standard models of random graphs, where nodes are represented by random latent variables and edges are drawn according to a similarity kernel. This allows us to overcome the difficulties of dealing with discrete notions such as isomorphisms on very large graphs, by considering instead more natural geometric aspects. We first study the convergence of GCNs to their continuous counterpart as the number of nodes grows. Our results are fully non-asymptotic and are valid for relatively sparse graphs with an average degree that grows logarithmically with the number of nodes. We then analyze the stability of GCNs to small deformations of the random graph model. In contrast to previous studies of stability in discrete settings, our continuous setup allows us to provide more intuitive deformation-based metrics for understanding stability, which have proven useful for explaining the success of convolutional representations on Euclidean domains.
Incorporating Physical Knowledge Into Machine Learning for Planetary Space Physics

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. In comparison, the previous mission to Saturn, Voyager over 20 years earlier, had onboard a ~70 kB 8-track storage ability. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of “black box” or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. The present work uses Cassini data as a case study as these data are similar to space physics and planetary missions at Earth and other solar system objects. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetic environment, or magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine leaning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.
Antimicrobial Activity of Some Endemic Verbascum, Salvia, and Stachys Species

Methanol extracts obtained from endemic Stachys sivasica Kit Tan & Yildiz, Stachys anamurensis Sumbul, Stachys cydni Kotschy ex Gemici & Leblebici, Salvia aytachii Vural & Adiguzel, and Verbascum gypsicola Vural & Aydogdu have been investigated for their antimicrobial activity. Antimicrobial activity was determined with Escherichia coli ATCC 11230, Stapylococcus aureus ATCC 6538P, Klebsiella pneumoniae UC57, Pseudomonas aeruginosa ATCC 27853, Proteus vulgaris ATCC 8427, Bacillus cereus ATCC 7064, Mycobacterium smegmatis CCM 2067, Listeria monocytogenes ATCC 15313, Micrococcus luteus CCM 169, Candida albicans ATCC 10231, Rhodotorula rubra DSM 70403, and Kluyveromyces fragilis ATCC 8608 by the disk-diffusion method. Verbascum gypsicola extracts had strong antimicrobial activity against the gram-positive bacteria and the yeast cultures. The extracts of Stachys L. were effective only against bacteria. The extracts of Salvia aytachii demonstrated an antimicrobial effect against bacteria and the yeast cultures used in this study.
A mathematical model for automatic differentiation in machine learning

Automatic differentiation, as implemented today, does not have a simple mathematical model adapted to the needs of modern machine learning. In this work we articulate the relationships between differentiation of programs as implemented in practice and differentiation of nonsmooth functions. To this end we provide a simple class of functions, a nonsmooth calculus, and show how they apply to stochastic approximation methods. We also evidence the issue of artificial critical points created by algorithmic differentiation and show how usual methods avoid these points with probability one.
Horizontal gene transfer from bacteria and fungi as a driving force in the evolution of plant parasitism in nematodes

Phylogenetic studies have shown that parasitism of plants by nematodes has arisen independently on at least three separate occasions. We argue that horizontal gene transfer has played a critical role in the evolution of plant parasitism on each occasion. In addition, we discuss evidence that suggests this process has driven the evolution of other life strategies within the Nematoda and that it may be considerably more common within the Phylum than commonly thought. We review recent literature that shows horizontal gene transfer to nematodes has occurred from both bacteria and fungi.
Lifetime Production and Longevity of Cows Related to Their Sires’ Breeding Values

Abstract Canadian Record of Performance data on 30,738 Holstein daughters of 138 sires from 1,498 herds across Canada were used to study relationships of breeding values of sires for early production with later performance and longevity of daughters. Expected breeding values were estimated by multiplying the regression of future daughters on present daughters with twice the least square estimate of effects of genetic group plus sire within genetic group. Though sires ranked differently for yield and longevity traits, there were positive relationships between expected breeding values for lifetime production and longevity. Sires with expected breeding values for first lactation milk more than 0 kg or expected breeding values for first lactation fat more than 0 kg had considerably higher expected breeding values for total lifetime yields and longevity. Correlations of expected breeding values for first lactation yields with lifetime production and with longevity ranged from . 33 to . 89. Expected breeding values for first lactation milk and fat yields were good predictors of expected breeding values for milk and fat yields per day of productive life. Differences between sires in their heifer progeny tests will be maintained in their daughters’ total lifetime production.
Improved $q$-values for discrete uniform and homogeneous tests: a comparative study

Large scale discrete uniform and homogeneous $P$-values often arise in applications with multiple testing. For example, this occurs in genome wide association studies whenever a nonparametric one-sample (or two-sample) test is applied throughout the gene loci. In this paper we consider $q$-values for such scenarios based on several existing estimators for the proportion of true null hypothesis, $\pi_0$, which take the discreteness of the $P$-values into account. The theoretical guarantees of the several approaches with respect to the estimation of $\pi_0$ and the false discovery rate control are reviewed. The performance of the discrete $q$-values is investigated through intensive Monte Carlo simulations, including location, scale and omnibus nonparametric tests, and possibly dependent $P$-values. The methods are applied to genetic and financial data for illustration purposes too. Since the particular estimator of $\pi_0$ used to compute the $q$-values may influence the power, relative advantages and disadvantages of the reviewed procedures are discussed. Practical recommendations are given.
Anti-Inflammatory Effect of Deflazacort.

We compared the effects of deflazacort (DFZ) a methyloxazoline derivative of prednisolone (PRED), PRED and dexamethasone (DEX) on the IgE-mediated release of preformed (histamine) and de novo synthesized (leukotriene C4 (LTC4)) proinflammatory mediators from human peripheral blood basophils. Eighteen-hour incubation of basophils with pharmacological concentrations of DFZ (10--8 to 3 × 10--6M)concentration dependently inhibited (9-50%) the IgE-dependent release of histamine and LTC4 from basophils. DFZ had a similar anti-inflammatory efficacy as DEX (10--9 to 10--6M)and similar efficacy and potency as PRED (10--8 to 3 × 10--6M). We also evaluated the effects of DFZ (10--8 to 10--7M)in combination with cyclosporin A (CsA) on the IgE-mediated histamine and LTC4 release from basophils. Eighteen-hour incubation of basophils with low concentrations of DFZ (10--8 to 10--7M) produced an additive inhibition of the release of histamine and LTC4 caused by short-term incubation with CsA (30 ng/ml).
Bilocal quantum criticality

We consider 2+1 dimensional conformal gauge theories coupled to additional degrees of freedom which induce a spatially local but long-range in time $1/(\tau-\tau')^2$ interaction between gauge-neutral local operators. Such theories have been argued to describe the hole-doped cuprates near optimal doping. We focus on a SU(2) gauge theory with $N_h$ flavors of adjoint Higgs fields undergoing a quantum transition between Higgs and confining phases: the $1/(\tau-\tau')^2$ interaction arises from a spectator large Fermi surface of electrons. The large $N_h$ expansion leads to an effective action containing fields which are bilocal in time but local in space. We find a strongly-coupled fixed point at order $1/N_h$, with dynamic critical exponent $z > 1$. We show that the entropy preserves hyperscaling, but nevertheless leads to a linear in temperature specific heat with a co-efficient which has a finite enhancement near the quantum critical point.