Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationships between feeding practices and children ’ s fussiness with vegetables

Vegetables are commonly rejected by children, making it is important to consider factors which are associated with children’s fussiness with vegetables. The current study aimed to investigate whether fussiness with vegetables is associated with a number of factors including: caregiver and child vegetable consumption; caregivers’ general feeding practices; and caregivers’ vegetable-specific feeding practices. Caregivers (N=297) of preschool children completed questionnaire measures of their child’s fussiness with vegetables, as well as several caregiver and child factors hypothesised to be associated with children’s fussiness with vegetables. Findings indicate that children who are fussier with vegetables consume a smaller quantity of vegetables and almost all have caregivers who eat a smaller quantity of vegetables. Children’s fussiness with vegetables was not significantly related to any general feeding practices used by caregivers. However, children’s fussiness with vegetables was significantly associated with use of several vegetable specific feeding practices. Caregivers of fussier children used more encouragement/pressure to eat with vegetables (r=0.14, p=0.01), hid vegetables within other foods more often (r=0.30, p=<0.01), used more food rewards (r=0.19, p<0.01) and other rewards to encourage consumption of vegetables (r=0.21, p<0.01), and compromised more when feeding vegetables (r=0.14, p=0.01). These findings suggest that rather than caregivers’ general feeding practices being related to children’s fussiness with vegetables, caregivers’ specific feeding practices used when vegetables are rejected are more significant. This study highlights that provision of information for caregivers on methods to avoid when faced with a child who is fussy with vegetables could be important for promoting children's healthy diet.
Sample Attrition in Panel Data: The Role of Selection on Observables

The traditional formulation of the attrition problem in econometrics treats it as a special case of the partial-population section bias model in which selection (attrition) is based on model unobservables. This paper considers instead the treatment of attrition as a special case of selection on observables. The analysis compares and contrasts the identification assumptions and estimation procedures for this case with those of the usual case of selection on unobservables. Selection on observables case has rarely been considered in the econometric literature on the problem and hence the framing of the problem in these terms, as presented here, is apparently new. The selection on observables problem is made nontrivial by the assumption that selection occurs on endogenous observables; leading examples are lagged dependent variables from earlier periods in the panel. Among other things, it is shown in the paper that (i) weighted least squares using estimated attrition equations to construct the weights is one method of consistent estimation in thise case; (ii) simply conditioning on the observables does not, by itself, generate consistent estimates; and (iii) that the model is closely related to the choice-based sampling model.
The effect of meter-scale lateral oxygen gradients at the sediment-water interface on selected organic-matter based alteration, productivity and temperature proxies

Abstract. A valid assessment of selective aerobic degradation on organic matter (OM) and its impact on OM-based proxies is vital to produce accurate environmental reconstructions. However, most studies investigating these effects suffer from inherent environmental heterogeneities. In this study, we used surface samples collected along two meter-scale transects and one longer transect in the northeastern Arabian Sea to constrain initial OM heterogeneity, in order to evaluate selective aerobic degradation on temperature, productivity and alteration indices at the sediment-water interface. All of the studied alteration indices, the higher plant alkane index, alcohol preservation index, and diol oxidation index, demonstrated that they are sensitive indicators for changes in the oxygen regime. Several export production indices, a cholesterol-based stanol/stenol index and dinoflagellate lipid- and cyst-based ratios, showed significant (more than 20%) change only over the lateral oxygen gradients. Therefore, these compounds do not exclusively reflect surface water productivity, but are significantly altered after deposition. Two of the proxies, glycerol dibiphytanyl glycerol tetraether-based TEX86 sea surface temperature indices and indices based on phytol, phytane and pristane, did not show any trends related to oxygen. Nevertheless, unrealistic sea surface temperatures were obtained after application of the TEX86, TEX86L, and TEX86H proxies. The phytol-based ratios were likely affected by the sedimentary production of pristane. Our results demonstrate the selective impact of aerobic organic matter degradation on the lipid and palynomorph composition of surface sediments along a short lateral oxygen gradient and suggest that some of the investigated proxies may be useful tracers of changing redox conditions at the sediment-water interface.
Credit card fraud detection using deep learning

the growth of ecommerce websites people and financial companies rely on online services to carry out their transactions that has led to an exponential increase in the credit card frauds.Fraudulent credit card transactions lead to a loss of huge amount of money. The design of an efficient fraud detection system is necessary in order to reduce the losses incurred by the customers and financial companies. Research has been done on many models and methods to prevent and detect credit card frauds. Fraudsters masquerade the normal behaviour of customers and the fraud patterns are changing rapidly so the fraud detection system needs to constantly learn and update.Deep Learning has been used in many fields like in speech recognition, image recognition and natural language processing. This paper aims to understand how Deep Learning can be helpful in detecting credit card frauds. Deep learning package H2O is used in this paper to train a deep learning model. H2O deep learning framework is an efficient framework to handle large datasets and perform deep learning. The performance evaluation of the deep learning model is done on the UCSD Data Mining Contest2009 data and it is found that deep learning models give a high accuracy to detect the fraudulent transactions.
Productivity in innovation: the role of inventor connections and mobility

We study the transmission of knowledge arising from working relationships established by inventors, and its impact on firms’ innovation production. The study’s contribution to the literature is twofold. First, we consider those relationships that originate through inventor connections (”multi-applicant” inventors) and inventor mobility. Second, we analyse their effect on companies’ innovation production. The study focuses on the role played by geographical proximity, and the dynamic effects of knowledge flows. The geographical question is dealt with on a detailed level, by measuring knowledge spillovers observed within the same Local Labour System (LLS), between different LLSs of the region and, finally, with extra-regional LLSs. Dynamics are captured by measuring inventor mobility and connections occurring up to 20 years before patent filing. The analysis is carried out on the Italian region of Veneto and is based upon the original OECD REGPAT database of patent applications filed at the European Patent Office. The manual procedure we used to clean the data allows us to resolve some issues raised in the literature. Our results show that the impact of working relationships on innovation production depends on both geography and dynamics. Therefore, we can not conclude that productivity effects of knowledge flows occurring through the labour market are localized. However, we can conclude that working relationships have sizable productivity effects on innovation, either in the short or in the long run, depending on the geographical distance.
Bathymetric controls on calving processes at Pine Island Glacier

Abstract. Pine Island Glacier is the largest current Antarctic contributor to sea-level rise. Its ice loss has substantially increased over the last 25 years through thinning, acceleration and grounding line retreat. However, the calving line positions of the stabilising ice shelf did not show any trend within the observational record (last 70 years) until calving in 2015 led to unprecedented retreat and changed the alignment of the calving front. Bathymetric surveying revealed a ridge below the former ice shelf and two shallower highs to the north. Satellite imagery shows that ice contact on the ridge was likely lost in 2006 but was followed by intermittent contact resulting in back stress fluctuations on the ice shelf. Continuing ice-shelf flow also led to occasional ice-shelf contact with the northern bathymetric highs, which initiated rift formation that led to calving. The observations show that bathymetry is an important factor in initiating calving events.
Logistic regression methods application in medical information systems

Data analysis methods are widely used to solve many different problems. Intensive development in the field of knowledge discovery in database is a response to a sharp increase in the amount of information in electronic format. Classification is one of the main steps in data analysis. Taking into consideration medical data, it becomes extremely important to obtain knowledge containing valuable information about the patients with serious illness, e.g. cancer. In this paper we mainly focused on logistic regression model parameters. We built model, which shows the impact of predictors on the dependent variable. It will help to create the medical knowledge base as a next step. ZASTOSOWANIE METOD REGRESJI LOGISTYCZNEJ W MEDYCZNYCH SYSTEMACH INFORMACYJNYCH
Proposing a Novel Synergized K-Degree L-Diversity T-Closeness Model for Graph Based Data Anonymization

Privacy becoming a major concern in publishing sensitive information of the individuals in the social network data. A lot of anonymization techniques are evolved to protect sensitive information of individuals. kanonymity is one of the data anonymization framework for protecting privacy that emphasizes the lemma, every tuple should be different from at least k-1 other tuples in accordance with their quasi-identifiers(QIDs). Researchers have developed privacy models similar to k-anonymity but still label-node relationship is not well protected. In this paper, we propose a novel synergized k-degree l-diversity t-closeness model to effectively anonymize graphical data at marginal information loss, thereby controlling the distribution of sensitive information in graphical structure based on the threshold value. Experimental evidences indicate the substantial reduction in information loss ratio during synergy.
Positive Edge: A Pricing Criterion for the Identification of Non-Degenerate Simplex Pivots

The positive edge is a new pricing rule for the primal simplex: it identifies, with a probability error less than or equal to 2 in single precision binary floating-point format and 2 in double precision format, variables allowing for non-degenerate pivots. These are identified directly from a short calculation on the original coefficients of the constraint matrix. The complexity is the same as for the computation of the reduced cost. If such a variable has a negative reduced cost, it strictly improves the objective function value when entered into the basis. The preliminary computational experiments made with cplex show its high potential. We designed a simple algorithm using two external procedures: one identifies variables that allow for non-degenerate pivots while the other identifies variables with negative reduced cost. These are sent to the primal simplex algorithm of cplex. It has been tested on fourteen medium-sized aircraft fleet assignment instances (5000 constraints and 25 000 variables), two large-scale manpower planning problems (100 000 constraints and 450 000 variables), and nine pds instances from the Mittelmann library. All these problems are highly degenerate. On the first group, our algorithm is 7.4 times faster than cplex on average and the number of pivots is almost reduced by a factor 2. On the second and third groups, it is 50% faster and the number of pivots is decreased by 2.4 and 3.6, respectively. It has also been tested on Fome12 and Fome13 from the Mittelmann library. For these two highly dense problems, our simple implementation failed. The integration of the positive edge rule within a primal simplex code should prevent such cases by eliminating the external procedures and taking advantage of partial pricing strategies.
The Yellowstone "hot spot" track results from migrating basin-range extension

Whether the volcanism of the Columbia River Plateau, eastern Snake River Plain, and Yellowstone (western U.S.) is related to a mantle plume or to plate tectonic processes is a long-standing controversy. There are many geological mismatches with the basic plume model as well as logical flaws, such as citing data postulated to require a deep-mantle origin in support of an “upper-mantle plume” model. USArray has recently yielded abundant new seismological results, but despite this, seismic analyses have still not resolved the disparity of opinion. This suggests that seismology may be unable to resolve the plume question for Yellowstone, and perhaps elsewhere. USArray data have inspired many new models that relate western U.S. volcanism to shallow mantle convection associated with subduction zone processes. Many of these models assume that the principal requirement for surface volcanism is melt in the mantle and that the lithosphere is essentially passive. In this paper we propose a pure plate model in which melt is commonplace in the mantle, and its inherent buoyancy is not what causes surface eruptions. Instead, it is extension of the lithosphere that permits melt to escape to the surface and eruptions to occur—the mere presence of underlying melt is not a sufficient condition. The time-progressive chain of rhyolitic calderas in the eastern Snake River Plain–Yellowstone zone that has formed since basin-range extension began at ca. 17 Ma results from laterally migrating lithospheric extension and thinning that has permitted basaltic magma to rise from the upper mantle and melt the lower crust. We propose that this migration formed part of the systematic eastward migration of the axis of most intense basin-range extension. The bimodal rhyolite-basalt volcanism followed migration of the locus of most rapid extension, not vice versa. This model does not depend on seismology to test it but instead on surface geological observations.
Maximum earthquake magnitudes in the Aegean area constrained by tectonic moment release rates

SUMMARY Seismic moment release is usually dominated by the largest but rarest events, making the estimation of seismic hazard inherently uncertain. This uncertainty can be reduced by combining long-term tectonic deformation rates with short-term recurrence rates. Here we adopt this strategy to estimate recurrence rates and maximum magnitudes for tectonic zones in the Aegean area. We first form a merged catalogue for historical and instrumentally recorded earthquakes in the Aegean, based on a recently published catalogue for Greece and surrounding areas covering the time period 550 BC‐2000 AD, at varying degrees of completeness. The historical data are recalibrated to allow for changes in damping in seismic instruments around 1911. We divide the area up into zones that correspond to recent determinations of deformation rate from satellite data. In all zones we find that the Gutenberg‐Richter (GR) law holds at low magnitudes. We use Akaike’s information criterion to determine the best-fitting distribution at high magnitudes, and classify the resulting frequency‐magnitude distributions of the zones as critical (GR law), subcritical (gamma density distribution) or supercritical (‘characteristic’ earthquake model) where appropriate. We determine the ratio η of seismic to tectonic moment release rate. Low values of η ( 1.0). The seismic and tectonic moment release rates are then combined to constrain recurrence rates and maximum credible magnitudes (in the range 6.7‐ 7.6 m W where the results are well constrained) based on extrapolating the short-term seismic data. With current earthquake data, many of the tectonic zones show a characteristic distribution that leads to an elevated probability of magnitudes around 7, but a reduced probability of larger magnitudes above this value when compared with the GR trend. A modification of the generalized gamma distribution is suggested to account for this, based on a finite statistical second moment for the seismic moment distribution.
NOMLEX: a lexicon of nominalizations

NOMLEX (NOMinalization LEXicon) is a dictionary of English nominalizations currently under development at New York University. NOMLEX seeks not only to describe the allowed complements for a nominalization, but also to relate the nominal complements to the arguments of the corresponding verb. We consider both the main verbal arguments (subject, direct object, and indirect object), which may map into a variety of nominal positions, and the oblique verbal complements, which map more directly into nominal complements. The argument correspondences are specified through a combination of explicit information in the lexical entries and general linguistic constraints on the correspondences.
Generation and characterization of pigment mutants of Chlamydomonas reinhardtii CC-124

The induced mutagenesis method for deriving pigment mutants of a green microalga, Chlamydomonas reinhardtii CC-124 and their pigment composition as well as ability to assess mutability of contaminated aquatic ecosystems were studied. In the present study, 14086 mutants (colonies) were obtained by exposure of the wild strain, C. reinhardtii CC-124 , to 1, 2, 3, 5 min of ultraviolet (UV) irradiation. After screening, these mutants (colonies) revealed four pigmented mutants ( 124y-1, 124p-1, 124y-2 and 124p-2 ). Compared to the wild CC-124 , these mutants are characterized by a decrease in chlorophyll a & b content and an increase in carotenoids. The lowest decrease in chlorophyll a was 3 to 4 folds, while the highest increase in carotenoids was 2 to 4 folds. The result of bio-test, using the resulting pigment mutant of C. reinhardtii 124y-1 showed that mutagenic activity was observed significantly in both Tekeli River and Pavlodar Oil Refinery in Kazakhstan; the waste water of the Pavlodar Oil Refinery had hightoxicity while the water of the Tekeli River had medium-toxicity. Keywords: Ultraviolet (UV) mutagenesis, Chlamydomonas reinhardtii, biotesting African Journal of Biotechnology , Vol. 13(2), pp. 274-279, 8 January, 2014
Domain-Independent Text Mining Framework based on Open Information Extraction

We propose a robust hybrid text mining solution that combines Open Information Extraction (OIE), Knowledge Discovery from Databases (KDD) and Association Rules Mining (ARM) methods to perform domain independent text mining task. For OIE, we used ClausIE system with extra added features like "Co-Reference Resolution" and "Acronyms Detection" that allow the system to extract more information and can help later in the mining process. The added features in the proposed solution will enhance both the quality and the quantity of the extracted propositions regarding both precision and recall.
Personalized Item Ranking from Implicit User Feedback: A Heterogeneous Information Network Approach

In today’s era of the digital world with information overload, generating personalized recommendations for the e-commerce users is a challenging and interesting problem. Recommendation of top-N items of interest to a user of e-commerce is highly challenging using binary implicit feedback. The training data is usually very sparse and have binary values capturing a user’s action or inaction. Due to the sparseness of data and lack of explicit user preferences, the recommendations generated by model-based and neighborhood-based approaches are not effective. Of late, network-based item recommendation methods, which utilize item related metainformation, are beginning to attract increasing attention for binary implicit feedback data. In this work, we propose a heterogeneous information network based recommendation model for personalized top-N recommendations using binary implicit feedback data. To utilize the potential of meta-information related to items, we utilize the concept of meta-path. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various meta-paths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback data. To show the effectiveness, the proposed model is experimentally evaluated using the real-world dataset.
Field And Source Equivalence In Source Reconstruction On 3D Surfaces

This paper describes in detail difierent formulations of the inverse-source problem, whereby equivalent sources and/or flelds are to be computed on an arbitrary 3-D closed surface from the knowledge of complex vector electric fleld data at a specifled (exterior) surface. The starting point is the analysis of the formulation in terms of the Equivalence Principle, of the possible choices for the internal flelds, and of their practical impact. Love's (zero interior fleld) equivalence is the only equivalence form that yields currents directly related to the flelds on the reconstruction surface; its enforcement results in a pair of coupled integral equations. Formulations resulting in a single integral equation are also analyzed. The flrst is the single-equation, two-current formulation which is most common in current literature, in which no interior fleld condition is enforced. The single-current (electric or magnetic) formulation deriving from continuity enforcement of one fleld is also introduced and analyzed. Single-equation formulations result in a simpler implementation and a lower computational load than the dual-equation formulation, but numerical tests with synthetic data support the beneflts of the latter. The spectrum of the involved (discretized) operators clearly shows a relation with the theoretical Degrees of Freedom (DoF) of the measured fleld for the dual-equation formulation that guarantees extraction of these DoF; this is absent in the single-equation formulation. Examples conflrm that single- equation formulations do not yield Love's currents, as observed both with comparison with reference data and via energetic considerations. The presentation is concluded with a test on measured data which shows the stability and usefulness of the dual-equation formulation in a situation of practical relevance.