dataesr/scientific-paragraphs-categorization huggingface dataset:
The retrieval and classification tasks In our analysis we distinguished two tasks: retrieval and classification. For each task, at most three runs for each method have been considered for evaluation, being the result of either different parameter settings or more sub-Fig. 1  The collection of textured 3D models used in the comparative study. (a) (b) (c) (d) (e) (f ) (g) (h) (i) (l) Fig. 2  Two base models (a, f ) together with some purely geometric (be) and purely texture (gl) modifications. stantial method variations. Retrieval task. Each model is used as a query against the rest of the dataset, with the goal of retrieving the most relevant objects. For a given query, a retrieved object is considered highly relevant if the two models share both geometry and texture; marginally relevant if they share only geometry; not relevant otherwise.
For this task, a dissimilarity 572 × 572 matrix was required, each element (i, j) recording the dissimilarity value between models i and j in the whole dataset. Classification task. The goal is to assign each query to both its geometric and texture class. To this aim, a nearest neighbour (1-NN) classifier has been derived from the dissimilarity matrices used in the retrieval task. For each run, the output consists of two classification matrices, a 572 × 16 one for the geometric classifi-cation and a 572 × 12 one for the texture classification. In these matrices, the element (i, j) is set to 1 if i is classified in class j (that is, the nearest neighbour of model i belongs to class j), and 0 otherwise.
Classification performance measures. We also consider a set of performance measures for classification, namely confusion matrix, sensitivity, specificity and the Mattews correlation coefficient  [19, 49]. Confusion matrix. Each classification performance can be associated with a confusion matrix CM , that is, a square matrix whose order is equal to the number of classes (according to either the geometric or the texture classification) in the dataset. For a row i in CM , the element CM (i, i) gives the number of items which have been correctly classified as elements of class i; similarly, elements CM (i, j), with j = i, count items which have been misclassified, resulting as elements of class j rather then elements of class i. Thus, the classification matrix CM of an ideal classification system should be a diagonal matrix, such that the element CM (i, i) equals the number of items belonging to the class i.
A discrete version of the MAPT function is implemented following  [25]. Roughly, the map is estimated on a grid of voxels with side length s and for a set of corresponding sampled radius values r 1 ,. , r t. This grid partitions the mesh's bounding box, but only the voxels belonging to the inner region of the mesh are considered when creating the histogram. Histograms of MAPT are very good global shape descriptors, performing state of the art results on the SHREC 2011 non-rigid watertight contest dataset  [43]. For that retrieval task, the MAPT function was computed using 8 different scales (radius values) and the map values were quantized in 12 bins; finally the 8 histograms were concatenated creating an unique descriptor of length 96. The voxel size and the radius values were chosen differently for each model, proportionally to the cube root of the object volume, in order to have the same descriptor for scaled versions of the same geometry.
Joint Histograms of MAPT and greyworld RGB data To get run GG3, a new descriptor has been designed by concatenating the original APT histogram with those obtained from the 3 components of the selected (normalized) colour space. For each voxel, the APT is evaluated at a certain radius; the procedure is then repeated for all the radius values (r 1 ,. , r t ), and the t histograms are finally linearized and concatenated. In the present paper, a sampling grid with side length s = 3 √ V /18 has been used for each model, together with 9 sampled radii that are integer multiples of s (t = 9 values from 2s to 10s). The APT target set has been divided into 8 bins. As for the colour components, the above greyworld RGB representation has been adopted, and each channel has been quantized in 4 bins in the range [0, 2].
Runs Looking at how the performances degrade across the different families of transformations, it can be noted that all methods appear to be not too sensitive against transformations of type 2, while the worst results are distributed among transformations of type 1 (runs Gi3, LBG3, TAS and Ve2), and 3 (runs BCGS3 and GG1). Table  4  reports the best highly relevant performances in terms of Nearest Neighbour, First Tier and Second Tier evaluation measures. Additionally, the last column of Table  4  records the ADR measures. All the scores, which range from 0 (worst case) to 1 (ideal performance), are averaged over all the models in the dataset. Finally, Figure  8  shows the best run of each method according to the NDCG measure as a function of the rank p. In the present evaluation, the NDCG values for all queries are averaged to obtain a measure of the average performance for each submitted run.
Relevant evaluation. In this Section we analyse the performances of methods with respect to their capability of retrieving relevant items; in this case shape (dis)similarity depends only on geometric shape properties. In analogy to Section 5. 1. 1, Figure  9  shows the best runs for all methods in terms of the average precision-recall curve. Table  5  reports the mAP scores of those runs, which are now averaged over the 16 geometric classes composing the dataset. Also in this case, we report for how many classes the mAP score exceeds values 0. 40, 0. 55, 0. 70, 0. 85. 6 , first column), it seems that the overall relevant performance is still not ideal here. This is actually not surprising, since most of methods considered for evaluation have been specifically tuned for dealing with both texture and geometric shape modifications.
Second, the method represented by run LBG2 is spectral-based, and therefore is able to capture intrinsic shape properties. As a consequence, it is invariant to rigid shape transformations, as well as some non-rigid deformations such as pose variations and bendings, which are all present in the dataset. Finally, differently from runs Gi3 and Ve2, whose geometric contribution is intrinsic as well, the descriptive power of the spectral-based approach is improved by additional spatial information provided by the intrinsic spatial pyramid matching (see Section 4. 2 for details); • A similar reasoning about the invariance under rigid and non-rigid deformations also holds for run BCGS1, whose associated method can be considered as "mostly" intrinsic. Indeed, the geometric contribution relies in this case on a collection of descriptors which are mainly intrinsic, considering either spectral-based functions or geodesic distances or Gaussian curvature; • The relatively good performance of run TAS can be explained by the fact that it mixes an extrinsic approach with a "view-based" strategy that is widely acknowledged as the most powerful and practical approach for rigid 3D shape retrieval  [67].
Even when a 3D model is articulated, non-uniformly deformed or partially occluded, the number of views  (38, actually)  used in this implementation should limit the noisy effect possibly generated in the images captured around an object; • Run GG2 mainly focuses on radial and spherical local symmetries. While the approach appears to be robust when the analysis is restricted to a single class of geometric transformations, in the most general case (i. e. the whole dataset) results are affected by a non optimal trade-off between geometric and colourimetric contribution. In this way, the relevant retrieval performance is partially conditioned by the inter-class texture variations.
Note however, that the dataset classifications considered in this  task are a purely geometric and a purely texture ones. BCGS2 GG3 Gi3 LBG2 Ve2 TAS In particular, the latter does not coincide with the classification adopted in the highly relevant retrieval task, since geometric similarity is not involved. Also, results in Tables  7  and 8  are averaged on the dataset classes: this explains the slight discrepancy between the geometric TPR and the relevant NN measure that is averaged over all the elements in the dataset. As shown by Figure  10 , geometric confusion matrices reveal a good classification performance of the methods. Indeed, all matrices appear almost diagonal, meaning that almost all elements in the dataset should be correctly classified. This qualitative intuition is con-firmed by the TPR scores reported in Table  7.
We list a few as follows: • On the one hand, the retrieval performances are positive for all methods, in either the relevant or the highly relevant scenario, or both. On the other hand, the NDCG and ADR measures are specifically designed for interpreting a multi-level dataset classification as in this case, and thus offer a complementary evaluation to the above ones. As can be seen by Figure  8  and Table  4 , results in this sense are quite far from being optimal: indeed, the best possible value for the ADR score is 1, while the highest registered scores fluctuate around 0. 5; similarly, the highest possible area under a NDCG curve equals 1, while the best scores in this contribution are around 0. 75. In other words, the benchmark was challenging and call for further improvements and new strategies able to deal with non-isometric geometric deformations, as well as affine texture deformations; • Results achieved by some of the proposed runs suggest that a structured colourimetric analysis could be more informative that a purely histogram-bases one.
Acknowledgements Work developed in the CNR research activity  ICT. P10. 009 , and partially supported by  VISION-AIR ,  European  project " FP7 INFRASTRUCTURES " (2011-2015), the  Italian CNR   Flagship  project  INTEROMICS: In-terOmics   PB05 , research unit WP 15, and  IQmulus  ( EU FP7-ICT-2011-318787 ).
Simulations Which Validate the Proposed Method During 1999, N. Moubayed has established the link between the current ripples and the amount of mutual inductance when a double-star machine is fed by power inverters  [4]. Using simulations based on the d-q model of a synchronous machine, it can be shown the positive impact in magnetic coupling if the mutual inductance is low (Figure  15 ). Two machines will be compared. The first one, named MACHINE-1, is a conventional three-phase double-star machine with a large amount of mutual inductance. The second one, named MACHINE-2, is a double-star machine which uses FSCW and respects the design rule presented above. The amount of mutual inductance is low. Using MatLab simulations it is possible to apply two different supplies. The first star can be associated to a pure sinusoidal three-phase supply and the second star can be associated to a PWM supply.
Materials and Methods We analyzed pooled safety data from three multicenter, randomized, open-label phase III trials (ION-1 ClinicalTrials. gov no. : NCT01701401; ION-2 Clinical-Trials. gov no. : NCT01768286; and ION-3 Clinical-Trials. gov no. : NCT01851330) conducted in the United States, Italy, Germany, France, Spain, and the United Kingdom. 0] [11]  RBV was administered orally twice-daily, with the dose determined according to body weight (1,000 mg daily in patients with a body weight of <75 kg and 1,200 mg daily in patients with a body weight of 75 kg). [11]  Statistical Analysis. Demographic and clinical data were extracted and analyzed. Descriptive statistics, including means, medians, 25th-75th percentiles, frequencies, and percentages were used to summarize the data. The safety analysis was performed in the safety population (i.
He is on the data monitoring board for Tibotec/Janssen. Dr. Reddy consults for and received grants from Merck, Gilead, Bristol-Myers Squibb, Janssen, and AbbVie. Dr. Kwo advises for and received grants from Merck, Gilead, Bristol-Myers Squibb, Janssen, and AbbVie. Dr. Alqahtani consults for and received grants from AbbVie, Bristol-Meyers Squibb, Gilead, Janssen, and Merck. Dr. Afdhal consults for and advises for Gilead, Bristol-Myers Squibb, Echosens, GlaxoSmithKline, AbbVie, Janssen, and Merck. He owns stock in Spring Bank. Dr. Kowdley consults for, advises for, and received grants from Gilead. He consults for and advises for AbbVie. He advises for and received grants from Evidera. He advises for Achillion, Merck, Bristol-Myers Squibb, Novartis, and Trio Health. He received grants from Immuron, Intercept, and Tobira.
He received royalties from Up-To-Date. Dr. Marcellin consults for, advises for, is on the speakers' bureau for, and received grants from Gilead. Dr. Fried consults for and received grants from Genentech/Roche, Merck, Vertex, Gilead, Bristol-Myers Squibb, AbbVie, and GlaxoSmithKline. He consults for Tibotec/Janssen and Novartis. He received grants from Janssen. Dr. Ding is employed by and owns stock in Gilead. Dr. Yang is employed by and owns stock in Gilead. Dr. Pang is employed by and owns stock in Gilead. statistically significant. Statistical analysis was performed using SAS software (version 9. 2; SAS Institute Inc. , Cary, NC). Details concerning eligibility criteria for these studies are included in the Supporting Appendix. Presence of cirrhosis was determined at screening in one of three ways: a liver biopsy showing cirrhosis (e.
Amazon Temperatures Using gridded temperature data from the CRUTS 3. 21 (Climate Research Unit time-series dataset version 3. 21  [Harris et al. , 2013] ), we compare recent annual mean temperatures for the Amazon basin with all tropical land (Figure  2 ). All our subsequent analyses include the Tocantins River, which drains a catchment at the southwestern end of the Amazon basin, although it is not strictly part of the Amazon basin. A strong temperature increase since the midseventies of the twentieth century is evident for both records, but before approximately the 1970s the Amazon seems to have experienced approximately 25 year long swings of warming and subsequent cooling with an amplitude of ∼ 0. 5 ∘ C which are absent in the pantropical land record. Similar temperature trends have previously been reported also by  Victoria et al.
Summary and Conclusions We have analyzed precipitation and temperature variation of the Amazon basin over the last few decades using various datasets summarized in Table  2. Riverine records and climatologies show an increase in precipitation in most of the basin, except the southwest and some of the south. The increase is concentrated in the wet season, while there is a slight reduction in precipitation during the dry season. There is also an increase in the frequency of extreme floods and drier than usual conditions over the same period. The precipitation trends are in agreement with trends of net water vapor transport into the basin, which is increasing, and the increased water vapor inflow is concentrated in the wet season. Climatologies show that the basin has experienced strong warming over approximately the last three and a half decades (since 1980).
Background: Patient-mediated knowledge translation (PKT) interventions engage patients in their own health care. Insight on which PKT interventions are effective is lacking. We sought to describe the type and impact of PKT interventions. Methods: We performed a systematic review of PKT interventions, defined as strategies that inform, educate and engage patients in their own health care. We searched MEDLINE, EMBASE and the Cochrane Library from 2005 to 2014 for English language studies that evaluated PKT interventions delivered immediately before, during or upon conclusion of clinical encounters to individual patients with arthritis or cancer. Data were extracted on study characteristics, PKT intervention (theory, content, delivery, duration, personnel, timing) and outcomes. Interventions were characterized by type of patient engagement (inform, activate, collaborate).
Knowledge synthesis design A systematic review was conducted  [27]. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria guided the conduct and reporting of the review  [28]  (Additional file 1). Data were publicly available so institutional review board approval was not necessary. A protocol for this review was previously published  [26]. The methods described here provide details about updates to that protocol. The original protocol, published in 2011, was based on a conceptual framework inclusive of a variety of PKT objectives, settings, format, content and delivery. When the systematic review was completed, several years had passed, necessitating an update. However, considerably more research on patient engagement had emerged, requiring reconsideration of the scope of the review.
Search strategy and screening process A comprehensive literature search was conducted by using several indexed sources. ARG and a trained research assistant conducted searches with input from a medical librarian. MEDLINE, EMBASE and the Cochrane Library were searched on September 9, 2014 from 2005 to 2014 inclusive. We limited the search to research published in the most recent decade to characterize patient-mediated interventions most likely to be applied currently. The search strategy applied to all three indexed sources (Additional file 2) was purposefully broad to be as inclusive as possible because the scoping exercise revealed that relevant articles on PKT were not consistently indexed. Searches in all databases were last updated on February 2, 2015 to ensure that eligible studies published in 2014 were captured.
Data extraction A data extraction form was developed to collect information on study design, number and type of participants, intervention design including mention of theory used to design intervention, content, mode of delivery, duration, personnel delivering the intervention, timing (before, during or after consultation) and impact, including satisfaction or harms associated with the intervention, which referred to patient-related outcomes that were reported by studies. Given research findings which demonstrated that interventions targeting both patients and providers may be more effective than targeting one group alone  [11, 12] , we also extracted data on clinician or organizational interventions that were meant to prepare for or support the delivery of PKT interventions, or the impact of PKT interventions on clinicians or organizations.
Data analysis Summary statistics were used to describe the number of studies by topic, country, year of publication, study design, risk of bias, the number of studies employing single and multifaceted interventions and the number that employed theory in intervention design. Data could not be pooled due to heterogeneity in study design, interventions and measures of impact reported across eligible studies. To categorize the interventions used in eligible studies, we compiled a framework based on three categories of engagement described in other published research (inform, activate, collaborate)  [15, 31, 32]  and support for engagement in each of these three categories from a previously published meta-review that focused on self-management but offered a range of relevant supports for engagement that might be provided to patients during clinical encounters  [33]  (Table  1 ).
The findings may not be transferrable to PKT interventions delivered in home, community or other settings that were not the ambulatory clinical encounter. Although we searched standard indexed sources of published medical literature, the search strategy may not have identified all relevant studies. We did not search the grey literature, assuming that most empirical research on PKT interventions would be found in indexed databases. Publication bias, or the tendency for journals to publish positive results, may have influenced the number and type of studies that were retrieved. However, we employed rigorous methodology that complied with standards for the conduct and reporting of systematic reviews  [28]  and used a unique conceptual framework to describe the features of PKT interventions, thereby providing insight on the design of a range of options that could be employed by others.
Mortality estimates are based on analysis of latest available national information on levels of mortality and cause distributions as at the end of 2010 together with latest available information from WHO programs, IARC and UNAIDS for specific causes of public health importance. Cause of death categories and their definitions were defined using the International Classification of Diseases, Tenth Revision (ICD-10). Cardiometabolic conditions and cancer includes malignant and other neoplasms, diabetes mellitus, endocrine disorders, and cardiovascular diseases. Total non-communicable diseases additionally include diseases in sense organ, respiratory (non-infectious), digestive, genitourinary, skin and musculoskeletal, as well as congenital anomalies, oral conditions and neuropsychiatric conditions Data for Puerto Rico is from 2007, obtained from the Centers for Disease Control and Prevention, National Vital Statistics Reports Final Data for 2007.
Compiling data that could illustrate the nutrition transition was challenging due to the scarcity of historical data on carbohydrate consumption by source and level of processing by country, or of population-wide dietary surveillance data. We used FAO Food Balance data with the caveat that they include the commodity in its whole as well as milled form and we cannot distinguish the level of refinement of these foods. However, most of the grains in recent years were likely refined, as there is now more availability and demand for processed and pre-prepared foods, such as maize-, rice-, and wheat-based products  [88]. For example, in the US, the major global exporter of wheat, the efficiency of milling wheat grain into flour (extraction rate) has steadily improved over the past 20 years  [89] , which may result in more availability of refined flour rather than the fiber-rich whole grain.
In addition, we are evaluating the GI of several of these foods as a paucity of such data exists. Preliminary findings indicate that maize remains the primary staple, followed by beans, cassava and sweet potatoes. Similar to other African nations, we are noticing a shift from these foods towards more rice and wheat consumption. After we assess the current dietary patterns regarding carbohydrate consumption, we will then conduct focus group discussions to assess the acceptability of whole grain foods as substitutes for refined staple foods.
Researchers from the University of Puerto Rico Medical Sciences Campus in San Juan, Puerto Rico, conducted focus groups among obese non-diabetic adults to assess the perceptions, motivators and barriers for intake of beans, brown rice, SSB, and cold cereals. The team also conducted a 3-week pilot randomized intervention among overweight or obese adults without diabetes to determine compliance and acceptability of four possible replacement foods (brown rice, dry beans, high-fiber breakfast cereals, and coconut water), which were provided to participants and assessed repeatedly to help promote changes at home. The studies were preceded by blind taste tests of the four replacement foods. Analyses of these data are ongoing but preliminary results show that participants were generally receptive to incorporate brown rice and more dry beans in their diet.