dataesr/scientific-paragraphs-categorization huggingface dataset:
In fact, such a data type can be supplied as an algebra for enumerating the search space in symbolic form. While infeasible for realistic input sizes, it is instructive to do such enumeration on small inputs during program development. We will always present the components of an ICORE in the above order, although there is no intrinsic need for it. As these concepts are declarative, any order would have the same meaning, as given by Definition 4. We give explicit names to many ICORE components, and allow them to be imported by name and possibly extended in further ICOREs. We will make extensive use of inheritance (as in object oriented languages) by means of an extends. with. construct. Further notational conventions. As we deal with a large number of ICORE examples, introducing an even larger number of component names, we adhere to the following naming conventions.
Note that "AABB" marks two adjacent palindromes of length 2 in the third case. Of course, there are many more candidates which must be considered to find the minimum. As the input data types are normally clear when a problem is posed, the first step in ICORE design is to decide upon the signature of core terms ( In a sense, this overthrows common practice and most textbook advice on dynamic programming (unless you already adhere to the algebraic style). There, a data type representing candidate solutions is not defined explicitly. In the implementation, we clearly do not want to explicitly represent a search space of exponential size. But for developing an algorithm, the candidate trees are really at the heart of the design. We trust on our eventual ICORE compiler to perform the deforestation, taking care that candidates are evaluated instead of explicitly constructed.
Structural Alignment without Given Structures How to transform an alignment between structure-annotated sequences into an alignment between sequences whose structure is to be guessed? Just let us eliminate structure annotation from the input of STRUCTALI. The satellite signatures change from SAS to RNA, and we adjust the right-hand sides of the rewrite rules in consequence. We replace each symbol of SAS of the form < a , > a , or • a with the symbol a from RNA. As the structure is no longer given, we impose legal base pairing by means of the predicate basep in the rewrite rules. Otherwise, the new rules are a literal copy from STRUCTALI. This is what is done with ICORE SIMULTANEOUSFOLDING, presented on page 98. Operators of the core signature and the grammar are left untouched. Let us now study two simplifications of this new ICORE.
A Generic Structure Matcher A generic structure matcher allows us to fold a plain sequence into a target structure, using the full range of edit operations available in the core signature STRAL. For that, we start from ICOREs SIMULTANEOUSFOLDING and STRUCTALI. Recall that the rewrite rules of SIMULTANEOUSFOLDING reduce a core term to two plain RNA sequences, while STRUCTALI reduce the same term to two structure-annotated sequences. To obtain an ICORE for the sequence-to-structure matching problem, all we have to do is take half of each: rules of STRUCTALI for rewriting to the target structure t, and rules of SIMULTANEOUSFOLDING for rewriting to the RNA sequence s. We call this new ICORE S2SGENERIC (page 101). With inputs t from SAS and s from RNA, it will predict a structure for s that aligns best (however defined by algebra SCORE) to the t.
ICOREs can be easily coded in a functional-logic language. In such a language, we can define some function f , specify a value y and solve equational queries such as f (x) = y, producing all instances of x that satisfy the query equation. We have used the functional-logic language Curry  [49]  to prototype the ICOREs EDITDITANCE and TREEALIGN. Of course, the standard inference machine of such a language does not know that this particular type of query can be solved in polynomial time by employing dynamic programming. It actually implements a generate-and-test strategy over a search space of exponential size. We can only align trees up to five nodes in reasonable time. Still, this is a quick way to test if a given ICORE does what it is supposed to do. Languages such as Haskell, or Dyna could also be considered as host languages  [50, 51].
Pre and post measures included rate of missed doses and medication errors reported by patients, scores from the 4-item Morisky Medication Adherence Scale (MMAS-4), level of independence, self-perceived health status, and biochemical test results. In the experimental group, data were collected on their previous experience with information and communication technologies, their rating of ALICE, and their perception of the level of independence they had achieved. The intergroup intervention effects were calculated by univariate linear models and ANOVA, with the pre to post intervention differences as the dependent variables. Results: Data were obtained from 99 patients (48 and 51 in the control and experimental groups, respectively). Patients in the experimental group obtained better MMAS-4 scores (P<.
Study Design To evaluate ALICE we opted for a single-blind experimental design with 2 groups (control and experimental) and pre and post assessments using a randomized controlled trial (NCT02071498). Patients were randomly assigned to the control or experimental group. The control group was composed of participants who did not use ALICE, and the experimental group was composed of people who used this tool for 3 months. To maintain the blinding and be able to link the pre and post measurements, patients were assigned codes based on their date of birth and initials.
Measures All participating patients completed a questionnaire to assess the rates of missed doses and of medication errors, and adherence to treatment measured by the 4-item Morisky Medication Adherence Scale (MMAS-4), as well as self-perceived health status, the number of doctors seen, whether they used a physical pillbox, and who organized their medication (over the previous 3 months). Additionally, we recorded data on the sociodemographic characteristics of participants, namely sex, age, and civil status. Those in the control group received oral and written information regarding the main risks related to their medications and the most common errors of patients when taking medications. Participants in the experimental group were given a BQ tablet or an iPad with the ALICE app installed and personalized according to the medications they had been prescribed as listed in their medical record.
Patients in this group attended individual sessions of up to 2 hours to be shown how to use the app. During the study period, patients from the experimental group had a contact telephone number for any query regarding the use of the tablet or the app. Three months later, the measurements made preintervention were repeated and extra information was collected regarding treatment adherence based on the data provided by ALICE (post). Additionally, patients from the experimental group answered a series of questions to ascertain whether they, prior to the study, had experience with using tablets, smartphones, mobile phones, and computers. They were also asked to evaluate ALICE (its performance, functionality, usability, reliability, acceptability, usefulness, design, simplicity, accessibility, and problem-solving power, as well as overall satisfaction with the tool), whether they would recommend the app to relatives, friends, and acquaintances, and their perception of the degree of independence they had achieved because of ALICE.
Statistical Analysis To assess the effectiveness of ALICE, we built various univariate linear models. Where there were intergroup differences in preintervention measurements, we performed univariate linear model and ANOVA tests, using as the dependent variables the differences between the pre and post intervention measurements in MMAS-4 scores, self-perceived health status, levels of glycated hemoglobin (HbA1), low-density lipoprotein (LDL) cholesterol, blood pressure, number of medication errors, and missed doses related to medication reported by patients. Allocation to the experimental or control group was considered as the independent variable. In the experimental group, we carried out Pearson correlation analysis between MMAS-4 scores and treatment adherence assessed objectively by the number of alerts that had not been acknowledged or had been acknowledged late (data provided by ALICE).
Experimental Study: Participant Characteristics This study was carried out between June 2012 and May 2013. Data were obtained from 99 patients (48 and 51 in the control and experimental groups, respectively). Three patients in the control group declined to participate (Figure  2 ). The characteristics of those who participated are summarized in Table  1. A total of 72 of 99 patients (73%) took more than 5 types of drugs per day and 36 of 99 (36%) were under the care of more than one doctor. In the experimental group, 22 of 51 (43%) had a computer and 19 of 51 (37%) had an Internet connection at home, whereas 39 of 51 (76%) had a mobile phone and 9 of 51 (18%) a smartphone. On the other hand, 28 of 51 individuals (55%) were not familiar with ICTs, having never used a computer, tablet, or smartphone.
Principal Results Treatment adherence was higher in patients in the experimental group than in the control group. It is important to highlight that the ALICE app helped to solve a common problem in elderly patients; namely, remembering whether they have taken their medication. By using ALICE, the number of errors decreased, although the patients who benefited in this respect were those who previously made the most errors. Our data suggest that ALICE does contribute to reducing systematic errors, but not all medication errors. The self-perceived health status, levels of glycated hemoglobin, and blood pressure remained similar from the beginning to the end of the study, and although cholesterol levels increased, the changes were not clinically relevant. That is, ALICE did not improve the clinical status of patients; however, 3 months may not be long enough to observe differences.
Limitations The small number of participants and the number of months using ALICE affected our ability to detect differences between the group using the ALICE app and the control group (eg, in relation to biomarkers) as well as our ability to generalize the results. This type of app does not address the issue of where to store medication at home to have it accessible when it should be taken. The data collected do not include details about the number of drugs to be taken daily. No count was made of pills nor did we use any other system to check if patients actually took any medication after acknowledging the alert. Further, we cannot be sure that they took the correct dose or even the correct drug. We do not know whether ALICE would continue to be effective in the longer term. This is important because a notable reason patients cite for stopping taking medications is the feeling that they are not working for them.
OBSERVATIONS AND DATA REDUCTION ESO137-001 was observed with MUSE during the science verification run on UT June 21, 2014, under program 60. A-9349(A) (  PI Fumagalli, Hau, Slezak). Observations were conducted while the galaxy was transiting at airmass ∼ 1. 25, in photometric conditions and good seeing (∼ 0. 7 ′′ -0. 8 ′′ ). The disk and the inner tail of ESO137-001 ("Field A" in Figure  1 ) were observed with three exposures of 900s each, with dithers of 13 ′′ -16 ′′ and a rotation of 90 degrees in the instrument position angle at each position. A single 900s exposure ("Field B" in Figure  1 ) was then acquired to cover the extent of the primary tail. Natural seeing mode and slow guiding mode were used. The final data product is obtained using a combination of recipes from the early-release MUSE pipeline (v0.
Emission lines Maps of the Hα line flux FHα (Figure  2 ), radial velocity relative to systemic ∆v = vvsys (Figure  3 ), and of the velocity dispersion σ (Figure  4 ) are obtained from the final datacube using the IDL custom code KUBEVIZ. With KUBEVIZ, we fit a combination of 1D Gaussian functions to the Hα and [NII] λλ6548, 6583 lines, keeping the relative velocity separation and the flux ratio of the two [NII] lines constant. While measuring the intrinsic line width σ, we also fix the instrumental line width σins at each wavelength using an interpolation function constructed with a third degree polynomial fit to the resolution curve in the MUSE manual. At the observed wavelength of Hα computed for the systemic redshift of ESO137-001 (λ = 6664. 87 Å), we find R = 2611. 9 or σins = 49 km s -1 , in agreement with the resolution measured from skylines within a resampled datacube before sky subtraction.
ACKNOWLEDGMENTS It is a pleasure to thank  Mark Swinbank  for his advice on how to handle MUSE data, together with  Emanuele Farina ,  David Wilman , and  Elke Roediger  for comments on this manuscript. We thank  David J. Wilman  and  Joris Gerssen  for the development of the KUBEVIZ code, and  Eric Slezak  for his contribution to the  MUSE science verification programme. We thank the referee,  Jeffrey Kenney , for insightful comments on this manuscript. This work is based on observations made with ESO telescopes at the  La Silla Paranal Observatory  under programme ID  60. A-9349 (A). M. Fumagalli acknowledges support by the  Science and Technology Facilities Council  [grant number  ST/L00075X/1 ]. M Fossati  acknowledges the support of the  Deutsche Forschungsgemeinschaft via  Project  ID 387/1-1.
Introduction Ocean colour remote sensing from polar orbiting sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the MEdium Resolution Imaging Spectrometer (MERIS) has become wellestablished. Products for concentration of chlorophyll a and suspended particulate matter are widely used in marine science  (McClain, 2009)  and water quality monitoring  (Bresciania, Stroppiana, Odermatt, Morabito, & Giardino, 2011). A single sun synchronous polar orbiting sensor (SSO) can provide global coverage. However, the sampling frequency, typically once per day, is too slow for resolving tidal and subdiurnal processes, particularly in coastal waters, and the presence of clouds is a big limitation to data availability. A single geostationary orbiting sensor (GEO) provides coverage of only part of the earth but offers a vastly improved sampling frequency, typically one image per hour, and hence the possibility to resolve new processes from space.
This is illustrated in Fig. 1  where the tidal variability of suspended particulate matter is captured by data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and is comparable to that found by in situ data, but is completely missed by the single image of MODIS-AQUA. The probability of obtaining data during periods of scattered clouds is also greatly enhanced, as shown in Fig. 1  (SEVIRI data for the North Sea) and Fig. 2  with data from the Geostationary Ocean Color Imager (GOCI) for the Bohai Sea. The difference between acquisition frequency for a typical SSO with overpass time at 12:30 UTC and a GEO is shown in Fig. 3 , based on actual cloud-free observations of the Southern North Sea from SEVIRI. The simulated SSO sensor gives approximately 110 observation days per year compared to about 200 days/year for the GEO.
Combination of high frequency geostationary data with empirical orthogonal function (EOF)-based multitemporal analyses methods, e. g. Beckers and Rixen (2003) , can effectively fill gaps due to clouds and objectively identify outliers. The objective of this paper is to present a review of the state of the art of geostationary ocean colour and to outline opportunities and challenges based on recent results from the GOCI and SEVIRI missions and analogous developments in other remote sensing fields (sea surface temperature, land and aerosols). This builds on the review made by the IOCCG (2012) and incorporates the findings of more recent studies in this fast-moving field. The particular focus of the present review is on coastal water applications, a priority niche for GEO exploitation as seen in the recent proliferation of studies of tidal variability of suspended particulate matter  (Choi, Park, et al.
, 2014-this volume; Choi et al. , 2012; Doxaran et al. , 2014-this volume; He et al. , 2013; Neukermans et al. , 2009; Ruddick et al. , 2012)  and the related turbidity and diffuse attenuation  (Neukermans, Ruddick, & Greenwood, 2012). The present review concentrates on aspects of data processing algorithms that are specific to GEO ocean colour. A brief summary of the relevant SSO and GEO ocean colour satellite systems is given, highlighting the essential differences between the two orbits for data processing and exploitation. The impact of these differences on data processing algorithms is then assessed with some examples from new emerging methodologies. The new processes accessible to GEO are considered and the needs for corresponding new algorithms are outlined. The particular challenges of high zenith angle atmospheric correction are summarised.
Geosynchronous orbit The geostationary orbit is a special case of a circular geosynchronous orbit above the earth's equator. The more general class of geosynchronous orbits have an orbital period equal to the earth's rotation period, but the orbital plane may be inclined to the equator, giving a ground track which oscillates North and South of the equator during the day. This allows some viewing of higher latitudes, although this is achieved at the expense of some data in the other hemisphere, e. g. while the satellite is North of the equator, it is possible to view higher Northern latitudes but the higher Southern latitudes are then not observed. A nongeostationary geosynchronous orbit also loses the potential advantages of a constant viewing geometry. These orbits and the capabilities for better coverage of high latitudes are discussed in detail in IOCCG (2012), but are not considered further in this review because there are no immediate plans for launching such a mission.
Possible GEO and SSO constellations While single sensor GEO missions can provide data for a single region, such as Europe and Africa as shown in Fig. 6 , it is necessary to deploy a constellation of GEO missions to provide global coverage of tropical and temperate latitudes (excluding polar regions). Such a GEO constellation has theoretically been available since the 1990s for meteorological applications. However, owing to the typically regional nature of GEO missions, giving differences in instrument design, calibration and data distribution there are only a few examples of homogeneous global datasets, e. g. Knapp et al. (2011)  for general climate studies and  Freitas et al. (2013)  for land surface temperature. Govaerts, Lattanzio, Taberner, and Pinty (2008)  describe the consistent global merging of meteorological GEO data for surface albedo applications.
Viewing angles for SSO and GEO ocean colour One important additional difference in the orbits is the consequently different viewing (sensor) and sun zenith angles. These angles are often represented by the viewing and sun "air masses", given approximately by the secant of the respective zenith angle. These two components may be summed to give total air mass, which is equal to 2 for zenith sun and nadir viewing and increases, for example to 4 for sun and sensor zenith angles of 60°. The performance of atmospheric correction algorithms is often directly related to air mass (International Ocean Colour Coordinating Group (IOCCG), 2010) and high total air mass conditions may degrade greatly the data quality. This challenge may stimulate different data processing algorithms for GEO ocean colour as discussed in Section 4.
For SSO sensors, viewing zenith angles are limited by design to avoid high air mass atmospheric correction problems. For GEO sensors, limitations on viewing zenith angle would critically reduce the acquisition area and so there is a strong interest in pushing algorithms to deal with higher and higher viewing zenith angles as can be seen in Fig. 6. Similarly whereas an optimally low sun zenith angle is adopted for most SSO sensors, the very advantage of high frequency data from a GEO implies the need to acquire observations for the more challenging high sun zenith angles. The variation of air mass and Rayleigh reflectance at 635 nm is shown over the day for a location in the Southern North Sea for the Northern summer solstice and autumn equinox in Fig. 7. Rayleigh reflectance varies with wavelength, λ, approximately as λ -4 , and so will be proportionally about 5.
6 times larger at 412 nm. A second important aspect of the viewing geometry, in this case advantageous, is that the viewing zenith and azimuth angles are constant in time for a GEO sensor over the day and over the year. There is not yet sufficient experience with GEO ocean colour data processing to determine whether/how this constant viewing geometry could be exploited, although it seems already likely that there will be advantages for sensor calibration and product validation, as described below.
Spectral and radiometric characteristics for SSO and GEO ocean colour While a variety of engineering approaches are available for both SSO and GEO ocean colour sensors, the spectral and radiometric characteristics, as seen by the data user (ocean colour scientists in this context), can be quite similar and can be met by suitable engineering design. The requirements for spectral resolution are set by the target parameters, which are in turn set by the science questions and applications. For example, the need to remotely sense chlorophyll a concentration in clear and turbid waters triggers a need for a set of spectral bands which are common for both SSO and GEO. Similarly the radiometric sensitivity required for such an application is common to SSO and GEO operating at the same total air mass, even though this radiometric sensitivity may be achieved by very different engineering designs, e.
For example, continuously measuring in water optical instruments can give many matchups per day for a GEO sensor, an order of magnitude better than for SSO sensors, as demonstrated for the validation of SEVIRI-retrieved turbidity by  Neukermans et al. (2012). A similar order of magnitude increase in the number of reflectance matchups from autonomous radiometer systems such as those of the AERONET-OC network  (Zibordi et al. , 2009)  will be achieved when comparing GEO validation with SSO validation. In fact, the use of a GEO sensor may assist in the validation of SSO sensors, since it will be possible to evaluate temporal variability between the SSO acquisition and the in situ data acquisition and hence determine the associated uncertainty.
New processes and algorithms The new processes that could be resolved by high frequency data from GEO ocean colour will naturally stimulate the need for new algorithms to generate the most appropriate data corresponding to these processes. As a trivial example, tidal variability could be quantified using daily average and standard deviation, etc. Such processing is not conceptually different from similar multitemporal products generated at longer time scales, such as monthly or annual composites, described in detail in International Ocean Colour Coordinating Group (IOCCG) (2007). The timing of minimum/maximum concentrations in day or harmonic analysis of time series is also relevant for tidal processes. The estimation of daily primary production may be improved by the knowledge of (uncorrelated) variations of phytoplankton biomass and Photosynthetically Available Radiation (PAR) during the day  (Lee et al.