dataesr/scientific-paragraphs-categorization huggingface dataset:
Instruction Scheduling An important feature of the SW26010 is its dual instruction pipeline. Floatingpoint instructions and register communication instructions can be issued simultaneously by dual instruction pipeline. In some specific scenarios, great performance gains can be achieved by instruction scheduling, which was used in the design of swDNN  [3]  and swDGEMM  [5]. After comparison, we selected the instruction scheduling provided by swDGEMM. Through the instruction scheduling method, the execution cycle of the instruction stream is reduced from 26 to 16, the overall operation efficiency of the program is greatly improved.
Results and Analysis We use a number of sets of parameters to test our program performance and compare it to the K80 GPU using cuDNNv7. The final test results are shown in Fig. 3 , Fig. 4 , Fig. 5. Finally, we achieve a double-precision convolution performance over 2. 4 Tflops, achieving 81% of the processor's peak performance, which is higher than swDNN  [3] 's measured performance (only 54%) shown in their paper, and under different convolution filters, the performance is still stable(see Fig. 3 ). Compared to the K80 using cuDNNv7, we achieve.
MASK is available in 17 languages and deployed in 23 countries. The potential usefulness of MASK will be further explored by POLLAR (Impact of Air POLLution on Asthma and Rhinitis), a new Horizon 2020 project using the Allergy Diary. The scientific evidence of MASK-air is based on a validated "research" tool (The Allergy Diary up to 2018) that has led to large scale deployment (MASK-air, 2019-):  Validation of the App using COSMIN guidelines (28);  Baseline characteristics informed (29);  Work productivity associated with the control of allergic diseases  (30, 31) ;  EQ-5D is available and was found to correlate with baseline characteristics  (32) ;  Novel phenotypes of allergic diseases were discovered  (33) ;  Adherence to treatment is extremely low and novel approaches to inform the efficacy of treatment have been proposed  (34)  leading to novel studies to better understand guidelines  (35) (36) (37).
The HBNs were then converted to auditory format [auditory stimuli have been used in similar studies for e. g. ,  Klink and Wu (2014) ], in a female voice, using the Google translate speech (HBNs were written in the Hindi script and then converted to the audio format). This was done mainly because in the Hindi language, there are no differences between pronunciation and orthography. For e. g. , the words cell and sell may have the same pronunciation in the English, but a different orthography, but in the Hindi language, if the words have the same pronunciation they will have the same orthography or script (for e. g. , से ल) as well). To rule out the resemblance of HBNs to real/existing brands, a pre-test was conducted with 60 American participants recruited from Amazon Mechanical Turk (M Turk;  Paolacci & Chandler, 2014).
One participant who had provided the same Likert response to all questions, was excluded from the analysis. The data from the remaining 98 participants were analyzed (Final M age = 35. 95 years, SD = 9. 27, Males = 47, Females = 51). Seven subjects knew an additional foreign language other than English (these languages were Japanese, Tagalog, Russian, Ukranian, Hebrew, Cantonese, Spanish (two participants), and French; participants' proficiency in these languages was not asked). Participants were instructed to wear headphones throughout the study and some instructions were given orally to check if they were using the headphones.
| Procedure and design All studies reported were designed and managed using Inquisit 5 software (from millisecond. com) and comprised three blocks with a short break in between. In each block, participants listened to 84 HBNs (i. e. , 28 HBN groups chosen from Table  2  at random) and rated whether the HBN was appropriate for a basic brand or a luxury brand (on a Likert scale, 1 = Extremely basic brand name & 11 = Extremely luxury brand name; HBNs was continuously played on a loop till the participant provided a response; see Appendix 1 for the instructions given to participants).
It is also supported by the fact that even among the existing luxury brand names (see  Sung et al. , 2015, for   The perception of luxury of HBNs with more than three syllables was equivalent to the luxury appeal of HBN3S which raises questions about the incremental effect in luxury appeal for brand name that have a length of more than 3 syllables. Word length is believed to be 1 COCA is a well-cited linguistic database  (Davies, 2010)  compiled from the words often used in spoken languages, fiction, popular magazines and in newspapers and is frequently updated. It includes a collection of over 520 million words taken from over 220,225 texts published between 1990 and 2015, which are commonly used in spoken language, fiction, popular magazines, newspapers, and academic texts. This list is updated regularly and provides a current overview of the usage of the English language  (Schmitt & Schmitt, 2014).
Although the results of sound symbolism have been shown to generalize across languages, further research is needed to test these results in non-English speaking populations before we can generalize these results across all languages. One last potential limitation of this paper is that we have used a Hindi script to convert HBN to the sound stimuli. This was done primarily to keep the phonetic structure intact which is often not possible in English (for e. g. , pronunciation of the letter /o/ in the words go and to is different). For known English words or brand names, using text to speech conversion is not a problem, but for hypothetical words or non-words, it becomes challenging. It remains to be seen whether or not the Hindi accent impacted on our results but we believe this to be highly unlikely.
The detection codes were applied blindly to a realistic galaxy mock, built using PhotReal  (Ascaso et al. 2015)  on the Euclid wide light-cone  (Merson et al. 2013) , which was considered to be the best compromise available in terms of angular size (300 deg 2 ), depth (z > 2. 5), and realistic modeling of galaxy properties. We present the main assumptions and methodology of each of the competing codes and discuss the main properties of the simulated mock in the context of cluster detection. The code detections were matched to the true mock clusters and this information was used to evaluate the performance of the algorithms. Special care was given to the matching procedure by using several methods, allowing us to estimate the associated systematic uncertainties. In light of the mock properties, the performance comparison of the different algorithms participating in the challenge guided our selection of those now being validated and implemented in the Euclid pipeline.
5 Mpc. This way the adaptive kernel size in the densest region (corresponding to galaxy clusters) is about 1 Mpc (i. e. , the typical size of cluster cores). Structures in these density maps are detected using the source extraction software, SExtractor  (Bertin & Arnouts 1996) , in the different redshift bins with a detection threshold set to a given number of galaxies per Mpc 2. The initial structures are then assembled into larger structures using a minimal spanning tree FoF algorithm (see  Adami & Mazure 1999). Any two detections less than 1 Mpc apart and with ∆z ≤ 0. 05 are merged. A detailed description of each step of the algorithm, as well as a discussion of Table 1: Summary of properties and names of eight cluster finder algorithms that participated in CFC. The properties listed here correspond to those of the final CFC.
Euclid mock galaxy catalog The final Euclid CFC made use of a main mock galaxy catalog  (Ascaso et al. 2015)  in order to test the behavior of the detection algorithms on Euclid-like data. This mock includes photometric redshifts, z phot , and their errors. It was limited to H-band magnitudes brighter than H AB = 24 to mimic the context of the Euclid wide survey (H AB = 24 for 5σ point-source). A 20 deg 2 region including both photometric and spectroscopic redshifts was also provided as a calibration field for the photometric redshifts or for the detection code parameters. While it is not the purpose of this paper to make an assessment of the validity of the semianalytic models on which the mock is based, we do aim to verify the reliability of the model predictions. This is done in order to quantify how realistic the performance of the cluster finders are when applied to the mock.
Galaxy catalog The galaxy catalog was extracted from the  Ascaso et al. (2015)  mock, which was based on the H-band wide light-cone from  Merson et al. (2013). The light-cone was generated from the Millennium simulation  (Springel et al. 2005 ) using semianalytical modeling of galaxy formation with the GALFORM model  (Lagos et al. 2012). The mock was reprocessed with the software PhotReal  (Ascaso et al. 2015)  to obtain realistic galaxy photometry compliant with Euclid depth in Y JH (down to magnitude 24 at 5 σ, point sources) and grizY (down to magnitudes  25. 2, 24. 8, 24. 0, 23. 4 and 21. 7  at 10 σ, extended sources), assuming complementary ground-based DES data  (Mohr et al. 2012). This corresponds to the pessimistic case in  Ascaso et al. (2015) , as opposed to the combination of the Euclid observations with deeper ground-based photometry from LSST (the optimistic case in  Ascaso et al.
2015). In this sense the performance derived hereafter is expected to be conservative. The photometry was also modified by PhotReal using a set of empirical templates to fit observed spectral distributions and make the galaxy colors, luminosity and mass functions more consistent with current observations (see  Ascaso et al. 2015 , for more details). Photometric redshifts were estimated using the Bayesian Photometric Redshifts software (BPZ,  Benítez 2000; Benítez et al. 2004; Coe et al. 2006 ) applied to the PhotReal photometry. The most likely redshifts (PDF peaks) were derived, as well as their probability distribution functions. We note that the magnitude cut applied to the mock used in the present paper introduces and extra idealization. Indeed, in practice the Euclid catalog will extend to fainter magnitudes (albeit being incomplete), which may benefit to the detection codes, in particular for the detection of high redshift clusters.
The N-fragmentation rate and the N-over-merging rate are given in Figures  16  and 17  as a function of mass for different redshift bins. All cluster finders present a fragmentation rate that increases with mass and decreases with redshift. This is expected as more substructures become accessible as the mass increases. Similarly, substructures cannot be resolved at higher redshift as fewer galaxies are accessible. Fragmentation could also arise because a code detects lower mass structures that are inside the matching radius of a larger system. In this sense, a code that is more complete at lower masses will automatically have a higher fragmentation rate. We observe significant differences between the different cluster finders. While AMASCFI,  HCFA and PZWav present low fragmentation rates, AMICO and WaZP present a higher fragmentation rate, and sFoF tends to fragment clusters up to very low masses and with a large number of fragments (more than 5 fragments are common, as already illustrated in Figure  6  for this cluster finder).
A. 2. Voronoi The Voronoi diagram and its dual, the Delaunay triangulation, have long been known as a very useful and versatile mathematical techniques in a variety of topics -including 3D reconstruction and modeling of objects, visualization of medical datasets, shape analysis and pattern recognition, computer animation, to mention a few  (Okabe et al. 2000; Aurenhammer et al. 2013 , for wide a review of their applications in computational geometry). In short, a Voronoi tessellation on a two-dimensional (tridimensional) distribution of objects is a unique plane (volume) partition into convex cells (polytopes), each of them containing one, and only one, such object. These are the set of points which are closer to that object than to any other. Its main advantage consists in its being completely non-parametric and adaptive, thus very useful in all those cases when one does not desire to define a priori a specific spatial scale for the analysis.
Visualization recommender systems aim to lower the barrier to exploring basic visualizations by automatically generating results for analysts to search and select, rather than manually specify. Here, we demonstrate a novel machine learningbased approach to visualization recommendation that learns visualization design choices from a large corpus of datasets and associated visualizations. First, we identify fve key design choices made by analysts while creating visualizations, such as selecting a visualization type and choosing to encode a column along the X-or Y-axis. We train models to predict these design choices using one million dataset-visualization pairs collected from a popular online visualization platform. Neural networks predict these design choices with high accuracy compared to baseline models.
steep learning curves due to a reliance on manual specifcation through code  [7, 68]  or clicks  [2, 62]. As a result, data visualization is often inaccessible to the growing number of domain experts who lack the time or background to learn sophisticated tools. While required to create bespoke visualizations, manual specifcation is unnecessary for many common use cases such as preliminary data exploration and the creation of basic visualizations. To support these use cases in which speed and breadth of exploration are more important than customizability  [63] , systems can leverage the fnding that the properties of a dataset infuence how it can and should be visualized. For example, prior research has shown that the accuracy with which visual channels (e. g. position and color) encode data depends on the type  [5, 15, 67]  and distribution  [28]  of data values.
While recent systems like DeepEye  [33] , Data2Vis  [17] , and Draco-Learn  [37]  are exciting, they do not learn to make visualization design choices as an analyst would, which impacts interpretability and ease of integration into existing systems. Furthermore, because these systems are trained with annotations on rule-generated visualizations in controlled settings, they are limited by the quantity and quality of data. We introduce VizML, a ML-based approach to visualization recommendation using a large corpus of datasets and associated visualizations. To begin, we describe visualization as a process of making design choices that maximize efectiveness, which depends on dataset, task, and context. Then, we formulate visualization recommendation as a problem of developing models that learn to make design choices.
We train and test machine learning models using one million unique dataset-visualization pairs from the Plotly Community Feed  [46]. We describe our process of collecting and cleaning this corpus, extracting features from each dataset, and extracting fve key design choices from corresponding visualizations. Our learning task is to optimize models that use features of datasets to predict these choices. Neural networks trained on 60% of the corpus achieve ∼ 70 -95% accuracy at predicting design choices in a separate 20% test set. This performance exceeds that of four simpler baseline models, which themselves out-perform random chance. We report feature importances from one of these baseline models, interpret the contribution of features to a given task, and relate them to existing research. We evaluate the generalizability and uncertainty of our model by benchmarking against a crowdsourced test set.
We construct this test set by randomly selecting datasets from Plotly, visualizing each as a bar, line, and scatter plot, and measuring the consensus of Mechanical Turk workers. Using a scoring metric that adjusts for the degree of consensus, we fnd that VizML performs comparably to Plotly users and Mechanical Turkers, and outperforms two rule-based and two ML-based visualization recommendation systems. To conclude, we discuss interpretations, applications, and limitations of our initial machine learning approach to visualization recommendation. We also suggest directions for future research, such as aggregating public training and benchmarking corpora, integrating separate recommender models into an end-to-end system, and refning defnitions of visualization efectiveness.
PROBLEM FORMULATION Data visualization communicates information by representing data with visual elements. These representations are specifed using encodings that map from data to the retinal properties (e. g. position, length, or color) of graphical marks (e. g. points, lines, or rectangles)  [5, 12]. Concretely, consider a dataset that describes 406 automobiles (rows) with eight attributes (columns) such as miles per gallon (MPG), horsepower (Hp), and weight in pounds (Wgt)  [50]. To create a scatterplot showing the relationship between MPG and Hp, an analyst encodes each pair of data points with the position of a circle on a 2D plane, while also specifying other retinal properties such as size and color: To create bespoke visualizations, analysts may need to exhaustively specify encodings in detail using expressive tools.
But a scatterplot is specifed with the Vega-lite  [55]  grammar by selecting a mark type and felds to be encoded along the x-and y-axes, and in Tableau  [62]  by placing the two columns onto the respective column and row shelves. That is, to create basic visualizations in many grammars or tools, an analyst specifes higher-level design choices, which we defne as statements that compactly and uniquely specify a bundle of lower-level encodings. Equivalently, each grammar or tool afords a design space of visualizations, which a user constrains by making choices. We formulate basic visualization of a dataset d as a set of interrelated design choices C = {c}. However, not all design choices result in valid visualizations -some choices are incompatible with each other. For instance, encoding a categorical column with the Y position of a line mark is invalid.
Therefore, the set of choices that result in valid visualizations is a subset of the space of all possible choices. The efectiveness of a visualization can be defned by informational measures such as efciency, accuracy, and memorability  [6, 74] , or emotive measures like engagement  [19, 27]. Prior research also shows that efectiveness is informed by low-level perceptual principles  [15, 22, 31, 51]  and dataset properties  [28, 54] , in addition to contextual factors such as task  [3, 28, 53] , aesthetics  [14] , domain  [24] , audience  [60] , and medium  [36, 57]. In other words, an analyst makes design choices C max that maximize visualization efectiveness given a dataset and contextual factors. But making design choices can be expensive. A goal of visualization recommendation is to reduce the cost of creating visualizations by automatically suggesting a subset of design choices C r ec ⊆ C that maximize efectiveness.
ML-based Visualization Recommender Systems The guidelines encoded by rule-based systems often derive from experimental fndings and expert experience. Therefore, an indirect manner, heuristics distill best practices learned from another analyst's experience of creating and consuming visualizations. Instead of aggregating best practices learned from data and representing them in a system with rules, ML-based systems propose to train models that learn directly from data and can be embedded into systems as-is. A schematic comparison of ML-based visualization recommender systems can be found in the SM Section S2. DeepEye  [33]  combines rule-based visualization generation with models trained to 1) classify a visualization as "good" or "bad" and 2) rank lists of visualizations. The Deep-Eye corpus consists of 33,412 bivariate visualizations of columns drawn from 42 public datasets.
100 students annotated these visualizations as good/bad, and compared 285,236 pairs. These annotations, combined with 14 features for each column pair, train a decision tree for classifcation and a ranking neural network  [10]  for the "learning to rank" task. Data2Vis  [17]  uses a neural machine translation approach to create a sequence-to-sequence model that maps JSONencoded datasets to Vega-lite visualization specifcations. The model is trained using 4,300 automatically generated Vega-Lite examples, consisting of 1-3 variables, generated from 11 distinct datasets. Model predictions are qualitatively validated by examining the visualizations generated from 24 common datasets. Draco-Learn  [37]  learns trade-ofs between constraints in Draco, a formal model that represents 1) visualizations as logical facts and 2) design guidelines as hard and soft constraints.
The size of our corpus permits the use of 1) large feature sets that capture many aspects of a dataset and 2) high-capacity models such as deep neural networks. The third major diference is one of data quality. In contrast to the few datasets used to train the three existing systems, the datasets used to train VizML models are extremely diverse in shape, structure, and distribution. Furthermore, the visualizations used by other ML-based recommender systems are generated by rule-based systems and evaluated under controlled settings. The corpus used by VizML is the result of real visual analysis by analysts on their own datasets. However, VizML faces two major limitations. First, these three ML-based systems recommend both data queries and visual encodings, while VizML only recommends the latter.
DATA We describe our process for extracting features and design choices from the processed Plotly data. These are steps 1, 2 and 3 in Figure  1. In the SM Section S3, we describe our process for collecting and cleaning the corpus of 2. 3 million dataset-visualization pairs from the Plotly Community Feed  [44, 46]  and provide a description of the data. This paper is the frst time the Plotly corpus, generated by 143,007 unique users, is used to train visualization recommender systems. The corpus along with analysis scripts is publicly available at https://vizml. media. mit. edu.
Feature Extraction We map each dataset to 841 features, mapped from 81 singlecolumn features and 30 pairwise-column features using 16 aggregation functions. Detail on each of the features is found in Table  S2  in the SM Section S4. Each column is described by 81 single-column features across four categories. The Dimensions (D) feature is the number of rows in a column. Types (T) features capture whether a column is categorical, temporal, or quantitative. Values (V) features describe the statistical and structural properties of the values within a column. Names (N) features describe the column name. We distinguish between these feature categories for three reasons. First, these categories let us organize how we create and interpret features. Second, we can observe the contribution of diferent types of features.
Third, some categories of features may be less generalizable than others. We order these categories (D → T → V → N) by how biased we expect those features to be towards the Plotly corpus. We describe each pair of columns with 30 pairwise-column features. These features fall into two categories: Values and Names. Note that many pairwise-column features depend on the individual column types determined through singlecolumn feature extraction. For instance, the Pearson correlation coefcient requires two numeric columns, and the "number of shared values" feature requires two categorical columns. We create 841 dataset-level features by aggregating these single-and pairwise-column features using the 16 aggregation functions shown in Table  S2c  in SM Section S4. These aggregation functions convert single-column features (across all columns) and pairwise-column features (across all pairs of columns) into scalar values.