dataesr/scientific-paragraphs-categorization huggingface dataset:
The consequences on lifespan shortening induced by these temperature variations, represented with better accuracy by this work method, are still to be studied. The same is true for thermal breakdown forecasting based on full knowledge of CPV thermal response over years with high time resolution. Finally, concerning the implementation of Fresnel lenses by refractive surfaces generated by script with Tonatiuh, which is also a novelty presented in this work: Proper validation of the script-based model by comparing real and scrip lenses, including their geometry, is worth its own research and is beyond the scope of this work, focused on the association between the thermal quadrupoles model and ray tracing analysis. ∂θ * (x, y, z, p) ∂x = ∂θ * (x, y, z, p) ∂y = 0, para x = 0, x = X w , y = 0, y = Y L , t > 0 (A7) By taking account adiabatic conditions (A7), one can apply the Fourier cosine transform: θ(α, β, z, p) = X w 0 Y L 0 θ * (x, y, z, p) cos(α n x) cos(β m y)dy dx (A8) where the eigenvalues are: α = m π X W , β = n π Y L with n = 0, 1, 2, 3,.
We combine parallaxes from the first Gaia data release with the spectrophotometric distance estimation framework for stars in the fifth RAVE survey data release. The combined distance estimates are more accurate than either determination in isolation -uncertainties are on average two times smaller than for RAVE-only distances (three times smaller for dwarfs), and 1. 4 times smaller than TGAS parallax uncertainties (two times smaller for giants). We are also able to compare the estimates from spectrophotometry to those from Gaia, and use this to assess the reliability of both catalogues and improve our distance estimates. We find that the distances to the lowest log g stars are, on average, overestimated and caution that they may not be reliable. We also find that it is likely that the Gaia random uncertainties are smaller than the reported values.
The value of F is provided in the database as FitQuality_Gauss, and we also include a flag (denoted Fit_Flag_Gauss) which is nonzero if the standard deviation of the final fitted model differs by more than 20 percent from that of the computed pdf. Typically the problems flagged are rather minor (as shown in fig. 3  of  Binney et al. 2014). The uncertainties of the RAVE stellar parameters are assumed to be the quadratic sum of the quoted internal uncertainties and the external uncertainties (Table  4 of DR5 ). The external uncertainties are those calculated from stars with SNR> 40, except in the case of the IRFM temperatures for which a single uncertainty serves for stars of every SNR since the IRFM temperatures are not extracted from the spectra. We discard all observations with a signal-to-noise ratio less than 10, or where the RAVE spectral pipeline returns a quality flag (AlgoConv) of '1', because the quoted parameters for these observations are regarded as unreliable.
COMBINED DISTANCE ESTIMATES A fundamental element of Bayesian analysis is the updating of the probability of a hypothesis (for example, the hypothesised distance to a star) as more evidence becomes available. TGAS parallaxes provide new evidence regarding these distances, so we are required to take it into account when determining the distances. We can think of this as either an additional piece of input data, or as a prior on parallax for each star (in addition to the prior on distance implied by Equation  13 ) -the two statements are equivalent. In previous sections we have investigated the properties of the RAVE distance pipeline in the absence of TGAS parallaxes, and developed an understanding of the problems with each dataset. We now incorporate the new evidence from these parallax measurements to obtain more accurate distance estimates than either can provide in isolation.
Improvement Including the TGAS parallaxes in our distance estimation inevitably leads to an improvement in the formal uncertainties. From the discussion of the previous sections, we can claim with some confidence that, outside a few regions of parameter space (e. g. , low log g, the stripe near the ecliptic pole), the combination does not introduce significant systematic errors into one dataset or the other. We can make a naïve estimate of how the uncertainties will decrease when we combine the two datasets by approximating that the uncertainties from the RAVE-only distance pipeline (σ ,Sp ) are Gaussian, in which case we have a new expected uncertainty in parallax σ ,exp given by 1/σ 2 ,exp = 1/σ 2 ,Sp + 1/σ 2 ,TGAS. (28) Because the RAVE uncertainties are significantly non-Gaussian, we do significantly better than this is some regions of the HR diagram.
CONCLUSIONS We have produced new distance, age and stellar parameter estimates for stars common to RAVE and TGAS which reflect new measurements of parallax and T eff (from TGAS and the infra-red flux method, respectively). This allows us to produce distance estimates that are better than those that either RAVE or TGAS can achieve in isolation. It also allows us to make age estimates which have better than 30 percent precision for 25 percent of the stars in our sample, and estimates of the stellar parameters which are roughly twice as accurate as from RAVE spectra alone (when compared to external catalogues). RAVE is the spectroscopic survey with the largest number of sources in common with TGAS, and therefore this dataset has the largest number of sources with both radial velocities from spectroscopy and proper motions from space astrometry.
This last option provides the most complete description of what the distance pipeline finds, though it is clearly less important here than it was before TGAS parallaxes became available  (Fig 18). The apparatus we have used for this study is applicable to data from any spectroscopic survey. It is our intention to apply it to data from the APOGEE survey in the near future. We will also produce distance estimates for RAVE stars that do not have TGAS parallaxes, using the AllWISE photometry and IRFM temperatures. These will have smaller systematic errors than the DR5 distances, particularly for hot dwarfs, because of the use of IRFM T eff values. All of these distance estimates will be made available through the RAVE website (http://dx. doi. org/10. 17876/rave/dr. 5/033 and http://dx. doi. org/10.
17876/rave/dr. 5/034 for the distance estimates for sources with and without TGAS parallaxes, respectively). For TGAS sources they constitute a substantial improvement in distance and, therefore, velocity uncertainty over previous data releases. It is our hope that the new, more precise age and distance estimates are of great value in characterising the dynamics and history of our Galaxy. Parameter for multi-Gaussian fit ( µ 1 in Eq. 4) 1 31 -sig_1 Parameter for multi-Gaussian fit (σ 1 in Eq. 4) 1 32 -frac_1 Parameter for multi-Gaussian fit ( f 1 in Eq. 4) 1 33 -mean_2 Parameter for multi-Gaussian fit ( µ 2 in Eq. 4) 1 34 -sig_2 Parameter for multi-Gaussian fit (σ 2 in Eq. 4) 1 35 -frac_2 Parameter for multi-Gaussian fit ( f 2 in Eq. 4) 1 36 -mean_3 Parameter for multi-Gaussian fit ( µ 3 in Eq.
ACKNOWLEDGEMENTS The authors are grateful to the referee for numerous suggestions which improved the paper. Paul McMillan is grateful to  Lennart Lindegren  for suggesting looking at the variation on-sky, and to  Louise Howes  for a careful reading of the draft. Funding for the research in this study came from the  Swedish National Space Board , the  Royal Physiographic Society in Lund , and some of the computations were performed on resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at  Lunarc  under project  SNIC 2016/4-17. Funding for RAVE has been provided by: the  Australian Astronomical Observatory ; the  Leibniz-Institut fuer Astrophysik Potsdam (AIP) ; the  Australian National University ; the  Australian Research Council ; the  French National Research Agency ; the  German Research Foundation  ( SPP 1177  and  SFB 881 ); the  European Research Council  ( ERC-StG 240271 Galactica ); the  Istituto Nazionale di Astrofisica at Padova ; The  Johns Hopkins University ; the  National Science Foundation of the USA  ( AST-0908326 ); the W.
There is an increasing gap between fast growth of data and limited human ability to comprehend data. Consequently, there has been a growing demand of data management tools that can bridge this gap and help the user retrieve highvalue content from data more e↵ectively. In this work, we aim to build interactive data exploration as a new database service, using an approach called "explore-by-example". In particular, we cast the explore-by-example problem in a principled "active learning" framework, and bring the properties of important classes of database queries to bear on the design of new algorithms and optimizations for active learning-based database exploration. These new techniques allow the database system to overcome a fundamental limitation of traditional active learning, i. e. , the slow convergence problem.
INTRODUCTION Today data is being generated at an unprecedented rate. However, the human ability to comprehend data remains as limited as before. Consequently, there has been a growing demand of data management tools that can bridge the gap between the data growth and limited human ability, and help retrieve high-value content from data more e↵ectively. To respond to such needs, we build a new database service for interactive exploration in a framework called "explore-byexample"  [12, 13]. In this framework, the database content is considered as a set of tuples, and the user is interested in some of them but not all. In the data exploration process, the system allows the user to interactively label tuples as "interesting" or "not interesting", so that it can construct an increasingly-more-accurate model of the user interest.
Eventually, the model is turned into a user interest query 1  that will retrieve all relevant tuples from the database. In this work, we consider several target applications. First, when a scientist comes to explore a large sky survey database such as SDSS  [39] , she may not be able to express her data interest precisely. Instead, she may prefer to navigate through a region of the sky, see a few examples of sky objects, provide yes or no feedback, and ask the system to find all other (potentially many more) relevant sky objects from the database. Second, consider many web applications backed by a large database, such as E-commerce websites and housing websites, which provide a simple search interface but leave the job of filtering through a long list of returned objects to the user. The new database service in the explore-by-example framework will provide these applications with a new way to interact with the user and, more importantly, help the user filter through numerous objects more e ciently.
Our approach to building an explore-by-example system is to cast it in an "active learning" framework: We treat the modeling of the user interest as a classification problem where all the user labeled examples thus far are used to train a classification model. Then active learning  [5, 36, 41]  decides how to choose a new example, from the unlabeled database, for the user to label next so that the system can learn the user interest e ciently. The active learning based explore-by-example approach o↵ers potential benefits over alternative methods such as faceted search  [26, 34, 35]  and semantic windows  [25]  for several reasons. First, the user interest may include varying degrees of complexity, or the user does not have prior knowledge about the complexity and hence expects the system to learn a model as complex as necessary for her interest.
More specifically, if the user knows the relevant attributes and just wants to set the appropriate value for each attribute, an interactive GUI or faceted search  [26, 34, 35]  may be su cient. However, the user interest may involve more complex constraints, e. g. , "( rowc a 1 b 1 ) 2 +( rowc a 2 b 2 ) 2 < c", and "x +2. 5 ⇤ log 10 (y) < 23. 3" from the SDSS example query set  [38] , or "length⇤width > c" as seen in our car database. If the user knows the function shape and constants in advance, some of the above examples (e. g. , the ellipse pattern) can be supported by semantic windows  [25]  as pre-defined patterns. However, if the user does not have such prior knowledge, explore-by-example may work regardless of how complex the predicates are, and which functions and constants are used in the predicates.
This problem, referred to as slow convergence, is exacerbated when the user interest covers only a small fraction of the database (i. e. , low selectivity) or the number of the attributes chosen for exploration is large (i. e. , high dimensionality). In this work, we take a new approach to active learningbased database exploration. Instead of improving active learning in isolation from the database, we treat it as an internal module of the database system and ask the question: what query and data properties from the database can we leverage to address the slow convergence problem? Based on the common properties that we observed in query traces from the Sloan Digital Sky Survey  [38]  and a car database (described more in Section 6), we can indeed design new techniques that overcome or alleviate the slow convergence problem.
Some of the key query properties include: Subspatial Convexity: Consider the database attributes as dimensions of a data space D and map the tuples to the space based on the values of their attributes. Then all the tuples that match the user interest form the positive region in D; others form the negative region. We observe that in some lower-dimensional subspaces of D, the projected positive or negative region is a convex object. For example, the SDSS query trace  [38]  includes 116 predicates, among which 107 predicates define a convex positive region in the subspace formed by the attributes used in the predicate, and 3 predicates define a convex negative region in their subspaces. For the car database, the 70 predicates defined on numerical attributes all form a convex positive region. Figure  2  shows a range of predicates from these two databases and their positive and negative regions.
Conjunctivity: Conjunctive queries are a major class of database queries that have been used in numerous applications. For data exploration, we are interested in the "conjunctive" property of the set of predicates that characterize the user interest. Among 45 queries provided by SDSS  [38] , 40 queries are conjunctive queries. For the car database, all user queries use the conjunctive property. In this paper, we bring the subspatial convexity and conjunctive properties of database queries, treated as true (yet unknown) user interest queries, to bear on the design of new algorithms and optimizations for active learning-based database exploration. These techniques allow the database system to overcome a fundamental limitation of traditional active learning, i. e. , the slow convergence problem when data exploration is performed with high dimensionality and low selectivity of the user interest query.
Our dual-space model with factorization, dsmF , runs the polytope model in each subspace where the convex property holds. We formally define the class of queries that dsmF supports, the decision function it utilizes, and prove that it achieves a better lower bound of F1-score than dsm without factorization. (b) Online feature selection (Section 5): The user may start exploration with more attributes than those needed in the final model. To remove irrelevant attributes, we propose an online feature selection method that adaptively selects the top-k relevant attributes and leverages the convex property (if present) for optimization. 3. Evaluation (Section 6): We evaluated our system using two real datasets. The SDSS dataset  [39]  includes 190M tuples, for which the user interests are selective and their decision boundaries present varied complexity for detection.
Without knowing these queries in advance, dsmF can achieve F1-score of 95% within 10 labeled examples if the decision boundary B falls in a sparse region, and otherwise requires up to 100 labeled examples for 2D queries and 160-240 examples for 4D-6D queries while maintaining per-iteration time within 1-2 seconds. For these queries, our results show that dsmF significantly outperforms learning methods including Active Learning (AL)  [5, 15]  and Active Search  [16] , as well as recent explore-by-example systems, Aide  [12, 13]  and LifeJoin  [9]. Our user study using a 5622-tuple car database validates the subspatial convex property and the conjunctive property of user interest queries. It also shows the benefits of dsmF (a median of 10 labeled examples to reach high accuracy) over manual exploration (where the user wrote 12 queries and reviewed 98 tuples as the median), as well as over AL, even in the presence of noisy user labels.
System Overview Our data exploration system is depicted in Figure  1. The main concepts and modules are described as follows. Data space. When a user comes to explore a database, she is presented with the database schema for browsing. Based on her best understanding of the (implicit) exploration goal, she may choose a set of attributes, {Ai}, i = 1,. , d, from a table for consideration. These attributes form a superset of the relevant attributes that will eventually be discovered by the system. Let us consider the projection of the underlying table to {Ai}, and pivot the projected table such that each Ai becomes a dimension and the projected tuples are mapped to points in this d-dimensional spacethe resulting space is called a data space where the user exploration will take place. Initial examples.
To bootstrap data exploration, the user is asked to give a positive example and a negative example. If she does not have such examples, the system can run initial sampling  [12, 29]  over the data space to help her find such examples. Since the initial sampling problem has been studied before, our work in this paper focuses on data exploration after such initial examples are identified. Iterative learning and exploration. The iterative exploration starts with a given positive example set and a negative example set, which form the labeled dataset. In each iteration, the labeled dataset is used to train a user interest model, which is fast due to the small size of training data. Before the model reaches convergence or a user-specified accuracy level, it is used next to explore the data space and retrieve a new example for display.
In the next iteration, the user labels this example as positive or negative -such feedback can be collected explicitly through a graphical interface  [11] , or implicitly based on the user behavior. 2 The newly labeled example is added to the labeled dataset, and the above process repeats. Convergence and final retrieval. At each iteration, our system assesses the current model to decide whether more iterations are needed. The process is terminated when the model accuracy has reached a user-defined threshold. At this point, the model for the positive class is translated to a query which will retrieve from the database all the tuples classified as relevant. To provide better interpretability, our system can visualize the final (nonparametric) model and fit a corresponding parametric model, the form of which can be suggested by the user after seeing the visualization.
Active Learning for Data Exploration The problem of dynamically seeking the next example for labeling from a large database of unlabeled tuples is closely related to active learning. The recent results on active learning are surveyed in  [36]. Below, we summarize those results relevant to our work. Pool-Based Sampling. Many real-world problems fit the following scenario: there is a small set of labeled data L and a large pool of unlabeled data U available. In active learning, an example is chosen from the pool in a greedy fashion, 2 Methods for collecting user feedback are in the purview of humancomputer interaction and are beyond the scope of this paper. according to a utility measure used to evaluate all instances in the pool (or, if U is large, a subsample thereof). In our setting of database exploration, the labeled data L is what the user has provided thus far.
The pool U is a subsample of size m of the unlabeled part of the database. The utility measure depends on the classifier in use, as discussed below. Classification Model. Previous explore-by-example systems  [12, 13]  used decision trees to build a classification model. It works well if the user interest pattern is a hyperrectangle in the data space, whereas real-world applications may use more complex predicates. To support higher complexity, our system uses more powerful classifiers such as Support Vector Machines (SVM) or Gradient Boosting. The new techniques proposed in our work do not depend on the specific classifier; they can work with most existing classifiers. But the implementation of active learning does depend on the classifier in use. For ease of composition, in this paper we use SVM as an example classifier.
DUAL-SPACE MODEL For interactive data exploration, active learning algorithms often require a large number of user-labeled examples to reach high accuracy, known as the slow convergence problem. One reason for slow convergence is the limitation of uncertainty sampling itself: it may exert a lot of e↵ort searching in sparse, noisy, or irrelevant regions of the input space  [36]. With the goal to provide a service for database exploration, our work takes a new, a database centric approach to tackle the slow convergence problem. We observe from existing query traces that in some lower-dimensional subspaces, the projected positive or negative region of user interest query is often a convex object. In this section, we utilize such subspatial convexity and introduce a dual-space (data and version space) model, which enables improved accuracy and provable lower bounds on the model accuracy.
We also o↵er a test at the beginning of the data exploration process to choose between two polytope models, Q 2 Q + c or Q 2 Q c. The details are deferred to Section 4 where we o↵er a test procedure for all the assumptions made in the work. Three-Set Metric. Our goal is not only to provide a new data space model, as described above, but also to design a new learning algorithm that enables a provable bound on the model accuracy. As stated before, our accuracy measure is F1-score. Formally, F1-score is evaluated on a test set Dtest = {(xi, yi)}, where xi denotes a database object and yi denotes its label according to the classification model. Then F1-score is defined as: F1-score = 2 • precision • recall precision + recall , where precision is the fraction of points returned from Dtest by the model that are positive, and recall is the fraction of positive points in Dtest that are returned by the model.
However, capturing F1-score in our data exploration procedure is di cult because we do not have such a labeled test set, Dtest, available. We cannot a↵ord to ask the user to label more to produce one since the user labor is an important concern. To bound the F1-score with limited labeled data, our idea is to run our polytope model, FD : R d ! { 1, 0, 1}, on an evaluation set. We define the evaluation set D eval as the projection of Dtest without labels yi's. Then for each data point in D eval , depending on which region it falls into, D eval can be partitioned into three sets accordingly, denoted as D + , D and D u. We can compute a metric from the number of data points in the three sets, as follows. Definition 3. 4 (Three-Set Metric) Denote D + = D eval \ R + , D = D eval \ R , D u = D eval \ R u , and |S| means the size of set S.
At a specific iteration of exploration, the three-set metric is defined to be |D + | |D + |+|D u |. We will prove shortly that this metric is a lower bound of F1-score evaluated on Dtest. As more labeled examples are provided, data points will be moved from D u to either D + or D. Eventually, with enough training data the uncertain set shrinks to an empty set and the Three-Set Metric rises to 100%, reaching convergence.