dataesr/scientific-paragraphs-categorization huggingface dataset:
Apparatus The benchmark data acquisition was conducted online using a web browser with a javascript library, p5. js (https://p5js. org/). Each participant accessed the web server hosted in our laboratory and engaged in the tasks. The platform in our experiment was organized by a python web framework, Django (https://www. djangoproject. com/). The informed consent and the schedule management of the 2-days experiment were controlled using the platform. Our test battery, including instructions and practice trials for each task, was implemented with the javascript library p5. js. Users can run our test battery either with our Django platform or separately using a shared webserver. All codes and data are available from the following link: https://github. com/ flowersteam/cognitive-testbattery.
Memorability Task The memorability task measures human memory performance for natural scene images (Figure  4B ;  Isola et al. , 2011; Bylinskii et al. , 2015 Bylinskii et al. , , 2021;; Khosla et al. , 2015). Our experiment extracted images with intermediate memorability scores from the FIne-GRained ImageMemorability (FIGRIM) dataset  (Bylinskii et al. , 2015)  because it has been shown that cognitive factors are more effective for these images. Each trial began with the presentation of a natural scene photograph for 1000 ms. During the presentation, participants were asked to remember each photograph and answer whether the photograph is presented twice or not, by pressing the key "J" as soon as possible. After each presentation, participants received feedback if the response was correct or not for 1,400 ms.
There were two blocks for the memorability task. On each block, participants viewed a set of 120 images within a specific natural scene category, "bedroom" or "kitchen, " in the dataset. We chose the image of hit rates on the interval [0. 60, 0. 70]. Forty images were the targets and displayed twice for each block. Forty images were the fillers and displayed once. Eight of the targets were presented with a long distance of 100-109 images between an image and its repeat. Thirty-two of the targets were presented with a short distance of 2-5 images.
Basic Performance Figure  5  shows the results of basic performances for each task. Different panels show different tasks. Blue circles of each panel indicate the individual performance of the response probability or reaction time for each task condition. The thin green line connects each individual performance across different stimulus parameters. We analyzed the data using Bayesian statistical methods to estimate the mean parameters (accuracy and reaction time) and their 95% credible intervals, shown in the red squares and error bars in Figure 5  (Andrews and Baguley, 2013; Makowski et al. , 2019). Our main motivations to use bayesian inference were the access to credible intervals and to the a posteriori distribution (not only to a point estimate)  (Kruschke, 2021). We performed the model inference by Hamiltonian Monte Carlo with the NUTS sampler using PyStan.
While the smaller value in the reaction time means a fast (better) response, the larger value in the accuracy means better performance. Therefore, the first component can be associated with a general ability factor, the shared ability across different cognitive tasks to solve them  (Steyvers and Schafer, 2020). The latent factors after the first one in PCA are constrained by the orthogonality of the input parameters. To interpret the factors conservatively, we also conducted the independent component analysis (ICA), where the latent factor orthogonality is not constrained by the input parameters. We used the FastICA implemented in the python scikit-learn library with the six parameters. The results showed that the hierarchical clusterings of the ICA were similar to PCA (Figure  9 ). It is noteworthy that the different conditions within the same task were not always clustered in near categories.
It is noteworthy that we share all source codes and data to conduct the cognitive assessment experiment from our repository (https://github. com/flowersteam/cognitivetestbattery). Not only can users conduct our experiment as we did on their own server, but also they can do it more flexibly. One use-case is to conduct our test battery on a shared server. Another case is to conduct it in the laboratory environment. In this case, users can strictly control the monitor size and viewing distance and run the experiment using a web browser.
The paper presents the third data release of Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), a wide-field multi-band imaging survey with the Subaru 8. 2m telescope. HSC-SSP has three survey layers (Wide, Deep, and UltraDeep) with different area coverages and depths, designed to address a wide array of astrophysical questions. This third release from HSC-SSP includes data from 278 nights of observing time and covers about 670 square degrees in all -.
Updates from PDR2 This release includes data taken between March 2014 and January 2020 from 278 nights in total (including nights lost to weather and other reasons). The release includes data from an additional ∼ 100 nights since PDR2 and hence represents a major increase in terms of the data volume and area coverage. There are also changes in the data products. Major updates from PDR2 can be summarized as follows. • The Deep/UltraDeep (D/UD for short) layers are now ∼ 80% complete and we choose to stop there (i. e. , we do not take any further data in D/UD). Most of the Deep fields have ∼ 1. 5 times longer integration times compared to PDR2. UD-SXDS is much deeper (×3 times longer integration times) than PDR2 due to a transient survey carried out in that field. The overall increase in D/UD-COSMOS is relatively minor because the transient survey in that field was performed earlier in the survey  (Yasuda et al.
Jointcal The first is an update in the astrometric calibration. We used to use an algorithm termed meas_mosaic  (Bosch et al. 2018a ) to solve for astrometry in the joint calibration step, but its memory usage becomes prohibitively expensive when we try to fit all visits in the UD fields. In order to improve the memory efficiency, we switch to using the LSST jointcal 1  package, which uses sparse linear algebra for efficient memory usage. This is an older version of the code used in  Léget et al. (2021) , with considerable customization to integrate it with the rest of the LSST/HSC pipelines, and a less sophisticated model chosen  (conservatively)  to mimic what we have used in the past  (Bosch et al. 2018a ): a single per-visit, full-focal-plane, high-order polynomial to capture both optical distortions and atmospheric effects, composed with an affine transform for each CCD, fit to each band separately.
FGCM Photometric calibration is performed with the Forward Global Calibration Method (FGCM;  Burke et al. 2018). FGCM calibrates the full PDR3 dataset with a forward model approach that uses atmospheric model parameters in conjunction with scans of the instrument throughput as a function of wavelength. FGCM was originally developed for use with the Dark Energy Survey data  (Burke et al. 2018; Sevilla-Noarbe et al. 2021) , and has been incorporated into the LSST/HSC pipeline for use with HSC and LSST data. The FGCM model begins with measurements of the instrument throughput, including the mirrors, filters, and detectors. Filter scans are taken from  Kawanomoto et al. (2018) , and we average in the azimuthal direction prior to use in the model fit. We use the fiducial mirror and detector throughput measurements as reported at the Subaru website 2.
The The FGCM fit minimizes the variance in repeated observations of stars, and we use stars with a signal-to-noise ratio larger than 10 within the 2 arcsec aperture, which roughly corresponds to i < 23. As configured, we calibrate the 2 arcsecond aperture fluxes. Due to significant residuals in the background of the processed CCD images, we additionally subtract off a perstar local background estimate. Tests with a subset of the data show that this reduces residuals as a function of magnitude, as well as tightening the repeatability in the photometry of a given star. The local background subtraction introduces a few percent flux change at faint magnitudes of i = 22 -23 and is smaller at brighter magnitudes of i ∼ 20 (but still up to 0. 5% depending on the local source density) due to the over-subtraction of the sky.
(in prep). For good photometric results, we need to accurately constrain the degradation of the mirror surface over time. The default model in FGCM is for a piecewise-linear decay function, with discontinuous changes when the instrument/telescope underwent changes. We set October 2017, at the date of the Subaru primary mirror recoating as such a discontinuous point. Fig. 4  illustrates the throughput degradation from the fit. After the mirror recoating, the system throughput improves by ∼ 5% (g) and ∼ 2% (z). As expected, bluer bands show a larger change. Interestingly, we notice that the rate of decay increased between 22 April 2016 and 08 November 2016 in all bands. These dates coincide with increased activity of the Maunaloa volcano, and such increased decay may be due to volcanic fog. One particular challenge of performing a global calibration of the PDR3 dataset is that we replaced the r and i filters with the r2 and i2 filters with improved uniformity, respectively.
Value-added Products In addition to the data products from the pipeline, we serve value-added products as in our previous releases, including the COSMOS Wide-depth stacks, a collection of public spectroscopic redshifts, and random points. The COSMOS Wide-depth stacks and the random points are generated in the same way as in the previous release; the reader is referred to the PDR2 paper. In what follows, we briefly discuss the updated list of spectroscopic redshifts, and photometric redshifts based on the spectroscopic catalog. There is also a major update to the mask around bright stars, which we discuss in Section 4. 2. • Public spectroscopic redshifts: We have updated the list of public spectroscopic redshifts from the literature. The list includes redshifts from 2dFGRS  (Colless et al. 2003 ), 3D-HST  (Skelton et al.
2014; Momcheva et al. 2016 ), 6dF-GRS  (Jones et al. 2004; Jones et al. 2009)   (Lilly et al. 2009). As oneto-one correspondence between the spectroscopic objects and photometric objects is not always obvious, we match objects within 1 arcsec and all matched objects are stored in the database. In most cases, the most likely match will be the object with the smallest matching distance. Each spectroscopic survey has its own flagging scheme to indicate the reliability of a redshift measurement. We have homogenized the flags to make it easy for the user to make a clean redshift catalog. See the online documentation for the definition of the homogenized flag. We emphasize that the user should acknowledge the original data source(s) when using this product. • Photometric Redshifts: Photometric redshifts for objects in PDR3 have been computed using a few independent codes  (Tanaka et al.
Summary of masks Fig. 13  summarizes the sizes of masks discussed here as a function of stellar magnitude. The reflection ghost is the largest component at very bright magnitudes and the dip feature is the dominant feature for stars fainter than 10th magnitude. However, the dip mask introduces a significant loss in the survey area; about 80% of the Wide area is left after applying the halo, ghost, and blooming masks, but the fraction reduces to 50% when the dip mask is also applied because faint stars are numerous. If one does not care about a ∼ 10% detection loss around the detection limit around bright stars, the dip mask can be ignored. All these mask flags are available for each object in each filter at the database. At this point, the masks are defined only for broad-band filters. For narrow-band filters, one can use the broad-band mask that is closest in wavelength (e.
Photometry and Astrometry The overall astrometric and photometric accuracy of this release is similar to that of PDR2. There are changes in the way we calibrate the data (astrometric calibration by jointcal and photometric calibration by FGCM; Sections 3. 1 and 3. 2). But our primary calibration source has not changed; we still use the PanSTARRS1 DR2 catalog  (Schlafly et al. 2012; Tonry et al. 2012; Magnier et al. 2013; Chambers et al. 2016 ) astrometrically calibrated against Gaia DR1  (Gaia Collaboration et al. 2016a; Gaia Collaboration et al. 2016b). We plan to change the astrometric reference catalog to a newer Gaia release in our future public release. As in the two previous releases, we present a full set of quality assurance (QA) plots on the data release website, and the user should check these QA plots before utilizing the data for science.
Here we present some of the key example diagnostics. Fig. 14  shows the astrometric calibration accuracy in a portion of the Wide field. We compute an offset in R. A. by comparing the HSC and Gaia DR1 coordinates of common stars brighter than G = 20. Overall, our astrometric calibrations are good, but there are multiple small regions with rather large negative offsets. We have visually inspected some of these regions, but there are nothing obviously wrong in the images. It turns out that these are regions in which the astrometry in our own reference catalog (PS1 calibrated against Gaia) is problematic. The bad astrometry in the reference data unfortunately propagated to HSC. We hope to fix it in our future release. Fig. 14  shows only the offset, but we also estimate rms of the astrometric residual, which is a good indicator of overall astrometric accuracy.
We also note that the pipeline is not optimized for very crowded fields such as star clusters or dwarf galaxies around the Milky Way Galaxy. The user should carefully check the images and catalogs in crowded regions before using them. The NB387 data should also be used with caution because this is a rather difficult filter to calibrate. We infer NB387 magnitudes by applying color-terms to the PS1 gr photometry when we calibrate its zero-point. We use the stellar library from Pickles (1998) to derive the color-terms. Most of the stars in this library are close to solar metallicity, but the stars that we use for calibration are faint halo stars, which are likely to have subsolar metallicities. The NB387 filter is quite sensitive to the metallicity of stars and the PS1 gr photometry does not fully capture the metallicity variation.
FGCM and r/i vs. r2/i2 As already discussed in Section 3. 2, there is an issue with the photometric zero-point introduced by the local sky subtraction performed internally in FGCM. This has been mitigated by the stellar sequence regression (Section 3. 9) and we encourage the user to apply the zero-point offsets as well as the r/i into r2/i2 offsets computed in Section 3. 8. Note that these offsets have not been applied to the photometry in the database (it is as quoted by the pipeline). The user can apply these correction by joining the magnitude offset tables. See the data release website for details.
Multiband (U + grizy as well as NIR, where publicly available) photometry is carried out by the CLAUDS team using SExtractor  (Bertin & Arnouts 1996)  and an adaptation of hscPipe that has been adapted to handle CFHT U -band images. The combination of CLAUDS and HSC-SSP data enables research in many broad science areas by significantly enhancing photometric redshift performance (particularly at z < ∼ 0. 8 and z > ∼ 2) and permitting the selection of z ∼ 3 Lyman break galaxies and quasars. Several science projects have already been carried out with the joint CLAUDS+HSC-SSP datasets  (Halevi et al. 2019; Moutard et al. 2020; Huang et al. 2020; Golob et al. 2021; Thibert et al. 2021; Harikane et al. 2021) , while others are underway. The CLAUDS team anticipates releasing their U + grizy catalogs and processed images to the public in late 2021 or early 2022 (G.
The DUNES 2 team plans to publicly release the processed UKIRT/WFCAM images and associated source catalogs in early 2022. A complementary survey, DeepCos (PI: Y. -T. Lin), is also being carried out. The survey originally aimed to bring the DUNES 2 footprint to the depth of VIDEO and used WFCAM on UKIRT to further observe E-COSMOS and DEEP2-F3 in J and K. Due to operation issues with UKIRT, we have switched to using WIRCam on CFHT in 2020 and continued the survey. The survey now aims to provide a uniform JAB = 23. 7 coverage over ∼ 11 deg 2 area in three of the Deep fields (XMM-LSS, E-COSMOS, and DEEP2-F3). Such a data set will not only improve the photo-z accuracy of the D/UD fields, but will also play a critical role in the target selection for an upcoming survey with Prime Focus Spectrograph (PFS;  Tamura et al.
The remaining observing time will be spent on the Wide layer and final data set will include ∼ 1, 200 square degrees of FCFD area. We will not take additional D/UD data. We anticipate that the next data release will be the final data release from HSC-SSP. We do not have a specific release date yet, but we will keep the community informed on our website. Following the imaging survey, we plan to carry out a massive spectroscopic survey using PFS on the Subaru Telescope. The PFS survey will be based largely on the imaging data from HSC-SSP, and the combination of imaging and spectroscopy will allow us to explore deeper into the Universe. the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, the Queen's University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under Grant No.
Acknowledgments The  Hyper Suprime-Cam (HSC) collaboration includes the astronomical communities of Japan and Taiwan , and  Princeton University. The HSC instrumentation and software were developed by the  National Astronomical Observatory of Japan (NAOJ) , the  Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU) , the  University of Tokyo , the  High Energy Accelerator Research Organization (KEK) , the  Academia Sinica Institute for Astronomy and Astrophysics in Taiwan (ASIAA) , and  Princeton University. Funding was contributed by the  FIRST program  from  Japanese Cabinet Office , the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) , the  Japan Society for the Promotion of Science (JSPS) ,  Japan Science and Technology Agency (JST) , the  Toray Science Foundation ,  NAOJ ,  Kavli IPMU , KEK, ASIAA, and  Princeton University.
A-0839 , observations taken by the  3D-HST Treasury Program  ( GO 12177  and  12328 ) with the  NASA/ESA HST , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555 , data from the  VIMOS VLT Deep Survey , obtained from the  VVDS database operated by Cesam, Laboratoire d'Astrophysique de Marseille, France , data from the  VIMOS Public Extragalactic Redshift Survey (VIPERS ), which has been performed using the  ESO Very Large Telescope , under the " Large Programme "  182. A-0886 , and the participating institutions and funding agencies are listed at http://vipers. inaf. it. Funding for SDSS-III has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , and the  U.
Analogical proportions are statements of the form "A is to B as C is to D". They support analogical inference and provide a logical framework to address learning, transfer, and explainability concerns. This logical framework finds useful applications in AI and natural language processing (NLP). In this paper, we address the problem of solving morphological analogies using a retrieval approach named ANNr. Our deep learning framework encodes structural properties of analogical proportions and relies on a specifically designed embedding model capturing morphological characteristics of words. We demonstrate that ANNr outperforms the state of the art on 11 languages. We analyze ANNr results for Navajo and Georgian, languages on which the model performs worst and best, to explore potential correlations between the mistakes of ANNr and linguistic properties.
In the remainder of this paper we first introduce the problem of morphological analogies with a brief state of the art in Section 2. We describe the three components of our retrieval approach (ANNc, ANNr and the morphological embedding model) in Section 3, and detail our experiments in Section 4. First, we describe the dataset used in Subsection 4. 1, then compare performance with different training procedures and against baselines in Subsections 4. 2 to 4. 4. Finally, in Subsection 4. 5, we explore mistakes made by ANNr on Navajo and Georgian to get insight on the behavior and flaws of our approach. As per tradition, we conclude our paper and offer perspectives on some key aspects in Section 5.
Many approaches in embedding spaces, including ours, use the latter method as generation from an embedding space can be challenging. In our case the candidates are the set of all the words in the dataset, called the vocabulary. In the context of solving morphological analogies, the main trend follows the seminal work of Lepage and Ando  [18]  by exploiting the aforementioned postulates of analogical proportions. For instance, Lepage  [17]  uses these postulates to address multiple characteristics of words, such as their length, the occurrence of letters and of patterns. Following the results of Yvon  [32]  about closed form solutions, the Alea algorithm  [14]  proposes a Monte-Carlo estimation of the solutions of an analogical equation by sampling among multiple sub-transformations. Recently, a more empirical approach was proposed by Murena et al.
We detail two of the most used methods for solving analogies in embedding spaces, namely 3CosAdd  [24]  and 3CosMul  [19] , in Subsection 4. 3. These two approaches implicitly generate a solution e x and retrieve the closest candidate from the vocabulary. However, Chen et al. [5]  argue that the latter two methods significantly differ from human performance. Nonetheless, frameworks based on analogy datasets like those mentioned in  [21]  appear to bridge this gap in performance. Similarly to the method of Lim et al. [21] , ours learns a model for both the analogy detection and solving tasks in morphology. This approach models analogy as described by the analogy data, unlike 3CosAdd and 3CosMul that rely on fixed formulas and on the expected properties of the embedding space.
Those layers also encode the necessary information from e B and e C , respectively, while taking e A into account. Finally, fully connected layer 3 (with no activation function) generates e x the embedding of the predicted x from all the extracted information. We refer to this model as the Analogy Neural Network for retrieval (ANNr). Embedding model. Most deep learning methodologies rely on real-valued vector representations of the data called embeddings. For many applications, there are embedding models available and that have been pretrained on large amounts of data, as it saves users the cost of fully training them. It is commonly accepted that the quality of such representations, which corresponds to the amount and nature of the encoded information as well as the properties of the embedding space, is a key factor to achieve higher performance.