dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and methodology We use the trading days' closing prices of the most liquid 27 stocks and additionally the benchmark index (VN30 INDEX) from July 1, 2013 to June 29, 2018, covering full five years. 3 The VN30 Index accounts for 80% market cap and 60% of trading volumes of the Vietnamese market. This Index is also used as a benchmark for derivatives products. Moreover, this study focuses only in high liquidity, high free float and big cap companies, so there is no need to take all the stocks into account. On top of that, VNIndex covers more than 700 firms which makes it impossible in practice to analyze every firm stock included in this index. Since many studies on Vietnam within the context of technical trading use samples ending in 2013, we believe that it is opportune to start the sample with 2013 to capture the latest dynamics of this market which have not been reported in academic studies yet.
Furthermore, the research approach of this paper towards practical strategy that investors can implement with ease: we choose only the most liquid stocks, among the most biggest companies. Table  2  shows the descriptive statistics and provides useful information on our dataset, including those for the aggregate index. We can see that HOA PHAT GROUP has the highest daily average return (0. 1483%) over the sample period, followed by VINCOM (0. 1363%). The mean returns also show the general uptrend of the Vietnamese stock market over the past five years. Among all 27 sample stocks, only five of them have a negative mean return. Median returns, on the other hand, are all very close to zero. Maximum and minimum daily returns for the sample stocks are on par with their emerging market peers. In a single day, it is possible to see a maximum return of 12.
This paper is devoted to two different two-time-scale stochastic approximation algorithms for superquantile estimation. We shall investigate the asymptotic behavior of a Robbins-Monro estimator and its convexified version. Our main contribution is to establish the almost sure convergence, the quadratic strong law and the law of iterated logarithm for our estimates via a martingale approach. A joint asymptotic normality is also provided. Our theoretical analysis is illustrated by numerical experiments on real datasets.
One can also use our method to estimate online 95% confidence intervals for the superquantile ϑ α as explained in Remark 3. 4. This is illustrated in Figure  3  with the Exponential and Gamma distributions with a = 2/3 and b = 1. 7. 1. Real data. We finally illustrate, as a proof of concept, the use of our two algorithms on financial real-data that are freely available on the R-package tseries (EuStockMarkets dataset). Some more recent ressources may also be downloaded on the Yahoo! Finance website. We consider the four time series of the financial stock-markets DAX, CAC40, SMI, FTSE between 2014 and 2018 and compute the CVaR of the weekly log-returns, that are common indicators in the analysis of financial markets. It is commonly admitted as a reasonnable approximation that in non-exceptionnal situations, the log-returns are not far from an independent and identically distributed set of observations.
This work is partially funded by the  Catalan Agency for Management of University and Research Grants (AGAUR)  and the  European Social Fund (ESF) of the Government of Catalonia  through the grant  FI-SDUR-AGAUR 2020  (ref. 2020-FISDU-307 ). It is also partially funded by  European Union and Normandy Region. Europe  is involved in Normandy through the  European Funds for Regional Development.
Procedures To implement the group identity treatment variables, subjects were invited separately to the same sessions depending on whether they registered in our subject pool database (managed using HROOT, Bock, Baetge, and Nicklisch, 2014) as students currently enrolled in the business school or not. We have separate registration lists that allow us to distinguish the group to which subjects belong. Upon arrival, participants are called one by one by their name to check the registration information: they enter the private (oath) office at this stage. The first ten participants whom we call all are enrolled in the business school list, and are assigned to a computer whose predetermined role is set to sender. This allows us to control the group identity of senders in all conditions, and to implement the oath procedure on senders only in the Oath conditions.
Once in the laboratory, participants are informed about the instructions of each step of the experiment on their screen (the experiment is computerized using a software developed on Z-tree,  Fischbacher, 2007). They can push a button located inside their cubicle to ask a question in private to a monitor at any point in time. All payments in the experiment are expressed in Experimental Currency Unit. The exchange rate is 1 ECU = 0. 3 euros. Participants are paid a fixed fee for of 5 euros for answering the post-experimental survey, which is added to the payment that results from their decision in the payoff condition of the sender-receiver game that is randomly drawn (and the outcome from the dice-under the cup task for receivers). The average individual payoff is 12. 02 euros for an average 1 hour participation.
NASA's Regional Planetary Image Facility (RPIF) Network has effectively served the U. S. planetary science community for over 45 years. The RPIFs were originally conceived as libraries and research centers with reference collections of NASA mission photographic and digital image products, geologic maps, and planetary mission documents, literature, and legacy products. Their primary goal was to provide researchers, students, and media easy access to planetary image products and local planetary expertise, and to introduce the public to results from NASA's planetary missions and research through annual open houses. Now, as NASA considers a wholistic and efficient Planetary Data Ecosystem for the next decade and beyond, the network of facilities, data, and resources amassed within the RPIF Network can be efficiently and immediately leveraged and transformed to meet the evolving needs of the U.
S. planetary science community. As the next generation of planetary scientists will rely more heavily on digital data from the PDS and other digital archives and tools to accomplish research, a new generation of Planetary Data Utilization Centers (PDUCs), created from members of the RPIFs, can serve as loci for enabling this research, as well as providing expertise, training, and products that supplement the PDS for the general science community. These centers would offer specialized training to use planetary data in geospatially-registered formats, including such software packages as JMARS, ISIS, ArcGIS, QGIS, Ames Stereo Pipeline as well as commercial offerings such as ENVI and SOCET SET, among others. Providing help to the general user to gain access to and process PDS data, including the more arcane data, would also be part of the PDUCs role, as well as hosting unique digital data sets to support future planetary research.
g. , Lunar Orbiter, Apollo, Mariner 9, Mariner 10, Viking, Voyager, and Magellan) and digital data on CD-ROM from several missions  (Magellan, Voyager, Galileo, Mars Global Surveyor). The individual specializations often reflect local expertise and/or mission involvement of researchers at the host institutions. Such data sets are not just historical artifacts but continue to be used by researchers due to their unique perspectives and easy access. They also are used for hands-on teaching and provide a resource for media inquiries, books (research and historical documents), and displays in various museums across the country. Additionally, each RPIF node hosts its own unique collection of data accumulated by planetary scientists in their region, as well as rescued mission and research data sets (and literature) from retired investigators or closed centers.
What has changed since the last decadal survey? The volume and handling of NASA Planetary Data and the need for an overarching support system. Over the course of the last decade, NASA and the US planetary science community directed more attention to the long-term usability of planetary data, looking beyond the PDS (which is a long-term storage archive of raw data and calibration files from NASA missions) in order to develop the concept of Planetary Spatial Data Infrastructures (PSDIs). PSDIs are structures that enable long-term usability of planetary data, typically data from multiple missions, in a geospatially registered format for each planetary object in the Solar System. PSDIs include not only the data themselves, but also the data accessibility, standards, and policies to enable their use, and the people with critical skills to engage and train non-experts in their use  (Laura et al.
, 2018). MAPSIT, the Mapping and Planetary Spatial Infrastructure Team, is a NASA assessment group that was set up in 2014 to develop policy guidelines and recommendations for NASA in dealing with issues of long-term planetary data usability (see this website for definitions, details on PSDIs, and current recommendations: https://www. lpi. usra. edu/mapsit/). As study of the long-term challenges of enabling access and use of planetary spatial data have become apparent over the last decade, and with attention to MAPSIT's recommendations thus far, NASA Headquarters has developed an initiative called the Planetary Data Ecosystem, which is intended to be an overarching support network to help ensure standardization, discoverability, accessibility, and usability of planetary science data. In its nascent stages at present, NASA's Planetary Data Ecosystem review will be initiated in late 2020/early-2021.
Since 2005, the RPIF Network directors have recognized a key need in the US planetary science community for: 1) the training on how to access, create, and use planetary data, in particular geospatially-registered data from multiple missions, to conduct new research, and 2) a need to make accessible calibrated, higher-level digital data products from past planetary missions and NASA-funded research projects prior to the 2000s, that do not exist in the PDS, or are not appropriate for the PDS archive.
Justification: First, training to use planetary data has been conducted mostly in an ad hoc manner, e. g. , a senior staff researcher or graduate student trained a junior graduate student in data processing software; a student traveled to the USGS to learn ISIS (software for image processing) or SOCET SET (software to make digital terrain models). These training methods are not ideal in many circumstances (e. g. , What if a student cannot travel to the USGS? What if a knowledgable student gets a job or a staff researcher retires and leaves before training a replacement?), and do not even help more senior scientists overcome the data processing barrier needed to work with a new mission or planetary target. To address this problem, in the 2010s the USGS supported a bi-annual series of Planetary Data Workshops both to offer training in some types of the software tools to use planetary data, and to assess needs for new planetary data tools and software in the community of data users.
At this same time, several RPIFs also began conducting planetary data training events, both at their home institutions, and at national conferences (Table  2 ). Such training events at host institutions, however, were constrained by funding, e. g. , local expertise could not be tapped without compensation. Second, many historic planetary missions and major research projects developed higher-level products (e. g. , mission-related thematic or geologic maps, such as NASA Dawn mission global geologic maps of (4) Vesta and (1) Ceres, or NASA-DOD Clementine data products, such as mission planning, sensor calibration charts, and orbit-by-orbit mission profile, Clementine 2 mission to Mars). Many of these data products were never intended for the PDS, or have incompatible formats, but are still useful to future researchers.
These digital resources need to be curated in long-term institutional web archives (websites) with appropriate contextual information to enable future use. Third, research often depends on multiple formats or synoptic views that may be available as hard-copy (mosaics and maps) within a facility, rather than recreated from scratch. The combination of on-line data with readily available reference products saves time and provides different perspectives. Fourth, planetary research now involves combined data sets (imaging, remote sensing, and geophysics) that require multiple backgrounds and expertise for training and integration. Based on the feedback we've received at training events thus far, we think this need for training in planetary data usability defines a new role for the RPIF nodes, to fill a necessary gap in the Planetary Data Ecosystem, which we call Planetary Data Utilization Centers.
asu. edu/field_image_archive/), essentially acting as a remote node of the PDS Geoscience Node. Because no is everlasting without maintenance (security updates are periodically required, etc. ), a small amout of support is required to maintain these websites as PDS-equivalent archives. 3) Maintain a set of computer workstations, and necessary equipment, software and, most importanty, expertise, that is not commonly available to individuals (or is too expensive), to enable digital planetary data training, use, and access for research. Training to use planetary data with existing software (NAIF SPICE, ISIS, JMARS, ArcGIS, Socet Set, Ames Stereo Pipeline, etc. ) requires computer workstations as well as specialized equipment for data processing, digitiziation, geologic mapping, and making DTMs.
5) Maintain and develop new resources to visualise 3D digital planetary data using VR. Some US PDUCs (Cornell and Brown) have a COVE (Co-operative Visualisation Environment) and/or a state-of-the-art projection-based VR theater (YURT, using 69 HD projectors), as well as the UK has an 8 x 4 foot Geowall screen and dual projectors to allow groups of planetary data users 3D visualisation experiences for training and research. In the future, more COVEs could be created for surface rover data visualisation alongside VR and AR technology in "safe spaces" for one to a few individuals individuals to work co-operatively. This includes virtual field geology tours to MER, MSL, M2020 and RFEXM22 landing sites as planned in the UK. 6) Generate new supporting materials from PDS data. Examples: The Hawaii PDUC has created > 100 digital elevation models from MRO Context Camera images of Mars.
These are presented in a similar manner to mission teams via clickable maps showing craters, volcanoes and other landforms on Mars (https://www. higp. hawaii. edu/prpdc/CTX_DEMs/toplevel. html). The Lunar and Planetary Institute's RPIF has generated a series of maps of the Moon's South Pole (2019-2020) to support current VIPER and Artemis planning and the next generation of lunar human exploration. The maps make use of recent LRO data sets and are presented as quick-look products that reduce data processing efforts for mission planners. Many of the online materials at the LPI RPIF website are quick-look, easily searchable digital products that have served many researchers and mission planners.
, and how these data sets can be used with specific software for different types of research. Specifically: 1) PDUCs enable usability of PDS data: The Planetary Data System (PDS) is a digital storage archive, but the data therein are not immediately usable for many types of scientific studies by both experienced users and those who have no prior experience processing and working with planetary data. PDUCs contain trained staff to inform "customers" on how to find and extract PDS data and make them usable for scientific analysis. Staff members are typically trained, for example, to use Geographic Information Systems (GIS) software to enable spatial and temporal correlation of various planetary mission data sets so that users can conduct interdisciplinary studies via data fusion. They are also trained in the USGS' Integrated Software for Imagers and Spectrometers (ISISv3), which is the standard for image processing and mosaicking of digital images for most NASA and ESA planetary images.
PDUC staff members are also trained in various other data acquisition and analysis tools (e. g. , NAIF SPICE, JMARS, ENVI®, etc. ) to help users work with publically released planetary data. The importance of this technical support should not be underestimated, and PDUCs are often the only source for immediate help for students and professors at universities, smaller liberal arts colleges, or community colleges within a given region that do not have well-established planetary science programs or limited knowledge of PDS resources. Based on past experience with workshops, help involves hands-on training, not just remote learning, due to the range of experience levels and specific trouble shooting. Consequently, there is a need for regional nodes within a network of expertise. 2) PDUCs contain and produce higher-order data products: Most NASA missions produce derived data products (e.
g. , image mosaics of various extents; topical maps of specific features types, at scales varying from local to global; geological maps; or integrated data sets of grayscale/color images, spectral, radar, radiometric, stereo/topographic, or atmospheric information). These derived data products typically have not been released through the PDS. PDUCs typically collect these products to make them available to users. Most PDUCs also have the capability to use image processing software to produce derived data products, such as new digital elevation models that are not available from the mission teams. 3) PDUCs contain donated research holdings relevant to PSD activities: As most PDUCs are located at institutions that are heavily involved, if not leading, major NASA planetary missions, they often collect donations by local individuals (e.
g. , retiring faculty or scientists) or local companies that have unique planetary-related research materials, that would otherwise have been lost or destroyed and that are not digitally available. PDUCs have the resources to digitize these materials and make them publically available on their institutional websites, or through the PDS, which is relevant to ongoing and future planetary research. 6. Recommendations. Based on our experience during the past decade and the evolution of NASA's outlook and interest in maintaining the usability of planetary data, we recommend the following: 1) NASA continue development of the Planetary Data Ecosystem, building on the findings of MAPSIT, the PDS, and the RPIF network, particularly related to the development of planetary spatial data infrastructures to enable planetary research in coming decades.
2) As part of a vibrant Planetary Data Ecosystem, NASA enable a funding mechanism for Planetary Data Utilization Centers (PDUCs), new facilities that would leverage the resources of the old RPIF Network but focused on the objectives outlined above, most specifically 1) the training of users in planetary data, 2) making accessible new digital data sets from past planetary missions and historic planetary research projects, and 3) producing new online supporting materials (e. g. , derived data sets such as DTMs, "user guides" to PDS data formats, etc. ). PDUCs would thus serve as "community memory" for mission documentation and methodologies. NASA should encourage cost sharing with host institutions, who were eager participants in the RPIF network for several decades. 3) NASA, through the Planetary Data Ecosystem, enable better communication between the PDS nodes and the PDUCs.
PDUCs are uniquely able to capture community feedback on PDS resources such as data search tools, that could enable the PDS to improve its services. 4). Support PDUCs in providing (or co-hosting) multiple planetary data users workshops targeting researchers and students at institutions across a given region. Local and visiting facilitators (e. g. , USGS, nearby institutions with unique expertise) would schedule the workshops according to need and demand. 5) Encourage and support the exchange of data between the US and international centers in terms of capturing areas of expertise within the Planetary Data Ecosystem.
hardcopy PAL*/aeolian experiment record Limited digitization 1 R. Greeley 35mm slide collection Digitization in progress UArizona GIS Laboratory/DTM production In person training Earth-based telescopic image collection Lunar Surveyor image database For delivery to PDS Communications of the Lunar and Hosted online Planetary Lab Jet Propulsion Lab. , Pasadena Historical planetary mission records In person only Un. Hawaii Clementine Mission Documentation On-line searchable data base ~100 MRO CTX DEMs On-line, with GUI and supporting text Three 3-D printers >120 *. stl files on-line University College, London 3D imaging center for DTM In person access to stereo production. iMars webGIS of WMS workstation (running USGS ISIS browse of ≈3000 CTX DTMs, some + SOCET) and large screen of the HRSC DTMs and all the UoA- MacPro and online access PDS, UCL and UoSeoul-HiRISE through http://i-mars.
eu/web- DTMs. gis. php Orsay, France Data from Lunar Orbiters, Digital/Online Clementine, Mariner 6-7-9-10, Viking 1-2 (Orbiters et Landers), Mars Pathfinder, Mars Global Surveyor, Mars Odyssey, Venera 15-16, Magellan, Voyager 1-2 and Galileo. Planetary GIS laboratory, Interactive In person access 1 e. g. , Digitizing the Unique Photographic-Film Archive of the Ronald Greeley Center for Planetary Studies, PDART grant NNX15AI81G, 2015-2019, PI David Williams, ASU; Digitization and Public Archiving of the Surveyor Imaging Dataset, LASER grant number NNX14AQ11G, PI Shane Byrne, UArizona).
Can this additional uncertainty be safely ignored, or is it sizeable? Does it decline with peer feedback? This has been our core motivation when designing the Finance Crowd Analysis Project (#fincap). 1 ur working definition of non-standard errors is the standard deviation across researchers for the results they report when independently testing the same hypotheses on the same sample. For example, in #fincap we provide researchers with identical data and then ask them to independently propose a measure for market efficiency, estimate its average per-year change, and report it. The NSE for this reported change is simply the standard deviation across researchers of their reported changes. Non-standard errors exist for a variety of reasons. They might, of course, be due to glitches in computer code or due to machine  1  To increase the credibility of the findings and address concerns of specification searching, we wrote a pre-analysis plan (PAP) and filed it with the Open Science Foundation at https://osf.
Project design The core of #fincap is letting multiple research teams (RTs) independently test the same six hypotheses on the same Deutsche Börse sample. We refer to these hypotheses as RT-hypotheses and to this sample as RT-sample. This is to avoid potential confusion with the hypotheses that we will test based on sample generated by RTs and peer evaluators (PEs). 11 he RT-sample is a plain-vanilla trade dataset for the EuroStoxx 50 index futures with, added to it, an agency/principal flag. 12 For each buy and sell, we therefore know whether the exchange member traded for its own account or for a client. The sample runs from 2002 through 2018 and contains 720 million trade records. These index futures are among the world's most actively traded index derivatives. They give investors exposure to Europe, or, more precisely, to a basket of euro-area blue-chip equities.