dataesr/scientific-paragraphs-categorization huggingface dataset:
The forecasts were created by media and by institutions that specialize in analyzing the art market. I excluded this criterion because the same group of experts-analysts, academics, practitioners-speaks through both. The collected forecasts have miscellaneous formsthere are 10 reports, 44 press articles and analyses, and 31 discussions and interviews with experts. In relation to this, the selection criterion was the size of media coverage. Data were selected for those that had the broadest reach and were created by prominent media and art institutions which are reference points for market participants. The last criterion was the place of origin. The forecasts come both from the world's center and periphery-46 of them were created within the largest art markets, the United States of America and the United Kingdom (in total, 64 percent of the global art market value in 2019), and 40 were created within the comparatively underdeveloped Polish art market (less than 1 percent) (McAndrew 2020a).
They facilitate worldwide networking and communication, enable transactions and acquisitions at a distance, and thereby provide the basis for new business opportunities on the art market  (Samdanis 2016). Moreover, they allow galleries to reach new markets and to connect these markets more effectively  (Adam 2014; Arora and Vermeylen 2013; Khaire 2015). In general, the Internet has facilitated global visibility of art market participants, making it easier for buyers and collectors to access a broader range of offerings on a global level. This may transform the traditional gallery business model. In recent years, numerous new digital business concepts and platforms have emerged within the art market that also offer new opportunities for galleries. Websites such as Artfacts 1 and the price database Artnet 2 provide information about market developments of artists  (Bayer 2018; Sidorova 2019; Velthuis 2014).
Methods This article is part of ongoing research and a series of interviews with artists, curators, and art dealers about digital art exhibitions, and their influence on gatekeepers and the changing experience culture. TikTok is one of the research fields in the study of digital art exhibitions. As there is no dataset or research for these research questions, it is crucial to understand how artists use TikTok to bypass primary gatekeepers, and why feminism is used as a determining label in the app. By analysing artists labelled with #feminsitartists, through their TikTok feeds, articles, and artist and indepth interviews, it is possible to reach a well-based understanding of the still-developing research field. The study begins by placing TikTok in the context of social media and distinguishes it from Instagram.
This can most clearly be seen in the publicly available art auction sales numbers aggregated by John Furphy in the Australian Art Sales Digest database (Figure  1 ). These records show that, other than 2017, the 2021 year was the most successful year for auction sales volumes in Australia since the previous peak of 2007. The AUD 121 million of sales in 2021 was the third-highest total in the thirty-three years for which records have been kept  (Furphy 2022). Even the 2020 year, at AUD 108 million of sales, was remarkably robust given the reduced mobility of third-highest total in the thirty-three years for which records have been kept  (Furphy 2022). Even the 2020 year, at AUD 108 million of sales, was remarkably robust given the reduced mobility of art collectors, border closures, restrictions on people gathering, and the postponement or cancellation of several major auctions.
Acknowledgments: A special thanks to  Atmadeep Ghoshal , undergraduate intern in Project " Folk Artists in the Time of Coronavirus"  ( 2020Coronavirus" ( -2021 ) ) for conducting telephonic interviews between December  2020-January 2021  with  Patuas of Naya. Copyright of all photographs included in figures belongs to  Anjali Gera Roy  for her Project " Folk Artists in the Time of Coronavirus"  ( 2020Coronavirus" ( -2021 )). Acknowledgments: The author wishes to thank her thesis director  Joyeux Prunel  for her ongoing support for this project, as well as  Sophie-Rose Schor  ( Doctoral Candidate at the University of Massachusetts-Amherst ) for her diligent editorial contributions. This project would not be possible without the time and generosity of those interviewed. She would also like to thank the team at  Alserkal Avenue  for providing her with archival information from its early days, and the attentive librarians at the  Jameel Arts Centre.
During each stride, the arms reciprocally flex and extent. 3) Human paces are regular and constrained: According to a statistical study presented by  [3] , within their dataset containing 13. 5km and 10145 strides of gait measurements collected by a foot mounted device, including fast, normal and slow walking, 99. 5% of strides were within 1. 55m and no stride exceeds 1. 75m. The mean and standard deviation of stride length are 1. 33m and 0. 18m.
All these methods assume that the walking heading is observable with handheld inertial sensors during one step/stride, In another word, a segment of signal cut within gait cycles does not provide enough information to recover a walking heading. The method  [16] : pedestrian Stride-length Estimation based on LSTM and Denoising Autoencoders (titled SELDA in the rest of the paper) is selected among the gait driven AI methods. The later is representative of the category with sufficient implementation and data processing details, along with a benchmarking dataset. RoNIN  [25]  is selected among the sampling frequency driven AI methods since the authors published their implementation, trained model weights and a part of their dataset. A. SELDA: pedestrian Stride-length Estimation based on LSTM and Denoising Autoencoders  [16]  presents a deep learning stride length estimation model taking as input stride event segmented acceleration and angular velocity readings, collected by a smartphone IMU.
1) SELDA Dataset: is publicly available  [33] , collected by 5 volunteers of different genders, ages and heights, using a Huawei mate 9 smartphone. Throughout the recordings, the users hold the phone horizontally in right hand in front of their chest. The stride length ground truth is provided by a foot mounted IMU module. The dataset contained more than 22 km, 10000 strides of gait measurements in natural motions such as fast walking, normal walking, slow walking, running, jumping. It covers indoor and outdoor environment including stairs, escalators, elevators, office environments, shopping mall, streets and metro station. 2) Data preprocessing: The raw triaxial accelerometer and gyroscope readings at 100Hz are segmented by stride events. The stride segments are zero-padded to reach a uniform length of 300.
We chose dropout rate = 0. 3. The autoencoder is trained in an unsupervised manner minimizing the error between the reconstructed input and the original input. Stride length regression. Once the autoencoder is trained, we retrieve features extracted by its encoder, as the input for the final stride length regression. The stride length is regressed by 3 consecutive dense layers. All layers are fine-tuned with the regression. 4) Adaptation for experimental assessment: We implement the SELDA model according to  [16]. 35 higher-level features are utilized in the article, but definitions of only 4 among them are available, thus we decide to use only 4 higher-level features. We train the SELDA model with SELDA dataset. To obtain the user's trajectory, we pile up 3 modules namely step detection, stride length estimation (SELDA) and heading estimation to build a complete pedestrian dead reckoning (PDR) algorithm.
B. RoNIN: Robust Neural Inertial Navigation RoNIN is a state-of-the-art pedestrian positioning algorithm using smartphone IMU measurements. Both RoNIN dataset, model implementation and trained model weights are publicly available  [35]. 1) RoNIN dataset: contains 42. 7 hours of IMU data collected in 3 buildings, by 100 participants and 3 android devices. Usual device carrying mode and human activities are considered (smartphone in a bag, in pocket, handheld, walking, sitting). A sample track contains synchronised accelerometer and gyroscope readings, game rotation vector provided by android API and ground truth user trajectories and orientations, provided by visual-inertial SLAM using a tango phone, placed on the participant's chest. All measurements are synchronized and sampled at 200Hz. A json file contains sensor biases, scale factors and necessary information for the spatial alignment procedure.
2) Data pre-processing: To use the RoNIN networks, acceleration and angular rate sequences must first be expressed in a navigation frame. At the beginning of each recording, an "alignment" procedure is performed between the IMU device and the tango phone, by attaching them together screen to screen during a static period. The alignment procedure allows to estimate the rotation quaternion from the device's body frame to the tango phone's body frame at the beginning of the recording: q imu_to_tango. This quaternion is available in the json files of RoNIN dataset as part of the input. We also need the tango phone's orientation (w. r. t the navigation frame) at the beginning of the sequence q init_tango_ori. Using these 2 quaternions, we can estimate the initial orientation of the IMU device w.
In the bottom figure, we plot the 3 curves, representing the variations of angular offsets between Euler angles given by the 2 algorithms. The under-script "a" stands for android and "u" stands for ULISS. ∆ roll = roll u -roll u (t = 0)-(pitch a -pitch a (t = 0)) (11) ∆ pitch = pitch u -pitch u (t = 0) -(roll a -roll a (t = 0)) (12) ∆ yaw = yaw u -yaw u (t = 0) + (yaw a -yaw a (t = 0)) (13) The figures show that the game rotation vector is good at estimating roll and pitch (related to the gravity), there is no huge difference between game rotation vector and MAGYQ result. The offset between game rotation yaw and ULISS yaw is almost constant during several minutes of recordings. We observe punctual peaks in the offset variation plot, which are due to the singularity points in both game rotation vector and ULISS attitude.
Artificial Line-Scan Images We developed a software with NI LabVIEW 2020 to generate artificial line-scan images as described in Supplementary Figures  2A, B  and Supplementary Information. This software can be downloaded from GitHub: https://github. com/ EmmanuelleChaigneau/Create_simulated_RBC_xtfast_Image. Using it, we generated 1. 2 s long artificial line-scans of constant velocity (V RBCapp image ) as in Supplementary Figure  2C. The distribution RBCs was set to be randomly varying using a flat distribution. The tube Hematocrit (H t ) was set to 35% which is the average of published values: for the cortex 22, 47, and 37%  (Santisakultarm et al. , 2012; Lyons et al. , 2016; Li et al. , 2019) , and 35% for the olfactory bulb  (Lyons et al. , 2016). White noise was added to all images so that the amplitude of noise was 1.
FUNDING Financial support was provided by the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Fondation pour la Recherche Médicale  ( EQU201903007811 ), the  Fondation Leducq Transatlantic Networks of Excellence program  ( 16CVD05 , Understanding the role of the perivascular space in cerebral small vessel disease), and by  French  state funds managed by the  ANR  within the  Investissements d'Avenir program  [ IHU FOReSIGHT  ( ANR-18-IAHU-0001 ),  RHU TRT_cSVD  ( ANR-16-RHUS-0004 )].
As the number of observations submitted to the citizen science platform iNaturalist continues to grow, it is increasingly important that these observations can be identified to the finest taxonomic level, maximizing their value for biodiversity research. Here, we explore the benefits of acting as an identifier on iNaturalist. In an increasingly human-modified world, biodiversity data are essential to the detection and understanding of local to global biodiversity change  [1]. Biodiversity monitoring is targeted in the draft post-2020 global biodiversity framework of the Convention on Biological Diversity, recognizing that up-to-date knowledge is needed to guide decision-making. In recent decades, there has been a massive increase in available biodiversity data-there are currently >2. 1 billion species occurrence records in the Global Biodiversity Information Facility, representing a 12-fold increase since 2007  [2].
This rise in biodiversity data is due in part to the growing popularity of citizen, or community-based, science. One of the most globally successful platforms is iNaturalist (www. inaturalist. org;  [3] )-a multitaxa platform and joint initiative of the California Academy of Sciences and the National Geographic Society. iNaturalist allows participants to contribute observations of any organism (e. g. ,  Fig 1) , or traces thereof, along with associated spatiotemporal metadata. Observations are then identified and verified to high taxonomic resolution by the iNaturalist community, in conjunction with the rapidly improving computer vision suggestions. An observation is deemed "Research Grade" when it meets the site's metadata quality criteria, and has 2 or more suggested identifications, more than two-thirds of which agree at a species level (i.
e. , 2/2, 2/3, 3/4, etc. ; although records identified to a level finer than family can also become Research Grade if no further progress in identification is deemed possible). While the quantity of data and contributors continue to increase on iNaturalist, one bottleneck to fully realizing the potential of these data for scientific research is the dearth of participants with reasonable expertise (i. e. , someone with the skills and ability to make informed identifications)-hereafter "identifiers"-actively participating in the community. The iNaturalist community-as of January 2022-consists of 2. 5 million users, 92% of whom only observe, <1% of whom have only made identifications, and 7% of whom both observe and identify. More recruitment of identifiers is clearly needed. Here, we provide our collective perspective on 7 reasons to contribute to iNaturalist as an identifier (Box 1).
How identifications can contribute to biodiversity research As an identifier on iNaturalist, you can identify observations that have been madeAU : Pleasenotethatthissta anywhere in the world, and your identification efforts can be prioritized for maximum knowledge increase. For example, correct identifications of poorly studied invertebrates from the tropics are arguably 4. iNaturalist is a ready-made, free, and easy-to-use data collection infrastructure • A computer or smartphone and an internet connection are the only requirements for using iNaturalist, with all aspects of the platform, including uploading, identifying, and downloading data, entirely free. • An important component of the iNaturalist infrastructure is the computer vision providing automated identification suggestions. • iNaturalist features a dedicated "Identify: tool (www.
You can enjoy yourself • Browsing photographs of even well-known species, and helping new naturalists to identify them, can be enjoyable and personally rewarding. • There is an official "iNat Observation of the Day" project (see here) showcasing such observations. Non-English translations of this manuscript are available here. more valuable than identifications of common birds in the United States. Past misidentifications can be transparently amended on iNaturalist, and the person who made the observation, and any potential future identifiers, can learn about the identification of that organism. Even if an observation is not identifiable to the species level, an expert identification to family or genus, coupled with teaching the observer what to look for and capture next time (i. e. , photographing particular attributes of organisms), is often useful for improving data quality in the future.
For these variants, numerous algorithms have been proposed in the literature, which can be mainly divided into the following categories. The first category of algorithms are heuristics based on the non-convex continuous optimization model, such as the quasi-physical quasi-human algorithm  ( Wang, Huang, Zhang, & Xu, 2002 ) , monotonic basin-hopping algorithm  ( Addis, Locatelli, & Schoen, 2008a ) , population basin-hopping algorithm  ( Addis, Locatelli, & Schoen, 2008b ) , simulated annealing algorithms  ( Müller, Schneider, & Schömer, 2009; Zhang & Deng, 2005 ) , tabu search algorithm  ( Fu, Huang, & Lü, 2013 ) , greedy vacancy search algorithm  ( Huang & Ye, 2010 ) , local search-based methods  ( Hifi & Hallah, 2007; Hifi & Yousef, 2019 ) , formulation space search methods  ( López & Beasley, 2013; 2016 ) , hybrid heuristic algorithms  ( Boll, Donovan, Graham, & Lubachevsky, 20 0 0; Zeng, Yu, He, Huang, & Fu, 2016 ) , jump algorithm  ( Stoyan, Scheithauer, & Yaskov, 2016; Stoyan & Yaskov, 2014 ) , and popular Packmol approach which is a package for building initial configurations for molecular dynamics simulations  ( Martínez, Andrade, Birgin, & Martínez, 2009 ).
Parameter settings and experimental protocol The proposed IDTS algorithm employs three main parameters whose descriptions and default values are given in Table  1  , which were empirically determined via a preliminary experiment. This setting can be considered as the default setting of the algorithm and was consistently used to conduct all computational experiments in Section 4. A sensibility analysis of these parameters is presented in Section 5. 1. The IDTS algorithm was implemented in the C++ language and complied using the g++ compiler with the -O3 option. The computational experiments with IDTS were executed on a computer with an Intel E5-2670 processor (2. 5 GHz and 2G RAM), running the Linux operating system. To assess the performance of the algorithm, we employed 320 popular instances with N ≤ 320.
Acknowledgments We are grateful to the reviewers for their valuable comments and suggestions which helped us to improve the paper. This work was partially supported by the  National Natural Science Foundation of China  (Grant no. 61703213  ), six talent peaks project  in Jiangsu Province  (Grant no. RJFW-011 ), and  Shenzhen Science and Technology Innovation Commission  under Grant nos. JCYJ20180508162601910  and  2019-INT003.
Based on dynamic capability and contingency theory, authors examined the direct connection between Service innovation, Marketing innovation and Customer satisfaction, and the interacting mechanism of competitive intensity. The research model and hypotheses were developed from extant studies. Respondents from 300 microfinance banks participated in the survey. Analyses in Smart PLS software showed that Service innovation positively and significantly affects Customer satisfaction. Marketing innovation promotes customer satisfaction. Furthermore, the influence of Service and Marketing innovation on Customer satisfaction is greater in a competitive environment. Therefore, microfinance bank managers must continue to invest in innovation-related capabilities (service and marketing) to keep their customers satisfied.
According to the Central Bank of Nigeria (2016), "the number of MFBs declined from 879 to 820 in 2013 and grew to 987 in 2016, total asset declined by 5. 1% (N343. 9 billion to N326. 2 billion), total deposit liabilities declined by 6. 1% from N159. 5 billion in 2015 to N149. 8 billion in 2016, net loans and advances increased by 6% to N178. 0 billion in 2016 from N167. 9 billion in 2015 and investments increased by 13. 5% from N17. 7 billion in 2015 to N20. 1 billion in 2016. " These data reflect the complex nature of the microfinance industry in Nigeria. Thus, the microfinance sector is considered appropriate to test the hypotheses because of the environment in which they operate. Authors selected microfinance banks from three geopolitical zones. These zones have a strong presence of microfinance banks.
Four experts and two researchers evaluated our questionnaire before sending them to participants. The survey was conducted from September 2018 to October 2018. The authors employed a cross-sectional quantitative research approach. This approach is appropriate for testing hypotheses quantitatively. Online surveys and emails were used to collect data from 325 participants who were conveniently selected. The online survey was sent to the e-mails of participants to ensure that relevant individuals complete the survey. 300 responses were suitable for the analysis conducted. This represents a 92% response rate which is adequate for data analysis and drawing conclusion  (Bryman & Bell, 2015).
This was caused by an interruption in communications between the instrument and spacecraft that resulted in no collection of data during this time. 2 Two years later, TESS reobserved HD 73583 as part of its extended mission in Sector 34 between 2021 January 13 and 2021 February 9 in camera 2 with a 2-min cadence. We downloaded the Sector 34 HD 73583's light curve from the MAST archive. We found that the expected transit signals associated with HD 73583 b were consistent with the transits observed in Sector 8. We also detected by eye two extra transits in Sector 34 that do not have a counterpart in Sector 8 at times 2232. 17 and 2251. 06 BTJD, where BTJD = BJD -2457 000 is the Barycentric TESS Julian date. These two new transits have similar depths ( ∼1130 ppm), suggesting that they are caused by the MNRAS 514, 1606-1627 (2022) same transiting object with a period of 18.
According to TESS ' Sector 34 release notes, orbit 76 suffered from significant spacecraft motion. 3 This suggests that the apparent PDCSAP light-curve corruption is likely caused by an o v erfitting of the Cotrending Base Vectors (CBVs) when trying to correct the significant spacecraft motion. For this reason we decided to perform our own light-curve correction using the lightkurve software (Lightkurve Collaboration 2018 ). Briefly, 3 Sector 34 release notes can be found in this link. we use the CBVCorrector class to perform a 'Single-scale' and 'Spike' CBV correction. We first set a regularization term alpha = 1 × 10 - 4. This produces a corrected light curve that is visibly similar to the PDCSAP one with an o v erfitting metric of 0. 5 that is smaller than the recommended threshold of 0.
These tests increased our confidence that none of the transit signals are the result of systematic effects, such as a temperature change in the satellite, or that they are astrophysical false positives such as background eclipsing binaries or a Solar system object passing through the field of view. HD 73583's TESS light curves show out-of-transit variability likely caused by activity regions on the stellar surface and/or instrumental systematics. For further transit analysis in this manuscript, we chose to remo v e the low-frequency trends in order to work with flattened light curves. We detrended the TESS light curves using the public code citlalicue (Barrag án et al. 2022 ). Briefly, citlalicue uses Gaussian Processes (GPs) as implemented in george  (Ambikasaran et al. 2015 )  to model the out-of-transit variability in the light curves.
Ground-based transit obser v ations We conducted ground-based photometric follo w-up observ ations as part of the TESS Follow-up Observing Program (TFOP;  Collins et al. 2018 ). We used the TESS Transit Finder , which is a customized version of the Tapir software package  (Jensen 2013 ) , to schedule our transit observations. A summary of the observations is provided in Table  2. We observed a full transit event of HD 73583 b simultaneously using four Next-Generation Transit Survey (NGTS;  Wheatley et al. 2018 )  0. 2 m telescopes located at ESO's Paranal Observatory, Chile. Each NGTS telescope has an 8 square degree field of view and a plate scale of 5 arcsec pixel -1. The observations were taken using a custom NGTS filter (520-890 nm) with 10 s exposure times and at airmass < 1. 95. Each telescope independently observed the transit event in the multitelescope operational mode as described in  Bryant et al.