dataesr/scientific-paragraphs-categorization huggingface dataset:
In this work, we survey the equatorial pitch angle distributions of protons in the inner magnetosphere of Jupiter. We combine data from Juno's JADE and JEDI particle detectors to examine distributions of protons with energies between 10 and 700 keV. Our work extends previous work from  Shen et al. (2022)  by including the <50 keV proton population between 6 and 20 R J in terms of equatorial pitch angle. In Section 2, we describe the methodology we use to analyze and combine multiple charged particle datasets from Juno. In Section 3, we describe the observations in the region past Io to Ganymede. Using the pitch angle distributions, we discuss implications for ion-neutral charge exchange and proton transport in the magnetosphere. We conclude in Section 4 with a summary of our results and address the question of whether proton distributions observed previously were consistent with charge exchange with a neutral torus.
Methodology We analyze data collected by the plasma and energetic particle instruments onboard the Juno spacecraft during its prime mission. The JADE-I instrument  (McComas et al. , 2017)  measures lower energy plasma and energetic ions between 10 eV/q to 50 keV/q. The JEDI instrument  (Mauk, Haggerty, Jaskulek, et al. , 2017; Mauk et al. , 2023)  measures higher energy ions, including protons between 40 keV and 20 MeV. JADE-I has a field-of-view that covers the full sky for each Juno spin period of ∼30 s and therefore has complete pitch angle coverage on a cadence of 30 s. However, extracting the pitch angle distribution in the plasma rest frame requires prior knowledge of the plasma flow velocity with respect to the spacecraft, which is non-trivial (see Appendix in  Szalay et al. , 2020).
We chose these energy ranges as charge-exchange cross-sections decrease sharply above ∼100 keV for both H + -O and H + -H 2 reactions (see Supporting Information S1) and the lifetime for an energetic particle to charge exchange with a neutral atom is inversely proportional to its velocity  (Lagg et al. , 1998). JEDI proton channels below 50 keV are ignored to avoid data that is contaminated by penetrating electrons and electronic noise  (Mauk, Haggerty, Paranicas, et al. , 2017). The data from both JADE and JEDI instruments are averaged to 5-min cadence for analysis. We constructed proton equatorial pitch angle distributions between M-shells M = 5 and M = 30. An M-shell is defined by the maximum radial extent of the magnetic field line. We use M-shell instead of L-shell to differentiate between the dipolar field at Earth versus Jupiter where the non-dipolar field contributions from the internal magnetic field and current sheet are significant.
Data Availability Statement All data analyzed in this work is publicly available from the NASA Planetary Data System Plasma Interactions Node. In particular, we use data from the JADE, JEDI and magnetometer instruments, which is available at the following URLs-https://doi. org/10. 17189/1519715, https://doi. org/10. 17189/1519713, https://doi. org/10. 17189/1519711. The JupiterMag community code can be accessed at https://github. com/ mattkjames7/JupiterMag.
Data currently suggest that both repeating and nonrepeating classes of FRBs have dispersion measures (DMs), a quantity equal to the integral of the free electron density along the line of sight, and sky locations consistent with being drawn from the same population. However, the two classes have been shown to differ in their intrinsic temporal widths and spectral bandwidths (CHIME/ FRB  Collaboration et al. 2021). Whether genuine nonrepeating sources have a different origin compared to that of their repeating cousins is an unresolved question. The first discovery of an FRB was made over a decade ago by Parkes 64 m radio telescope  (Lorimer et al. 2007 ). This burst, FRB 010724 or FRB 20010724A, known as the Lorimer burst, first indicated an extragalactic origin for FRBs through its observed DM.
A number of studies have looked at the possibility of GW emission associated with FRBs indirectly, using radio observations to search for coherent FRB-like emissions associated with short, hard gamma-ray bursts (GRBs;  Anderson et al. 2018; Rowlinson & Anderson 2019; Bouwhuis et al. 2020; Gourdji et al. 2020; Rowlinson et al. 2021). The identification of an FRB within the sensitive reach of GW interferometric detectors could provide conclusive proof of an association or constrain the parameters of the emission mechanisms for a given FRB. The increased population of detected FRBs from the CHIME/FRB Project therefore offers a unique chance of achieving this endeavor. The first search for GW counterparts to transient radio sources was conducted by  Abbott et al. (2016). This used a minimally modeled coherent search (X-Pipeline) ±2 minutes around the detection time of 6 Parkes FRBs using GW data from GEO600  (Grote 2010 ) and initial Virgo  (Accadia et al.
It also incorporates a skew at large σ DM to reflect the possibility of overdensities along the line of sight. Finally, for a prior on DM host , we adopt a lognormal distribution with median e μ = 68. 2, and logarithmic width parameter σ host = 0. 88 as in  Macquart et al. (2020). The quantities outlined above have a large range of uncertainty, and there could be additional contributions, e. g. , circumburst material. As a result, redshift values calculated from DMs are generally taken as upper limits. We perform MCMC sampling using the emcee package (Foreman-Mackey et al. 2013) based on an affine-invariant sampling algorithm (Goodman & Weare 2010) using 256 walkers of 20,000 samples. The inferred values of z, and thereby luminosity distance, and their 90% credible intervals are thus determined for each FRB, based on the observed values of DM T , R.
PyGRB-Modeled Search for Binary Mergers The modeled search for GWs associated with FRB events makes use of the PyGRB data analysis pipeline  (Harry & Fairhurst 2011; Williamson et al. 2014) , and the search is configured to be similar to the search for GW signals coincident with GRBs in O3a  (Abbott et al. 2021). This is a coherent matched-filtering pipeline that compares the GW detector network data with a bank of pregenerated waveforms, including the inspiral of BNS and NSBH binaries. PyGRB uses the PyCBC  (Nitz et al. 2020 ) open-source framework for distribution of the analysis of the GW data across large computing clusters, and also relies on several elements of the LALSuite software library (LIGO Scientific Collaboration 2018). The PyGRB analysis searches the combined detector data in the range 30-1000 Hz.
RAVEN Coincident Analysis To perform a wider sweep of the O3a data, we also looked for coincidences between these CHIME/FRB events and existing GW candidates using the tools of the Rapid, onsource VOEvent Coincidence Monitor (RAVEN; Urban 2016; Cho 2019) to query the Gravitational-Wave Candidate Event Database (GraceDB;  Pace et al. 2012). This query to GraceDB tests whether any GW candidates were found by any of the modeled or generic transient low-latency GW search pipelines within a time window around the FRB events. The queries used the same on-source search windows as our modeled and generic transient searches, with [-10, +2 s] and [-600, +120 s] windows around the FRB triggers, respectively. We then computed the joint false-alarm rate of any coincident GW candidate within these windows using the overall rate of FRB events in the CHIME/FRB sample calculated across the full span of the O3a observing run and the false-alarm rate of the GW candidate.
We define D 90 as the distance within which 90% of the simulated GW signals injected into the off-source data were recovered with a significance greater than the most significant on-source trigger. systematically a factor of 2 smaller for the SG and ADI models used in that study. We find that this is a result of the sky locations surveyed by CHIME corresponding with a region of weak sensitivity for the Virgo interferometric detector, due to their relative locations on the surface of the Earth. The average antenna responses for the LIGO Hanford (H1) and LIGO Livingston (L1) detectors are of order 0. 72 and 0. 65 respectively; the same metric for the V1 instrument is 0. 28. This has a severe effect when V1 is one of only two detectors in a network, a situation that has occurred 55% of the time for the generic transient analysis of nonrepeating FRBs.
Two of the repeats occurred after O3b; another episode, despite being consistent with the localization of the other associated bursts, had no intensity data saved. Therefore, we discuss here only the initial burst FRB 20200120E, for which GW data exists. At the time of FRB 20200120E, only H1 data were available; thus, a generic transient search was not conducted. Likewise, since this is a repeating event, it does not pass our criteria for conducting a modeled search. Due to these restrictions, only a RAVEN coincidence search was conducted within a [-6000, +6000] s time window. No coincidences were found with sufficient significance as determined by the coincident falsealarm rate. Given the relative close proximity of this burst, further repeat emissions will be of interest for GW follow-up during the fourth observing run of Advanced LIGO, Advanced Virgo, and Kagra (O4; Abbott et al.
CHIME/FRB is deploying a set of Outrigger telescopes located at sufficient distances to allow autonomous very-longbaseline interferometry on CHIME/FRB detected bursts  (Cassanelli et al. 2022; Mena-Parra et al. 2022). This development promises subarcsecond localizations on hundreds of FRBs per year allowing host galaxy identification and redshift determination through optical follow-ups or through cross matching of positional data with photometric galaxy surveys  (Shin et al. 2023). The resulting sample of FRBs at low redshift will be a significant development for GW detection networks, particularly as the sensitive volume increases with future observation runs and should allow targeted searches to obtain statistical evidence toward supporting or ruling out GW-FRB associations. Note. The distances represent the lower bounds of 90% credible intervals from the MCMC inference described in Section 3.
Acknowledgments This material is based upon work supported by  NSFʼs LIGO Laboratory , which is a major facility fully funded by the  National Science Foundation. The authors also gratefully acknowledge the support of the  Science and Technology Facilities Council (STFC)  of the  United Kingdom , the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/ Germany  for support of the construction of  Advanced LIGO  and construction and operation of the GEO 600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS) , and the  Netherlands Organization for Scientific Research (NWO) , for the construction and operation of the Virgo detector and the creation and support of the EGOconsortium.
The authors gratefully acknowledge the support of the  NSF ,  STFC ,  INFN , and  CNRS  for provision of computational resources. This work was supported by  MEXT ,  JSPS Leading-edge Research Infrastructure Program ,  JSPS Grant-in-Aid for Specially Promoted Research   26000005 ,  JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2905  ( JP17H06358 ,  JP17H06361 , and  JP17H0636 ),  JSPS Core-to-Core Program  (A)  Advanced Research Networks ,  JSPS Grant-in-Aid for Scientific Research  (S)  17H06133  and  20H05639 ,  JSPS Grant-in-Aid for Transformative Research Areas (A )  20A203  ( JP20H05854 ), the  joint research program  of the  Institute for Cosmic Ray Research, University of Tokyo ,  National Research Foundation (NRF) ,  Computing Infrastructure Project of KISTI-GSDC, Korea Astronomy and Space Science Institute (KASI) , and  Ministry of Science and ICT (MSIT) in Korea ,  Academia Sinica (AS) ,  AS Grid Center (ASGC)  and the  Ministry of Science and Technology (MoST) in Taiwan  under grants including  AS-CDA-105-M06 ,  Advanced Technology Center (ATC) of NAOJ , and  Mechanical Engineering Center of Kō Enerugī Kasokuki Kenkyū Kikō (KEK).
The problem of handwritten text recognition has been one of the oldest and most widely explored problems in this field  [6]. However, in some cases, like the one this paper explores, the previously proposed solutions to these problems are somewhat ineffective because they are unable to adapt to new or unseen data quickly. Since there is a shortage of the amount of handwritten data accumulated for the recognition of Urdu characters, this paper proposes a few-shot learning based system that is able to recognize individual Urdu characters and digits from an extremely limited amount of given examples. We test our improved prototypical network based approach against other methods and are able to achieve better accuracy and outperform other methods. We note an average accuracy of 92% on characters in a 5-way-5-shot setting over multiple datasets, while showing an average classification accuracy of 90% for classifying numerals in a 5-way-5-shot settings.
To assess relevance and generalization capabilities of our proposed approach, we tested our solution on two different datasets. These datasets are the one created by Husnain et al. [18]  , and the UHaT dataset  [5]. We show in Table  1  and Table  2  that our proposed method was able to consistently outperform previous few-shot solutions for recognition tasks. The next section presents the related works (section II), our proposed approach (section III) and its evaluation (section IV).
Based on experiments done in the paper, the PNN was able to outperform other classifier architectures by a margin of 6%. Previous studies done for lone character recognition  [39]  were able to sufficiently recognize handwritten Urdu characters using a feature vector that it built by the analysis of the strokes that were used to construct the character. There have been more recent works that tackle the problem of Urdu Character Recognition. For instance, Ahmed et al. [2]  presented the offline-UNHD dataset for Urdu characters. Experiments were performed using a 1-dimensional Bi-Directional LSTM to classify individual character labels. Other works such as  [35]  use Transformer architectures with a CNN backbone for unconstrained character classification. The large variability of Urdu characters and numerals representation is a challenge for deep-learning models as they need a large set of annotated data to learn their representation.
To the best of our knowledge, only two public properly collated datasets could be used to train a model with a limited number of samples per class. The dataset used by Husnain el al. [18]  proposed around 500 samples per class, while the Urdu Handwritten Text (UHaT)  [5]  dataset proposes around FIGURE  1 : Urdu character set with phonemes and Urdu numerals with their corresponding Roman equivalences (from  [18] ) 700 samples per class. The lack of properly structured and annotated data has always been a major hindrance for these types of tasks. Most recent methods  [2] ,  [35] ,  [28]  that involve the use of data-intensive and hungry architectures like LSTMs and Transformers usually are unable to perform when provided with sparse datasets. With continually changing styles and patterns, the Urdu language provides just that in the real world.
B. FEW SHOT LEARNING Deep learning methods have been observed to perform extremely well in settings and environments where there is an abundance of data available. Most conventional neural network architectures are designed in such a way that the influence of a particular example within the training set would be extremely small in terms of bringing about a change in the final results. These networks are designed to capture and learn the general features that are displayed throughout the dataset  [37] , which usually has a varied amount of examples. There are however some areas that require fast adaption to new kinds of data, with a minimal number of given examples. The concept of one-shot, few-shot or k-shot learning is motivated by the general idea of how humans are able to learn a new class of objects with just a few given examples.
However, it has been seen that combinations of parametric and non-parametric methods have yielded the best results  [23] ,  [41]. In this paper, we explore Prototypical Networks, which do not suffer from severe overfitting and according to our knowledge are able to achieve state of the art performance on the Omnilogot dataset  [41]. Furthermore, with the scarcity of annotated data for Urdu characters, we found that these networks consistently outperformed other few-shot learning approaches. We were also able to improve the performance of a vanilla Prototypical Network on both the datasets of Urdu characters and numerals by adding modules for regularizing individual weights given to initial prototypes and balancing the distances between the clusters that are formed in the embedding vector space.
III. PROPOSED APPROACH The approach that we followed was majorly derived from classical recognition problems in pattern recognition. We first preprocessed the images in the dataset to remove any unnecessary noise, resize them to a standard size, keeping the aspect ratio fixed. We also rotated them in multiples of 90 degrees to generate more classes. These images were then passed through a self-supervised encoder to obtain a generic representation of the features of the images. Finally, we use an improved Prototypical Network model (described in Sections 3. 3, 3. 4 and 3. 5) to structure the representation space which we used to classify unseen data with a minimal number of examples. Figure  3  gives a allegorical overview on the step-by-step process taken by our approach to classify handwritten Urdu text.
A. PREPROCESSING As in all pattern recognition processes, data need to be cleaned to remove noises and irrelevant features. To this end, the images of the dataset are sent through basic preprocessing steps to prepare the individual images to be encoded. First, the images are processed to remove noise using methods that were used in noteworthy work conducted by Bieniecki et al. [8]. All the images are grayscaled and resized to 28 × 28 pixels (if there were any images that were not of this size) while keeping the aspect ratio fixed at all times. We also (similar to the authors of  [41] ) rotated the images by angles of 90 • , 180 • and 270 • in order to augment the variability of images and gain in genericity. This last step was also done keeping in mind real-world uses of Urdu OCR applications.
To further improve the performance of the prototypical networks on the character and numeral dataset, we proposed two additional modules to be added to the original network. These were the Weight Distribution module, which gave weights to the initial class prototypes based on their amount of representativeness of the class; and the Distance Scaling module, which was able to regulate inter-and intra-class distances between the prototypes in the embedding space. While concepts similar to the Weight Distribution module have been studied in the context of Prototypical Networks before  [41] , we increment this approach by using an attention-based order to get representative prototypes of different classes. We describe our proposed approach in more detail in Section 3. 4.
F. POST-PROCESSING Complementing the distance scaling module, in order to achieve better accuracy on all characters of the dataset, we also had to post-process our results. This was necessary in order to account for the diacritics that the characters in the Urdu language contain. We explain this problem and our process in more detail in this section. Each character in the Urdu language is comprised of two ligatures: the primary and secondary ligatures. In some cases, the secondary ligature may not exist. Different Urdu characters join with each other and form different ligatures based on joiner rules  [29]. These ligatures often take the form of diacritics like dots or dashes that are appended somewhere with the character. We use a connected component labelling process similar to  [46]  in order to separate secondary ligatures from primary.
B. DATASETS In order to assess the relevance of our proposed approach, we tested our results on two comprehensive datasets with individually separated Urdu characters. These two datasets are the Urdu Handwritten Text Dataset (UHaT)  [5] , and the one used by Husnain et al. in  [18]  (where they used a CNN to classify individual Urdu characters). They were chosen as they correspond to the most relevant and the most recent datasets related to our task. Some recent works  [2] ,  [28]  have been proposed in the literature and allow us to propose a fair comparison of our results. The UHaT dataset  [5]  contains samples from more than 900 individuals who wrote characters and digits of the Urdu language. The training set for each character contains 700 images on average. The number of images in each of the set of characters is uneven.
For example: the class alif contains 697 images and ayn contains 811 images. Similarly, the training set for digits has 140 images on average. Since we are conducting the research in a few-shot environment, we randomly sampled 40 images from each class and used it as a training set; and 10 images as testing. Each of the testing images are 28 × 28 in size. The other dataset we used for the experiment was collated by Husnain et al. [18]. In their paper, the authors classified Urdu characters and digits using convolutional neural networks. The dataset was built by collecting images from 500 different Urdu speakers belonging to a variety of social groups. Each of the authors was directed to write the character and the numeral in their own writing. The dataset also recorded the age, gender, hand preference while writing, physical impairments (if any) and the profession of each of the writers, but that data was not used in this experiment.
As with the UHaT dataset, we randomly sampled 40 images from each class for training and 10 images from the query set each class for testing. For both datasets, we have intentionally limited the number of samples in the training phase as this paper intends to demonstrate that it is possible to make a Urdu Handwritten Recognition system using few samples. This is done with the idea to mimic the human behavior, which is able to generalize knowledge based on few learning samples. Some examples of those datasets are provided in figure  5.
C. IMPLEMENTATION DETAILS We conducted a large number of few-shot learning experiments on both the datasets using many random sets in order to assess the generality of the proposed approach. We used the Adam optimizer with an initial learning rate of 1 × 10 -3. All of our models were implemented in Tensorflow and were trained on Google Collaboratory, on a single NVIDIA K80 GPU. The training time for the Prototypical Network on each of the datasets was around a day. We trained our models for both datasets in the same way to avoid the possibility of ambiguity in the final results. We trained our model separately on all 40 classes of Urdu characters and the 10 numeric classes and tested each trained model with 5 classes at a time (5-way classification). The reason for this was that most pre-existing literature  [41] ,  [45]  demonstrated their results on the Omnilogot dataset on the basis of a 5-way classification.