dataesr/scientific-paragraphs-categorization huggingface dataset:
Barrientos, J. I. Hormaza, M. F. Martínez, P. Manosalva, J. Patel, and G. Wilkie during the IX World Avocado Congress held on September 2019 in Medellín (Colombia). Finally, we thank the team that maintains the avocado genebank A. Caicedo, A. Hernandez, E. Rodriguez, and D. Cañar for their dedication that motivates this work. Author contribution NA, CSI, YR, and RHP designed the sampling elite and CGB collection, and NA led tissue elite collection. BCJA, and DP performed DNA extraction and genomic library preparation. BCJA carried out bioinformatic analyses to discover SNPs and filtered and prepared input datasets. BCJA, CAJ, LHF, CSI, RHP, and YR made data analyses and interpreted results. All authors wrote and approved the submitted version.
, and the outgroup, P. schideana (Rendón-Anaya et al. 2019). Four SNP datasets were generated, and each included different samples (CGB, EC, RM, and O) depending on the analyses. The SNPs ranged from 227 in dataset 4, which included EC and Pure RM genotypes, to 26048 in dataset 3, which contained CGB and Pure RM genotypes (Table.
Introduction Semantic parsing aims to transform a natural language utterance into a structured representation that can be easily manipulated by a software (e. g. , to query a database). As such, it is a central task in human-computer interfaces. Andreas et al. (2013)  first proposed to rely on machine translation models for semantic parsing, where the target representation is linearized and treated as a foreign language. Due to recent advances in deep learning and especially in sequence-to-sequence (seq2seq) with attention architectures for machine translation  (Bahdanau et al. , 2015) , it is appealing to use the same architectures for standard structured prediction problems  (Vinyals et al. , 2015). This approach is indeed common in semantic parsing  (Jia and Liang, 2016; Dong and Lapata, 2016; Wang et al.
different predicates) to a bi-lexical dependency parsing problem. As such, we tackle the same semantic content as aforementioned previous work but using a different mathematical representation  (Rambow, 2010). We identify two main benefits to our approach: (1) as we allow crossing arcs, i. e. , ''non-projective graphs'', all datasets are guaranteed to be fully covered and (2) it allows us to rely on optimization methods to tackle inference intractability of our novel graph-based formulation of the problem. More specifically, in our setting we need to jointly assign predicates/ entities to words that convey a semantic content and to identify arguments of predicates via bi-lexical dependencies. We show that MAP inference in this setting is equivalent to the maximum generalized spanning arborescence problem  (Myung et al.
Semantic Grammar and AST The semantic programs we focus on take the form of a functional language, i. e. , a representation where each predicate is a function that takes other predicates or entities as arguments. The semantic language is typed in the same sense than in ''typed programming languages''. For example, in GEOQUERY, the predicate capital 2 expects an argument of type city and returns an object of type state. In the datasets we use, the typing system disambiguates the position of arguments in a function: For a given function, either all arguments are of the same type or the order of arguments is unimportant-an example of both is the predicate intersection river in GEO-QUERY that takes two arguments of type river, but the result of the execution is unchanged if the arguments are swapped.
Supervised Training Objective We define the likelihood of a pair x, y ∈ C via the Boltzmann distribution: p μ,φ (x, y) = exp(μ x + φ y -c(μ, φ)), where c(μ, φ) is the log-partition function: c(μ, φ) = log x ,y ∈C exp(μ x + φ y ). During training, we aim to maximize the loglikelihood of the training dataset. The loglikelihood of an observation x, y is defined as: (μ, φ; x, y) = log p μ,φ (x, y) = μ x + φ y -c(μ, φ). Unfortunately, computing the log-partition function is intractable as it requires summing over all feasible solutions. Instead, we rely on a surrogate lower-bound as an objective function. To this end, we derive an upper bound (because it is negated in ) to the second term: a sum of log-sum-exp functions that sums over each cluster of vertices independently and over incoming arcs in each cluster independently, which is tractable.
Weakly Supervised Training Objective Unfortunately, training data often does not include gold pairs x, y but instead only the AST, without word anchors (or word alignment). This is the case for the three datasets we use in our experiments. We thus consider our training signal to be the set of all structures that induce the annotated AST, which we denote C *. The weakly supervised loss is defined as: i. e. , we marginalize over all the structures that induce the gold AST. We can rewrite this loss as: ˜ (μ, φ; C * ) = log x,y ∈C * p μ,φ (x, y), = ⎛ ⎝ log x,y ∈C * exp(μ x + φ y ) ⎞ ⎠ -c(μ, φ). The two terms are intractable. We approximate the second term using the bound defined in Section 3. 1. We now derive a tractable lower bound to the first term. Let q be a proposal distribution such that q(x, y) = 0 if x, y / ∈ C *.
Experiments We compare our method to baseline systems both on i. i. d. splits (IID) and splits that test for compositional generalization for three datasets. The neural network is described in Appendix B. Datasets. SCAN (Lake and Baroni, 2018) contains natural language navigation commands. We use the variant of  Herzig and Berant (2021)  for semantic parsing. The IID split is the simple split  (Lake and Baroni, 2018). The compositional splits are primitive right (RIGHT) and primitive around right (ARIGHT)  (Loula et al. , 2018). GEOQUERY  (Zelle and Mooney, 1996)  uses the FunQL formalism  (Kate et al. , 2005)  and contains questions about the US geography. The IID split is the standard split and compositional generalization is evaluated on two splits: LENGTH where the examples are split by program length and TEMPLATE  (Finegan-Dollak et al.
, 2015). BERT2SEQ replaces the encoder with BERTbase. GRAMMAR is similar to SEQ2SEQ but the decoding is constrained by a grammar. BART  (Lewis et al. , 2020)  is pre-trained as a denoising autoencoder. Results. We report the denotation accuracies in Table  1. Our approach outperforms all other methods. In particular, the seq2seq baselines suffer from a significant drop in accuracy on splits that require compositional generalization. While SPANBASEDSP is able to generalize, our approach outperforms it. Note that we observed that the GEOQUERY execution script used to compute denotation accuracy in previous work contains several bugs that overestimate the true accuracy. Therefore, we also report denotation accuracy with a corrected executor (see Appendix C) for fair comparison with future work.
B Experimental Setup The neural architecture used in our experiments to produce the weights μ and φ is composed of: (1) an embedding layer of dimension 100 for SCAN or BERT-base  (Devlin et al. , 2019)  for the other datasets, followed by a bi-LSTM  (Hochreiter and Schmidhuber, 1997)  with a hidden size of 400; (2) a linear projection of dimension 500 over the output of the bi-LSTM followed by a TANH activation and another linear projection of dimension |E| to obtain μ; (3) a linear projection of dimension 500 followed by a TANH activation and a bi-affine layer  (Dozat and Manning, 2017)  to obtain φ. We apply dropout with a probability of 0. 3 over the outputs of BERT-base and the bi-LSTM and after both TANH activations. The learning rate is 5 × 10 -4 and each batch is composed of 30 examples.
Data In this analysis, the Australian Bureau of Meteorology Space Weather Services' ionosonde data are used. Figure  1    The Geoscience Australia's Ginan system was used for performing the PPP calculations 1. PPP is a high accuracy positioning method used to correct errors in GNSS positioning based on the robust modelling and estimations of systematic errors in the GNSS signals. The specific Ginan setup used in this analysis closely follows the Ginan PPP example "Ex11", in which the PPP is performed in 'static mode' using the ionospherefree combination 2. The carrier-phase measurements between the GPS satellites and receivers were used to calculate the TEC along the signal path (i. e. , the slant TEC, sTEC) following similar methodology to  Le Huy et al. (2016)  and T. Dao et al. (2020). The possible jumps in the sTEC values estimated from the carrier-phase measurements due to cycle slips were eliminated by comparing them against the sTEC estimated from the pseudo-range measurements that were smoothed by a fourth-degree polynomial approximation.
TID signatures are also clear in the data on January 15 for all stations, beginning at approx. 4:30 UT at Niue (top panel) and at approx. 10 UT at Cocos Keeling Islands, with the other stations showing the TID signatures at times in between. All stations, with the exception of Darwin which was suffering some intermittent hardware issues during this period, show the presence of spread-F traces in the hours following the TIDs, and in all cases, the spread-F traces are persistent for a number of hours. In particular, the TID activity at Norfolk Island begins shortly before 6 UT, with Spread-F present from 09:15 UT until 13:30 UT. Thus, the occurrence of spread-F traces measured by the Norfolk Island ionosonde span local times (LT) 21:15-01:30. At Townsville, the TIDs are first observed close to 7 UT (17 LT) and bring with them batches of Spread-F.
The Spread-F then intensifies close to 11:24 UT (21:24 LT) and remains strong until 15 UT (1:00 LT), before finally ceasing at 17:30 UT (03:30 LT). Next, the GPS data from receivers close to the Norfolk Island and Townsville ionosonde stations are examined as initial examples of the features and trends present throughout the GPS data. Figure  3  shows the observed vertical TEC (VTEC, upper panels) and the rate of TEC index (ROTI, lower panels) observed by the Norfolk Island  (NORF, left)  and Townsville (TOW2, right) GPS receivers on January 15, 2022. The VTEC data for both stations show the presence of TIDs that disrupt the diurnal pattern starting at ∼ 6 and 7 UT for NORF and TOW2, respectively, in good agreement with the ionosonde observations in Fig. 2. In the NORF data, the TIDs are observed in the individual satellite traces as rather small-amplitude wave structures compared to the variations over the 24-hour period.
2. Next, we illustrate how the Ginan PPP software achieves convergence, as defined here by a 3-D position error of less than 10 cm. Figure  4   Given the TID and subsequent Spread-F activity shown in the ionosonde data in Fig. 2 , one might expect that PPP errors for nearby GPS receivers would noticeably increase during these periods. However, Fig. 4a  shows that despite the onset of TIDs at approx. 6 UT for the NORF station, the position error remained below the 10-cm threshold. The same is true in Fig. 4d  for the TOW2 station, for which the onset of TIDs was approx. 7 UT. While Fig. 4b  shows that the NORF station position error increased following the commencement of Spread-F close to 9 UT, it remained well below 10 cm. Interestingly, despite the commencement of rather strong Spread-F at 11:24 UT at TOW2, Fig.
The PPP convergence time is a complicated parameter that is dependent on the number and geometry of available satellites, and the ability of the algorithm to model and account for the signal errors including the atmospheric components caused by the ionosphere and troposphere (e. g. ,  Zumberge et al. , 1997; Leick et al. , 2015; Choy et al. , 2017; Kouba et al. , 2017; Teunissen & Montenbruck, 2017). To further explore the possible impact of the disturbed ionosphere on the PPP convergence time, a numerical experiment was performed using the GPS CORS across the Australian region. The Ginan software was used to perform the PPP processing using data from staggered start times throughout January 13-15, 2022, in 30-min steps. In other words, the experiment simulated a series of 'cold starts' of the PPP algorithm every 30-mins during January 13-15, 2022.
5-hour period of strong Spread-F activity detected by the Townsville ionosonde. Finally, it is also worth noting that the magnitude of the ∆T C increases vary between the stations, from an increase of 2 hours observed at the Cocos Keeling Islands to an increase of 3. 6 hours observed at Townsville and 3. 8 hours at Jakarta. To further explore the impact of the disturbed ionosphere on the GPS PPP convergence time across the region, Fig. 7   east Asia remain at 3-4 hours as the eastern Australian stations approach 0. There is a slight increase in ∆T C across Australia's northeast once again at 13 UT to more than 3 hours, before almost all stations across the region approach ∆T C = 0 by 15 UT. An interactive map showing these data is included in the Supplementary Materials (S1). In an effort to diagnose the physical phenomena that may be responsible for these impacts on PPP convergence, ionospheric observations made by these GPS stations are examined next.
Open Research The Bureau of Meteorology Space Weather Services' World Data Centre provides ionosonde data via their website; https://www. sws. bom. gov. au/World Data Centre. Geoscience Australia provides GNSS data for all of the stations used in this analysis via their Global Navigation Satellite System Data Centre; https://gnss. ga. gov. au/. Geoscience Australia's Ginan platform is also accessible via their github repository; https://geoscienceaustralia. github. io/ginan/. The higher level analysis products, particularly the PPP convergence times that were calculated using Ginan, are available on the Zenodo data repository (doi:10. 5281/zenodo. 7694409). search. The authors would also like to acknowledge the advice and assistance of R. Maj in running Geoscience Australia's Ginan platform.
Data In this analysis, the Australian Bureau of Meteorology Space Weather Services' ionosonde data are used. Figure  1  shows the locations of the ionosonde stations (black triangles) and Tonga volcano (red cross). The stations are Niue (NUE5F; 19. 07   The Geoscience Australia's Ginan system was used for performing the PPP calculations 1. PPP is a high accuracy positioning method used to correct errors in GNSS positioning based on the robust modelling and estimations of systematic errors in the GNSS signals. The specific Ginan setup used in this analysis closely follows the Ginan PPP example "Ex11", in which the PPP is performed in 'static mode' using the ionospherefree combination 2. The carrier-phase measurements between the GPS satellites and receivers were used to calculate the TEC along the signal path (i.
This methodology was adopted for the present study. Figure  2  shows these range-time-intensity-style plots for the ionosondes during January 14-15,  Given the TID and subsequent Spread-F activity shown in the ionosonde data in Fig. 2 , one might expect that PPP errors for nearby GPS receivers would noticeably increase during these periods. However, Fig. 4a  shows that despite the onset of TIDs at approx. 6 UT for the NORF station, the position error remained below the 10-cm threshold. The same is true in Fig. 4d  for the TOW2 station, for which the onset of TIDs was approx. 7 UT. While Fig. 4b  shows that the NORF station position error increased following the commencement of Spread-F close to 9 UT, it remained well below 10 cm. Interestingly, despite the commencement of rather strong Spread-F at 11:24 UT at TOW2, Fig.
6. east Asia remain at 3-4 hours as the eastern Australian stations approach 0. There is a slight increase in ∆T C across Australia's northeast once again at 13 UT to more than 3 hours, before almost all stations across the region approach ∆T C = 0 by 15 UT. An interactive map showing these data is included in the Supplementary Materials (S1). In an effort to diagnose the physical phenomena that may be responsible for these impacts on PPP convergence, ionospheric observations made by these GPS stations are examined next. To explore the potential link between ROTI and PPP convergence time, Figure  9  is similar to Fig. 7  but shows the 30-min maximum ROTI value for each GPS receiver; the full interactive map is available in the Supplementary Materials (S2). These maps show a lot of similar trends to those in Fig.
Moreover, if it were TIDs from Tonga then the propagation would have been expected to much larger than 30 m/s. Such low eastward propagation speeds are typical of the F-region dynamo in the dusk sector, within which EPBs drift following their non-linear growth to the topside F re-gion (e. g. ,  Kelley, 2009; Chapagain et al. , 2013, and references therein). Therefore, the local time of the Spread-F observed over Townsville, the VTEC depletion depth of 42 TECU and the associated eastward propagation speed in results presented here strongly indicate that the structure responsible for hours of increased ROTI, and subsequently increased PPP convergence times, in northeastern Australia is an EPB, in agreement with  Rajesh et al. (2022). It can be seen from the full animation of Fig. 11  (Supplementary Material (S3)) that data points associated with the depletion over northeastern Australia appeared as far south as 18 • S, equivalent to 30 • S magnetic latitude with an apex altitude of 1900 km over the magnetic equator.
Acknowledgments  B. A. Carter ,  S. Choy  and  T. Dao  would like to acknowledge the support of  FrontierSI  (project  PA1002A ). M. Le Huy  was supported by the  Vietnam Academy of Science and Technology  under the grant number  NVCC 12. 02/22-22. R. Pradipta 's time was partially supported by grant  FA9550-20-1-0313  from the  Air Force Office of Scientific Re -49  ( 11 ),  e2022GL099002. doi: https://doi. org/10. 1029/2022GL099002   Le Huy ,  M. ,  Tran Thi ,  L. ,  Fleury ,  R. ,  Amory-Mazaudier ,  C. ,  Le Truong , T. ,  Nguyen Chien , T. , &  Nguyen Ha , T. (2016). TEC variations and ionospheric disturbances during the magnetic storm in March 2015 observed from continuous GPS data in the Southeast Asia region. Vietnam Journal of Earth Sciences, 38 (3), 287-305. doi: https://doi. org/10.
shows the locations of the ionosonde stations (black triangles) and Tonga volcano (red cross). The stations are Niue (NUE5F; 19. 07 • S, 190. 07 • E), Norfolk Island (NLK5D; 29. 03 • S, 167. 97 • E), Canberra (CBR5F; 35. 32 • S, 149. 00 • E), Townsville (TVL5F; 19. 63 • S, 146. 8 • E), Darwin (DWN5D; 12. 45 • S, 130. 95 • E), Perth (PER5D; 31. 94 • S, 115. 95 • E), Learmonth (LEA5D; 22. 25 • S, 114. 08 • E) and Cocos Keeling Islands (CCK5F; 12. 20 • S, 96. 80 • E). Each ionosonde generates ionograms by sweeping through radio frequencies between 2-22 MHz, transmitting and receiving ionospheric echoes that indicate the electron density for a given virtual height determined by time-of-flight. In this analysis, the ionosonde data were used to indicate the presence of TIDs and spread-F traces. The ionosonde data complement the primary dataset used in this study, namely the GPS receiver data.
12. 20 • S, 96. 80 • E). Each ionosonde generates ionograms by sweeping through radio frequencies between 2-22 MHz, transmitting and receiving ionospheric echoes that indicate the electron density for a given virtual height determined by time-of-flight. In this analysis, the ionosonde data were used to indicate the presence of TIDs and spread-F traces. The ionosonde data complement the primary dataset used in this study, namely the GPS receiver data. GPS Continuously Operating Reference Station (CORS) data from stations located across Australia, spanning into the Southeast Asian region and across the South Pacific, were used in this analysis. While previous works have employed such GPS CORS data to analyze ionospheric TEC fluctuations caused by the eruption, this study focuses on the impact of this eruption on the GPS application of PPP.
Single electrons trapped in quantum dots hosted in silicon nanostructures are a promising platform for the implementation of quantum technologies. In this study, we investigated the required conditions to attain the single-electron regime in an Ultra-Thin Body and Buried oxide (UTBB) Fully Depleted Silicon-On-Insulator (FD-SOI) quantum dot device fabricated using the standard manufacturing process of STMicroelectronics. The cryogenic temperature operation of the quantum dot device is simulated and analyzed using the 3D Quantum Technology Computer Aided Design (QTCAD) software developed by Nanoacademic Technologies, achieving convergence down to 1. 4 K. We report here simulations exploring single-electron occupancy of a single side-gate activated corner quantum dot and compare them to experimental data collected from the measurements on a device with the same geometry.
Cryogenic temperature quantum technology Computer Aided design simulation tool The quantum dot device presented here was modeled in 3D using the QTCAD simulation tool  [18, 19]  developed by Nanoacademic Technologies Inc. This finite-element modeling (FEM) software permits the simulation of the electrical and quantum performance of the nanostructure at cryogenic temperatures. In this work, the FEM mesh generating tool Gmsh  [20]  was used to define the 3D geometry of the quantum dot structure producing a firstorder mesh containing 2. 5 million nodes. Local mesh refinements were manually implemented in areas of fast potential variations allowing to improve precision and convergence. The various materials and regions were then specified, followed by the electrodes and the structure doping profile.
Acknowledgements The authors of this paper would like to thank all the colleagues from  STMicroelectronics  who contributed to the design and fabrication of the samples, and especially  F. Arnaud ,  C. Gardin ,  A. Poulin ,  K. Tournon ,  N. Guitar ,  C. Charbuillet , and  T. Bédécarrats. They  would also like to thank all the colleagues from the Institut quantique for their technical support during the experimental process, and especially  M. Lacerte ,  C. Lupien ,  B. Rivard , and  S. Pelletier. This work was supported by the  French program Conventions Industrielles de Formation par la Recherche (CIFRE) ,  Canada First Research Excellence Fund (CFREF) ,  Natural Sciences and Engineering Research Council of Canada (NSERC), and Ministère de l' Économie et de l'Innovation du Québec.
Introduction: One of the main issues in the field of clinical research is to enhance clinical databases with information from imaging (CT, MR, PET-scan), contouring (RTstruct), or produced by TPS such as dose distribution (RTdose) or treatment plans (RTplan). To perform these analyses automatically, we propose the new open-source package "espadon", developed in R environment. This package also opens up numerous perspectives for TPSindependant calculation, automation and processing of DICOM data. Results: The espadon package converts DICOM objects into espadon objects. Several tools have been developed to manipulate these objects and extract the desired information. In addition to decode DICOM files and pseudonomize them, the great advantage of espadon is that it presents the links between patient data (images, structures, treatment plans) in a didactic way, respecting the dates of the examinations.
It can visualize volumes or structures in 2D or 3D, resample volumes, segment them, and change geometric frames of reference. It integrates dose-volume histogram functions on a selection, with Monte Carlo calculations of random shifts of contours. It offers the automatic calculation of several usual radiotherapy indices, as well as the calculation of Gamma and Chi indices. Conclusions: Espadon is a toolkit designed to be easily used by radiotherapists, medical physicists or students. Espadon's functions are implemented in an R script, and allow the automatic extraction or calculation of data from DICOM files, which can be used for statistical modelling or machine-learning in the R environment. This package is available on the Comprehensive R Archive Network (CRAN) repository.