dataesr/scientific-paragraphs-categorization huggingface dataset:
Excited-state transition density matrix analysis To analyze the nature of the excited states occurring during TD-DFTB surface hopping dynamics, we interfaced the TheoDORE program with the DFTB+ program. TheoDORE is a program suite intended for the electronic wave function analysis based on state and transition density matrices between electronic states. 42 The implemented methods are particularly suitable for obtaining insight into excitonic structure in large conjugated systems  43  and are, therefore, a natural choice for analyzing CPPs. The most important part of this analysis refers to the determination of charge-transfer character of the excited states and their spatial localization properties by computing charge transfer and fragment participation ratio numbers. Additionally, the TheoDORE program also implements the computation of the natural transition orbitals of the excited states and their participation ratio of natural transition orbitals.
025 fs, applying interpolated electronic properties between classical steps. The nonadiabatic couplings between excited states were evaluated numerically using the orbital derivative method. 19, 36   he dynamics was propagated for 3 ps. The excited states occurring during dynamics were analyzed based on their transition density matrices. For this purpose, the molecules are fragmented into phenylene rings. CT, PR, and PRNTO values for the active excited state during the trajectories were computed using the TheoDORE program. For the comparison with the TD-DFTB results, we also computed vertical excitation energies of both molecules with the long-range corrected TD-LC-DFTB method implemented in the DFTB+ code and with TD-DFT based on three different functionals (PBE, 53 B3LYP,  54  and CAM-B3LYP  55  ), using the GAMESS-US code.
Acknowledgments This Project was funded by the  Deanship of Scientific Research (DSR) King Abdulaziz University, Jeddah , under grant no. 43-130-35-RG. The authors, therefore, acknowledge DSR support for Scientific Research. Also, the authors appreciate the kind cooperation provided by the  Deanship of Scientific Research (DSR) ,  King Abdulaziz University. MB and LS work was supported by  Excellence Initiative of Aix-Marseille University  (A*MIDEX) and the project  Equip@Meso  ( ANR-10-EQPX-29-01 ), both funded by the  French Government "Investissements d'Avenir" program. MB also acknowledges funding from  HPC  resources from  GENCI-CINES  (Grant  2017-A0010810012 ). TN would like to thank the  Laboratoire d'Excellence iMUST  for financial support.
Recognizing and distinguishing antonyms from other types of semantic relations is an essential part of language understanding systems. In this paper, we present a novel method for deriving antonym pairs using paraphrase pairs containing negation markers. We further propose a neural network model, AntNET, that integrates morphological features indicative of antonymy into a path-based relation detection algorithm. We demonstrate that our model outperforms state-of-the-art models in distinguishing antonyms from other semantic relations and is capable of efficiently handling multi-word expressions.
For example, for the term pair unhappy/joyful, we record the negating prefix of unhappy using a new path feature and replace the word embedding of unhappy with happy in the vector representation of the dependency path between unhappy and sad. The proposed model improves the path embeddings to better distinguish antonyms from other semantic relations and gets higher performance than prior path-based methods on this task. We used the antonym pairs extracted from the Paraphrase Database (PPDB)  (Ganitkevitch et al. , 2013; Pavlick et al. , 2015b)  in the paraphrasebased method as training data for our neural network model. The main contributions of this paper are: • We present a novel technique of using paraphrases for antonym detection and successfully derive antonym pairs from paraphrases in the PPDB, the largest paraphrase resource currently available.
The Paraphrase Database The Paraphrase Database (PPDB) contains over 150 million paraphrase rules covering three paraphrase types: lexical (single word), phrasal (multiword), and syntactic restructuring rules, and is the largest collection of paraphrases currently available. PPDB. In this paper, we focus on lexical and phrasal paraphrases up to two words in length. We examine the relationships between phrase pairs in the PPDB focusing on phrase pairs that are most salient for deriving antonyms.
We use   (Pavlick et al. , 2015a)  of less than 2. 5. In the above example, unhappy/sad, we first derive happy/sad as an antonym pair and expand it by considering all synonyms of happy as antonyms of sad (e. g. joyful/sad), and all synonyms of sad as antonyms of happy (e. g. happy/gloomy). Table  2  shows the number of pairs derived at each step using PPDB. In total, we were able to derive around 773K unique pairs from PPDB. This is a much larger dataset than existing resources like Word-Net and EVALution as shown in Table  3. Analysis We performed a manual evaluation of the quality of the extracted antonyms by randomly selecting 1000 pairs classified as 'antonym' and observed that the dataset contained about 63% antonyms. Errors mostly consisted of phrases and words that do not have an opposing meaning after the removal of the negation pattern.
Dataset Neural networks require a large amount of training data. We use the labelled portion of the dataset that we created using PPDB, as described in Section 3. In order to induce paths for the pairs in the dataset, we identify sentences in the corpus that contain the pair and extract all patterns for the given pair. Pairs with an antonym relationship are considered as positive instances in both classification experiments. In the binary classification experiment, we consider all pairs related by other relations (entailment, synonymy, category, unrelated, other) as negative instances. We also perform a variant of the multiclass classification with three classes  (antonym, other, unrelated). Due to the skewed nature of the dataset, we combined category, entailment and synonym/paraphrases into one class.
For both classification experiments, we perform random split with 70% train, 25% test, and 5% validation sets. Table  5  displays the number of relations in our dataset. Wikipedia 3  was used as the underlying corpus for all methods and we perform model selection on the validation set to tune the hyper-parameters of each method. We apply grid search for a range of values and pick the ones that yield the highest F 1 score on the validation set. The best hyper-parameters are reported in the appendix.
Baselines Majority Baseline The majority baseline is achieved by labelling all the instances with the most frequent class occuring in the dataset i. e. FALSE (binary) or UNRELATED (multiclass). Distributed Baseline The method proposed by  Schwartz et al. (2015)  uses symmetric patterns (SPs) for generating word embeddings. The authors automatically acquired symmetric patterns (defined as a sequence of 3-5 tokens consisting of exactly 2 wildcards and 1-3 words) from a large plain-text corpus, and generated vectors where each co-ordinate represented the co-occurrence in symmetric patterns of the represented word with another word of the vocabulary. For antonym representation, the authors relied on the patterns suggested by  (Lin et al. , 2003)  to construct word embeddings containing an antonym parameter that can be turned on in order to represent antonyms as dissimilar, and that can be turned off to represent antonyms as similar.
Path-based and Combined Baseline Since AntNET is an extension of the path-based and combined models proposed by  (Shwartz and Dagan, 2016)  for classifying multiple semantic relations, we use their models as additional baselines. Because their model used a different dataset that contained very few antonym instances, we repli-cated the baseline (SD) with the dataset and corpus information as in Sectionn 5. 1 rather than comparing to the reported results.
Results Table  6  displays the performance scores of AntNET and the baselines in terms of precision, recall and F 1. Our combined model significantly 5  outperforms all baselines in both binary and multiclass classifications. Both path-based and combined models of AntNET achieve a much better performance in comparison to the majority class and SP baselines. Comparing the path-based methods, the AntNET model achieves a higher precision compared to the path-based SD baseline for binary classification, and outperforms the SD model in precision, recall and F 1 in the multiclass classification experiment. The low precision of the SD model stems from its inability to distinguish between antonyms and synonyms, and between related and unrelated pairs which are common in our dataset, causing many false positive pairs such as difficult/harsh, bad/cunning, finish/far which were classified as antonyms.
Comparing the combined models, the AntNET model outperforms the SD model in precision, recall and F 1 , achieving state-of-the-art results for antonym detection. In all the experiments, the performance of the model in the binary classification task was better than in the multiclass classification. Multiclass classification seems to be inherently harder for all methods, due to the large number of relations and the smaller number of instances for each relation. We also observed that as we increased the size of the training dataset used in our experiments, the results improved for both path-based and combined models, confirming the need for large-scale datasets that will benefit training neural models. Effect of the Negation-marking Feature In our models, the novel negation marking feature is successfully integrated along the syntactic path to represent the paths between x and y.
In order to evaluate the effect of our novel negation-marking feature for antonym detection, we compare this feature to the distance feature proposed by Nguyen et al. (  2017 ). In their approach, they integrate the distance between related words in a lexicosyntactic path as a new pattern feature, along with lemma, POS and dependency for the task of distinguishing antonyms and synonyms. We re-implemented this model by making use of the same information regarding dataset and patterns as in Section 5. 1 and then replacing the direction feature in the SD models by the distance feature. The results are shown in Table  7  and indicate that the negation marking feature and the replacement of the embeddings of negated words by the ones of their base forms enhance the performance of our models more effectively than the distance feature does, across both binary and multiclass classifications.
Conclusion In this paper, we presented an original technique for deriving antonyms using paraphrases from PPDB. We also proposed a novel morphologyaware neural network model, AntNET, which improves antonymy prediction for path-based and combined models. In addition to lexical and syntactic information, we suggested to include a novel morphological negation-marking feature. Our models outperform the baselines in two relation classification tasks. We also demonstrated that the negation marking feature outperforms previously suggested path-based features for this task. Since our proposed techniques for antonymy detection are corpus based, they can be applied to different languages and relations. The paraphrasebased method can be applied to other languages by extracting the paraphrases for these languages from the PPDB and using a morphological analysis tool (e.
g. Morfette for French  (Chrupala et al. , 2008) ) or by looking up the negation prefixes in a grammar book for languages that do not dispose of such a tool. The LSTM-based model could also be used in other languages since the method is corpus based, but we would need to create a training set for new languages. This would not however be too difficult; the training set used by the model is not that big (the one used here was around 6000 pairs) and could be easily labelled through crowdsourcing. We release our code and the large-scale dataset derived from PPDB, annotated with semantic relations.
Acknowledgments This material is based in part on research sponsored by  DARPA  under grant number  FA8750-13-2-0017  (the  DEFT program ). The U. S. Government is authorized to reproduce and distribute reprints for Governmental purposes. The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of  DARPA  and the  U. S. Government. This work has also been supported by the  French National Research Agency  under project  ANR-16-CE33-0013  and partially supported by an  Intel ICRI-CI  grant, the  Israel Science Foundation  grant  880/12 , and the  German Research Foundation  through the  German-Israeli Project Cooperation (DIP, grant   DA 1600/1-1 ). We would like to thank our anonymous reviewers for their thoughtful and helpful comments.
Seismic signal windowing is the preliminary step for many analysis procedures in engineering seismology (standard spectral ratio, quality factor, general inversion techniques, etc. ). Moreover a noise window is often necessary for the data quality control through the signal-to-noise verification. Selecting the noise window can be challenging when large heterogeneous datasets are considered, especially when they include short preevent noise signals. This study proposes a fully automatic and configurable (i. e. , with default parameters that can also be user-defined) algorithm to windowing the noise and the P, S, coda and full signal once the P-wave (T P ) and S-wave (T S ) first arrivals are known. An application example is given on a KiK-net dataset. A Matlab language implementation of this algorithm is proposed as an online resource.
g. ,  Maggi et al. 2009)  or based on a model using the information extracted from seismic bulletins (e. g. ,  Kishida et al. 2016). When working with a large and heterogeneous dataset, once the T P and T S first arrivals have been picked, defining a specific window can be complicated. This complexity increases when a noise window has to be assessed for SNR computation. Indeed, timeseries extracted from triggered instruments and/or generated automatically from regional or national networks, often present short and variable pre-event noise durations. When a large dataset has to be processed, as for generalized inversion techniques (GIT-e. g. ,  Drouet et al. 2008)  or ground motion prediction equations (e. g. ,  Laurendeau et al. 2013) , then a complex automatic procedure has to be used to avoid the introduction of poor quality data into the processing and to minimize the number of rejected data due to difficult window selection.
The motivation behind the present study was to provide a suitable windowing tool for spectral estimation on different phases with due account to signal-to-noise ratio issues. A method to select the phase windows of any dataset for which the T P and T S first arrivals are known is proposed, and a suitable solution to estimate the noise window from heterogeneous datasets with variable noise level and duration is provided. An automatic Matlab algorithm was developed and tested on a KiK-net Japanese dataset that is composed of more than 2000 manually picked events with short and variable durations of pre-event noise  (Laurendeau et al. 2013). The records are accelerograms from local to regional events that are used between 0. 25 and 30 Hz mostly. This study was initially developed for the application of GIT to a specific KiK-net subset  (Foundotos et al.
2016-same issue) , and for the correction of the KiK-net surface records for local site effects for prediction of hard-rock reference ground motion prediction  (Laurendeau et al. 2016-same issue) : more details on the dataset can be found in these papers. After a short reminder on the relationships between window duration and the associated minimum frequency valid for the FAS, (f min ), we present the model proposed for the seismic-phase windowing in a first step, and the methodology used for the noise window selection in a second step. In a final step, some examples are given to discuss the windowing obtained for local, regional and teleseimic events, as well as for complex examples including after-or fore-shocks. 2 Spectral resolution and sensitivity to time window duration Many studies have tested the sensitivity of their data to signal window duration (e.
The higher is N, the better is the spectral resolution. Based on our experience, taking N = 10 is enough to give the assurance of good spectral resolution. However, taking such a high N number is not always possible, as this depends on the seismic phase or noise duration available, and on the minimal frequency necessary for the application. When it is required, the N-value can be optimized by tested it values for different signals. Figure  1  shows the sensitivity of the FAS to the S-wave window duration (D S ). The colors from blue to yellow show the results for window lengths from 2. 5 to 60 s. In this example and for various other KiK-net signals tested (not represented here), the minimal N-value for this dataset is around three. Indeed, clear discrepancies appear at low frequency for the shortest window, generally just below the f min criteria (vertical lines) obtained with N = 3.
Because the KiK-net dataset present very limited duration of noise for the analyses and tests with N = 3 appear satisfying, we keep this N-value in the following examples. In agreement with the literature, we find a good stability of the FAS over the frequency range where the N-value criterion is satisfied (Fig. 1 ). Thus, the FAS seems weakly dependent on the duration of the signal considered, provided the most energetic part of the signal is common to every window. It means that small variations on the duration of the selected phase window would lead to negligible change on the FAS.
(  4 ) with the magnitude and rupture distance for the KiK-net dataset example. The maximum duration due to the source is around 17 s, and that due to the propagation is around 38 s. The total duration has a minimum of 1. 2 s and a maximum of 55 s. However, in the following, we consider a target minimal frequency of 0. 3 Hz and a minimum of three wavelengths included inside the window (N = 3). Equation (1) finally gives a minimal duration D Smin = 10 s for applications on the KiK-net dataset.
Noise window selection The noise window selection generally consists of taking the duration of the target window and reporting it before the first P-wave arrival. Here, a more complex scheme has been developed, to take into account the availability of data with short windows of pre-event noise. Figure  4  shows the pre-event noise duration that is available for the KiK-net dataset. This shows generally that these durations are short and variable, which makes the noise window selection difficult. Only one noise window of duration (D N ) is selected to assess the SNR of several seismic phase windows of different durations. The duration of the target noise window (D t ) has to be at least as long as the longest seismic signal window requested (D P , D S , D C or D All ), or long enough to satisfy the f min criterion.
Thus, FAS estimated from these windows of different durations have to be normalized by the square root of the duration to obtain the Fourier amplitude spectrum density (FASD ¼ FAS= ffiffiffi ffi D p ) that is length-independent, for SNR purposes especially. Fig. 4  The number of records versus the pre-event noise duration available In the present example, we consider a minimal noise duration D min of 10 s to be consistent with the minimal S-wave window duration taken previously. However, 10 s of noise is not available before the event for all of the records of the KiK-net dataset, as shown in Fig. 4. The dataset contains 311 records without 10 s of pre-event noise. Thus, different noise window definitions are tested, for which the energy was then compared. The idea was to select a noise window with sufficient duration, and also a window with a representative level of energy (without strong seismic signal included).
Thus, a constant window duration is not adapted when working with datasets composed by both local and regional earthquakes while our formulation gives a suitable solution. Concerning the other phases of the signal, we used the classical P wave formulation that seems appropriate, while our coda wave window formulation begins very close to the one predicted by  Aki (1969) , as expected. The T end95 cumulative energy estimation is always later than the manually picked one, and this duration increase may be accentuated for longer records. The three noise windows have the same duration and present similar FASD. However, long pre-event noise is available and this may not be true for each record of every dataset. Figure  8  presents two examples recorded during the 2014 Cephalonia seismic crisis in Greece, for which the selection of noise and phase windows is uneasy and might be unreliable.
Seismic signal windowing is the preliminary step for many applications in seismology and for SNR verification. While the vast majority of previous studies have used very simple windowing formulations, such as constant duration, we propose a more complex method that takes into account source and propagation terms. This study provides a suitable solution for heterogeneous datasets where the P-wave and S-wave first arrivals have been picked beforehand. The earthquake signal phases are selected exclusively from the T P and T S parameters for the majority of the events, which makes the windowing independent of the uncertainties present in the information given by the seismic bulletin. For strong earthquakes (M [ 6) that have source durations that are not negligible, a source term is estimated through the inverse of the corner frequency evaluated from the magnitude.
Selecting the noise window can be challenging when large heterogeneous datasets are considered, especially if for some events the duration of the available pre-event noise is short. The noise window has to be the most representative of the noise level, and long enough to allow SNR estimation with good resolution at low frequency. To get around this issue, we defined and tested three different windows: one pre-event and two post-event windows. A scheme is proposed for selecting of the best noise window in terms of duration (as long as possible) and mean energy (as low as possible), without including undesirable transients. This approach gave good results on the KiK-net dataset, with a very limited number of rejected signals. A Matlab algorithm was developed and can be adapted to each dataset through a few parameters to be defined by the user.
Acknowledgements This work was conducted within the framework of the  CASHIMA  project ( Cadarache Seismic Hazard Integrated Multidisciplinary Assessment ), jointly funded by the  French Alternative Energies and Atomic Energy Commission (CEA) , the  Laue-Langevin Institute (ILL)  and the  International Thermonuclear Experimental Reactor  (ITER) organization, and the  SIGMA  project ( Seismic Ground Motion Assessment ) funded by  ElectriciteDe France (EDF) ,  CEA, AREVA  and the  Ente Nazionale per l'Energia Elettrica (ENEL). We are thankful to  Christopher Berrie  and to the two anonymous reviewers for their careful suggestions and corrections. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4.