dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison to the existing code to perform joint analysis We compared the functionalities and computational performances of JASS against other approaches for the joint analysis of GWAS summary statistics. Out of the eight stud-ies (Supplementary Table  S3 ), two did not provide code  (14, 33) , two provided loose R functions  (11, 12) , one provided Python 2. 7 scripts (3), one provided code in a nonopen source software  (9)  and two provided R packages  (10, 13). The quality of documentation varied greatly from one method to another going from its absence to a wiki describing in depth routine usage. Only one package offered a command line interface. None of the advanced features offered in JASS, such as the management of missing values, an accompanying Python 3 package to harmonize and clean heterogeneous GWAS summary statistics and the possibility to deploy an interface server, were available in other approaches.
Detection of new associations with JASS We deployed an online implementation of JASS that currently includes 154 publicly available GWAS summary statistics (http://jass. pasteur. fr/index. html, Supplementary Table  S4 ). These GWAS cover several common diseases (e. g. asthma, type 2 diabetes, cardiovascular diseases) and quantitative traits (e. g. body mass index, total cholesterol). All GWAS were preprocessed using the JASS companion package and were aligned to a reference panel of European ancestry sample from the 1KG Project Phase 3 data  (28). Rare (MAF < 1%), non-biallelic and strand-ambiguous SNPs were filtered out from the reference panel. These 154 GWAS represent only a fraction of all GWAS publicly available; however, there are already 2. 3 × 10 46 possible combinations of phenotypes that can be analyzed jointly from this subset.
As there is currently no established strategy to determine which specific set of phenotypes should be considered for joint test when addressing a specific biological question, it illustrates the strong need of disseminating to the community a fast and user-friendly tool for multi-GWAS analysis. Our package offers the possibility of performing multiple exploratory analyses extremely fast. To illustrate and demonstrate the potential of JASS, we performed three real data applications using these data. Example 1. In this first example, we considered a simple scenario where an investigator is interested in complementing results from genome-wide genetic association studies of multiple insulin phenotypes. The goal is to perform a multivariate analysis to identify additional genetic variants associated with insulin phenotypes missed by the univariate analyses.
DISCUSSION The past few years saw a dramatic increase in the number of publicly available GWAS summary statistics for a broad range of phenotypes. This wealth of data is coming along a strong interest from the community for multitrait analysis, and multitrait association testing in particular. In this study, we present JASS, a command line and web-based package dedicated to the joint analysis of GWAS summary statistics. JASS addresses the need for a fast and user-friendly tool to perform various joint analyses of summary statistics. Our package includes the two most popular multivariate approaches, an omnibus test and a weighted sum of Z-scores, but allows for alternative approaches to be implemented by advanced users. JASS also includes several complementary functions both for a dynamic synthesis and visualization of large-scale results and for the preprocessing of heterogeneous GWAS data, a critical step for valid multivariate analysis.
Using existing GWAS summary statistics, we showed the flexibility and strong computational performances of our package as compared to the existent. We further performed three arbitrary real data applications to demonstrate the potential of JASS. These examples cover various scenarios, including the joint analysis of related phenotypes for the identification of new associated loci missed by univariate analyses, the exploratory analysis of alternative weighting schemes to identify specific genetic components across a large number of traits and the validation of suggestive associations for a given phenotype using multivariate analysis applied to correlated phenotypes. We also deployed a publicly available web instance of JASS (http://jass. pasteur. fr) currently including 154 preprocessed and harmonized GWAS summary statistics, and covering several common diseases and quantitative traits.
This installation-free instance of JASS allows nonexperts to performed various complementary analyses relevant to their specific study. Our JASS software is complementary to standard metaanalysis approaches, as implemented, for example, in METAL  (37)  or GWAMA  (38). These meta-analysis tools  focus on combining association results from independent GWAS performed for the same phenotypes, and are typically used in consortium data. They incorporate approaches for the combined analysis of effect estimates, such as the inverse-variance weighted meta-analysis, or P-values, such as the Fisher's test, whose validity relies on the assumption of independence between GWAS. The primary objec-tive of JASS is to combine statistics from multiple phenotypes from potentially correlated GWAS, and is therefore a generalized version of the aforementioned approach.
Figure2. JASS architecture. The JASS Python package is composed of a central 'analysis module' that defines computational features, enables reading input data and writing results to HDF files, and generating static plots as PNG files. These features can be accessed from a command line interface or from an embedded web application powered by a Flask server that uses the connexion library and a JavaScript user interface using the plotly library that enables the execution of joint analysis and the exploration of the results. In the latter case, the management of the analysis jobs requires the setup of a Celery server.
DATA AVAILABILITY A JASS public server currently including 154 clean and harmonized GWAS is available at http://jass. pasteur. fr For local use, the source code of JASS can be found at https://gitlab. pasteur. fr/statistical-genetics/jass. Installation, configuration and data import instructions are included and linked from the README. md file. The JASS software is released under the terms of the MIT license (see https://gitlab. pasteur. fr/statistical-genetics/ jass/blob/master/LICENSE). The preprocessing package is available at https://gitlab. pasteur. fr/statistical-genetics/ JASS Pre-processing. The imputation package is available at https://gitlab. pasteur. fr/statistical-genetics/raiss. The nextflow pipeline integrating all JASS preprocessing steps (GWAS harmonization, GWAS imputation and H 0 covariance matrix computation) is available at https: //gitlab.
The second edition, ASVspoof 2017, focused instead upon replay spoofing attacks and countermeasures. The ASVspoof 2019 edition is the first to consider all three spoofing attack types within a single challenge. While they originate from the same source database and same underlying protocol, they are explored in two specific use case scenarios. Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques. Replay spoofing attacks within a physical access (PA) scenario are generated through carefully controlled simulations that support much more revealing analysis than possible previously. Also new to the 2019 edition is the use of the tandem detection cost function metric, which reflects the impact of spoofing and countermeasures on the reliability of a fixed ASV system.
This paper describes the database design, protocol, spoofing attack implementations, and baseline ASV and countermeasure results. It also describes a human assessment on spoofed data in logical access. It was demonstrated that the spoofing data in the ASVspoof 2019 database have varied degrees of perceived quality and similarity to the target speakers, including spoofed data that cannot be differentiated from bona fide utterances even by human subjects. It is expected that the ASVspoof 2019 database, with its varied coverage of different types of spoofing data, could further foster research on anti-spoofing.
Born in 2013  [4] , ASVspoof sought to collect and distribute common datasets of bona fide and spoofed speech signals and to establish common platforms for the comparative evaluation of different spoofing countermeasure solutions. The result is a series of bi-annual anti-spoofing challenges whereby participants working with a common database produce scores that reflect the likelihood that speech recordings are bona fide (or spoofed) speech. Participant scores are then compared by the challenge organizers with ground-truth labels in order to estimate and compare the performance of each antispoofing solution. The first ASVspoof challenge held in 2015 focused on the development of countermeasures for the detection of speech synthesis and voice conversion spoofing attacks  [5]. The second edition, held in 2017, focused instead on the development of countermeasures for the detection of replay spoofing attacks  [6].
While ASVspoof 2015 and 2017 showed that there is great potential in distinguishing between bona fide and spoofed speech, the threat of spoofing is very much a moving target. Speech synthesis and voice conversion technologies have evolved considerably over the last four years, whereas a much more controlled setup is needed to study more thoroughly the threat of replay attacks and the limits of replay spoofing countermeasures. It is critical that progress on anti-spoofing keeps pace with developments in technologies and techniques that can be used to deceive ASV systems. The ASVspoof 2019 challenge, accompanied by a new dataset, brings a number of advances compared with previous editions  [7, 8]. It is the first to consider all three spoofing attack types within a single challenge: speech synthesis, voice conversion, and replay.
While they originate from the same source database and same underlying protocol, they are explored in two specific scenarios illustrated in Figure  1 , namely logical and physical access, with distinct datasets for each scenario. The ASVspoof 2019 database of bona fide and spoofed speech signals includes synthetic speech and converted voice signals generated with the very latest, state-of-the-art technologies, including Tacotron2  [9]  and WaveNet  [10]. Under well Figure  1 : LA access and PA access adopted for ASVspoof 2019 controlled conditions, the best of these algorithms produces spoofed speech that is perceptually indistinguishable from bona fide speech. ASVspoof 2019 thus aims to determine whether advances in speech synthesis and voice conversion technology pose a greater threat to the reliability of ASV systems or whether, instead, they can be detected reliably with existing countermeasures.
Also new to the 2019 database is carefully controlled simulations of replayed speech that support much more revealing analyses than possible previously. The resulting database is suited not only to the study of ASV replay spoofing and countermeasures but also the study of fake audio detection in the case of, e. g. smart home devices. To reflect as best as possible the practical scenario in which the nature of spoofing attacks is never known in advance, a detailed description of the ASVspoof 2019 database was deliberately withheld from participants during the evaluation period. Now that the evaluation period is complete, a detailed description can finally be released into the public domain. This paper describes (i) the database design policy, (ii) the evaluation protocol for both logical and physical access scenarios, (iii) the techniques and algorithms used in creating the spoofing attacks in each scenario, (iv) their impact on the reliability of ASV systems, and (v) two baseline spoofing countermeasures and their performance for each scenario.
Database partitions and protocols In contrast to the ASVspoof 2015 and 2017 databases, the 2019 edition focuses on all three major forms of spoofing attack, namely replay, speech synthesis, and voice conversion. To support such a broader assessment, the ASVspoof 2019 database is designed to reflect two different use case scenarios, namely logical and physical access control. Also different is the strategy of assessing spoofing and countermeasure performance on ASV, instead of standalone countermeasure performance. All of these differences demand an entirely new database design policy. It is described here. Training, development, and evaluation partitions are described first. Protocols for the generation of spoofing attacks in each scenario are described next. Described last are ASV protocols for assessment with and without spoofing attacks and countermeasures.
Database partitions The ASVspoof 2019 database is based upon the Voice Cloning Toolkit (VCTK) corpus  [11] , a multi-speaker English speech database recorded in a hemi-anechoic chamber at a sampling rate of 96 kHz. It was created using utterances from 107 speakers (46 male, 61 female) that are downsampled to 16 kHz at 16 bits-per-sample. The set of 107 speakers is partitioned into three speaker-disjoint sets for training, development, and evaluation. This partitioning scheme is illustrated in the top panel of Instead of the equal error rate (EER) metric, as used for the 2015 and 2019 editions, ASVspoof 2019 adopted a new ASVcentric metric referred to as the tandem detection cost function (t-DCF)  [12]. Use of the t-DCF means that the ASVspoof 2019 database is designed not for the standalone assessment of spoofing countermeasures (CMs) but their impact on the reliability of an ASV system when subjected to spoofing attacks.
The database partitioning must then support experimentation and evaluation for both ASV and CMs. This implies the need for a non-overlapping subset of speaker enrollment data that are needed for both development and evaluation partitions. After the pruning of low-quality recordings, and while keeping to the same number of enrollment utterances for all speakers of the same gender, there are 11 enrollment utterances for female speakers and 19 utterances for male speakers for both development and evaluation partitions. The enrollment data for the development and evaluation speakers is shown in Figure  2  with tag #1 and #2, respectively.
Logical access The construction of the LA database is illustrated in the middle panel of Figure  2. Unlike the usual ASV databases, it includes data to be used by attacker and defender sides. The application of speech synthesis is referred to as text-to-speech (TTS) and voice conversion (VC) algorithms from here on. TTS algorithms convert text input to speech, and VC algorithms conduct transformations of input source speech to target speech. These two algorithms can also be combined. Defender side. ASV/CM training data comprises 2,580 bona fide utterances (#5) and 22,800 spoofed utterances (#7) generated by using four TTS and two VC algorithms. The ASV/CM development partition contains 1,484 bona fide target utterances (#6. 1), 1,064 bona fide non-target utterances (#6. 2), and 22,296 spoofed utterances (#8) generated with the same TTS and VC algorithms.
Physical access The construction of the PA database is illustrated in the bottom panel of Figure  2. Its design respects the same high-level partitioning scheme. There are, however, some differences. First, since replay attacks stem only from the recording and presentation of bona fide access attempts, there is no need for replay training data. Next, replay spoofing attacks are generated according to different replay configurations, rather than with different spoofing algorithms. A replay configuration is defined by an acoustic environment (e. g. room dimensions) and an attack type (e. g. the physical placement of the microphones and loudspeaker in the acoustic environment). The top row in the lower panel in Figure  2  illustrates the protocol for a single acoustic environment for replay spoofing attacks generated according to one of nine different attack types.
Spoofing algorithms for LA subset The LA subset of the new ASVspoof 2019 database is the result of more than six months of intensive work, including contributions from academic and industrial research laboratories. Currently, there are so many different types of TTS and VC systems in the research field and market, and new methods are proposed almost every week at ArXiv. Therefore, it is difficult to define either the baseline or major methods for TTS and VC. Nevertheless, we considered various types of approaches to the extent that we could, and 17 totally different types of TTS and VC systems were constructed using the TTS/VC training data (#3 and #4 in Figure  2 )  2. Six of them are designated as known spoofing systems, with the other 11 being designated as unknown spoofing systems. Table  1  summarizes the spoofing systems, which are fundamentally diverse.
The known spoofing systems (A01 to A06) include two VC and four TTS systems. Then A07 to A19 (apart from A16 and A19) are the eleven unknown spoofing systems, and A16 and A19 are the known reference systems using the same algorithms as A04 and A06  3. The details of each spoofing system are described below. A01. A neural-network (NN)-based TTS system. This system follows the standard NN-based statistical parametric speech synthesis (SPSS) framework  [14]  and uses a powerful neural waveform generator called WaveNet  [10]. Attackers may use A01 to attack ASV/CM systems because the WaveNet vocoder can produce high-quality speech that fools CMs. A01 uses the Festival lite (Flite)  [15]  as the front-end to convert text into a sequence of linguistic features such as phone and pitch-accent labels.
Attackers may consider this system if they want to generate fake speech at a high speed. Another difference from A01 is that A08 replaces the VAE with one-hot speaker vectors. A09. An NN-based SPSS TTS system  [30]. Attackers may try A09 because it is designed for real-time TTS on mobile devices to reduce computation load and disk footprint. A09 uses two LSTM-based acoustic models and a vocoder called Vocaine  [31]. The first LSTM model is a duration model that takes as input a vector describing the linguistic characteristics for each phoneme in a similar way to the above Merlin toolkit and predicts the number of acoustic frames required for that phone. Given the predicted number of acoustic frames and a very similar linguistic input, the second LSTM model predicts the acoustic features that are used by Vocaine to generate waveforms.
A13. A combined NN-based VC and TTS system. Attackers may use A13 because it not only combines TTS and VC but also includes a waveform modification approach to produce output waveforms. Speech generated from such a system may be difficult to detect by CMs. The TTS system in A13 is a product called VoiceText Web-API, which is publicly available for non-commercial uses  5. This TTS system uses a unit selection approach and was built with 40 hours of a single speaker's recordings. Using this TTS system, a parallel database of the TTS voice and bona-fide speech of target speakers in the LA subset was constructed, and a conventional VC model was trained. The VC model consists of a highway network and a 3-layered feedforward network, where its inputs are MCCs of the source speaker and outputs are those of the target speaker.
The ASR model was trained using an in-house dataset that contains around 3,000 hours of speech. Then, an LSTM-based acoustic model was constructed for each target speaker that predicted acoustic features from the bottleneck features. The acoustic features used are 41-dimensional MCCs, F0, and 5-dimensional BAPs, all of which were extracted using STRAIGHT  [39]. For waveform reconstruction, the STRAIGHT vocoder was used. A15. Another combined VC and TTS system similar to A14. However, A15 uses speaker-dependent WaveNet vocoders rather than the STRAIGHT vocoder to generate waveforms. Attackers may use A15 if they intend to improve the speech quality and increase the chance to fool a CM. These WaveNet vocoders (µ-law companding and 10-bit quantization) were trained by pre-training them using an in-house multi-speaker corpus and fine-tuning it using the speech of target speakers  [38].
Besides the 2D visualization, we carried out a clustering process to find out which attacks are naturally grouped together. To this end, we used the same (pre-processed) x-vectors to compute all pairwise distances between attacks. If we let X i and X j denote the x-vector collections of attacks i and j (i, j = 1,. , 20), we define the distance of X i and X j as D(X i , X j ) = 1 |X i | x∈X i min y∈X j 1 -s(x, y) , (1) where s(x, y) is the cosine similarity of x-vectors x and y, and |X i | denotes the number of x-vectors in X i. We then run agglomerative clustering on the resulting 20 × 20 distance matrix   By comparing Figure  3  with a similar figure plotted for spoofing systems in ASVspoof 2015 (i. e. , Figure  2  in  [52] ), we can see that the ASVspoof 2019 database includes more spoofing systems that overlapped with the bona fide data in the 2D feature space.
The PA scenario is thus relevant not just to ASV but also to the detection of fake audio, a problem that plagues a host of other applications such as voice interactive devices and services, e. g. , smart-speakers and voicedriven assistants. Whereas the 2017 ASVspoof database consists of real replay recordings, the 2019 edition consists of simulated replay recordings in a manner similar to  [53]   7. Simulation was chosen 7 We have released another data set that was recorded and replayed in real rooms (https://www. asvspoof. org/user/register). But this real replayed data set is irrelevant to the published results of the ASVspoof challenge 2019. Neither is it included in the released ASVspoof 2019 database. in order to collect a large database of replay recordings with a carefully controlled setup.
Simulation procedure The approach used to simulate room acoustics is that described in  [55]. Simulations were done using Roomsimove 9   [56] , which takes into account the entire acoustic environment, including room size, reverberation, and varying source/receiver positions, which includes source directivity. The same software and technique has been applied successfully for data augmentation in well-known x-vector-based speaker and speech recognition recipes  [57, 58]. As illustrated in the bottom panel of Figure  2 , bona fide and replay utterances were simulated 10  in 27 different environments. An environment was defined according to a combination of room size S, reverberation level R, and talker-to-ASV distance D s. With each parameter being categorized into three intervals as per Table  2 , a single acoustic environment is specified according to a tuple (SRD a ).
CB, CC. The full simulation procedure is illustrated in Figure  8. By combining the 9 different replay configurations with the 27 different acoustic configurations, there was a total of 243 different evaluation conditions. For maximum precision, all simulations were performed with the original VCTK data whose sampling rate is 96 kHz. For consistency with the LA setup, the resulting 96-kHz data was then downsampled to 16kHz with 16 bits-persample resolution. Bona fide presentations were generated from the simulation of acoustic environment effects only (SRD s ). In contrast, replay attacks were generated from the simulation of recording effects (SRD a ), loudspeaker effects (Q), and replay presentation (SRD s ). All simulations were made with the Roomsimove default microphone and talker heights of 1.