dataesr/scientific-paragraphs-categorization huggingface dataset:
ASV and CM baseline systems As for the 2017 edition, countermeasure (CM) baseline systems were provided to all ASVspoof 2019 participants. In addition, participants were also provided with ASV scores for all bona fide and spoofed trials. For the challenge itself, these were provided for the training and development partitions only. The now-public version of the database also contains ASV scores for the evaluation partition. ASV scores, produced by a fixed ASV system designed by the organizers, support the use of the t-DCF metric  [12] , which assesses the impacts of both spoofing and CMs on ASV performance. Participants may then focus exclusively on the design of CMs without the need to develop and optimize an additional ASV system. The baseline ASV and CM systems are described here. modeling approach.
ASV baseline The ASV system uses DNN-based x-vector speaker embeddings  [57]  together with a probabilistic linear discriminant analysis (PLDA)  [59]  backend. The x-vector extractor is a pretrained 11 neural network available for the Kaldi  [60]  toolkit. The network is trained with MFCC features extracted from audio data from 7,325 speakers of the VoxCeleb1 and Vox-Celeb2  [61]  databases. The x-vector model consists of a 5-layer deep time-delay neural network (TDNN), followed by statistics pooling and two fully connected layers before a softmax output. The statistics pooling layer converts the frame-level TDNN output to utterance-level representations by computing the mean 11 http://kaldi-asr. org/models/m7 and standard deviation of features over time. The x-vector embeddings are obtained from the first fully connected layer after the pooling layer.
The dimension of the embedding layer is 512, and the embeddings are extracted without the application of the ReLU activation function or batch normalization of the embedding layer. The network was trained with a stochastic gradient descent algorithm. Further details concerning network parameters and data preparation are available in  [57]. The original Kaldi recipe was modified to include PLDA adaptation using disjoint, bona fide, in-domain data. Bona fide CM training data (#5 and #17 in Figure  2 ) was used as indomain data for both the LA and PA scenarios. Note that adaptation was performed separately for the LA and PA scenarios since bona fide recordings for the latter only contain simulated acoustic and recording effects. The within-class and betweenclass covariances were adapted using Kaldi's domain adaptation technique (Algorithm 1 of  [62] ) with scaling factors of α w = 0.
Analysis of baseline ASV performance First, we assessed the impact of the different attacks with the baseline ASV system. In Table  6 , we show the ASV performance for the LA subset using the x-vector system described in Section 5. 1. The performance was measured in terms of EER computed with a tool developed for the challenge  12. The first row of the results shows the performance with the bona fide imposters, whereas the rest of the rows show the results for all 19 attacks, i. e. , A01-A19, split into development and evaluation sets. The results indicate that the spoofed data created with various VC and TTS methods drastically degraded the ASV performance. However, the severity of the attacks varied across different methods. For instance, A04, the TTS attack created with the waveform concatenation method, increased the EER from 2.
Human assessment While Section 6 demonstrated how the baseline ASV and CM performed on the ASVspoof 2019 database, we describe in this section a human assessment on the LA subset of the database. The results show whether the spoofed data was perceptually similar to the bona-fide data of target speakers and whether the spoofed data could be detected by the human subjects. Note that we did not conduct human assessment on the PA subset because the human perception of the bona fide and replayed speech may be affected by the varied types of headphones used by the human subjects during the crowd-sourced assessment. This uncontrolled acoustic environment will make it difficult to interpret the assessment results on PA subset. We designed the human assessment protocol on the basis of the ASVspoof LA evaluation protocol.
In general, we can see that the spoofed data in the LA database had disparate perceived quality and similarity to the target bona-fide data. Among the spoofing systems, the DET curve of A10 was close to the diagonal line in Figure  15. According to the results of a Mann-Whitney U test  [68] , the difference between the spoofed data from A10 and the bona-fide utterances of the target speakers was marginally significant (p = 0. 012) in terms of the quality and insignificant (p = 0. 81) in terms of the similarity to the enrollment utterances. These results demonstrate that the spoofed utterances from A10 sounded the same as the target bona-fide speaker and sounded very natural from the human listener's perspectives, clearly showing that the state-of-the-art TTS has the capability of producing synthetic speech that is perceptually indistinguishable from bona fide speech.
Interestingly, the human assessment also showed different results from those obtained using the baseline ASV and CM. For example, while A13 was good at fooling the baseline ASV and CM, it was easily detected by the human evaluators as spoofed data as Figure  15  shows. Likewise, A17 was also good at fooling the baseline CM, but it was also easily detected by the human evaluators. In all, the LA subset of the ASVspoof2019 database includes spoofed data with varied degrees of perceived quality and similarity to the target speakers. Some of them are strong spoofing methods even for the baseline ASV and CM. Such variation is essential for the LA task so that the performance of ASV and CM can be examined under various conditions.
Summary This paper described the design, protocol, and spoofing attack implementations of the ASVspoof 2019 database. This is the first database that considers all three types of spoofing attacks, and it has two specific use case scenarios: logical access and physical access scenarios. Spoofing attacks within the logical access scenario have been generated with the latest speech synthesis and voice conversion technologies, including stateof-the-art neural acoustic and waveform model techniques, and 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. Replay spoofing attacks within the physical access scenario have been generated through carefully controlled simulations that support much more revealing analysis than possible previously.
Both the acoustic environment and attacks' factors, including the physical placement of the microphones and loudspeaker in the acoustic environment, were systematically simulated. This paper also reported their impact on the reliability of the baseline ASV systems and two spoofing countermeasures and their performance using the traditional metric EER and newly introduced metric t-DCF for each scenario. This database, including ground truth labels, meta labels, and baseline ASV scores, is freely available for both academic and commercial purposes at the Edinburgh DataShare 14 under the 14 https://doi. org/10. 7488/ds/2555 Open Data Commons Attribution License. We strongly believe that the ASVspoof 2019 database will further accelerate and foster research on speaker recognition and media forensics.
Performance of integrated system in terms of min-tDCF and of standalone countermeasures in terms of EER (%) on development set (LA subset) of ASVSpoof 2019 dataset. Results are shown for two baselines, B1 (CQCC-GMM) and B2 (LFCC-GMM), separately, combined with fixed ASV system based on x-vector. Last row describes results for "pooled condition" when trials from all the attacks are considered for evaluation.
Acknowledgements The work was partially supported by  JST CREST  Grant No. JPMJCR18A6  ( VoicePersonae  project),  Japan ,  MEXT KAKENHI  Grant Nos. ( 16H06302 ,  16K16096 ,  17H04687 ,  18H04120 ,  18H04112 ,  18KT0051 ),  Japan , the VoicePersonae and RESPECT projects funded by the  French Agence Nationale de la Recherche (ANR) , the  Academy of Finland  ( NOTCH  project no. 309629 entitled " NOTCH: NOn-cooperaTive speaker CHaracterization "). The authors at the  University of Eastern Finland  also gratefully acknowledge the use of the computational infrastructures at CSC -the  IT Center for Science , and the support of the NVIDIA Corporation the donation of a Titan V GPU used in this research. The numerical calculations of some of the spoofed data were carried out on the TSUBAME3. 0supercomputer at the  Tokyo Institute of Technology.
Z. Wu, P. L. De Leon, C. Demiroglu, A. Khodabakhsh, S. King, Z. Ling, D. Saito, B. Stewart, T. Toda, M. Wester, J. Yamagishi, Anti-spoofing for text-independent speaker verification: An initial database, comparison of countermeasures, and human performance, IEEE/ACM Transactions on Audio, Speech, and Language Processing 24 (4) (2016) 768-783. doi: 10. 1109/TASLP. 2016. 2526653.
Note that A04/A16 also serves as an anchor to our ASVspoof 2015 database. The spoofing method for A04/A16 was used for the ASVspoof 2015 database and was notated S10 in the previous database. S10 was found to be the most difficult spoofing attack. Since the protocols for ASVspoof 2015 and 2019 are totally different, we cannot directly compare results for the two databases, but A04/A16 would imply how CM research advanced rapidly.
Multiple surgical targets for treating obsessive-compulsive disorder with deep brain stimulation (DBS) have been proposed. However, different targets may modulate the same neural network responsible for clinical improvement. We analyzed data from four cohorts of patients (N = 50) that underwent DBS to the anterior limb of the internal capsule (ALIC), the nucleus accumbens or the subthalamic nucleus (STN). The same fiber bundle was associated with optimal clinical response in cohorts targeting either structure. This bundle connected frontal regions to the STN. When informing the tract target based on the first cohort, clinical improvements in the second could be significantly predicted, and vice versa. To further confirm results, clinical improvements in eight patients from a third center and six patients from a fourth center were significantly predicted based on their stimulation overlap with this tract.
Other invasive therapies, such as cingulotomy and capsulotomy also aimed at disrupting connectivity from frontal regions by lesioning white-matter bundles  21. It could recently be shown that such tract-or network-based concepts may be used to predict clinical improvements across DBS centers and surgeons for the case of Parkinson's disease  22, 23. Based on modern neuroimaging methods and high-resolution connectomic datasets, connectivity of DBS electrodes to specific cortical regions was associated with stronger therapeutic effects in various diseases treated with this surgical procedure  22, [24] [25] [26]. For the case of OCD, Baldermann et al. 24  recently demonstrated that structural connectivity from DBS electrodes to medial and lateral prefrontal cortices was associated with stronger symptom alleviation.
After calculating the tract exclusively based on data of one cohort (e. g. ALIC), we cross-predicted outcome in the other cohort (e. g. STN), and vice versa. We then tested predictive utility of this tract in two additional cohorts from a third and fourth center. Finally, we set the resulting tract target into the larger context of OCD-DBS literature and tested, whether it could be used to explain outcomes of reported clinical studies with different surgical targets.
30  ). This resulted in a total of 32 data points. In the second test cohort (London; both ALIC and STN target; N = 6, four electrodes in each patient), stimulation parameters resulted from an optimized phase following parameter optimization. Electrode localization confirmed accurate placement to each of the three target regions for all patients of the four cohorts (Fig. 1 ). Connectivity analysis. Connectivity analysis results seeding from electrodes of the two training cohorts (Cologne and Grenoble) based on the N = 985 HCP normative connectome are shown in Fig. 2. The overall connectivity of electrodes to other areas in the brain (without weighing for clinical improvement) was strikingly different between the two cohorts (Fig. 2 , top row). This is hardly surprising as it mainly reflects the overall structural connectivity profiles of the two DBS targets.
The resulting positive discriminative tract traversed slightly dorsal to the group of electrodes of the ALIC cohort and coursed centrally or slightly ventral to the electrodes of the STN cohort. This tract was then used to predict outcome in two independent test cohorts of patients that underwent surgery in a third and fourth center (Madrid & London; Fig. 4, bottom ). Although the surgical target of the Madrid cohort was the NAcc, electrode placement was comparable to the ALIC/Cologne cohort (Fig. 1 ). Here, improvements were taken for each contact pair that had been switched on during a 3-month interval, leading to 32 data points (Fig. 4 , bottom left, active contact pair color coded). In the London cohort, patients had received two electrodes to each target (four in total) and fiber T-scores scores were summed up across targets.
6. The bundle most negatively associated with clinical improvement was the posterior limb of the anterior commissure, connecting bilateral temporal cortices. To properly define the anatomical course of this tract, we openly released it as an atlas in stereotactic (MNI) space within Lead-DBS software (www. lead-dbs. org). Of note, Lead-DBS is scientific and not clinical software and the tract should not be vacuously used for any form of clinical decision making  37.
Discussion We analyzed data from four cohorts of OCD patients with different DBS targets using a connectomic approach. Strikingly, the same optimal tract target emerged when separately analyzing either an ALIC-DBS or STN-DBS cohort, alone. Among other regions, this bundle connected dorsal anterior cingulate and ventrolateral prefrontal cortices to the anteriomedial STN. When the tract was calculated on either cohort alone, it could be used to cross-predict clinical improvement in the other cohort, respectively. Furthermore, variance in clinical outcomes in two independent test cohorts from a third and fourth center could be significantly predicted based on stimulation overlaps with the tract. Finally, literature-based stimulation sites for OCD seemed to cluster close to the identified tract.
Similarly, connectivity from electrodes to M1 was associated with tremor improvement in Essential Tremor  48. 2 ). a, b Reported average targets mapped to standard space. c The degree of weighted overlap between stimulation sites and the identified tract. These were correlated with reported average %-Y-BOCS improvements of published studies (where available, other sites marked in gray; see Supplementary Methods for details). Gray shaded area represents 95% confidence intervals. Supporting the first hypothesis, our study was able to predict symptom-specific clinical improvement across DBS targets and centers based on connectivity data. Although the tract that our data seems to shape out is predictive for Y-BOCS improvement, different tracts could have emerged when repeating the analyses for depressive or cognitive flexibility symptoms (as analyzed by Tyagi et al.
13  ). Going further, shared symptom networks could be present in other diseases for which multiple surgical targets are investigated. Major depression and Tourette's syndrome are obvious examples and extensive work in this direction is currently ongoing  14, 49, 50. Similar concepts could even be applied to more established targets such as STN vs. GPi DBS  51  or symptom-specific alleviations across diseases. Potentially, DBS surgery in the (distant) future could involve detailed preoperative phenotyping to establish a broad patient-specific symptom score. Based on databases of clinical improvements along affected symptom axes, a mix of networks that should be modulated to alleviate each patient's specific symptom profile could be identified. Such concepts are still mostly speculation but could be investigated in future studies.
This said, we must emphasize that the present study investigated data on a group level and utilized connectivity data from individuals without OCD. As mentioned by others in the very context, we could not agree more that surgical decision making for DBS should not be based on such aggregated normative data, alone  37. Further studies are required to determine whether individual patient connectivity or generic connectome data (or both) could assist with optimizations in surgical targeting or DBS programming by determining crossing sites of symptom networks for specific patients. Several limitations apply for the current work. First and foremost, the retrospective character of the study is not ideal to compare and study effects of clinical outcome which is why we kept clinical information to a minimum and instead referred to the underlying clinical studies.
In DBS, the technique has been applied before and has led to models that could be used to predict improvements in out-of-sample data  22, 24. In addition to the practical advantage of being applicable to cases where patientspecific data is lacking, normative data also has the theoretical advantage of better data quality. In the present case, a connectome dataset was derived from a high N of 985 subjects scanned under research conditions by a specialized imaging center  52. It may be logistically challenging to acquire data of such quality in a clinical routine setting (e. g. pre-operatively) in individual patients but could be feasible in specialized centers. Still, studies have pointed out that tractography-based DBS targets pointed to coordinates that were sometimes >2 mm apart from each other when repeating analyses on test-retest scans of the same subject  56.
The normalization strategy that was applied was found to automatically segment the STN as precisely as manual expert segmentations  60  and each step of the pipeline was carefully assessed and corrected if needed by a team with long-standing expertise in this area  58, 61. Besides, both post-operative CT (33 patients) and post-operative MRI (17  patients) were used for electrode localization in the current dataset. Although studies have reported similar agreement between the results based on the two modalities, this might still lead to slight inconsistencies across patients. A larger dataset acquired with a homogeneous protocol would be ideal to validate our results, in the future. Finally, given the correlative nature of the study, our findings may not differentiate between local and global effects.
For instance, the tracts may have spuriously originated in the ALIC group because a more dorsal stimulation resulted with better clinical outcome. The congruency between results derived from STN-and ALIC-cohorts resulting in the same fiber bundle still suggests that the identified tract could play a causal role. However, such a claim would need to be confirmed e. g. using optogenetics or electrophysiology. Four main conclusions may be drawn from the present study. First, we show that the overall connectivity profiles of STN-and ALIC-DBS electrodes project to largely different areas in the brain. Second, data in each target alone singled out the same fiber bundle that was associated with long-term improvement of OCD symptoms when modulated either at level of the STN or the ALIC. Third, we demonstrated that it is possible to cross-predict clinical improvement of OCD patients across DBS target sites (ALIC/STN) and centers (Cologne/Grenoble).
In our analysis, this led to 32 data points (i. e. stimulation-based outcomes). Patients' demographic details are provided in Table  1. All patients gave written informed consent. The protocols were approved by the Ethics Committee of each center, respectively. The current study was further approved by the local ethics committee of Charité-University Medicine Berlin in accordance with the Declaration of Helsinki. For all patients in the four cohorts, high-resolution structural T1-weighted images were acquired on a 3. 0-Tesla MRI scanner, before surgery. Post-operative computer tomography (CT) was obtained in thirty-three patients after surgery to verify correct electrode placement, while 11 patients from the Grenoble cohort and the six London patients received post-operative MRI instead. Post-operative MRI parameters were as follows.
Grenoble cohort: T1-weighted 3D-FFE scans were acquired on a 1. 5 T Philips MRI scanner with a 1. 0 × 1. 0 × 1. 5 mm  3   DBS lead localization and VTA estimation. DBS electrodes were localized using Lead-DBS software (http://www. lead-dbs. org)  58. Post-operative CT and MRI scans were linearly coregistered to preoperative T1 images using Advanced Normalization Tools (ANTs, http://stnava. github. io/ANTs/)  62. Subcortical refinement was applied (as a module in Lead-DBS) to correct for brain shift that may have occurred during surgery. Images were then normalized into ICBM 2009b Nonlinear Asymmetric ("MNI") template space using the SyN approach implemented in ANTs, with an additional subcortical refinement stage to attain a most precise subcortical alignment between patient and template space ("Effective: Low Variance" preset as implemented in Lead-DBS).
13  , which reported on the rich London dataset in more depth. Connectivity analysis. Structural connectivity between VTAs and all other brain areas was calculated based on a normative connectome as similarly done in previous work  22, 24, 32, 58, 64. Specifically, a whole-brain connectome based on state-ofthe-art multi-shell diffusion-weighted imaging data from 985 subjects of the Human Connectome Project (HCP) 1200 subjects data release  52  was calculated in each patient using Lead-Connectome (www. lead-connectome. org). Whole-brain fiber tracts were normalized into standard space using a multispectral warp based on T1-weighted, T2-weighted, and diffusion-weighted acquisitions using ANTs (using the same "Effective Low Variance" preset implemented in Lead-DBS). In each subject, a total of 6000 fibers were sampled and aggregated to a joint dataset in standard space, resulting in a set of 6,000,000 fibers across 985 HCP subjects.
Acknowledgements We would like to thank  Kristy Kultas-Ilinsky  and  Igor Ilinsky ,  Rudolf Nieuwenhuys  and  Suzanne Haber  for counseling regarding the anatomical validity of tractography results presented in this study. We would like to thank  Hagai Bergmann ,  Helen Mayberg ,  Paul Krack ,  Christian Moll ,  Todd Herrington ,  Eduardo Alho  and  Erich Fonoff  for very helpful general advice and feedback related to the present work. We would like to thank  Cyril Pernet ,  Wolf-Julian Neumann  and  Roxanne Lofredi  for statistical consultation and help on the manuscript. This work was supported by the  German Research Foundation (Deutsche Forschungsgemeinschaft, Emmy Noether Stipend 410169619  to  A. H. ,  SPP 0141  to A. A. K. and  KFO 219  to J. K. ). The study was further funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -Project-ID  424778381 -TRR 295.
Contributions and Variants To date six benchmark models have contributed results: ARTS 2. 3  (Buehler et al. , 2018) , provided by the University of Hamburg; LBLRTM v12. 8  (Clough et al. , 2005) , provided by Atmospheric and Environmental Research; the SOCRATES narrow-band configuration described in section 2. 1, provided by the UK Met Office; the Reference Forward Model  (Dudhia, 2017) , provided by the NOAA Geophysical Fluid Dynamics Lab; GRTCODE, a new line-by-line code developed at GFDL; and 4AOP  (Chéruy et al. , 1995; Scott & Chédin, 1981) , provided by the Laboratoire de Météorologie Dynamique. Half the models use spectroscopic information from HITRAN 2012  (Rothman et al. , 2013) , while GRTCODE results are based on HITRAN 2016  (Gordon et al. , 2017 ), 4AOP uses GEISA 2015 (Jacquinet-Husson et al.