dataesr/scientific-paragraphs-categorization huggingface dataset:
Our different experiments have shown the potential of a stratification-based strategy, but also the challenges ahead. The main issue, in our opinion, remains on finding the correspondences of the atlases inferred from each of the groups. This matching has to deal simultaneously with two sources of cross-subject variability unfortunately looking very similar: the spurious variability induced by the weaknesses of the atlas inference process and the actual normal and pathological variability, which is largely unknown. The spurious variability induced by the bundle atlas inference process has various origins. Some of them stem from the quality of the tractography itself. A poor quality diffusion dataset induced for instance by subject motion may reduce the number of reliable streamlines and increase the amount of spurious ones.
14 ms; repetition time TR = 2400 ms; inversion time TI = 1000 ms; flip angle FA = 8; FOV = 224 mm; matrix = 320; voxel size = 0. 7 mm isotropic; resolution bandwidth RBW = 210 Hz/pixel; Echo Spacing ES = 7. 6 ms). It also provides a Spin-echo EPI multi-shell HARDI dataset along 90 optimized diffusion-weighted directions, b = 1000, 2000, and 3000 s/mm 2 , (111 slices; TE = 89. 5 ms; TR = 5520 ms; FA = 78; matrix = 168,144; voxel size = 1. 25 mm isotropic; RBW = 1488 Hz/pixel; echo spacing ES = 0. 78 ms; partial Fourier factor PF = 6/8; GRAPPA = 2; total scan time = 16 min and 46 s).
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement no. 785907 & 945539  ( HBP SGA2 & SGA3 ), from the  FR-MDIC20161236445 , the  ANR-19-CE45-0022-01   IFOPASUBA , the  ANR-14-CE30-0014-02  APEX the  ANR-20-CHIA-0027-01  FOLDDICO. P. Guevara received funding from  ANID-FONDECYT 1221665  and  ANID-Basal  Projects  FB0008  ( Advanced Center for Electrical and Electronic Engineering ,  AC3E ) and  FB210017  ( Centro Nacional de Inteligencia Artificial, CENIA ). HCP Data were provided by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University.
INTRODUCTION The IOHprofiler  [2]  is a benchmarking tool to compare the performance of iterative optimization algorithms. It has several modules, each with a different benchmark-related purpose. One of such modules is the IOHanalyzer  [6] : a web-based interface for visualizing and statistically assessing the differences in performance of the algorithms. The IOHanalyzer proposes analyzing the data from different perspectives. The best well-known measures are probably the expected value of the objective function with a fixed evaluation budget (quality), and the expected number of function evaluations to obtain a target value of the objective function (evaluations). For the sake of simplicity, we now focus on comparing the quality of the algorithms, although the methodology discussed in the following is also applicable to the evaluations.
Motivation The histogram and the box/violin plot are two visualization tools designed to summarize samples. They are also two of the most used visualizations to compare samples, each with its limitations. To illustrate such limitations, let us look at an example. Let us assume that we are interested in comparing ADAM and RMSProp: two gradient descent-based algorithms, useful for training neural networks. Specifically, we compare them in an image classification task in the MNIST dataset. We use the term observation of the quality to refer to training the neural network in the train-set and measuring the accuracy in the test-set once. Since the parameters of the neural networks are randomly initialized, each observation can be different  [3]. We save 1000 observations of the quality for each algorithm.
Acknowledgments This work is partially supported by the data science and artificial intelligence chair for digitalized industry and services,  Telecom Paris, Institut Polytechnique de Paris , and by the  Basque Government  through the  BERC 2022-2025  and the ELKARTEK programs ( KK-2020/00049 ,  SIGZE ,  KK-2021/00065 ) the Research Groups  2019-2021  ( IT1244-19 ) and by the  Spanish Ministry of Economy and Competitiveness , through  BCAM Severo Ochoa excellence accreditation   SEV-2017-0718  and the research project  PID2019-106453GA-I00/AEI/10. 13039/501100011033.
An important challenge in the field of network biology is to develop inference methods that retrieve actual regulatory relationships while limiting the number of false positives, and that are not overly sensitive to noise. Random forest-based methods are efficient at detecting true regulatory relationships, but create a high proportion of false positives and thus, pruning networks built with these approaches is necessary to avoid spurious regulatory relationships. To solve this issue, Kimura et al. built a pipeline combining an efficient random forest-based network inference method with a series of feature selection methods, which significantly improved the quality of the inferred network. Network inference methods should also lead to consistent results across datasets obtained from the same biological condition.
This is particularly important for data-driven approaches applied to single-cell datasets, which are known to have a high level of inherent noise. Kang et al. propose a blueprint to benchmark network inference approaches in this context, that includes most genes present in the network, taking into account both their presence in the network and the weight of their relationships, and assesses the biological soundness of the inferred networks by comparing them to gold-standard regulatory relationships extracted from public databases. Once researchers have properly filtered their data, inferred the networks, and filtered out spurious connections, the networks are ready for analysis. A common way of making sense of genomic networks, which often include thousands of nodes and many more edges, is to look for modules or communities, i.
Three other articles focus on the comparison of networks between conditions. In a perspective piece, Weighill et al. , highlight the promise of using a weighted gene degree, or "gene targeting score," toglobally compare networks inferred from data representing different conditions, in order to identify key regulatory processes in disease. Lim et al. developed Constrained Random Alteration of Network Edges (CRANE), a new algorithm to identify robust diseaserelated regulatory modules. Finally, Arbet et al. share a new algorithm aimed at identifying differentially co-expressed modules and propose an R implementation, discoMod. This tool tests whether connections between co-expressed genes differ between conditions, and allows the user to assess how regulatory relationships within a module vary between conditions.
Finally, two papers tackled an important issue in the genomic network field: how to disseminate genomic network results and make them usable by the broader scientific community. Yang et al. built a web platform that hosts co-expression network results from Gastrodia elata, an important herb in traditional Chinese medicine. The platform gives access to a series of tools that facilitate result-browsing and allows the user to perform functional analysis of genes. Garcia-Ruiz et al. propose CoExp, a web platform that allows researchers to manipulate, compare, and analyze 109 co-expression networks. Importantly, the types of web tools presented here, based on open data and widely used programming languages and softwares, can be emulated and applied to a wide range of topics and organisms. Rapidly developing research on how to best infer the genomic networks has led to the publication of algorithms and software that are crucial tools in systems biology.
For simplicity, the focus of this text is on pollen monitoring, however, the same concepts also apply to the detection of fungal spores and other large biological aerosols. It is also important to note that no single method can reasonably be expected to be able to identify all bioaerosol present in the atmosphere since they cover a huge size range from viruses in droplets (micrometres in size) through to pieces of plant debris (millimetres in size). Furthermore, the term real time is used to describe all cases where measurement systems can provide data within a matter of a few hours from the time the observation was taken. This thus also includes near real-time provision of data and only the term real time is used from here on. Other papers in this special issue describe in more detail the reasoning and aims of the EUMETNET AutoPollen Programme, standards that are under development for automatic monitoring, site and network requirements, as well as the impact that the provision of real-time information will have across various domains.
In the dynamics of an invasion, control measures are more effective and costs significantly reduced if action is taken at an early stage of the invasion. It is therefore important that automatic instruments can easily be trained to identify non-native or invasive taxa to better understand the dynamics of these species and survey their spread, to provide an opportunity to improve environmental protection efforts and avoid the development of new sensitizations in an area. This can be achieved by community efforts to produce training datasets for a range of taxa from different areas that can be used to further develop particle classification software. While monitoring for such purposes are certainly important, given the slower timescales of ecological processes it is not as imperative that it is done in real time.
Unknown pollen taxa can be labelled manually and added to the database for software training. The first version of this instrument was tested in 2006 after which it was further developed before being implemented as part of the ePIN (Electronic Pollen Information Network) automatic pollen monitoring network in Bavaria as of 2019  (Oteros et al. , 2020). As such, it is one of the first instruments to be used operationally over an entire network and not just for research purposes. Helmut Hund GmbH has announced the launch of a smaller version of this instrument, the BAA500e, although it is not yet commercially available.
This includes for numerical forecast models, as well as for medical, climate change, forestry, and agricultural applications. Measurement techniques and identification algorithms will need to be improved to enlarge the number of taxa that can be discriminated, in particular, this includes fungal spores. Furthermore, standards need to be developed regarding the data and metadata provided, in addition to how particle identification algorithms are developed and validated. This will enable traceable data quality across the entire measurement chain. Changes to instrumentation as well as identification algorithms will improve device performance over time, but care needs to be taken to ensure homogenous datasets can be produced. This will require detailed protocols to be kept, analysis software is versioned, and that all data are stored in case reanalyses need to be carried out at a later date.
At the community level, new expertise covering topics such as software development, machine learning techniques, and dealing with big data is required. In addition, guidelines need to be developed for routine instrument and algorithm testing, as well as those related to assessing data quality. All these tasks are being carried out by a number of groups, most of them through or as part of the EUMETNET AutoPollen Programme. While no automatic instrument can yet identify all the pollen and fungal spore types required by end users (see Table  1 ) and no standardised method to calibrate automatic instruments has been established, there is continual development in the field and these monitors have already proven their worth-both in terms of the higher temporal resolution information they can provide and their ability to do so in real time.
Indeed, a number of sites or networks across Europe already have enough confidence in these technologies to publish their data in real-time online. As the technology further develops and matures, automatic instruments can be expected to provide more and better quality data which can be used across a growing number of domains. Acknowledgements The authors would like to thank all EUMETNET AutoPollen members for their participation and engagement, without which this paper would not have been possible. The authors would warmly like to acknowledge the input from the following instrument manufacturers: Droplet Measurement Technologies, Helmut Hund GmbH, Plair SA, PollenSense, Swisens AG, Nam Cao at the University of Graz, and Yamatronics Co. Open Access This article is licensed under a Creative Commons Attribution 4.
Ltd. Branko Sikoparija acknowledges financial support from the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant No. 451-03-9/2021-14/200358) and the BREATHE project funded by the Science Fund of the Republic of Serbia (Grant No. 6039613). Lenka Hájková was supported by the Technology Agency of the Czech Republic Project No. SS02030040 (PERUN) and by the project SustES-Adaptation Strategies for the Sustainability of Ecosystem Services and Food Security in Adverse Natural Conditions (CZ. 02. 1. 01 / 0. 0 / 0. 0 / 16 019 / 0000797). Célia M. Antunes and Helena Ribeiro thank the Earth Sciences Institute, which is funded by the Portuguese National Funds awarded by FCT-Foundation for Science and Technology, I. P. , projects UIDB/04683/2020 and UIDP/04683/2020.
Jeroen Buters acknowledges the ePIN-Valid No AP-2460-ePIN-V11-U1-D44520/2018 grant for support. Ingrida Sauliene thanks the European Social Fund (project no. 09. 3. 3-LMT-K-712-01-0066) under grant agreement with the Research Council of Lithuania (LMTLT). Olga Sozinova acknowledges EC ERDF PostDoc Latvia project No 1. 1. 1. 2/ VIAA/2/18/283 for financial support.
2. Several files were created to generate the jamming signal, consisting of signal samples in the time domain. Equation (1) was implemented in MATLAB with a fixed amplitude and bandwidth and a variable sweep period. All files are loaded in the arbitrary signal generator, where they are periodically generated. The jamming signal amplitude is 250 mV at the signal generator output, while its frequency band is 840 MHz to 980 MHz. A 12 V amplifier is connected at the signal generator output to boost the jamming signal power. During the experiments, different sweep periods were evaluated in the 1 µs to 50 µs range. We used two identical SX1272 transceivers, each one containing the implementation of the LoRa communication in the 868 MHz frequency band (EU863-870  [26] ) with a particular set of parameters.
This type of equipment allowed us to operate at the physical layer only, without all processes related to the upper layers of the LoRaWAN protocol. We connected both transceivers to a laptop with a pair of USB cables. These connections allowed us to burn the embedded software for the transceivers and read information about the data traffic. During the execution of the tests, timestamps are added to each data sent. The payload content is displayed, and, more specifically, the values of two counters (one present at the beginning of the payload and the other at the end) are highlighted. Simultaneously, we obtained the receiver report delivering the same type of information. The difference between the two reports (i. e. , the reports provided by the transmitter and the report provided by the receiver) should be null when no interference occurs, indicating that 100% of the data sent are correctly received.
Still, the reports can differ in the presence of interference since not all the transmitted bytes are received. It was possible to stop the transmissions anytime. Furthermore, a specific Python routine was designed to obtain a summarized report of errors. This type of report contains some error indicators, whose details are given in Section 4. 3. Our idea is to demonstrate that the LoRa system can change its operating frequency when a jamming signal with the characteristics described in Section 3 is detected. The new frequency must be located in the "dead area" between two spectral components of the jamming signal (see Figure  5 ).
Signal-to-Interference Ratio As exposed in Section 3, the power measurement needed for the SIR estimation is not straightforward in this investigation. A measurement method based on the instantaneous power levels will not lead to consistent results, due to the fast dynamics of the jamming signal, compared to the LoRa symbol time. In this context, our first proposal to assess the SIR value was a methodology based on a time-domain analysis  [23]. This proposal consists in separately measuring signal and interference within a time interval of T sym , using an oscilloscope, and then post-processing the signals in MATLAB. The post-processing consists in a filtering stage, followed by an average power calculation. However, this method is time consuming because it demands post-processing in a computational environment.
Introduction Fine-tuning a multilingual language representation (be it written or spoken) such as the one uncovered by wav2vec  [1]  from raw speech or by mBERT  [2]  from texts, is today at the core of many workflows in speech recognition (and, more broadly, in Natural Language Processing). It is widely considered to be the most promising way to develop NLP and speech systems beyond the thirty or so languages (representing only 0. 5 % of the world's linguistic diversity) for which there are large amounts of annotated data  [3]. In this work we explain how this approach can be used to develop a phonemic transcription system for the documentation of "under-documented", "under-resourced" languages (mostly endangered and unwritten), taking as examples Japhug, an endangered language of the Trans-Himalayan (Sino-Tibetan) family.
• Endangered/little-described languages have structural features of their own, which may be widely different from those of the languages routinely taken into account in the work of the speech processing community. It has even been argued that highly elaborate linguistic structures and typological oddities are more likely to be found in minority languages, for sociolinguistic reasons  [13, 14]. For example, Japhug has a degree of morphosyntactic complexity that is particularly impressive, especially in view of its areal context  [15]. Knowing whether models pre-trained on the most common languages (and more generally the neural architectures used for these languages) are also able to correctly represent languages in all their diversity would (i) help out with the modeling of these languages and (ii) improve our understanding of these models (e.
g. speech recognition or speaker identification). These representations can be learned from a corpus containing data in several languages in order to learn a multilingual representation that can encode data from any language. The multilingual aspect of these representations is particularly appealing because it opens the possibility of developing speech recognition systems for the languages that constitute the focus of fieldwork language documentation tasks: languages for which there is no available corpus of sufficient size to train a neural representation from scratch. In our experiments, we used the XSL-R multilingual model  1  trained in an unsupervised way on a corpus of 56,000 hours of recordings in 53 languages, and we used the HuggingFace API  [18]  to use and fine-tune it. Application to phoneme prediction To develop a phonemic transcription system, we simply fine-tune a wav2vec model on a corpus made of speech utterances associated to their phonemic transcription.
The use of CTC loss function spares us from having to explicitly specify an alignment at the phonemic level and we can directly use the annotations that are collected to document a language and that are accessible in open archives such as the Pangloss Collection, with time codes at the level of the sentence (each typically corresponding to less than 10 seconds of audio signal). More precisely, as wav2vec makes prediction at the character level, the sequences to be predicted are made up directly of the phonemic transcriptions, including the spaces separating words as well as the punctuation marks. We apply a very simple preprocessing step consisting only in deleting some comments of the field worker who annotated the data (that are all between brackets) and in adding spaces before the punctuation marks.
A phonemic transcription system for Japhug We use the approach described in the previous paragraph to train a phonemic transcription system for Japhug. More precisely, we extract from the Pangloss Collection  [12]  a corpus containing 3 hours of audio recordings manually transcribed and segmented into utterances. 2 We use 90% of this corpus as a train- ing set and the remaining 10% as a validation set. This corpus is much larger than the corpora usually gathered for documenting a language (Japhug is an exception in this respect since nearly 32 hours of data have been collected, annotated and are freely available  3  ). We use the validation set to tune the various hyperparameters (in particular: the learning rate, the learning rate schedule, the batch size, and the various drop-out parameters).
In this work we have used pyctcdecode, 6  a CTC beam search decoder that finds the sequence of phonemes that maximizes a linear combination of the language model score and the acoustic model score. 7 n all our experiments we consider a standard n-gram language model estimated from phonemic transcription using modified Kneser-Ney smoothing as implemented in KenLM  [21]. We train two language models considering as training data transcriptions pre-processed in the same way as the training data of the acoustic model. The first one, referred to as small in what follows, is trained on the same exact data as the acoustic model. The second one, large, is trained on a corpus corresponding to 10 hours of audio recordings (8,400 sentences and 94,082 tokens) containing the data used for the training of the acoustic model.
Conclusion We have shown, in this work, how the performance of a neural phonemic recognizer built by fine-tuning a wav2vec model can be improved by plugging it into a simple countbased language model, even if the latter is estimated on a very small corpus. Our experiments on an endangered language, Japhug (Trans-Himalayan/Tibeto-Burman), show that this approach can significantly reduce the WER, reaching the stage of automatic recognition of entire words. If the quality of the transcriptions predicted by our system is already sufficient to help linguists in their language documentation work, the per-.
Acknowledgments We wish to express our deepest gratitude to the main Japhug language consultant, Tshendzin. Financial support was given by  Agence Nationale de la Recherche  as part of grants  ANR-10-LABX-0083  ( Laboratoire d'excellence "Empirical Foundations of Linguistics ",  2011-2024 ) and  ANR-19-CE38-0015  (" Computational Language Documentation by 2025 ",  2019-2024 ). Financial support was also contributed by the  Institute for Language Diversity and Heritage (ILARA-EPHE ). An important part of the linguistic resources used in the present work was collected in the course of the project " Himalayan Corpora: Parallel corpora in languages of the Greater Himalayan area " ( ANR-12-CORP-0006 ).