dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Figure  11  summarizes out results. First, we evaluate a flight call detection system that does not rely on deep learning, but purely on feature engineering. Under the names of "Tseep" and "Thrush", this system has long remained the standard for detecting sparrows, warblers and thrushes respectively [?]. We re-implement these detectors in Python, with help from the original authors. We obtain an F 1 -score of 3%. As shown on Figure  11  (curve A), this low F 1 -score can be explained by a low precision; that is, a large proportion of false positives in comparison with true positives. Then, we evaluate a version of BirdVoxDetect yet without PCEN nor data augmentation. Instead, we train the network on batch-normalized log-mel-spectrograms from BirdVox-222k. We obtain an F 1 -score of 5%: see curve B in Figure  11.
Deep learning architecture The architecture of our multilevel taxonomic classifier corresponds to a non-hierarcical multitask model (abbreviated Non-H. MT) presented in prior species classification research  [52]. Although this prior publication reported that a hierarchically structured classifier (TaxoNet) achieved the best classification performance on its evaluation dataset, we were not able to replicate the results with the new data and now find that the non-hierarchical multitask model performs best. The architecture of BirdVoxClassify is similar to that of BirdVoxDetect, as it also composes three convolutional layers and two fully connected layers, with no bias weights for any layer. Before the first layer, we perform batch normalization on the PCEN-gram to stabilize and accelerate training  [53].
Data curation To train and validate BirdVoxClassify, we present an updated version of the BirdVox American Northwest Avian Flight Call Classification (BirdVox-ANAFCC, or ANAFCC for short) Dataset  [52] , which we refer to as ANAFCC-v2. This dataset aggregates isolated flight calls from different data sources: BirdVox-full-night, CLO-43SD, CLO-SWTH, CLO-WTSP [?], the Macaulay Library [?], Xeno-Canto [?], and Old Bird [?]  6. An expert ornithologist (AF of the authors) verified and re-annotated each clip and aligned each flight call precisely at the center of its corresponding clip. We map the resulting annotations onto our taxonomy as shown in Figure  5. This new version of ANAFCC, v2. 0, contains additional flight calls from full-night which did not appear in the initial release, v1. 0. In order to better match our heterogenous development set to data found in realistic acoustic monitoring scenarios, we create training and validation subsets by finding a suitable partition of the ANAFCC data sources that is appropriately sized and have species distributions similar to that of the 296h dataset.
Evaluation To evaluate BirdVoxClassify, we present a new version of the BirdVox 14 Species Dataset (BirdVox-14SD)  [52] , which we refer to as BirdVox-14SD-v1. We evaluate the predictions with and without enforcing hierarchical consistency to understand the impact of our hierarchical consistency procedure. We use F1 score for each class and overall to evaluate classifier performance, as shown in Figure  11.
Results With our hierarchical consistency procedure, the classifier achieves an F1 score of 72. 82% whereas it achieves an F1 score of 66. 71% without our hierarchical consistency procedure. We May 26, 2022 14/20page. 20  Example use case: Swainson's Thrush (Catharus ustulatus)   Direct estimation from the BirdVox machine listening system on the full-season dataset (6,671 hours from nine sensors). Bottom: frequency of occurence of the Swainson's Thrush among eBird checklists in Tompkins County for the same period. necessarily taken over the same subset of sensors between one day and the next. With that caveat in mind, we observe some consistent trends: busy migration activity on Sep. 13 th and 14 th , followed by a decrease to almost zero detected flight calls, followed by a large peak on Sep.
With this paper, we aim to fill this gap in research by providing large-scale open audio datasets with expert annotation: BirdVox-full-season, BirdVox-296h, and BirdVox-14SD-v1. 1. In future work, we will evaluate whether pairing machine listening with radar measurements and/or crowdsourced observations benefits the species-specific monitoring of bird migration at the macroscopic scale. May 26, 2022  16/20page. 20.
Acknowledgments This work is supported by  NSF  awards  1633259  and  1633206 , the  Leon Levy Foundation , a  Google faculty  award, and an Atlanstic2020 project on " Trainable Acoustic Sensors " (TrAcS). We thank  Jessie Barry ,  Ian Davies ,  Tom Fredericks ,  Jeff Gerbracht ,  Sara Keen ,  Holger Klinck ,  Anne Klingensmith ,  Ray Mack ,  Peter Marchetto ,  Ed Moore ,  Matt Robbins ,  Ken Rosenberg , and  Chris Tessaglia-Hymes  for designing autonomous recording units and collecting data. We acknowledge that the land on which the data were collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy.
Furthermore, we count the spatiotemporal temporal distribution of flight calls per sensor location and per two-hour segment within the full night. Following this coarse spatiotemporal estimate, we extract 222k audio clips at random within the time regions containing no flight calls. Combining the 35k positive clips (centered around one flight call) and the 187k negative clips (containing no flight call) yields the 222k dataset. Note that the ratio of positives to May 26, 2022 10/20page. 20 negatives in 222k is equal to 187k/35k ≈ 5. 3.
Such a randomization procedure allows to augment any given audio sample more than once. Specifically, we draw ten instances of pitch shifting, ten instances of time stretching, and ten instances of pitch shifting and time streching in combination. This corresponds to 31 versions of each audio sample in total: i. e. , one original version and 30 augmentations. After augmenting each of the 189k samples in the training set of BirdVox-222k, we obtain a dataset of 31 × 189k = 5. 9M samples. This dataset represents 633 gigabytes of data on disk.
The sections above have focused on the evaluation of individual components in BirdVox: sensor fault detection, flight call detection, and species classification. It remains to be seen how BirdVox operates once these elements are integrated within a given application. For this purpose, we run the complete BirdVox pipeline on all recordings from the BirdVox-full-season dataset. This dataset contains 6,671 hours of audio; which, given a hop length of τ =15 ms, translates to 6671 × 3600 15 • 10 -3 ≈ 1. 601 • 10 9 (4) instances of Fast Fourier Transform (FFT). The convolutional neural network in BirdVoxDetect predicts event detection function at a rate of 20 Hz, hence a total of 6671 × 3600 × 20 ≈ 4. 803 • 10 8 predictions. Furthermore, the number of synapses in the first layer of BirdVoxDetect is equal to 128 × 104 × 24 ≈ 2.
995 × 10 5. Because each synapse is encoded over 32 bits, or four bytes, the throughput of our computation is at least (2. 995 • 10 5 ) × 4 × (4. 803 • 10 8 ) ≈ 613 • 10 9 (5) bytes, or 613 terabytes. Lastly, the output contains 6671 × 3600 × 20 × 4 ≈ 1. 921 • 10 9 bytes; i. e. , around two gigabytes. These numbers demonstrate the need for parallel computing in the analysis of the full-season dataset. For this reason, we use the high-performance computing facility of New York University 7 and parallelize execution massively over hundreds of CPU cores. In this way, the computation completes within a few hours.
Reconstruction methods The MarlinTPC software package  [16]  was used to reconstruct and analyze the data. This package is based on the linear collider software suite  [17] [18] [19]. The individual steps of the reconstruction are introduced briefly in the following. The amplified electron avalanche after the GEM stack is collected on the pads of the segmented readout anode. The signal on a single pad is called a pulse. The readout electronics measures the collected charge in time bins. The charge of a pulse is the sum of the ADC counts in these bins from the rise of the signal over a start threshold to its fall under a stop threshold. In addition to the thresholds, a minimum number of samples over threshold is required. The arrival time of each pulse is derived from the inflection point of its rising edge.
Drift velocity A key parameter for a TPC is the electron drift velocity in the drift volume. It has been determined from the reconstructed mean arrival time for several measurement runs at various positions of the beam along the drift direction. The position of the beam is determined from the position of the movable stage, since no absolute reference is available. Figure  5  shows the beam position inside the drift volume plotted against the signal arrival time. The slope of a linear fit to the data points corresponds to the drift velocity. The fit results are given in table  1 , including a comparison with values from a Magboltz  [22]  simulation. The errors on the measured values originate mainly from the uncertainties of the stage position measurement and less from the uncertainty of the time measurement.
The behavior can be described by the function  [23]  PRF ( ) = PRF,0 2 + t 2 • , (1) which allows to determine the transverse diffusion constant t. Since this is also required as an input to the fit of the transverse point resolution , as is explained in section 4. 3, a   2 , including simulated values of the corresponding diffusion constants, taking into account differences in measured gas pressure, temperature and contamination with air and water vapor. As expected from the Magboltz simulation, the diffusion measured in the 2016 data set is smaller than in the 2013 data set, mostly due to a difference in the gas pressure. However, the measured diffusion constants from both data sets are significantly larger than the corresponding simulated results. As discussed in  [10] , the simulation only describes the ideal case of perfectly homogeneous and parallel electric and magnetic fields.
For the reconstruction of the ionization clusters, the source-extractor software package  [41]  has been integrated into the MarlinTPC reconstruction. The simulation is performed for square pads, varying the pad pitch between 55 µm and 6 mm. Figure  16  shows an example of the resulting charge deposition on pads with a pitch of 220 µm. Also shown are the incident particle track, the corresponding signal electrons and the reconstructed cluster centers. In the simulation the gain of the GEM stack was adjusted by varying the GEM voltages between 230 V and 280 V per GEM, resulting in overall gains between ∼500 and 62,000. GEM voltages above 280 V were not used since this is considered the limit of stable operation without frequent discharges. For each pad size and method, the gain resulting in the best separation power was used.
Human exposure to per-and polyfluoroalkyl substances (PFAS) has been associated with numerous adverse health effects, depending on various factors such as the conditions of exposure (dose/concentration, duration, route of exposure, etc. ) and characteristics associated with the exposed target (e. g. , age, sex, ethnicity, health status, and genetic predisposition). The biological mechanisms by which PFAS might affect systems are largely unknown. To support the risk assessment process, AOP-helpFinder, a new artificial intelligence tool, was used to rapidly and systematically explore all available published information in the PubMed database. The aim was to identify existing associations between PFAS and metabolic health outcomes that may be relevant to support building adverse outcome pathways (AOPs).
The collected information was manually organized to investigate linkages between PFAS exposures and metabolic health outcomes, including dyslipidemia, hypertension, insulin resistance, and obesity. Links between PFAS exposure and events from the existing metabolicrelated AOPs were also retrieved. In conclusion, by analyzing dispersed information from the literature, we could identify some associations between PFAS exposure and components of existing AOPs. Additionally, we identified some linkages between PFAS exposure and metabolic outcomes for which only sparse information is available or which are not yet present in the AOP-wiki database that could be addressed in future research.
All AOPs, independent of their status (from under development to validated by the OECD), are accessible on the AOP-wiki database (https://aopwiki. org/, accessed on 28 February 2022)  [5]. Per-and polyfluoroalkyl substances (PFAS) are a very large group of xenobiotics that are used for a multitude of applications due to their unique physicochemical properties (e. g. , heat and acid resistance, water and oil repellency, and high surface activity) (see Glüge and co-workers  [6] ). PFAS are persistent and ubiquitous in the environment, and measurable levels of some PFAS are detected in human populations globally  [7]. Toxicological and epidemiological studies have reported that exposure to certain PFAS and/or their mixtures may potentially interfere with a wide range of adverse health effects  [8] ; for instance, thyroid disruption, reproductive toxicity, reduced birth weight, various metabolic outcomes, and decreased immune responsiveness  [9, 10].
This tool allows researchers to accelerate the identification of existing knowledge between an environmental stressor (e. g. , a PFAS) and biological events involved in AOPs (i. e. , MIE, KE, and AO) through the scientific published abstracts. Owing to the rapid increase in the amount of released chemicals in the environment, chemical pollution has been recognized as a growing peril and a potential risk to human health. With the increasing amount of scientific literature in the PubMed database, it becomes feasible and valuable to investigate in a systematic manner the published linkages between stressors and health outcomes. Combined with manual curation, the AOP-helpFinder also allows researchers to identify stressor-event relationships in full texts. In previous studies, the tool was applied to bisphenol compounds  [19, 20] , to a set of pesticides  [21] , and more recently, to decipher non-validated methods to characterize endocrine-disrupting chemicals  [22].
Application of the AOP-helpFinder 2. 1. 1. Development of the Dictionaries of Stressors and Events to Be Screened In order to explore the associations between events (e. g. , "metabolic health outcomes") and stressors of interest (i. e. , PFAS), the AOP-helpFinder tool needs two input dictionaries that will be automatically screened in order to identify the co-occurrence in abstracts between at least one event and a stressor. The stressor dictionary contained a list of 59 PFAS that was created by experts. The majority of the PFAS were selected in accordance with the HBM4EU Scoping Document  [25] , and the list was additionally complimented by the judgment of experts. The second dictionary, including events related to metabolism, i. e. , MIEs, KEs, and AOs, was subsequently generated. This dictionary contained two distinct lists of events: one using data from the AOP-wiki database and a second one using medical subject headings (MeSH) knowledge.
To define the AOP-wiki event list, we went through the downloaded full list of events from the AOP-wiki database (as of March 2020) and selected 17 AOPs of interest linked to metabolism. These selected AOPs were mostly related to lipid metabolism and liver steatosis, which are related to metabolic outcomes. Moreover, one AOP for obesity and two AOPs for hypertension were added after a manual search. From this, a first list containing a selection of 154 events extracted from the AOP-wiki database (https://aopwiki. org, accessed on 27 October 2020) was created. To select these events, all event types (MIE, KE, and AO) were considered, and only the ones of interest (i. e. , related to metabolic outcomes) were kept. The second list included 19 MeSH specifically related to metabolic syndrome.
Automatic Screening of the Literature Using the AOP-helpFinder Tool-Multistep Procedure First step: All available abstracts (>33 million) from the PubMed database were screened using the previously defined list of 59 PFAS in a. txt format (Table  S1 ). Only abstracts mentioning at least one of the PFAS from the list were selected and extracted in xml format files using the PubMed API (https://pubmed. ncbi. nlm. nih. gov/, accessed on 27 October 2020). All the extracted. xml files were the ones that were used for the text-mining step, meaning identifying abstracts that co-mention at least one PFAS (from list one) and one biological event (from list two, Table  S2 ) (see step two). Second step: The dictionary of events containing the two lists of biological terms related to metabolism was used to automatically screen the abstracts identified during the first step.
Since adults and elderly individuals are more prone to insulin resistance due to several age-related risk factors compared to children and adolescents, it is inconclusive to what extent these associations are related to the exposure to PFAS. While it was not possible to identify some MIE/KE within the collected data using the AOP-helpFinder, probably due to the selected parameters, it was possible to fill some data gaps with some additional literature research using, e. g. , search platforms such as Google Scholar. More recently, for example, one study reported that an exposure to a mixture of PFAS can lower the total brain dopamine concentration in mice as well as the tyrosine hydroxylase transcript level in mice  [56] , which are KE1450 and KE1452 from AOP72. Obesity, the AO of AOP72, is associated with PFAS exposure, as reported in some epidemiological studies.
Tributyltin is the stressor of AOP72, and our collected data indicate that PFAS potentially influences the same molecular pathways, increasing the risk of obesity. Furthermore, although we found that PFAS can increase the intracellular calcium levels (KE1339 of AOP226), it was shown that PFAS could cause a decrease in serotonin transporter activity (KE1320 of AOP226)  [57]. While our study indicated some MIE/KE that are associated with PFAS exposure and metabolic outcomes, we also identified some gaps that could be addressed in the future, and we hope that our results can support the knowledge and developmental processes of AOPs. The results from in vitro and in vivo as well as epidemiological studies indicate that PFAS exposure is associated with hypertension, which is the AO of AOP149. However, to the best of our knowledge, it is still unknown if PFAS exposure decreases the expression of guanosine triphosphate cyclohydrolase 1 (GTPCH-1) or if it decreases tetrahydrobiopterin levels, KE935 and KE934 of AOP149.
Computational sciences, including systems toxicology, are of great interest to accelerate the identification of known relationships between the chemical exposome and biological events at various levels of biological organization. Today, a huge amount of data, e. g. , derived from high-throughput and omics studies, are generated and available in databases and in the literature. The AOP-helpFinder tool, for which performance has been evaluated previously  [19] , has the ability to automatically and rapidly screen the scientific literature and allowed us to easily identify existing knowledge on PFAS and related metabolic events. Therefore, the development and assembly of AOPs is less laborious and time-consuming. These tools have also the potential to identify existing knowledge gaps. Nevertheless, AOP-helpFinder, similar to other computational approaches, is knowledge-based, and can only be used to identify existing, previously described, and available information.
High-Level Simulations Software simulations are necessary to validate and tune the control algorithm shown in Figure  7  and overall confirm the proper operation of the proposed DC microgrid. Highlevel simulations on Matlab-Simulink are first studied to observe long-term power flows on the DC microgrid with a time frame of a few days. The impact of the control algorithm on the power flows on the DC microgrid and the SoC of all NGs must be thoroughly analyzed. The microgrid simulated interconnects five NGs, as shown in Figure  8 , based on five NGs of Ambohimena (cf. Figure  3 ). To enable long-term simulations within a few minutes, averaged models are used for the DC-DC converters  [35]  and the batteries to model the NGs and the interconnection modules connecting the NGs to the DC bus. Furthermore, the simulation model includes the resistance of the DC cables and can also easily be extended with additional NGs and even with NGs without batteries or communal loads.
Real data collected on the field in Ambohimena are used for the NG consumption. The same irradiation profile is used for all NGs [36]. Hence, the DC bus voltage stabilizes at 54 V, indicating a weak microgrid. Then, during the first day, once NGs 1, 54 and 211 reach a SoC above 60%, they start to support NG 29 and NG 168, which helps them to reach back the medium SoC zone. Note that the selfish behavior for the NGs is implemented here, with a SoC max initially at 95% then at 80% to favor self-recharging. Therefore, the NGs wait until their SoC reaches 95% before supporting medium zone NGs. Over the next few days, as shown in Figure  10 , NGs 1, 54 and 211 frequently support NGs 29 and 168 and bring them back within the strong SoC zone in 2 days, as confirmed in Figure  11. Figure  9  illustrates well that the DC bus voltage translates the averaged level of available energy on the microgrid.
Conclusions This paper presents the successful development of a DC microgrid with decentralized production and storage designed for the progressive building of electric infrastructure for rural Africa, from software simulations and lab test bench to field deployment in Madagascar. Firstly, an architecture and a control algorithm were designed through software simulations on Matlab-Simulink. Then, a lab test-bench enabled to thoroughly and experimentally validate the proposed microgrid. Finally, after the in-house development of an interconnection module, a first microgrid interconnecting five nanogrids was successfully installed in Ambohimena in Madagascar, completing the whole development of a DC microgrid from the lab to the field. This technical and scientific accomplishment is an important milestone for Nanoé's electrification model which validates its technological approach and highlights the importance of public-private partnership to tackle rural electrification challenges.
Although these techniques are still routinely used, the rapid implementation of high-throughput nextgeneration sequencing (NGS) methods, especially targeted DNA panels, in clinical laboratories has led to the emergence of a fairly large number of pipelines and algorithms able to detect CNVs from NGS data. [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28]  Most of these studies use the read-depth approach, relying on the hypothesis that the number of reads aligned to a genomic region is proportional to the copy number of the region. In multiple sample methods, CNVs are detected by comparing the read counts of the sample of interest to the read counts of a reference sample. The proper building of the reference is one of the main difficulties. To that end, two main solutions exist: (i) to gather a database of normal samples or (ii) to add normal samples into the NGS run.