dataesr/scientific-paragraphs-categorization huggingface dataset:
In designing our draft graphical notation for computational neuroscience we tried to respect the lessons learned while developing SBGN as reported in  [84]. In particular neither position nor color carry inherent meaning and we started from notations already used in the literature. Le Novère et al. point at the relevance of software tools using the graphical notation for dissemination. In this spirit, the recent release of NEST Desktop  [108]  already adheres to our proposed graphical notation.
Balanced random network The balanced random network model used in Figs  1  and 8  is based on the model introduced by Brunel  [1]. Our implementation extends the script brunel delta nest. py which is part of the NEST source code 11  by the option to switch between a "fixed in-degree" and a "fixed out-degree" version. Details about the model description are summarized in Tables  2  and 3  and the parameters are given in Table  4.
Acknowledgments The authors would like to thank  Daniel Hjertholm  for inspiring work on testing connectivity generation schemes,  Sebastian Spreizer  for immediately adopting the graphical notation in NEST Desktop,  Espen Hagen  for detailed comments on the manuscript,  Hannah Bos  for fruitful discussions, and our colleagues in the Simulation and  Data Laboratory Neuroscience of the Jülich Supercomputing Centre  for continuous collaboration. This project has received funding from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreement 720270  ( HBP SGA1) 785907 (HBP SGA2) 945539 (HBP SGA3 ), and  754304  ( DEEP-EST ); the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -368482240/GRK2416: " RTG 2416 Multi-senses Multi-scales "; the  Priority Program (SPP 2041  " Computational Connectomics ") of the  Deutsche Forschungsgemeinschaft ; the  Helmholtz Association Initiative and Networking Fund  under project number  SO-092  ( Advanced Computing Architectures, ACA ) ; and the  Excellence Initiative  of the German federal and state governments (neuroIC001): " ERS: disziplinärer Paketantrag NeuroIC: NeuroModelingTalk (NMT) Approaching the complexity barrier in neuroscientific modeling ".
Algorithm 1 COL0RME, Step I: Support Estimation Require: r y ∈ R 𝑀 4 , r x 0 ∈ R 𝐿 2 , 𝜆 > 0 repeat 𝑠 𝑘+1 = arg min 𝑠 ∈R + F (r x 𝑘 , 𝑠) r x 𝑘+1 = arg min r x ∈R 𝐿 2 + F (r x , 𝑠 𝑘+1 ) + R (r x ; 𝜆) until convergence return Ω x , 𝑠 ȳ + GT CEL0 result ℓ 1 result TV result (a) (b) To evaluate the performance of the first step of the method COL0RME using the different regularization penalties described above, we created two noisy simulated datasets, with low background (LB) and high background (HB), respectively and used them to apply COL0RME and estimate the desired sample support. More details on the two datasets are available in the following sub-section 6. 1. The results obtained by using the three different regularizers are reported in Figure  3. In this example we chose the regularization parameter  heuristically, while more details about the selection of the parameter are given in the subsection 5.
( 24 ) As far as ℓ 1 is used as regularization term in (6), we report in Figure  8  a graph showing how the PSNR value of the final estimated intensity image (i. e. after the application of the second step of COL0RME) varies for the two datasets considered depending on. It can be observed that for a large range of values , the final PSNR remains almost the same. Although this may look a bit surprising at a first sight, we remark that such a robust result is due, essentially, to the second step of the algorithm where false localizations related to an underestimation of  can be corrected through the intensity estimation step. Note, however, that in the case of an overestimation of , points contained in the original support are definitively lost so no benefit is obtained from the intensity estimation step, hence the overall PSNR decreases.
Simulated Data To evaluate the method COL0RME we choose images of tubular structures that simulate standard microscope acquisitions with standard fluorescent dyes. In particular, the spatial pattern (see Figure  11a ) is taken from the MT0 microtubules training dataset uploaded for the SMLM Challenge of 2016 1. The temporal fluctuations are obtained by using the SOFI simulation tool  [28]. This simulation software, implemented in MATLAB, generates realistic stacks of images, similar to the ones obtained from real microscopes, as it makes use of parameters of the microscope setup and some of the sample's main properties. For the experiments presented in this paper, we generate initially a video of 700 frames, however we evaluate the methods using the first  = 100,  = 300,  = 500 and  = 700 frames, so as to examine further the trade-off between temporal and spatial resolution.
The frame rate is fixed at 100 frames per second (fps) and the pixel size is 100 nm. Regarding the optical parameters, we set the numerical aperture equal to 1. 4 and the emission waveleght to 525 nm, while the FWHM of the PSF is equal to 228. 75nm. The fluorophore parameters are set as follows: 20ms for on-state average lifetime, 40ms for   (12)  for several values of  within a specific range. Tha data used are the HB dataset with  = 500 frames (Figure  11d ) and the ℓ 1 -norm regularization penalty. The red cross shows the PSNR value μ obtained by applying the Discrepancy Principle. We note that such value is very close to one maximizing the PSNR metric. off-state average lifetime and 20s for average time until bleaching. The emitter density is equal to 10. 7 emitters/pixel/frame, while 500 photons are emitted, on average, by a single fluorescent molecule in every frame.
We create three datasets with the main difference among them being the background level, as in real scenarios the background is usually present. More precisely we create: the no-Background (NB) dataset where the background is equal to 0 photons/pixel/frame, the low-Background (LB) dataset, where the background is equal to 50 photons/pixel/frame and finally, the most realistic of the three, the high-Background (HB) dataset, where the background is equal to 2500 photons/pixel/frame. In all three datasets, Poisson and Gaussian noise is added to simulate among others, the photon and the read noise, the two main types of noise in fluorescence microscopy (see (  2 )). In order to give a visual inspection of the background and noise, in Figure  10 , one frame of the HB dataset is presented before and after the background/noise addition.
As we want, also, to provide a quantitative assessment, we measure the quality of the reconstruction of the final sequence of  frames (y  ,  = 1, 2,. , ) using the Signal-to-Noise-Ration (SNR) metric, given by the following formula: SNR dB = 10 log 10 1 𝑇 𝑀 2 𝑇 𝑀 2 𝑖=1 (R 𝑖 ) 2 1 𝑇 𝑀 2 𝑇 𝑀 2 𝑖=1 (R 𝑖 -K 𝑖 ) 2 , ( 36 ) where R ∈ R   2 is the reference image and K ∈ R   2 the image we want to evaluate, both of them in a vectorized form. As reference, we choose the sequence of convoluted and down-sampled ground truth frames (see one frame of the reference sequence in Figure  10a ). The SNR values for a sequence  of  = 500 frames for the NB, LB and HB dataset are 15. 84dB, 15. 57dB and -6. 07dB, respectively. A negative value is computed for the HB dataset due to the very high background used in this case.
The diffraction limited image (the average image of the stack) of each dataset as well as the ground truth intensity image are available in Figure  11. In the LB dataset, due to the high signal values, the background is not visible. Further, as the observed microscopic images and the reconstructed ones belong to different grids, coarse and fine grid respectively, their intensity values are not comparable and we can not use the same colorbar to represent them. The intensity of one pixel in the coarse grid is the summation of the intensities of  ×  pixels in the fine grid, where  is the super-resolution factor. For this reason, we use two different colorbars, presented in Figure  12. The comparison of the method COL0RME with other state-of-the-art methods that take advantage of the blinking fluorophores is available bellow.
Regarding the method COL0RME-CEL0 and COL0RME-ℓ 1 , a regularization parameter equal to  = 5 × 10 - 3  ×   0  and  = 5 × 10 -3 ×  ℓ 1  , respectively, is used in the support estimation. The hyper-parameters  and  are set as follows:  = 10 6 ,  = 20. For the method COL0RME-CEL0 the algorithmic restarting approach is used for a better support estimation, while the maximum number of restarts is set to 10. For the method SRRF we are using the NanoJ SRRF plugin for ImageJ 2. Concerning the method SPARCOM, we make use of the MAT-LAB code available online 3. As regularization penalty we choose the ℓ 1 -norm with a regularization parameter equal to 10 -10 and we avoid the post-processing step (the convolution with a small Gaussian function) for most precise localization. Finally we test the method LSPARCOM, using the code that is available online  4  and the tubulin (TU) training set that is provided.
In Figure  13  we compare the reconstructions of the methods COL0RME-CEL0, COL0RME-ℓ 1 , SRRF, SPARCOM and LSPARCOM for the LB dataset and in Figure  14  for the HB dataset, for a sequence of T = 500 frames. Results for different stack sizes, are available in the Supplementary Figures  S1, S2  and S3. In both datasets, LB and HB dataset, and for a sequence of T= 500 frames, the better reconstruction is the one of the method COL0RME-ℓ 1 , as it is able to achieve a more clear separation of the filaments in the critical regions (yellow and green zoom boxes). The method COL0RME-CEL0 achieves also a good result, eventhough the separation of the filaments, that are magnified in the green box, is not so obvious. The same happens also when the method SPARCOM is being used. Finally, the reconstruction of the methods SRRF and LSPARCOM, is slightly misleading.
The data we are using have been obtained from a Multi-Angle TIRF microscope, with a fixed angle close to the critical one. A sequence of 500 frames has been acquired, with an acquisition time equal to 25s. The diffraction limited image, or with other words the mean stack image, can be found in Figure  15  under the title ȳ. The FWHM has been measured experimentally and is equal to 292. 03nm, while the CCD camera has a pixel of size 106nm. The results of the method COL0RME-CEL0 and COL0RME-ℓ 1 and more precisely the intensity and the background estimation, can be found in Figure  15. For the method COL0RME-CEL0 the regularization parameter  is equal to  = 5 × 10 -4 ×  0  and the algorithmic restarting approach has is used (maximum 10 restarts). Regarding the method COL0RME-ℓ 1 the regularization parameter  is equal to  = 5 × 10 -6 ×  ℓ 1  , a relatively small value so as to be sure that we will include all the pixels that contain fluorescent molecules.
Funding Statement. The work of  VS  and LBF has been supported by the  French government , through the  3IA Côte d'Azur Investments in the Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-19-P3IA-0002. LC acknowledges the support received by the academy on complex systems of  UCA JEDI  and the one received by the  EU   H2020 RISE program  NoMADS, GA 777826. The work of JHG was supported by the  French Agence Nationale de la Recherche  in the context of the project  Investissements d'Avenir UCAJEDI  ( ANR-15-IDEX-01 ). Support for development of the microscope was received from  IBiSA  ( Infrastructures en Biologie Santé et Agronomie)  to the MICA microscopy platform.
The steady decline of avian populations worldwide urgently calls for a cyber-physical system to monitor bird migration at the continental scale. Compared to other sources of information (radar and crowdsourced observations), bioacoustic sensor networks combine low latency with a high taxonomic specificity. However, the scarcity of flight calls in bioacoustic monitoring scenes (below 0. 1% of total recording time) requires the automation of audio content analysis. In this article, we address the problem of scaling up the detection and classification of flight calls to a full-season dataset: 6672 hours across nine sensors, yielding around 480 million neural network predictions. Our proposed pipeline, BirdVox, combines multiple machine learning modules to produce per-species flight call counts.
Contributions In this article, we present BirdVox, a project for bird migration monitoring via machine listening techniques. Compared to previous studies on flight calls, the main novelty of BirdVox resides in its unprecedented scale: nine sensors which span an area of 10 9 m 2 and remain active for 10 2 days, containing 10 5 -10 6 flight calls from 10 2 -10 3 species. The BirdVox project encompasses two contributions: first, a robust machine listening pipeline for the automatic detection and classification of flight calls, named BirdVoxDetect; and secondly, a large-scale evaluation of BirdVoxDetect versus competing methods in real-world conditions of deployment, by way of a new dataset named BirdVox-full-season. Figure  1  outlines the functioning of BirdVox. The pipeline comprises six stages in total, which are grouped into three blocks: audio signal processing, machine learning, and statistical modeling.
Expert annotation of flight calls We divide all recordings in the full-season dataset into two-hour segments. The starting times of these segments are expressed in Coordinated Universal Time (UTC) and range from 6 p. m. to 6 a. m. by increments of two hours. Note that the local time in Ithaca, NY, corresponds to Eastern Standard Time (UTC-05:00) in winter and Eastern Daylight Time (UTC-04:00) in summer. Furthermore, for each nocturnal recording in full-season, we extract the audio segment corresponding to the two hours preceding sunrise. To determine the time of sunrise on any given day, we rely on open data from the weather station of the Ithaca Tompkins Regional Airport (KITH). This operation results in a collection of 3, 131 segments, amounting to 6, 262 hours of audio. We select 150 segments at random from the aforementioned collection.
Among them, 100 segments are synchronized with UTC, ranging between 6 p. m. and 6 a. m, while the remaining 50 correspond to the two hours preceding sunrise. The reason why we assign a larger relative proportion to the latter is that the density of flight calls is higher at dawn than at dusk or at night  [40]. In 2018 and 2019, an expert ornithologist (AF of the authors) annotated each of these 150 segments by means of the Raven Pro sound analysis software 3. The annotation task consisted in pinpointing and labeling every flight call in the time-frequency domain. It took 570 hours to complete this first round of annotation. A second round of annotation, conducted in 2021, revealed that two segments were not admissible for nocturnal flight call detection because they had mistakenly been extracted after sunrise.
For example, "other American Sparrows" includes eight species, e. g. Field Sparrow. 3 Official website of Raven: https://ravensoundsoftware. com. May 26, 2022 6/20page. 20 Figure  5. Taxonomy of labels in the 296h dataset. The coarse, medium, and fine level of the taxonomy correspond to order, family, and species respectively. Species within the same bracket belong to the same family of the Passeriformes order. Likewise, "other Cardinals" includes three species, e. g. Indigo Bunting; "other Thrushes" includes four species, e. g. Veery; and "other New World Warblers" includes 16 species, e. g.
Magnolia Warbler. Due to the varying distance between the sensor and the source, some of the flight calls are too faint to be confidently labeled in terms of species, even to an expert ear. However, they may be identifiable at a coarser taxonomic level. In those instances, automatic species classifiers can only be evaluated against the human ground truth up to a certain level of granularity  [41]. For this reason, we release three variants of the annotation, respectively denoting order, family, and species. We name BirdVox-296h (or "296h" for short) the annotated dataset of 150 two-hour segments, as a subset of BirdVox-full-season. For the sake of research reproducibility, we upload a copy of BirdVox-296h to the Zenodo repository of open-access data  4.
5 ms is the hop size of the mel-frequency spectrogram. The recursive implementation above is more computationally efficient than FFT-based convolution while having a smaller memory footprint. Proposition IV. 1 from  [43]  gives the following connection between constants T , τ, and s: s = 1 -cos 2πτ T 3 -cos 2πτ T -1 -cos 2πτ T. (3) In this paper, we set T = 60 ms, ε = 10 -6 , α = 0. 8, δ = 10, and r = 0. 25. A previous publication [32, Figure  4  specifically] has shown empirically that, on the full-night dataset, this choice of parameters Gaussianizes the distributions of normalized spectrogram magnitudes, consistently across sensors. This is in contrast with the original publication on PCEN  [42] , in which the proposed default parameters are optimized for automatic speech recognition in a noisy indoor environment, but inadequate for flight call detection.
Random forest classifier We extract sensor fault features on the full-season dataset. This operation results in 12k feature vectors, i. e. , one every half-hour segment. We manually label two half-hour segments in the dataset: one in which a sensor fault is present and the other in which no sensor fault is present. With scikit-learn v0. 20. 1  [44] , we train a random forest classifier on the two sensor fault feature vectors corresponding to these two segments. We set the number of ensembled decision trees in the random forest equal to 100.
Active learning for efficient audio annotation Because the classifier described above is trained on a tiny dataset (two samples), it does not generalize well to unseen recording conditions. To improve accuracy, it is necessary to refine the decision boundary between classes, and thus label more samples. However, the annotation of sensor faults from bioacoustic recordings is a particularly tedious task. Furthermore, the relatively rare proportion of sensor faults in full-season (estimated between 1% and 5% of the audio data) causes a class imbalance problem, which hampers the statistical generalization of the classifier. We address the issue of annotation efficiency in the sensor fault detection task by adopting an active learning paradigm. Instead of annotating audio segments drawn uniformly at random in full-season, we execute an algorithm which iteratively queries the human annotator with the most informative unlabeled sample.
Qualitative evaluation with t-SNE embedding We propose to shed light on the active learning process described above by visualizing the performing a t-distributed stochastic neighbor embedding (t-SNE) of the full-season dataset  [46]. The t-SNE algorithm learns a nonlinear mapping from a feature space in dimension ten to a embedding space in dimension two. In doing so, t-SNE minimizes the Kullback-Leibler divergence between the joint probability distribution of samples in the feature space and that of samples in the embedding space. Therefore, spatial proximity in the 2-D embedding space denotes acoustical similarity in terms of median PCEN-gram features. We use the implementation of scikit-learn with all parameters set to their default values as of v0. 20. 1. Figure  7  illustrates the outcome of t-SNE embedding.
Data curation We train BirdVoxDetect on BirdVox-222k (or "222k" for short), a new derivative of the BirdVox-full-night dataset. BirdVox-full-night (or "full-night" for short) comprises 62 hours of audio in total, as recorded on the night of September 23rd, 2017 by six different sensors. In 2017, an expert ornithologist (AF of the authors) spent 102 hours annotating each of these six recordings and found 35k flight calls from passeriformes. We extract 35k audio clips from full-night, each lasting two seconds and centered around one annotated flight call. We group these 35k audio clips into 352 segments, each of them of size 100, according to their spatiotemporal contiguity in the sensor network. Then, we run a pretrained flight call detector full-night: this detector combines spherical k-means (SKM) and a support vector machine (SVM), and is thus an instance of "shallow learning".
Data augmentation We augment the 222k dataset with three kinds of digital audio effects: pitch shifting, time stretching, and the combination of pitch shifting and time stretching. We sample the pitch interval at random from a normal distribution with null mean and half-unit variance, as measured in semitones according to the 12-tone equal temperament. Furthermore, we sample the time stretching factor at random from a log-normal distribution with parameters µ = 0 and σ = 0. 05.
Training We train BirdVoxDetect on the augmented training subset of BirdVox-222k via the Adam algorithm, an improved variant of stochastic gradient descent. We leave the hyperparameters of Adam to their default values: i. e. , a learning rate of 10 -3 , decay rates of β 1 = 0. 9 and β 2 = 0. 999, and a denominator offset of ε = 10 -7  [50]. We formulate the flight call detection task as binary classification and choose binary cross-entropy as objective function. Similarly to  [40] , we regularize this objective function by penalizing the L 2 norm of the synaptic weights in the penultimate layer, with a multiplicative factor set to 10 -3. To implement the training procedure efficiently, we use the pescador Python package, which offers utility functions for shuffling and streaming heterogeneous data  5.
For each of the 299 segments and the 31 augmentations, we construct a "stream": that is, an infinite generator which yields positive and negative samples with equal probability. At the beginning of each epoch, pescador draws one augmentation uniformly at random (out of 31) for each of the 299 segments. We then define batches by "multiplexing" the streams corresponding to these 299 segment-augmentation pairs, so that each stream contributes one and only one sample per batch. In this way, we guarantee that the 299 samples in each batch reflect the acoustical diversity of the full-night dataset. We repeat the process 100 times per epoch, thus yielding 29. 9k samples per epoch in total. Note that this number roughly corresponds to the number of flight calls in the training set. On every epoch, we re-draw a new augmentation for each of the 299 available segments and multiplex the corresponding streamers.
Evaluation After training on 222k, we evaluate BirdVoxDetect on 296h. Note that 222k and 296h arise from the same recording locations but are disjoint in time. Furthermore, the dataset 222k was constructed from a single night of data acquisition whereas 296h is more diverse, as it involves recordings between August and November 2015. We run BirdVoxDetect on each of the two-hour segments in 296h according to a hop duration of 50 ms, thus producing an event detection function at a rate of 20 Hz. We select local peaks in the detection function above some fixed absolute threshold τ ∈]0, 1[. Then, we compare the set of detected peaks to the human-provided checklist of flight call timestamps. We define matching pairs between detected events and a reference events if their timestamps are within 500 ms of each other.
We optimize the cardinality of this matching while guaranteeing that each reference peak matches a single detected peak at most, and vice versa. For this purpose, we solve a bipartite graph matching problem via the match events function of the mir eval Python package  [51]. This operation yields a number of true positives, false positives, and false negatives. We may convert these integer counts into information retrieval metrics: precision, recall, and F 1 -score. We repeat the process for sweeping values of the threshold parameter τ in to derive a precision-recall curve.