dataesr/scientific-paragraphs-categorization huggingface dataset:
While all of these models do indeed achieve good levels of performance on their test sets, none of them manage to take advantage of the neuromorphic-friendly nature of event data, since analog blocks or additional non-spiking information prevent a deployment on neuromorphic chips. More interesting to this work are spiking neural networks applied to event vision, be it for depth or for optical flow estimation. As far as optical flow is concerned, it is worth citing the works of  Hagenaars et al. (2021) , which achieves state-of-the-art levels of performance on the MVSEC Dataset with a fully spiking architecture. More recently,  Kosta and Roy (2022)  showed that spiking neural networks can indeed compete with their analog counterparts in terms of accuracy, showing top results both in the MVSEC and in the DSEC Dataset.
Finally,  Zhang et al. (2023)  achieves a remarkable accuracy on the MVSEC Dataset with a U-Net-like architecture and a self-supervised learning rule. However, all of these models are not implementable on neuromorphic hardware, since they either use upsampling techniques which are incompatible with the spiking nature of these devices (e. g. , bilinear upsampling), or re-inject intermediate, lower-scale analog optical flow predictions, thereby violating the spiking constraint by introducing floating point values in an otherwise binary model. In addition, the choice of a self-supervised learning rule, usually linked to a photometric loss function presented in  Yu et al. (2016) , means that optical flow estimations are only provided for pixels where events occurred, therefore creating non-dense flow maps.
Looking at depth prediction though, we do find some interesting strategies for fully deployable neuromorphic models. Finally, authors in  Rançon et al. (2022)  presented in their StereoSpike model a fullyspiking, hardware-friendly network achieving remarkable accuracy on the MVSEC Dataset, thanks to stateless spiking neurons that have greatly inspired our work. While we have focused on optical flow and depth predictions with event cameras, there have also been preceding works achieving top results on other computer vision tasks using event datasets and spiking neural networks. Such is the case of the works of  Kim et al. (2022) , who performed semantic segmentation via supervised training of a SNN, or the method described in  Kirkland et al. (2022)  that addressed instance segmentation on event data using a biologically-plausible learning strategy.
Training dataset Our study focuses on driving scenes, and we chose the DSEC Dataset by  Gehrig et al. (2021a)  to train our model. Unlike previous state-of-the-art datasets, such as the Muti-Vehicle Stereo Event Camera (MVSEC) Dataset by  Zhu et al. (2018a) , which provided different working scenarios (indoors/outdoors, day/night, and four possible vehicle configurations: pedestrian, motorbike, car and drone), the DSEC dataset only consists of driving scenario sequences. However, it provides higher-quality ground-truth labels, thanks to the finer processing of the LIDAR measurements. In addition, this dataset also includes masks for invalid pixels, i. e. , pixels where the optical flow ground-truth is unknown. As such, our metrics have only been evaluated on the valid pixels. Furthermore, this dataset provides an open benchmark to submit the results, which we used to determine our test metrics and compare ourselves to other works.
Spiking neuron model For our network, we chose a simple neuron model that can be easily implemented with open-source Python libraries, in addition to being much less computationally expensive than closer-to-nature neuron mathematical models. This model is the  McCulloch and Pitts (1943). It was implemented using the Spikingjelly library, developed and maintained by  Fang et al. (2020) , due to their full integration with the Pytorch library. Our model is based on a stateless approach: the neuron's potential is reset after each forward pass. Indeed, the mathematical neuron model presented by McCulloch and Pitts consists of stateless neurons with Heaviside activation functions. This is equivalent to stateless integrate-and-fire neurons, i. e. , stateless artificial neurons working as perfect integrators, but which are reset at every time step.
Supervised learning method Our model was trained with supervised learning using the surrogate gradient descent, using a sigmoid function as our surrogate gradient model. The ground-truth optical flow values were those provided in the DSEC database. While traditional selfsupervised methods restrict their optical flow processing to pixels where events occurred (e. g. ,  Zhu et al. , 2018b , Hagenaars et al. , 2021 , or Kosta and Roy, 2022 , to cite a few examples), our approach permits dense estimations (thanks to surrogate gradient learning). We trained our model on the valid pixels given by the dataset masks at each timestep. Our loss function included two terms: • A standard MSE-like loss between the value of the predicted flow and its corresponding ground truth, with the following formula: L mod = N pixels (pred x -gt x ) 2 + (pred y -gt y ) 2 N pixels (2) The term N pixels represents the number of valid pixels to be trained at each timestep.
Training details All of our calculations were performed on either NVIDIA A40 GPUs, or in Tesla V100-SXM2-16GB GPUs belonging to the French regional public supercomputer CALMIP, owned by the Occitanie region. Trainings were realized with a batch size of 1, since it is the optimal value we have found for our task. Although unconventional, this result is in line with the one found in  Rançon et al. (2022) , where a batch size of one was found optimal for depth regression from event data using stateless spiking neurons. We used an exponential learning rate scheduler, and have implemented random horizontal flip as a data augmentation technique to improve performance. Furthermore, thanks to our stateless approach, we were able to train our network with shuffled samples, instead of being forced to use the input frames sequentially.
Results We divided our dataset into a train and a validation split, and our performance levels are reported with regard to the validation set. The exact sequences used in each split can be found in the Supplementary material. Nevertheless, we resort to the official DSEC benchmark to compare ourselves to the state-of-the-art, since it represents an objective, third-party test set. We now proceed to present the results we obtained in our studies. Due to the number of tests that we have run, all of the corresponding plots are provided in the Supplementary material.
Comparison with the state-of-the-art We trained our best architecture on the whole DSEC dataset for a total of 100 epochs. We evaluated our model on the official  test set provided by DSEC. Results are shown on Table  3. In order to provide a fair comparison, we only included results on the official benchmark, and not those reported on custom validation sets. While still far from the best models, we demonstrate the power of spiking neural networks when applied to dense regression in computer vision, achieving good levels of performance with a fraction of parameters when compared to other models. We also provide some of our model's results on the validation set, which can be found in Figure  3. These pictures show that, even if the network was not explicitly trained to distinguish image contours (since it was only trained on a selection of valid pixels at each timestep), it is nonetheless capable of extracting structural information within the scene and generalizing it, as illustrated in the rightmost images (unmasked predictions) for the given examples.
Model evaluation on the MVSEC dataset In order to analyze the generalization capabilities of our method, we also tested our model on the Multi-Vehicle Stereo Event Camera Dataset (MVSEC), introduced in  Zhu et al. (2018a). We started by analyzing our model performance on the indoor flying sequences. To do so, we took a model pre-trained for DSEC, and optimized its weights on the MVSEC Dataset over 35 epochs. We followed a training approach akin to the one adopted for the DSEC dataset, i. e. , we only considered pixels with either zero-valued ground-truth (x-and y-components of the optical flow vector below a small threshold thr = 1e -5) or with unknown flow values as invalid, and only trained on valid pixels. The results we obtained, as well as a comparison with other state-of-the-art models, can be found in Table  4.
Nevertheless, the MVSEC dataset was recorded with a different event camera, and therefore may demand a different kernel size to achieve top performance levels. • Our frame duration and overall temporal context have been designed for a specific camera configuration and resolution. Again, the use of a lower resolution camera leads to different optical flow dynamics, and therefore to potentially different temporal representation. • Our training procedure (learning rate, scheduler, etc. ) has not been designed for such a low-resolution estimation, and therefore further optimizations are needed to increase accuracy. • Finally, the outdoor_day2 sequence of the MVSEC dataset, used for training on driving scenarios, consists of only 9 min of recording where high frequency vibrations are constantly affecting the event camera (see  Zhu et al.
The works of  Khalfaoui-Hassani et al. (2021)  present a way of increasing kernel sizes without an increment in network parameters, capable of achieving state-of-the-art performances. While only applied so far for 1-and 2-dimensional convolutions, their method could easily be adapted to our 3d approach. Moreover, publicly available datasets usually lack challenging conditions, such as crossing pedestrians or vehicles, which can limit the network's generalization capabilities. While we believe that our proposed model is capable of understanding such situations (see Figure  3C , where traffic signals are easily recognizable), it would be desirable to train on more challenging scenarios. Finally, we would like to address hardware efficiency and implementation. We acknowledge that our approach does not provide energy savings during training, since it is performed on GPUs using standard ANN learning techniques, and therefore suffers from the same energy consumption constraints as these networks (plus the added memory usage due to the stockage of the neuron's membrane potential.
Acknowledgments This work was granted access to the HPC resources of CALMIP supercomputing center under the allocation 2022-p22020. The authors would also express their gratitude to the  CerCo's NeuroAI team  and specially to  Mr. Khalfaoui-Hassani , for their constant support and insightful feedback. We would also like to thank  Mr. Fang , developer of the SpikingJelly library, for the constant support he has provided during the whole timespan of this study.
Funding This research was supported in part by the  Agence Nationale de la Recherche  under Grant  ANR-20-CE23-0004-04  DeepSee, by the  Spanish National  Grant  PID2019-109434RA-I00/ SRA (State Research Agency /10. 13039/501100011033 ), by a  FLAG-ERA  funding ( Joint Transnational Call 2019 , project  DOMINO ), and by the  Program DesCartes  and by the  National Research Foundation ,  Prime Minister's Office, Singapore  under its  Campus for Research Excellence and Technological Enterprise (CREATE) Program.
For this reason we choose targets showing < 20 days orbital period. The major reason behind having radii < 8R E is the statistical validation tool called TRICERATOPS  (Giacalone et al. 2021). TRICERATOPS under-predicts the false positive probability for planetary candidates having radii ≥ 8R E. A total of 343 candidates from the Exoplanet Follow-up Observing Program (ExoFOP) website database are considered in this study. To identify possible binary stars, use of Renormalized Unit Weight Error (RUWE) score  (Lindegren 2018 ) from Gaia EDR3 is done. Targets with an RUWE score of ≥ 1. 4 or null  (Lindegren 2018 ) are eliminated. Additionally, targets with stellar companions, lacking SPOC pipeline data, or without available stellar parameters were also excluded from this study. A visual inspection of the remaining targets was performed to eliminate any signals that were consistent with star variability, eclipsing binaries, or instrumental systematic effects.
Before conducting a thorough and computationally expensive probabilistic analysis for transit signals, it is important to check the origin of the detected signal preliminarily. We employed the open source package Lightcurve Analysis Tool for Transiting Exoplanet (LATTE, Eisner (  2022 )) which runs multiple diagnostic tests providing an approximate indication that the signal may be originating from the target star rather than any nearby sources. We discuss our interpretation of these tests in section 2. 1. However, it should be noted that this preliminary test provides only approximate information and does not provide a high level of confidence regarding the origin of the signal. Further analysis and verification are required. The results of the LATTE tests for all of the considered candidates have been uploaded to a publicly available GitHub repository 4  for further examination.
Results of diagnostic tests The LATTE program performs multiple tests on a chosen TESS target by considering all the transit events observed by the TESS to be passed through diagnosis. Taking inspiration from SPOC pipeline data validation, LATTE performs multiple checks for background flux, in-out transit flux, pixel-level light curves, and centroid correlation/positions for each transit. The checks at the level of each transit event being tested individually help us to rule out any possible False alarms (FA), or account for any instrumental error affecting transits. We utilized comparative plots of background flux and overall flux to rule out the possibility of any background object such as a solar system object or asteroid in line-of-sight mimicking a transit-like event. Each time TESS attains the perigee of its eccentric orbit around the Earth enhanced scattered light in the telescope optics can cause the background flux to sharply increase.
However, no significant differences were found upon visual inspection. It is to note that the targets with almost similar magnitude within the aperture might still show correlations in centroid positions and hence, despite the apparent lack of visual shifts it is imperative to account for nearby neighbours while performing validation. Hence, the insights from these diagnostic tests helped increase our confidence to follow up with TRICERATOPS for a deeper analysis. The results of these tests for other candidates have been uploaded in GitHub. By observing those plots, it is inferred that the source of the transit-like signal is approximately related to the target pixel. Furthermore, we performed ground-based follow-up observations, high-resolution imaging, and false positive probability calculation using TRICERATOPS to validate the planetary nature of the given transit signal.
Shane/ShARCS We observed TIC 470987100 (TOI-1732) on UT 2020 December 01 using the ShARCS camera on the Shane 3-meter telescope at Lick Observatory  (Kupke et al. 2012; Gavel et al. 2014; McGurk et al. 2014). Observations were taken with the Shane adaptive optics system in natural guide star mode in order to search for nearby, unresolved stellar companions. We collected sequences of observations using K S filter (λ 0 = 2. 150 µm, ∆λ = 0. 320 µm). We reduced the data using the publicly available SImMER pipeline  (Savel et al. 2020 (Savel et al. , 2022)). 6 Our reduced images and corresponding contrast curves are shown in Figure  4. Our observations achieve contrasts of 4. 5 (Brγ) and 2. 7 (J) at 1. We find no nearby stellar companions within our detection limits.
VLT/NaCo Observations for TOI-323 was performed using NaCo instrument with K filter. NaCo is the Paranal Observatory's instrument, which is a combination of NAOS  (Rousset et al. 2000)  (Nasmyth Adaptive Optics System) and CONICA  (Lenzen et al. 1998 ) (Near-Infrared Imager and Spectrograph) installed on the Very Large Telescope (VLT). It is able to compensate for the atmospheric variabilities and provides a diffraction-limited resolution for observing wavelengths ranging from 1 to 5 microns. It can collect imaging data with broad and narrow band filters, a field of view of 14 -56 , and a pixel scale of 13-54 mas per pixel.
Light Curve Follow-up Observations The TESS pixel scale is ∼ 21 pixel -1 and photometric apertures typically extend out to roughly 1 arcminute, generally causing multiple stars to blend in the TESS aperture. To rule out a nearby eclipsing binary (NEB) or shallower nearby planet candidate (NPC) blend as the potential source of a TESS detection and attempt to detect the signal on-target, we observed our target stars and the nearby fields as part of the TESS Follow-up Observing Program 7  Sub Group 1 (TFOP; Collins 2019). In some cases, we also observed in multiple bands across the optical spectrum to check for wavelength dependent transit depth differences, which can be suggestive of a planet candidate false positive. We used the TESS Transit Finder, which is a customized version of the Tapir software package  (Jensen 2013) , to schedule our transit observations.
STATISTICAL VALIDATION With the advent of dedicated space missions for finding exoplanets, the number of possible planet-like candidates has increased rapidly. This creates a potential bottleneck between finding an exoplanet candidate and confirming the discovery with multiple follow-up observations. It is expected that TESS alone would be adding 12,000 potential exoplanets in the database over its 7-year extended mission lifetime  (Kunimoto et al. 2022 ). As a result, statistical validation of exoplanets becomes a viable alternative to confirming each candidate with dedicated observations with follow-up telescopes. Furthermore, statistical validation of such likely candidates could also act as a vetting and prioritization procedure for space missions and surveys such as JWST  (Gardner et al.
on behalf of the Gemini partnership: the  National Science Foundation (United States) ,  National Research Council (Canada) ,  Agencia Nacional de Investigación y Desarrollo (Chile) ,  Ministerio de Ciencia, Tecnología e Innovación (Argentina) ,  Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brazil) , and  Korea Astronomy and Space Science Institute (Republic of Korea). This work makes use of observations from the LCOGT network. Part of the LCOGT telescope time was granted by NOIRLab through the  Mid-Scale Innovations Program   (MSIP). MSIP  is funded by  NSF. This paper makes use of observations made with the MuSCAT2 instrument, developed by the  Astrobiology Center , at TCS operated on the island of Tenerife by the  IAC  in the  Spanish Observatorio del Teide. This paper is based on observations made with the MuSCAT3 instrument, developed by the  Astrobiology Center  and under financial supports by  JSPS KAKENHI  ( JP18H05439 ) and  JST PRESTO  ( JPMJPR1775 ), at  Faulkes Telescope North  on Maui, HI, operated by the  Las Cumbres Observatory.
R. S. -FNRS)  under grant  PDR T. 0120. 21 , with the participation of the  Swiss National Science Fundation  (SNF). M. G. is F. R. S -FNRS  Research Director. E. J. is F. R. S-FNRS Senior Research  Associate. The postdoctoral fellowship of KB is funded by  F. R. S. -FNRS  grant  T. 0109. 20  and by the  Francqui Foundation. This publication benefits from the support of the  French Community of Belgium  in the context of the  FRIA Doctoral Grant  awarded to MT. F. J. P. acknowledges financial support from the grant  CEX2021-001131-S  funded by  MCIN/AEI/ 10. 13039/501100011033. This research received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement n •  803193/BEBOP ), and from the  Science and Technology Facilities Council (STFC ; grant n •  ST/S00193X/1 ).
3. 6). The promising results of previous forays into this topic argue strongly that a comprehensive study of tidal streams in the nearby universe will provide a new perspective on galaxy formation. With the Stellar Stream Legacy Survey we aim to collect a systematic deep, broad-band images of stellar streams around a much larger sample of galaxies than any previous survey of such features. This is now possible thanks to the latest generation of large-area imaging surveys. The quality of these data offers a unique opportunity to reveal asyet hidden details of galaxy assembly in the local Universe and to make meaningful connections with theoretical predictions from the ΛCDM paradigm. In this paper we describe the scientific motivation and design of our Stellar Stream Legacy Survey. As a proof of concept, we present first results for a sample of 24 stellar streams in the local Universe.
The DESI Legacy Imaging Surveys The Stellar Stream Legacy Survey is based on a custom re-reduction of public data from the DESI Legacy Imaging Surveys project  (Dey et al. 2019)  2 , which combines images from several new wide area photometric surveys, by using a single analysis pipeline. This provides high quality multi-color imaging with significantly greater depth and resolution than previous surveys such as SDSS or Pan-STARRS. The diffuse light feature detection limit of co-added images from the DESI Legacy Imaging Surveys is similar to that previously obtained with amateur telescopes  (Martínez-Delgado et al. 2010 , 2015) , but additionally provides broadband photometry in standard filters 3  (see  Sec. 3. 6 ) and sub-arcsec seeing, which allows us to trace fine details of streams at larger distances.
Low galactic latitude regions of the survey affected by the higher stellar density of the Galaxy are perceived with this Gaia overlay. The galactic referential is plotted in green. North Galactic Cap region at 𝛿 ≤ +32 • and the South Galactic Cap region at 𝛿 ≤ +34 •. Both DECaLS and DES data were obtained with the Dark Energy Camera (DECam) mounted on the Blanco 4-m telescope, located at the Cerro Torrolo Inter-American Observatory  (Flaugher et al. 2015). In the Northern Hemisphere, the DESI Legacy Imaging Surveys comprise images from the BASS and MzLS surveys. These surveys have imaged the Dec. ≥ +32 • footprint of the upcoming Dark Energy Spectroscopic Instrument (DESI) survey, as a complementary program to DECaLS. BASS provides 𝑔 and 𝑟 coverage using the 90Prime camera at the prime focus of the Bok 2.
3m telescope located on Kitt Peak, adjacent to the 4-m Mayall Telescope. The 90Prime instrument is a prime focus 8k×8k CCD imager, yielding a 1. 12 deg field of view, with a scale of 0. 45 pixel -1  (Williams et al. 2004). The survey has 𝑔-and 𝑟-band photometry over 5000 deg 2. The BASS survey tiled the sky in three passes, similar to the DECaLS survey strategy. At least one pass was observed in both photometric conditions and seeing conditions better than 1. 7. The Mayall 𝑧-band Legacy Survey (MzLS) imaged the same sky area with a similar observing strategy, using the MOSAIC-3 camera  (Dey et al. 2016)  at the prime focus of the 4m Mayall telescope at Kitt Peak National Observatory, in photometric conditions and with seeing better than 1. 3 arcsec. The deep images shown in this paper are based on data release 8 (DR8) of the DESI Legacy Imaging Surveys, although future work will exploit the slightly greater coverage, depth and improved reduction of the more recent DR9.