dataesr/scientific-paragraphs-categorization huggingface dataset:
All measurements are realized in ambient atmosphere at room temperature. Single-shot measurements of the voltage excitation signal, electrical response, and optical response are shown in figure  5. Clear optical response is detected for electrical pulse widths of   0. 4, 0. 8 and 1. 0 ns. In all cases, the applied voltage amplitude was set at 22 V. For pulses shorter than 0. 4 ns, no significant optical responses were observable. Note that the widths of the optical response pulses are roughly equal to the electrical pulse width, but the height ratios of current density and optical power differ. These data will be used to calibrate our theory.
Acknowledgments This work was supported by the  French Agence Nationale de la Recherche  through the  Programs 453 Investissement d'Avenir  under Grant  ANR-11-IDEX-0005-02 , by the  Labex SEAM: Science Engineering, 454 Advanced Materials , and  PRC DEµS. This study was also supported by the  IdEx Université de Paris ,  ANR-18-IDEX-0001  and in part by the  Research Project Zwaartekracht  funded by the  Netherland Organization for Scientific Research (NWO). The authors would like to thank  J Solard  and  D Kocic  for their technical support.
72 mg/m 3 in humans. The confidence of U. S. EPA in the RfC value was medium, reflecting the medium confidence in the main study and in the database, which lacks information on chronic n-hexane exposure. The U. S. EPA concluded that there are no data on the potential oral toxicity of nhexane in humans and that the animal database is limited to two sub-chronic studies, unsuitable for calculating an RfD. Furthermore, the toxicity database of n-hexane was found to be insufficient for the evaluation of carcinogenic potential in humans  [48]. In 2009, the U. S. EPA proposed a provisional RfD value (p-RfD) based on an additional literature review, which, however, did not reveal any new relevant data. A provisional value alone is not sufficient to characterise the adverse effects of a chemical; moreover, the confidence in the proposed p-RfD value of 0.
2,5-HD caused chromosomal damage by inducing the formation of micronuclei in bone marrow cells, and significantly (p < 0. 05) higher levels of micronucleated polychromatic erythrocytes were observed compared to the control groups. In conclusion, 2,5-HD causes oxidative and chromosomal damage characterised by micronuclei formation and DNA fragmentation  [93]. In assessing the carcinogenic effects of n-hexane, the U. S. agency EPA concluded in 2005 that the available database did not contain sufficient information to evaluate the carcinogenic potential. In the main animal study, an increased incidence of combined adenomas and hepatocellular carcinomas was observed in female rats exposed to commercial hexane (mixture of isomers), with a statistically significant trend towards an increased incidence of pituitary adenomas.
Backpropagation algorithms on recurrent artificial neural networks require an unfolding of accumulated states over time. These states must be kept in memory for an undefined period of time which is task-dependent. This paper uses the reservoir computing paradigm where an untrained recurrent neural network layer is used as a preprocessor stage to learn temporal and limited data. These so-called reservoirs require either extensive fine-tuning or neuroplasticity with unsupervised learning rules. We propose a new local plasticity rule named P-CRITICAL designed for automatic reservoir tuning that translates well to Intel's Loihi research chip, a recent neuromorphic processor. We compare our approach on well-known datasets from the machine learning community while using a spiking neuronal architecture.
Each paired neuron is associated with a neuron of the reservoir and integrates the post-synaptic activity of neuron i. When the branching factor of neuron n i is above the targeted branching factor, n i fires, causing a depreciation of all synaptic connections with n i as the presynaptic neuron. An overview of this concept can be visualized in figure  1  and equation 3. We name this model P-CRITICAL (or Paired neuron CRITICAL). We simulated that concept using the PyTorch  [20]  python library. We introduce two constants, α and β, that are used as learning rates for this new plasticity rule. The reservoir is initialized with a W R where the connectivity is decided with a distanced-based small-world topology (similar to  [17] ). We then connect the reservoir to the paired reservoir using weight tensor W T R -the transpose of W R.
The exponential can be approximated using a numerically decaying synaptic trace. For all reservoirs, 20% of the neurons are randomly chosen to be inhibitory. The weights are sampled from a uniform random distribution with range [0. 2, 0. 5[ for synapses coming from excitatory neurons and [0. 1, 0. 3[ for synapses coming from inhibitory neurons. All synaptic weights with an inhibitory pre-synaptic neuron are set to negative. The P-CRITICAL learning rule only affects connections coming from excitatory neurons, as inhibitory neurons cannot cause post synaptic spikes. By design, we can translate P-CRITICAL easily on Intel's Loihi using their on-chip local learning rules. All weights are scaled from PyTorch's 23 bits mantissas (32 bits floating point implementation) to Loihi's 8 bits (+ 1 sign bit).
IV. EXPERIMENTS AND RESULTS We tested our method against two well-known datasets of the machine learning and spiking neural network community: N-MNIST  [43]  and N-TIDIGITS  [44]. Both of these datasets were created using event-based sensors from previously recorded data. N-MNIST comes from the saccadic presentation of the well-known handwritten digit recognition dataset MNIST to the event-based camera ATIS sensor  [45]. N-TIDIGITS was recorded from TIDIGITS, an audio representation of spoken digits, using the spiking silicon cochlea sensor CochleaAMS1b  [44] ,  [46]. Successful training on these datasets could be a significant step to end to end training of low energy event-based hardware. As mentioned, the main goal of P-CRITICAL is to tune a reservoir to the input spike train representation as to offer stability.
For these tests, we use 5 seconds of continuous activity randomly sampled from the N-TIDIGITS dataset. Both methods revealed a fairly consistent branching factor of 1 with the P-CRITICAL learning rule, after a small adaptation period. Spike activity and branching factor estimations are illustrated in figure  3. Finally, we also compare the time-binned spike counts from the self-induced activity in a Poincaré plot in figure  4. We once again used 5s of activity from the N-TIDIGITS datasets where features were randomly connected to two reservoir neurons each. We removed the first 2. 5s of the spike train to be sure that the reservoir had converged to the target branching factor of one. We then compute the spike counts using 5 ms bins and plot these counts for consecutive time periods. We compare this with a model of slope one, which represents a σ = 1.
Only the single digit samples of the dataset were used for training. We use the Adam  [47]  optimizer with a learning of 10 -3. As expected, reservoirs that were tuned using spectral radius normalization outperformed random reservoirs. We observe, however, an increased accuracy on the test set with all experiments where P-CRITICAL plasticity was enabled. We obtained with P-CRITICAL an average accuracy of 71. 26 ± 0. 92% (figure  5 ). We executed the exact same reservoir experiment on the Loihi research chip and observed a 64. 1% accuracy. As the reservoir is slightly more noisy because of bit-depth, we ran the model for 10 more epochs when using the loihi model with a weight decay of 10 -2. Finally, optimizing the eigenvalues spectrum offered all tested reservoirs a mean accuracy boost of 16.
77% on N-TIDIGITS at no task-specific optimization cost. To the knowledge of the authors, this is the first LSM-based reported accuracy for the N-TIDIGITS dataset. 2) Handwritten Digit Classification: For N-MNIST, we use a 8640 neurons reservoir (with j = 4 4 3 ) with PyTorch. In all cases, only the ON polarity of the input spike trains as available in the N-MNIST dataset was kept. For the readout layer, we used the Adam optimizer with amsgrad  [51] , a learning rate of 1e -5 and a batch size of 10. We observe a 95. 22 ± 0. 09% accuracy on the test data. As N-MNIST is more substantial in the amount of data, only 1 epoch through the whole dataset was necessary to achieve these results. We conducted a second faster experiment with only 1156 neurons in the reservoir. To do so, the 3D input spike train of shape 34x34xTime was split into subspike-trains, or quadrants, of shape 17x17xTime.
Model Reservoir size Details Mean accuracy (%) Model with σ = 1 5ms bins Figure  4 : Poincaré visualization of the spike count time-binned with T bins = 5 milliseconds. A slope of one was added as a comparison for a model with σ = 1. The spikes come from the self-induced reservoir activity after presentation of the N-TIDIGITS dataset. We first tuned the reservoir over 2500 milliseconds of continuous input. We then simulate the model for another 2500 milliseconds, which resulted in the presented spike counts. We used a small-world reservoir of 512 neurons with constant j = 4. We ran all efficiency experiments using Nx SDK version 0. 9. 5 on Nahuku 32 board ncl-ext-ghrd-01 with power probing. We also benchmarked power-efficiency for both implementations. The PyTorch version consumes 46W of dynamical power.
The plasticity rule was able to increase the test accuracy of unoptimized reservoirs for various high-level tasks coming from different sensory inputs that were captured with event-based sensors. We aim for P-CRITICAL to extend current reservoir computing methods such that they can be implemented on a neuromorphic processor and offer low-power edge devices the ability to train without requiring extensive computation or cloud server access. Reservoir computing is a good alternative to RNNs for faster training times, and plasticity-enabled reservoirs are well suited for neuromorphic engineering applications as they are slow on conventional computer architecture. This is because plasticity rules often prevent batching of the input data while the recurrent dynamics of reservoirs forces sequential simulations.
Figure3: Spike activity for 5000 ms of randomly chosen samples from the N-TIDIGITS dataset. Plot A shows the input spikes mapped to the 512 neurons inside the reservoir and summed. Each of the 64 input features was connected to two reservoir neurons. Plot B shows the spike count summed of the reservoir in which the input spike raster was subtracted, therefore showing the self-induced activity inside the reservoir. Plot C shows the branching factor, estimated over the selfinduced reservoir activity with two different methods as done in [18] : the total spike count and a topology-aware method. This branching factor estimation is Gaussian filtered over time with constant σ = 0. 7. The total spike count method consists of dividing the total spike count at t+1 divided by the total spike count at t.
ACKNOWLEDGMENT The authors would like to thank  Intel  for giving us access to the Loihi chip, for travel grants and guidance, along with  Hydro-Québec ,  Université de Sherbrooke, Compute Canada  and the  Natural Sciences and Engineering Research Council of Canada (NSERC)  for funding this research. We'd also like to thank the many reviewers and the developers of all the mentioned python libraries. Finally, we thank  Simon Brodeur  for his valuable insights on the CRITICAL plasticity rule.
(2021). This method allows one to correct for residual calibration artifacts while permitting fast and flexible reimaging of a smaller portion of the dataset. We focused on a squared region of 1. 1 deg 2 centered on the ring and obtained a new image with WSClean  (Offringa et al. 2014 ) at 30 ′′ resolution. In this image, obtained using a minimum uv cut of 60λ (corresponding to an angular size of ∼57 ′ ), discrete sources were subtracted from the visibilities using the clean components created from a previous imaging run at higher resolution (created with a minimum uv cut of 1000λ to filter out the ring).
(2016)  estimates sensitivity limits of 0. 25 mJy for an assumed fractional pulse width of 10% (0. 8 mJy for 50%) at 7σ. The observation was also searched for emission from single pulses using tools from the PRESTO software suite  (Ransom 2001). After RFI masking, dedispersed time series were created for DMs up to 80 pc cm -3 , with steps of 0. 002 pc cm -2 up to 10 pc cm -3 at the native time resolution, 0. 005 pc cm -3 from 10 to 22. 5 pc cm -3 for a downsampled time resolution of 0. 655 ms, and 0. 02 pc cm -3 at 1. 31 ms. Single pulse candidates with DMs below 0. 2 pc cm -3 were discarded due to pollution by zero DM RFI, while all other events above a significance of 7σ were visually inspected. Unfortunately, no astrophysical pulses were detected as all remaining events could be attributed to time ranges affected by RFI.
Optical observations We also observed the region around Calvera and a section of the radio ring with the Wide Field Camera (WFC) on the 2. 5 m Isaac Newton Telescope (INT) in La Palma. Our initial aims were to search for optical forbidden line emission from the radio shell which would indicate radiative SNR shocks, and for the presence of a bow shock near Calvera which could help us constrain its direction of motion. The region was observed for three nights  (2022, March 19, 20, and 23)  in the Hα and r-band filters. In order to reduce the optical observing data, we used the automated astronomical data reduction pipeline from THELI  (Erben et al. 2005; Schirmer 2013 ). THELI provided the astrometry and relative photometry to combine the observations of a single observation night. For every single observation, THELI subtracted the sky with a model.
Discussion We are fairly confident that this ring of radio emission seen in LOFAR, WENSS, and the NVSS is the remnant of the explosion that formed Calvera. The main pieces of evidence are the presence of an X-ray plasma with enhanced metals and the rarity of a chance alignment with a pulsar at such high Galactic latitudes. We discard the H II region interpretation given the lack of infrared and Hα emission; the ORC interpretation remains a possibility, but the difference in size with the other known ORCs and the lack of a clear host galaxy candidate weaken it. An additional possibility is that the ring is a SNR unrelated to Calvera; although, this would require two supernova explosions occurring in a single line of sight a high Galactic latitude, which is unlikely. More data are necessary to determine whether the ring is a SNR or an ORC.
The camera sensor allows to capture an image of 320 × 256 pixels up to 3. 1 kHz in full frame an up to 100 kHz with a reduced spatial resolution of 64 × 4 pixels. Transverse crack propagation in [0 m /90 n ] s can be considered as almost instantaneous from side to side  (Berthelot 2003; Huchette 2005). This hypothesis remains valid with the loading rate increase  (Berthe and Ragonet 2018). Based on this assumption, only one edge of the different specimens has been monitored to maximize the IR camera frame-rate for the high loading-rate tests. More information about the camera acquisition parameters and the image analysis will be given in Section 2. 3. At last, the synchronization of each measurement system and the data acquisition are performed by means of a Dewetron DEWE-2600 acquisition system at 1 MHz.
Microscope Imaging Microscopy: We used an inverted NIKON TE300 microscope equipped with motorized x, y, and z displacements and shutters. Images were collected using a 20 × S plan Fluor objective (NA 0. 45 WD 8. 2-6. 9 mm). Bright-field images were collected in direct illumination (no phase). Fluorescence acquisitions were performed using either the green channel filters for GFP and FAST : HBR-2,5-DM (Ex. 482/35, DM 506 Em. FF01-536/40) or the red filter for m-Cherry (Ex 562/40nm DM 593 Em. 641/75). Excitation was performed using an LED box (CoolLed pE-4000). For dynamics measurements, confocal images were collected using a spinning disk Crest X light V2 module (Gataca, France distribution) with an axial resolution of 5. 8 µm.
Image Analysis Image intensities: Time-lapse images were analyzed to derive the kinetics of E. coli biomass accumulation in the channel based on FAST fluorescence intensity, as previously detailed  (Monmeyran et al. , 2018). Image intensity per pixel, averaged for the whole image or on defined regions of interest (ROIs), was collected using the NIKON proprietary software NIS. Subsequently, the data sheets edited by NIS were exported to MATLAB for further analysis of biofilm development kinetics. Background was subtracted using the contribution to the fluorescence intensity of a channel containing medium without any bacteria (details in Figure  S2 ). All curves were averaged over at least three independent positions and two independent replicates.
Dynamics Monitoring The individual dynamics of E. coli cells were tracked in the biofilm surface layer using confocal time-lapse acquisitions performed with a 60x, 1. 4NA objective. Series of 50 images were recorded at an acquisition frequency of 90 frames per hour every 2 hours, for 24 h. The image stacks were binarized using the ImageJ IsoData threshold calculation tool, which iteratively takes into account average background and average object intensities. Next, objects comprising between 40-2,000 pixels in area (1px=0. 1075 µm) were tracked in 2D over each temporal series using the ImageJ MTrack2 algorithm. These size limits ensured the tracking of a single cell to clusters of a few (3-4) cells. The minimal trajectory held 2 frames and the maximal accepted displacement between two frames was 60 pixels.
e. , H = 5 H = 7. 5 s and H = 10 s. The frequency response of the network improves with incorporation of IR, with better response for higher value of H. This demonstrates the successful use of synchronous GDS for frequency control study within a transmission network. An additional simulation with different rates of data exchange is undertaken to assess the impact of transmission rate. The results for four different transmission rates (1kHz, 2kHz, 3kHz and 4kHz) have been presented in Fig. 17. As can be observed, the rate of transmission does not impact the time delay. The jitter and variability in the delay remains in the same order of magnitude. With appropriate time delay compensation implemented, the errors in frequency response are negligible. VIII. FUTURE OUTLOOK While the detailed models developed in this paper and the consequent establishment of the boundaries of stability will further the understanding of the applicability of GDS and support safe implementations, there are still areas that present exciting directions for further exploration to support widescale adoption of the concept: • As the concept of GDS becomes established, facilitated by adoption by research laboratories across the world, it becomes essential to maintain a database of such infrastructures and the services they offer.
Sample definition Our sample was composed only of certified projects under the five following sustainable urban labels: LEED-ND (US Green Building Council-USA), BREEAM Communities (Building Research Establishment -UK), DGNB Urban Districts (German Sustainable Building Council -Germany), Green Star Communities (Green Building Council Australia -Australia) and EcoQuartier (Ministère de la Transition Écologique et Solidaire & Ministère de la Cohésion des territoires et des relations avec les collectivités territoriales -France, phase 3 and 4). These labels were chosen because they are all well-established sustainable neighbourhood design frameworks, widely used at this scale  (Chastenet et al. , 2016; Grazieschi et al. , 2020; Reith & Orova, 2015; Sharifi et al. , 2021). They also offer good international coverage to the sample, and they have an accessible online projects database, allowing us to compose a representative sample of projects to interrogate.
Survey data analysis The data was analysed using descriptive statistical analysis on Microsoft Office Excel 365, using functions as total count, frequency, quartiles and mode. We also realised a t-test to verify the hypothesis that projects in which the ecological diagnostic deeply informed the design process had a higher average of different strategies used than the rest of the sample. The t-test was applied to compare the average number of different strategies used between projects that had a diagnostic that deeply informed the design and the remaining projects (no diagnostic, diagnostic that did not deeply inform the project or not sure how it informed). To analyse the frequency of observation of each of the 42 proposed strategies, we created groups of strategies through quartile analyses.
, 2022)  to gather site data and mobilize citizens. Regarding finance viability, in the context of market-based urban development, projects must seek an economic and financial equilibrium and integrating sustainable strategies can be perceived as an additional cost. For instance, research on nature-based solutions explores new implementation models for these strategies, covering financial mechanisms  (Jeuken, Breukers, Elie, & Rugani, 2020). Nevertheless, further research is still fundamental to foster regenerative projects. A clue is the valuation of immaterial benefits created for society and nature by these projects and the payment for the produced ecosystems services (Bellver-Domingo, Hernández-Sancho, & Molinos-Senante, 2016), a mechanism very little explored at this scale. A remarkable example at the building scale is the Bullit Center, in Seattle, USA, a regenerative building certified LBC.
Le Maistre et al. [9]  have classified the interior structure of Phobos into four families: rubble pile, heavily fractured, porous, and icy body. The interior structure can be inferred from the moments of inertia (MoI) of Phobos. The MoI is related to the central gravitational parameter, GM, 2nd-degree gravity coefficients, C 20 and C 22 , and libration amplitude. A number of flybys performed in past missions (e. g. , the Viking, Phobos-2, and Mars Express) provided valuable radio tracking data that elucidated the GM of Phobos. One latest GM derived radio tracking data during flybys has an uncertainty of 0. 3%  [10]. The estimation of Phobos' ephemeris and 2nd-degree gravity coefficients done by Jacobson and Lainey  [11]  shows an 8. 7% disagreement on C 22 from that of the homogeneous model, which corresponds to the shape model derived from observed images  [12, 13].
The irregularily shaped Phobos has dimensions of 26 × 22 × 18 km. As it is much more smaller than Mars, whose dimension is 6780 km, and orbits closely (i. e. , with a semi-major axis of 9377. 2 km) around Mars, dynamics in its vicinity is dominated by the gravity of Mars. The sphere of influence of Phobos is small and even below its surface. Therefore, classical Keplerian orbits do not exist around Phobos. The retrograde quasi-satellite orbit (QSO) found in the Mars-Phobos three-body problem is a means for spacecraft to orbit around Phobos in the sense of relative motion  [31, 32]. Previous work has computed a database of periodic three-dimensional QSO (3D QSO) around Phobos in the circular-restricted three-body problem (CR3BP)  [33, 34]. Figure  2  presents families of periodic QSO described by the x-amplitude, A x , and z-amplitude, A z.
Acknowledgments: The authors appreciate  Sebastien Le Maistre (Royal Observatory of Belgium)  for allowing the usage of his distribution map in Figure 13. This work benefits from the computer clusters tycho and cerfeuil, financed and managed by  Observatoire de Paris and IMCCE. The first author appreciates the constant support from the  informatics department of IMCCE  delivered by  Stephane Vaillant  and  Mickaël Gastineau , and also wants to thank  Zhuoxi Huo  ( QXSLab ) for an invited visit,  Jinsong Ping  ( NAOC/CAS ) for his advice on the Doppler error analysis, and  Long Long  ( CAST ) for making the 3D model of the CubeSat.