dataesr/scientific-paragraphs-categorization huggingface dataset:
x 1. Arguably, the most important factor to consider when designing CUDA-based algorithms is its memory hierarchy. Under CUDA, each GPU thread has its registers, all threads in a block can access the same block-wise shared memory, and all threads across all blocks can access the same global memory. To process data using CUDA, it must first be loaded into the global memory from the CPU memory, and then be fetched to registers or shared memory by individual threads. Likewise, the output of the CUDA program can only be transferred to CPU memory via global memory. The memory size increases in order of per-thread registers, perblock shared memory and global memory, with the global memory being much larger than the former two. For example, for the Quadro RTX 6000 GPU, the maximum number of 32-bit registers per thread is 255, the maximum amount of shared memory per block is 64KB, while its global memory size is 24GB.
C. GPU-accelerated Raw Feature Extraction The sequential version of our feature extraction workflow, as illustrated in Algorithm 1, can be too inefficient to handle massive datasets. It's main bottleneck is the nested loops in lines 6-24. This is because while the number of ROIs is usually limited, they can often cover large areas. Therefore, for each of them, both the number of examples and the number of patches in each example can be huge, and looping over all of them can be highly time-consuming. Fortunately, note that we conduct the same operations (namely the same core method for raw feature extraction, and the same statistics for feature aggregation) to all patches and examples, which makes the feature extraction procedure amiable to the aforementioned SIMT architecture of the GPU. For the rest of this section, we focus on GPU-based raw feature extraction.
D. GPU-based feature aggregation We now discuss GPU acceleration for feature aggregation. From Section III-D (in particular lines 20-23 in Algorithm 1), we can conclude that the aggregated features for an ROI can be divided into segments with an ID of (ex, f Scale, i), in which ex is the ID of an example in the ROI, f Scale is the one of the feature scales, and i is an index of the output vector of the core method. Each segment consists the 7 statistics mentioned in Section III-D. Among them, mean and std can be obtained in constant time if we pre-calculate the two sums mentioned in the discussion on ST AT in the previous section (although here the values to sum up are raw feature values, rather than elevation values). These sums can be obtained by CUDA-based parallel reduction  [35]. For the order statistics, we can first sort the raw features that correspond to the current segment, which can be performed on GPU using existing software tools such as Thrust 1   [36]  and CuPy 2   [37] , and then trivially obtain them in constant time.
V. EXPERIMENTS For empirical study, we conduct 2 sets of experiments: First, we present a demo of our FARMYARD pipeline to showcase its utility in a real-world geoscientific research project. Second, we conduct experiments on both real and simulated large planetary LiDAR datasets to demonstrate the efficiency of our Pseudo-One-Pass Sweep (POPS) based raw feature extraction framework. All experiments were ran on a Ubuntu 18. 04 server with 2 NVIDIA Quadro RTX 6000 GPUs (only one is used at a time), 1 AMD EPYC 7402P 24-core CPU @2. 8GHz, and 125GB memory. Unless otherwise stated, all CPU-based code is implemented in Python 3. 8, while all GPU-based code is implemented in CUDA-C  [14]  and linked with the Python code using the PyCUDA 9   [42]  package. All results are averaged over 10 runs.
4) Feature extraction: • Raw feature extraction: We set both the example scale exScale and example step exStep to 2000m. We fix the feature stride-to-scale ratio stsRatio to 0. 5 for all 9 feature scales that are included in the aforementioned options for f Scales. Also, we implement the raw feature extraction in such a way that it is executed for all 9 feature scales in a single run on GPU (rather than one separate run for each option of feature scale). This can maximally leverage the ability of our POPS framework to handle multiple scales. • Feature aggregation: We use the CuPy  [37]  package to sort raw features in each segment (see Section IV-D) before we pass them on to our own CUDA-C code. 5) Feature evaluation: • Data partition: For our Domain-Shifted Partition (DSP), since we are trying to find features related to inter-planet differences, we set the target category (TC) to planets with the target labels (TLs) being Earth and Mars.
For non-target categories (NTCs), we consider landscapes and geolocations. The nontarget labels (NTLs) for the former are volcano, drainage, landslide and crater, and those for the latter are ::: Etna, :::::::::: Emi Koussi, ::::: Vesuve, ::::::: Elysium, ::::::: Olympus, :::::: Pavonis, ::::: Borku, ::::::: Ngazima, :::::::: Sycamore, ::::: Durius, ::::: Elaver, :::::: Warego, ::::::::: Blackhawk, ::::::: Socompa, ::::::: Coprates, :::::: Ganges, :: Ius and :::::::::: Gosses Bluff. The number of folds K is set to 3, as larger settings can not lead to partitioned datasets that meet the criteria of DSP. The numbers of examples in each fold is shown in Table  I   1. • Cross validation (CV): For data balancing, we consider 5 balancing methods: no balancing, 2 oversampling methods which are SMOTE  [23]  and ADASYN  [24] , as well as 2 undersampling methods which are random undersampling (RUS) and undersampling with instance hardness threshold (IHT)  [25].
For the latter four, we use the implementation provided by the Imbalancedlearn  [26]  library. For the choice of classifiers, we apply 8 classifiers, including 4 SVM classifiers with different kernels (linear, RBF, polynomial, and Sigmoid) which are implemented in the ThunderSVM  [39]  package, XGBoost  [22]  and 3 k-NN classifiers (the number of neighbors k are set to 1, 5 and 10) which are implemented using the KNN CUDA package (see Section IV-E). • Feature visualization: For feature visualization, we use the t-SNE algorithm  [27] , which is a dimensionality reduction algorithm that can facilitate visualization of high-dimensional datasets in 2D space. In our case, we visualize both the raw and aggregated features extracted using each feature template for all examples. The raw feature vector for each example is obtained by concatenating the features of all patches in it.
For example, we originally intended to include the terrestrial Ambrym Volcano in our dataset. However, after inspecting its raw 3D point cloud (Fig. 11 ), we decided that the data was so sparse that it was impossible to conduct raster interpolation to it that was faithful to the groundtruth. We note that this is not an isolated case. Rather, we have faced with such difficulties for many regions on the Earth that would have been included were not for data sparsity. Moreover, even with the current dataset with relatively dense raw data, raster interpolation still has a significant impact on the CV results. To illustrate, the CDD of the CV scores of P CA -2D -small with different IDW α settings is shown in Fig. 12. As is indicated, different interpolation configurations can lead to significantly different CV scores.
Specifically, we report the running time of POPS, POPS-NMS and their CPU-based counterpart when applied to extracting raw features from the dataset used in Section V-A, where we have also specified the parameters for feature extraction. The results are shown in Fig. 17. Specifically, for P CA, POPS is more than 3 orders of magnitude faster than the CPU-based framework; for ST AT where POPS has higher per-patch time complexity, it is still over 2 orders of magnitude faster. Also, POPS is nearly 2x as fast as POPS-NMS, which can make a difference when the absolute running time is relatively long, such as the case with ST AT.
Standard compression tests Standard compression tests against a hard substrate were performed to assess the mechanical properties of the bilayered silk-parylene probes. The axial compression of the implant allowed us to evaluate its buckling force, which corresponds to the highest force that a sample can withstand before bending. To guarantee the stability of the test, the implants were held between two glass plates, and compression was carried out over a tip length of approximately 3. 4 mm. The silk-parylene probes were fixed on a MARK-10 ESM303 test bench coupled with a MARK-10 M5-012 force sensor. An internally developed LabVIEW program allowed the equipment to be controlled and the force to be monitored according to the displacement of the implant. Compression tests were performed at a slow speed of 2 mm min -1 for optimal monitoring of the buckling force.
All signals were digitized with a digitization rate of 20 kHz (power1401, CED, UK). The real-time display of the signals was achieved with an oscilloscope and Spike2 software (CED, UK). Spike sorting was conducted using Spike2. First, we computed the root mean square (RMS) of the voltage trace (running average of over 5 s). Next, 2 ms wide segments of voltage traces ("wavemarks") were generated whenever negative deflections, likely corresponding to action potentials, exceeded a threshold set at -4 times the RMS. The wavemarks extended from -0. 5 to +1. 5 ms relative to the peak of the negative deflection. The minimal wavemark overlap was set at 0. 5 ms. The wavemarks were then submitted to principal component analysis (PCA) and cluster analysis. Single units corresponded to clusters containing spikes of constant shape and displaying a refractory period of >1 ms were determined from interspike interval distribution histograms computed with a bin width of 0.
Acknowledgements We thank  Dr. Denis Calise  ( Institut des Maladies Métaboliques et Cardiovasculaires, Toulouse, France ) for his help regarding animal facilities. The authors acknowledge funding from the  Agence Nationale de la Recherche  ( ANR-19-CE19-0002-01  and  ANR ANR-15-CE19-0006 ). This work was supported by the  French RENATECH network.
Together, the firing properties of these cells-i. e. , their sequential activation or their activity decay at different timescales-with respect to experimental temporal boundaries, are thought to provide timestamps of episodic memory. Uncovering a neuronal code for the representation of time in the human hippocampal-entorhinal system is critical to shed light on the temporal mechanisms of memory formation. In stark contrast to real life, most lab-based episodic memory experiments are often dominated by a stimulus-response methodology-requiring intermittent sensory input and subject response-therefore, disrupting the natural temporal continuity of behavior. Understanding how time is encoded, however, requires a more naturalistic scenario where time is studied at multiple timescales over prolonged periods spanning seconds to many minutes.
Lastly, if the PTCs' periodicity was modulated by the content, one would expect that the activity of PTCs with similar dominant periodicities would be similar and, thus, highly correlated in time. This was not the case in our data and the distribution of correlation coefficients between adjacent PTCs' firing (defined as PTCs with dominant periodicities within a certain time interval, e. g. , 5s, 10s, 20s) was not significantly different from zero (p>0. 05 for all intervals, signrank test). However, one cannot fully rule out the possibility that the neuronal firing was partly modulated by nested event boundaries of the narrative content  (28). Therefore, to further assess the extent to which the PTCs' periodic firing was modulated by external stimuli, we recorded data from additional five participants who watched the same episode but each half of the episode was presented to them at different speeds.
Behavioral Tasks: The behavioral task (programmed in PsychToolbox, Matlab) consisted of participants watching an episode of the TV series 24 (season 6, episode 1, duration ~42 minutes) on a laptop. Afterwards, they were presented with short clips (duration = 1. 91 ± 0. 72 s) and were asked to make a choice on whether they had seen the clip or not (response time duration = 2. 39 ± 1. 66 s), using the keyboard. The clips were divided into targets (clips chosen from episode 1 that they had just watched) and foils (clips chosen from episode 2 that they had never seen). The episodes of this series happen in consecutive hours of the day and, therefore, the characters' appearances are very similar in the target and foil clips. Further, performance accuracy for each participant was computed as follows: (TP+TN)/(TP+TN+FP+FN), where TP, TN, FP, and FN are the true positive, true negative, false positive, and false negative respectively.
We also computed an alternative behavioral performance measure, specifically d' (d-prime) using the hit rate and false alarm rate values. These two measures of behavior (accuracy and d') were highly correlated (r=0. 974, p=4. 20x10 -5 , Pearson correlation). Nine out of the 10 participants performed the memory recognition test, and had extended electrophysiological recordings after the episode was over. Of these, behavioral performance data could be analyzed for eight participants. The number of presented clips, and hence the duration of the memory test, varied from participant to participant. Five additional participants performed an alternative version of the task. They watched the same episode of the TV series 24 but, here, each half of the episode was presented at different playback speeds.
In participants 1,3, and 5, the first half was presented at regular speed and the second half was presented at 1. 5x speed. In participants 2 and 4, this order was reversed. Data Acquisition: Electrophysiological data was recorded from implanted electrodes that terminated in a set of nine 40 micro-m Platinum-Iridium microwires  (44, 45). The number of electrode bundles, as well as their locations, were different for each participant and determined solely by clinical criteria. Wide-band local field potentials were recorded from eight microwires (the 9th microwire was used for referencing) using a 128-channel (or 256-channel) Neuroport recording system (Blackrock Microsystems, Utah, USA) sampled at 30 kHz.
Electrode Localization: A high-resolution post-operative CT image was obtained and co-registered to a pre-operative whole brain and high-resolution MRI for each participant using previous methods (Fig. 1c ; Table  S1 ). The location of the microelectrodes were determined by examining the location of the electrode artifact on the co-registered images. For further details, see ref. 21. Electrophysiological Analyses: Data were analyzed offline using custom code as well as functions and toolboxes in Matlab and Python. a) Spike detection and sorting: Spike detection and sorting was done using previous methods  (21) (22) (23) (24). Briefly, we applied a bandpass filter to the broadband data in the 300-3000Hz to detect spikes that were subsequently sorted using the Wave_clus toolbox. Furthermore, the automatically-detected clusters were manually inspected for: 1) spike waveforms; 2) presence of refractory spikes; as well as 3) the ISI distribution for each cluster.
Units with true autocorrelations that had values beyond the 2. 5% and 97. 5% of the shuffled data were identified. Further, we used a cluster-based permutation test  (46)  to correct for multiple comparisons (given the large number of lags that were being tested). Specifically, we used the function permutation_cluster_test from MNE Python package  (47)  and units with significant clusters were deemed to be periodic time cells (PTCs). The different steps of this procedure are demonstrated in Fig. S2. Here, T bin is the bin size in time (0. 1 s), H refers to the design matrix associated with the temporal covariates, in this case cosine functions with different periods (T i ), and betas are the parameters associated with the design matrix in time and a constant term. Note that the exponentiation is done element-size in this case.
Steps 1-3 were repeated 250 times (Fig. S4 ). g) Decoding time from PTCs' population activity: Decoding analysis was done using Linear Discriminant Analysis as a classification method. We divided the data into equally sized time epochs and we performed this for different bin sizes of  [1:10, 15, 30, 45, 60, 90]  seconds. The epoch number was used as the output of the classification model and the activity of the PTCs within each epoch was used as the input to the model. Further, we used a hold-out method, i. e. the model was trained on randomized 75% of the data and an independent 25% of the data were left aside for testing and the model performance was evaluated on the test dataset (Fig. 4 ). Additionally, the performance of the model was compared against shuffled data: the same classification method was applied on the temporally shuffled activity of the PTCs.
Figures and Tables For ease of readability, we have included the main figures and legends within the text. Bottom) The distribution of the dominant periodicity of the PCA eigenvectors (that explained 80% of the variance data) was z-scored with respect to the shuffled data (Methods, Electrophysiological Analyses). Note the periods that are over-represented (preferred timescales) in the distribution, which are consistent with the timescales shown in Fig. 3c. performed an alternative version of the task that consisted of watching one half of the episode at regular speed and the other half at a faster speed (1. 5x speed; see Methods). Table  S3. Number of PTCs recorded per participant and their timescales. The numbers reported here are from the original 10 participants. PTCs' timescales are represented by their dominant periodicities (shown are mean ± std within each participant).
86 GHz Flux The Global Millimeter VLBI Array (GMVA) observed Sgr A * on 2017 April 3, just 3 days ( ; 13,000 GM/c 3 ) before the EHT campaign. Issaoun et al. (2019)  estimate that the compact flux during this observation was F 86 = 2. 0 ± 0. 2 Jy (2σ errors). 159 Maximum likelihood m-ring parameters were found for each scan using the Julia package Comrade. jl (P. Tiede 2022, in preparation) in combination with a differential evolution-based optimizer found in the Julia package Metaheuristics. jl. The set of scripts used for the fits can be found in the GitHub repository https://github. com/ptiede/EHTGRMHDCal. To test the models, we compute a library of 86 GHz images for all GRMHD snapshots for all models and integrate over them to obtain F 86. We assume normally distributed measurement errors with σ = 0.
Conclusions We have made a first comparison of the EHT 2017 Sgr A * data to a state-of-the-art library of ideal GRMHD models. The models assume that the mass and distance to Sgr A * are known and that the central object is a black hole described by the Kerr metric. We use multiple simulation pipelines and find that, for a given model configuration, independent simulations are remarkably consistent (Appendix A). The model parameters are as follows: whether the horizon magnetic field is strong or weak (MAD or SANE, respectively); the black hole spin a * ; and the inclination angle i between the line of sight and the accretion flow orbital angular momentum vector. The eDF also has one or more parameters. In our "fiducial" model set, run with three independent codes, the eDF is determined using the so-called R high prescription (Section 2).
This paper is dedicated to the memory of  John F. Hawley , whose pioneering work on black hole accretion flows made this paper possible. We are grateful to an anonymous referee whose comments significantly improved this paper. The Event Horizon Telescope Collaboration thanks the following organizations and programs: the  Academia Sinica ; the  Academy of Finland  (projects  274477 ,  284495 ,  312496 ,  315721 ); the  Agencia Nacional de Investigación y Desarrollo (ANID) ,  Chile  via  NCN19_058  (TITANs) and Fondecyt  1221421 , the  Alexander von Humboldt Stiftung ; an Alfred P. Sloan Research Fellowship; Allegro, the  European ALMA Regional Centre node in the Netherlands , the  NL astronomy research network NOVA  and the astronomy institutes of the  University of Amsterdam, Leiden University and Radboud University ; the  ALMA North America Development Fund ; the  Black Hole Initiative , which is funded by grants from the  John Templeton Foundation  and the  Gordon and Betty Moore Foundation  (although the opinions expressed in this work are those of the author(s) and do not necessarily reflect the views of these Foundations); Chandra  DD7-18089X  and  TM6-17006X ; the  China Scholarship Council ;  China Postdoctoral Science Foundation  fellowship ( 2020M671266 );  Consejo Nacional de Ciencia y Tecnología  ( CONACYT, Mexico , projects  U0004-246083 ,  U0004-259839 ,  F0003-272050 ,  M0037-279006 ,  F0003-281692 ,  104497 ,  275201 ,  263356 ); the  Consejería de Economía, Conocimiento ,  Empresas y Universidad of the Junta de Andalucía  (grant  P18-FR-1769 ), the  Consejo Superior de Investigaciones Científicas  (grant  2019AEP112 ); the  Delaney Family via the Delaney Family John A.
Wheeler Chair at Perimeter Institute ;  Dirección General de Asuntos del Personal Académico-Universidad Nacional Autónoma de México (DGAPA-UNAM , projects  IN112417  and  IN112820 ); the  Dutch Organization for Scientific Research (NWO)   VICI award  (grant  639. 043. 513 ) and grant  OCENW. KLEIN. 113 ; the  Dutch National Supercomputers, Cartesius and Snellius (NWO  Grant  2021. 013 ); the  EACOA  Fellowship awarded by the  East Asia Core Observatories Association , which consists of the  Academia Sinica Institute of Astronomy and Astrophysics , the  National Astronomical Observatory of Japan, Center for Astronomical Mega-Science, Chinese Academy of Sciences , and the  Korea Astronomy and Space Science Institute ; the  European Research Council (ERC)   Synergy Grant  " BlackHoleCam: Imaging the Event Horizon of Black Holes " (grant  610058 ); the  European Union   Horizon 2020 research and innovation programme  under grant agreements  RadioNet  ( No 730562 ) and  M2FINDERS (No 101018682 ); the  Generalitat Valenciana postdoctoral  grant  APOSTD/2018/177  and  GenT Program  (project  CIDEGENT/2018/021 ); MICINN Research Project  PID2019-108995GB-C22 ; the  European Research Council  for advanced grant ' JETSET: Launching, propagation and emission of relativistic jets from binary mergers and across mass scales ' (Grant No.