dataesr/scientific-paragraphs-categorization huggingface dataset:
2) Desktop Performance The optimizations used in the fARMS algorithm provide a significant decrease in the computational complexity of the algorithm and allowed for real-time performance on embedded platform in some limited cases. Although event-byevent flow results will vary between the original ARMS and fARMS algorithms due to these optimizations, the previous sections have shown that the overall flow results generated by fARMS have equivalent or better accuracy across a wide range of visual scenes when compared to ARMS. Having demonstrated comparable flow accuracy, we now compare the throughput of both software algorithms on a desktop grade platform. As in  [6] , an Intel E5-1603 processor is used to collect the performance results, which are shown in Table  4. Both fARMS and ARMS use W m = 320 and η = 4, while the fARMS configuration uses the specified buffer length as determined for the embedded platform tests.
Real-time operation is defined in the same way as for the embedded tests. The results in Table  4  show that real-time performance is achievable with the fARMS algorithm for all but two datasets. The ARMS algorithm, however, is far from realtime in all cases with the specified parameters used. This demonstrates that the fARMS algorithm yields significant throughput performance improvements over the original realization presented in  [6]. Although these software results show real-time performance in many cases, they still fall significantly short of the throughput obtainable by the hARMS architecture. This shows that the hARMS architecture can yield performance improvements even in non-embedded computing environments.
Real-time speed was achieved for every dataset considered, with most datasets able to be processed in real time with low resource configurations of the hARMS architecture. Thorough analysis of the design space was performed to determine the relationship between the architecture parameters and the throughput, accuracy, resource utilization, and power of the resulting accelerator. Resource utilization scales linearly with the number of accelerator cores used, with FF and LUT utilization being the main constraints on design scaling. Unlike previous works that achieve aperture robust flow ( [5],  [7] ,  [16] ), the hARMS architecture can operate in real time, fully utilizes the temporal resolution of the sensor, and operates only on temporal contrast events. When compared to the estimated resource utilization in  [8] , the hARMS architecture offers configurations capable of achieving real-time performance with fewer resources.
Maximum throughput results using qVGA Bar-Square dataset. Hardware configurations are denoted as hARMS-P. The throughput of the hARMS design increases as P is increased. Wm is equal to 320 in all cases and N is 1000 for all cases. 10 6 10 5 Events/Second 10 3 10 4 10 2 10 1 4 8 16 32 Spatial Windows (2) fARMS hARMS-2 hARMS-8 hARMS-24 hARMS-1 hARMS-4 hARMS-16 FIGURE 6. Maximum Speedup 10 3 Speedup 10 2 10 1 10 0 4 8 16 32 Spatial Windows (2) FIGURE 7.
Numerical methodology The CFD investigation was performed using the hybrid RANS/LES method Zonal Detached Eddy Simulation (ZDES)  (Deck, 2012). The simulations were performed using the CFD code elsA developed by ONERA  (Mayeur et al. , 2016). This code solves the compressible Navier-Stokes equations in their integral forms over a multi block structured grid. It uses a finite volume method which consists of integrating the flow solution using a cell centred method.
The methodology to compute the swirl distortion descriptors is the same as in  Tanguy et al. (2017). The instantaneous ring-based distortion descriptors are computed and areaaveraged to obtain a single descriptor (SI, SP and SD) per snapshot. Therefore, the distortion assessment at the AIP for the experimental and numerical data is performed using the exact same post-processing tools. The distortion is computed over 9 rings and 72 rakes for both CFD and experimental data. For the baseline configuration, a total of 86 convective times (τ c ) which is equivalent to 0. 3 s, is used for the comparison with the experimental data. Although the sampling acquisition for the experimental and the ZDES results are significantly different (80 kHz for the ZDES compared with 3. 5 Hz for the S-PIV measurements), the statistics of the distortion descriptors provide a relevant comparison as the statistics for both datasets are converged.
High-throughput gene expression-based combinatorial drug screen Based on the promising results of the 4 × 4 drug combination experiment, we performed a high-throughput screen, using a total of 420 different combinatorial treatment conditions. For this study, we purposely selected drugs with low logD values (Supplementary Fig. 13  and Supplementary Data). This way, undesired exchange of typically hydrophobic compounds can be minimized  22. Additionally, we Color code is labeled for autosampler and Braille valves drugs (top panels). b Silhouette scores of sample clustering based on autosampler/Braille valves drugs and ordered / unordered combinations. Silhouettes scores are compared to random distributions (color code) created by permuting sample labels. c Pathway activity heatmap of samples.
PROGENy pathway activities were calculated for each sample (z-scores of pathway activities, color code), and the pathway activity matrix was hierarchically clustered. Drugs of combinations are color-coded (light-green: autosampler drug, blue: braille valves drug, cyan: Same drug from autosampler and Braille valves). d Drug-induced pathway activity changes. A linear model (pathway_activity ~YM155 + Imatinib + Trametinib) was fitted for each pathway, and the linear model coefficients (color code) for each drug is plotted as a heatmap. e Drug-induced MAPK activity changes. MAPK activity (y-axis) grouped based on autosampler drug (x-axis) and Braille valves drug (color code), n = 3 biological independent experiments for all samples, except for YM155_Imatinib and DMSO_Imatinib n = 2. The boxplots show the median and first and third quartiles as a box, and the whiskers indicate the most extreme data points within 1.
15 ), we performed the same dimensionality reduction (Fig. 5a ) and silhouette analysis (Fig. 5b ), as for the 4 × 4 screen. Again, samples clustered significantly better based on the used combination, than randomly expected (p values of silhouette scores vs. random distribution: <0. 01, 0. 47, and <0. 01 for autosampler drug, Braille valves drug, and combination, respectively). We also found that the low cell numbers did not impact the ability to distinguish treated from nontreated cells based on gene expression (Supplementary Fig. 16a ). To further investigate whether the gene expression values of the high-throughput screen are biologically meaningful, we compared the obtained gene expression signatures to those available for the same drugs in the public LINCS-L1000 dataset  8. As LINCS-L1000 contains only expression signatures of monotherapy drug treatments, we calculated consensus signatures for each drug of our high-throughput screen (Methods Functional genomic analysis of gene expression signatures) and compared these to consensus signatures generated from the LINCS-L1000 database across all available cell lines and concentration doses (note that LINCS-L1000 does not include data obtained directly from K562 cells).
For 32 drugs used in our microfluidic screen, corresponding data on LINCS-L1000 was available. To compare signature similarities of these, we calculated Spearman's correlation coefficients for all pairs of drugs across the two datasets. Our ROC analysis showed that signatures of the same drugs from the two screens (true positives) are more similar, than signatures of unrelated drug pairs (Fig. 5c , ROC AUC: 0. 59), and this area under the ROC curve is statistically significant compared to a random distribution created by permuting drug labels (Supplementary Fig. 17 , p = 0. 019). Taken together, we demonstrated that for the high-throughput combinatorial drug screen, the injection (Braille valve or autosampler) and barcoding mode did not bias the data and that drug signatures correlation between our data and LINCS-L1000 showed significant similarity.
Taken together, this illustrates how comprehensive information can be analysis of drug signature similarity to LINCS-L1000 data. For each drug, a consensus signature was calculated and similarity (Spearman's rank correlation) to corresponding LINCS-L1000 signatures was calculated. The similarity values were used as predicted values for ROC analysis, while true positives were the matched drug pairs between the high-throughput screen and LINCS-L1000. Source data are provided as a Source Data file. gained from gene expression profiles obtained in a highly multiplexed microfluidic format, sequencing only about 250 cells per drug treatment. This should make the workflow particularly interesting for use with very limited material, such as patient samples.
Performing such screens on patient biopsies will allow us to dissect the impact of tumor heterogeneity on drug responses and thereby to define more efficacious drug combinations and additionally gain insights into their potential resistance mechanisms. In order to enable the encapsulation of primary tumor cells (or microscopic fragments) and/or their expansion into spheroids, we aim at integrating support structures for cell adherence and growth in droplets as previously shown  33, 34. All generated datasets demonstrate that neither the barcoding approach nor the injection mode biased the gene expression-based readout. Monotherapies from both injection and barcoding modes had similar impacts, while their combinations had the strongest impact on gene expression and was the main driver of the observed clustering.
This confirms the highly precise and accurate operation of the presented microfluidic workflow and the specificity of the deterministic barcoding approach. Additionally, we found significant similarities between consensus gene expression signatures of monotherapies from our large-scale screen with drug signatures from the LINCS-L1000 dataset, illustrating a high level of reproducibility. In the b a Fig. 6  | Synergy predictions and validation of the high-throughput Combi-Seq screen. a Heatmap showing predicted synergy scores that were determined by comparing viabilities from all 420 drug combinations to corresponding single drug treatments. b Correlation between experimental and predicted cell viability (Pearson correlation r = 0. 66, p = 0. 018, R 2 = 0. 44). Drug synergy (y-axis) was measured for 12 combinations in a microtiter plate format (color code) and plotted against the predicted synergy value (x-axis), the shaded area shows a 95% confidence interval of the regression estimate.
To detect a comparable number of genes for all 420 drug combinations, a ten times higher coverage would have been necessary. We instead used the large screen to show that shallow sequencing data is sufficient to determine synergistic drug pairs. We mined the dataset with 420 treatment conditions for drug pairs with synergistic or antagonistic effects. We found that combining Trametinib with topoisomerase 2 (Top2) inhibitors (Doxorubicin or Razoxane) has antagonistic effects (Fig. 6b ). We hypothesize that the inhibition of the MAPK pathway by Trametinib and the resulting G1 cell cycle arrest, counteracts the DNA damage normally caused by Top2 inhibition when cells enter the subsequent S-phase. Among the predicted and validated synergistic combinations, we found the combination of Triciribine with Dacarbazine and YM155 with Imatinib among the top hits (Fig.
21b ). Using our "CombinatorialPlugFluidics" LabVIEW TM 2013 software (all required software can be downloaded from www. epfl. ch/labs/lbmm/ downloads). the movement of the pins was controlled, so that opening the collection channel resulted in the closing of the waste channel and vice versa. Closing a channel was achieved by a pin pushing into the elastic PDMS membrane. This could be opened again by moving the pin down. For all experiments, we used 20 syringes (Becton Dickinson) filled with 5 ml drug-barcode solutions and four syringes filled with 5 ml HFE oil (Novec™ 7500, 3 M). These were connected to the inlet ports of the valve chip with PTFE tubing, and fluids were injected at 500 µl h -1 using syringe pumps (Harvard Apparatus). The waste outlets were connected with a piece of PTFE tubing to direct fluids to a waste container.
Autosampler output tubing and HFE carrier phase supplemented with 1% Pico-Surf1 (Sphere Fluidics) were connected via the respective inlets and injected at 500 and 6000 µl h -1 , respectively. The droplet-maker chip was mounted on a microscope (Nikon, Eclipse Ti2-E) with an optical setup for measuring fluorescence intensities of compound plugs or droplets  14. Lasers with a wavelength of 375 or 488 nm were used to excite dyes and emitted light (450 or 520 nm) was measured using photomultiplier tubes. Lasers were focused at one of the three positions (M1-M3) shown in Fig. 1c  to measure fluorescent dyes that were injected together with the drug-barcode mixtures into the drop-maker. At positions M1 and M2, fluorescence signals were recorded using the PlugAcquisition LabView TM 2014 software. Fluorescence signals from droplets were acquired at position M3 using the DropletAcquisition LabView TM 2014 software (all required software can be downloaded from www.
Gene-expression data preprocessing We used the SCANPY pipeline  42  for gene expression preprocessing and quality control. Samples with low gene count and a high ratio of mitochondrial genes (>15 percent) and genes with a high dropout rate were filtered out. Read counts were normalized based on sequencing depth and z-score transformed. The batch effect (replicates) was removed by using the combat function of SCANPY. For dimension reduction, we used Principal Component Analysis, followed by t-distributed stochastic neighbor embedding (TSNE)  43. Additional data analysis was performed in custom Python 3. 7 scripts using NumPy  44  , and pandas as statsmodels libraries.
Clustering-based quality control of gene expression data To analyse the clustering of samples based on the different factors (Autosampler Drug, Braille Valves Drug, Combination) we used silhouette score analysis. Silhouette coefficient (ba)/max(a,b) was calculated for each sample, where a was the mean intra-and b was the mean nearest-cluster distance. For each clustering factor, the average of Silhouette Coefficients were calculated (scikit-learn Python library). As silhouette score is dependent on the number of clusters, we created random clusters by permuting sample labels, thus cluster membership.
Functional genomic analysis of gene expression signatures Pathway activities were calculated using PROGENy method  [17] [18] [19]. Individual drug-specific pathway activities were calculated by fitting a linear model (pathway_activity ~YM155 + Imatinib + Trametinib). To compare the similarity between gene expression signatures from the high-throughput screen and LINCS-L1000 dataset  8  , we calculated consensus signatures for each drug of the high-throughput screen. To calculate consensus signatures, we fitted a linear model (gene_expression ~Drug 1 + Drug 2 + … + Drug n ) for each gene of the expression matrix and used the linear model coefficients as drugspecific signatures. To compare signature similarities, we calculated Spearman's rank correlation coefficient between the drug signatures of the high-throughput screen and LINCS-L1000 signatures.
The similarity values were used as predicted values for ROC analysis, while true positives were the matched drug pairs between the highthroughput screen and LINCS-L1000. For cell viability predictions, we used the CEVIChE method. CEVIChE predicts cell viability from gene expression changes based on a linear model, trained on a large compedion of matched cell viability and gene expression dataset  18. As the measured genes of the highthroughput screen showed low overlap with the genes used by the original CEVIChE model, we retrained CEVIChE using only the genes measured in the high-throughput screen. This retrained CEVIChE model showed comparable performance (Pearson correlation between predicted and observed cell viability: 0. 31) to the original method. Plate-based viability measurements to validate hits from the microfluidic screen Drug-plates were prepared in advance in a 4 × 4 checkerboard for each combination, such that after the addition of cells, each drug was present at its GR35 concentration and a fourfold dilution series thereof.
Acknowledgements We thank all members of the  Genomics Core Facility at EMBL  for their help in preparing libraries and sequencing our samples, the EMBL electronic and mechanical workshop for building controllers and holders for the Braille valves, the  EMBL SIM  for maintaining the mask aligner,  Bart Deplancke  and  Cathrin Brisken  for their feedback on the manuscript, and all current and previous members of the  Merten lab  for discussions and feedback. B. S. thanks the  Premium Fellowship Program of the Hungarian Academy of Sciences  ( 460044 ).
Code availability Microfluidic control software can be downloaded from www. epfl. ch/labs/lbmm/downloads. The CAD files for the microfluidics chips are available from Zenodo: https://zenodo. org/record/6845607#. YtWZlMFBz1K. The code used to analyze the transcriptomic data can be downloaded from https://github. com/saezlab/Combi-Seqanalysis  46.
Therefore, for this call, the data correspond to simulated observations of two astrophysical objects. The first object corresponds to a target observed in low-resolution mode (R∼35) with the beam-combiner (K-band) GRAVITY-VLTI. 1  The data set consists of a series of observations with the Auxiliary Telescopes (ATs) with three different configurations. The second object corresponds to a target observed with the Sparse Aperture Masking mode (L-band) of the James Webb Space Telescope. 2  This data set contains a single snapshot with 21 squared visibilities and 35 closure phases. The data were stored and delivered in standard OIFITS 3 files. The GRAVITY data set was simulated using the noise model included in ASPRO2 * and the JWST data set was modeled assuming the expected noise performance of the space telescope.
THE CONTRIBUTIONS 3. 1 J. Young with BSMEM The BSMEM (BiSpectrum Maximum Entropy Method) software was first written in 1992 to demonstrate image reconstruction from optical aperture synthesis data. It has been extensively enhanced and tested since then. BSMEM was adapted during 2015-2016 to work with the "OImaging" graphical user interface developed at JMMC, and updated for the latest OImaging release in 2022. The 2022 contest entry was produced with v2. 3. 0 of BSMEM. BSMEM contains proprietary software, but may be downloaded for non-commercial use subject to a Creative Commons Attribution-NonCommercial-NoDerivatives 4. 0 International License †. The BSMEM algorithm applies a fully Bayesian approach to the inverse problem of finding the most probable wavelength-independent image given the evidence, making use of the Maximum Entropy approach to maximize the posterior probability of an image.
J. Kluska with MIRA and ORGANIC For this imaging contest we have used two image reconstruction algorithms: MiRA 6 and ORGANIC 7 together with the SPARCO approach 8 it allows the addition of geometric models to the image. We first used the SPARCO/MiRA to get images and an estimation of the SPARCO parameters (see below). We, then, used the best estimation of SPARCO parameters with ORGANIC to reconstruct the images. † http://www. mrao. cam. ac. uk/research/optical-interferometry/bsmem-software/.
SPARCO/MiRA MiRA is a gradient descent algorithm. SPARCO was developed to reconstruct images of the environments of young stars and use a hybrid approach combining image reconstruction and geometrical model fitting. Here, we use basic model with just two points sources for the dataset 1 and one point source for the dataset 2. As both data sets are found to be monochromatic we do not fit any chromatic dependency between the image and the models. However, we had to fit the relative flux contributions between the image and the point sources. This was done in a grid of flux ratios where the flux ratio giving the highest posterior probability (e. g. minimizes both the data likelihood and the regularization function) was chosen. Object 1: We used an image with 150x150 pixels and a pixel size of 0.
One of the detection is at ∼ 3σ. It is located at (-2. 8, -3,4) mas from the central star. Other tentatively detected point sources are at (0, 1) mas, (2. 4, 3. 2) mas, (-11. 0, 1. 8) mas, (11. 1, -1. 3) mas, and (-21. 0, 10. 4) mas. These detections are relatively point-symmetric pointing towards either an effect of the (u, v)-coverage or an absence of strong closure phase signal to focus the emission on one side of the ring. Object 2: For this dataset we used an image of 128x128 pixels and a pixel size of 12 mas. We have used SPARCO with just one point source. The fitted point-to-total flux ratio is 85. 7%. We used the quadratic smoothness regularization with a weight µ = 10 8. The resulting image has an arc bright from the North to the East that is detected at most than 5-σ. At the log scale one can see a faint point source at (15, -45) mas that is detected at less than 3 -σ.
SPARCO/ORGANIC ORGANIC stands for Object Reconstruction with Generative Adversarial Networks for InterferometriC data. It uses deep learning techniques and in particular two neural networks: one to generate images and one that is used as regularization. As the number of neurons is fixed, the images have a fixed size of 128x128 pixels. Only the pixel size is changed to adapt to the datasets. Both of the networks were trained on near-infrared images of radiative transfer models (MCMax; 9 ) of protoplanetary disks with varying sizes, inclinations, position angles, and surface density laws. These networks are thus not made to reconstruct point sources but disk like features instead. As a second step, the networks are retrained on the data itself to get the final image. The detailed procedure can be found in.
F. Millour/J. Drevon with MiRA and RHAPSODY We propose a contribution with 3 images this year, two images for Object No. 1 and one image for Object No. 2. As the Object no. 1 data is chromatic, we provide image cubes, one for each wavelength. The Object No. 2 entry is monochromatic, hence the image is at a single wavelength. For this contribution, we used two tools : MIRA+stats (MIRA+S), and RHAPSODY. MIRA+stats is an update to our 2016 participation by improving the statistical post-processing of the batch image reconstructions output from MIRA. As a recall, we reconstructed thousands of images using the MIRA software, varying randomly all the parameters of the reconstruction process (pixel size, number of pixels, number of iterations, start image). These images are then re-sampled to a fixed pixel size of 0.
Each of these layers is fully connected to its neighbours, resulting in a total of more than 107 connections and as many weights. These weights are optimized with a Stochastic Gradient Descent on several 107 unique images in order to minimize the square residuals between the outputs from the model and the original images used to compute the inputs. In the following paragraphs, I describe the specificities of the method applied for each dataset, as well as a short analysis of the reconstructed images. Object No. 1: The GRAVITY dataset was more difficult to handle, mostly because the visibility measurements spanned a wider range of frequencies and were low (but not zero) and complex, indicating that several unresolved components are present in the field of view. No other strong a priori on the source can be obtained with a basic analysis like for the case of the JWST dataset.