dataesr/scientific-paragraphs-categorization huggingface dataset:
FAUST View of Solar-type Protostars Figure 2: Zoo of preliminary spectra observed towards FAUST sources (see black labels) in a portion of 2 GHz (245. 7-247. 7 GHz) of the frequency Setup2 (see Table  1 ). The FAUST logo is also reported. The intensity scale is in brightness temperature (K). The spectra have been obtained merging ACA data with those at high-spatial resolution. The identification of the lines is out of the focus of the present paper. The goal of the sketch is to enlight the observed chemical differentiation, with some sources characterised by a rich spectra associated with iCOMs emission. Gas enriched in iCOMs around the IRS5 binary system, imaged on the 50 au scale. More specifically, the intensity-weighted velocity (also called moment 1) map of the HCOOCH 3 (18 4,14 -17 4,13 )A is shown, revealing a velocity gradient perpendicular to the axes of the protostellar jets (see the arrows in the Left panel) driven by N and S.
Acknowledgments This paper is written on behalf of the whole FAUST team (http://faust-alma. riken. jp)and it makes use of the following ALMA data: ADS/JAO. ALMA# 2018. 1. 01205. L. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.
Funding This project has received funding from: 1) the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program , for the Project The  Dawn of Organic Chemistry (DOC) , grant agreement No  741002; 2 ) the  PRIN-INAF 2016 The Cradle of Life -GENESIS-SKA (General Conditions in Early Planetary Systems  for the rise of life with SKA); 3) a  Grant-in-Aid  from  Japan Society for the Promotion of Science  (KAKENHI: Nos. 18H05222 ,  19H05069 ,  19K14753 ); 4)  ANR of France  under contract number  ANR-16-CE31-0013 ; 5) the  French National Research Agency  in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), through the funding of the  Origin of Life  project of the  Univ. Grenoble-Alpes , 6) the  European Union 's  Horizon 2020 research and innovation programs under projects Astro-Chemistry Origins (ACO) , Grant No  811312.
| SIFT IMPLEMENTATION In this section we present our hybrid Hardware/Software implementation of SIFT detector and descriptor computation and focus on the parts which have been implemented in FPGA. At the first step execution time of the SIFT algorithm using the GITW dataset  25  was measured on the ARM Cortex-A53 CPU of the Xilinx ZCU102 board and on an Intel Xeon E5-2620 server CPU for reference. Average execution time for the SIFT keypoints detection part was 193 and 46 ms on the ARM Cortex-A53 CPU and the Intel Xeon E5-2620 CPU, respectively. The descriptor computation part can be performed faster. It is executed in 62 and 17 ms on the CPU architectures. Based on the time required to execute different steps of the algorithm the system was partitioned between the FPGA and the embedded microprocessor.
For synchronization purposes the results of the previous stages (Gaussian filtered images, DoG images) are also loaded and buffered. The outputs of the module are: the filtered image, the DoG between the image filtered in the previous and the current stage. The buffered results from the previous stages are also sent to the next stage. After the CDFG module all these results will be processed by the SSE module. The results computed by the different stages of the CDFG module can be synchronised utilising this structure. The border adder/padding adds a frame around the input image. This is necessary for convolution computation at border pixels. The OpenCV 11 default border padding is called BORDER_REFLECT_101 and performs the padding by a mirror reflection. In that case if the input image is abcdefgh then the output is gfedcbkabcdefghkgfedcba.
| EXPERIMENTS AND RESULTS In this paper the implementations is compared in terms of precision of detection of keypoints, processing speed and dissipation power. The baseline for validation of different modules developed was the OpenSIFT implementation. 12 The experiments were conducted on the "Grasping-in-the-wild (GITW) dataset  25  " freely available for research at NAKALA CNRS server. 1  It is describe it in the following.
| Dataset The dataset GITW has been recorded for development of vision-based hybrid neuro-prostheses of new generation. 1  The dataset contains egocentric videos recorded by a camera worn on a shoulder of a person and on the eye-tracker glasses. The data set is recorded in a natural cluttered everyday life environment, that is, kitchens. The goal of SIFT point detection is matching of views by homography computation. In the visual scenario subjects are grasping objects of everyday life like a mug or a bowl. The duration of videos is about 10 s, the recording devices are eye-tracker with camera Tobii Glasses 2 and shoulder-mounted GoPro cameras. The resolution of the glass videos are HD (1280 pixels × 720 pixels), and the frame rate of the videos is 25 fps. The GoPro recorded videos resolution is Full HD (1920 pixels × 1080 pixels), and 60 fps frame rate.
The dataset contains 404 egocentric videos. It has 16 different categories of the recorded objects: Bowl, Plate, Wash Liquid, Vinegar Bottle, Milk Bottle, Oil Bottle, Glass, Lid, Saucepan, Frying Pan, Mug. The experiments were conducted on a subset of the whole dataset. The Bowl subdataset was chosen for this project. This subdataset contains 18 different "cases. " Each case contains an egocentric video and a bounding_box. txt file. The bounding_box. txt file contains the coordinates of a bounding box of the object of interest in each frame. All videos are recorded by different subjects in five different places. The overview of the "Bowl" subdatset is presented in the Table  1  in terms of duration, number of frames and number of bounding boxes for the Bowl object in videos. Figure  7  depicts some example videos from the Bowl subset.
| GFDG module validation To validate the GFDG module we focus on the comparison of the Gaussian filtering implemented in FPGA with regard to the reference software. The GFDG module was compared to the OpenCV 2. 4. 13. 7 cvSmooth function. This function is convolving an image with a Gaussian kernel and the output of the function is a Gaussianconvolved image. On the FPGA board OpenCV 4 has been used and the cvSmooth function renamed to GaussianBlur. This result and the module result are compared to each other and if the absolute difference between the two results is less than a predefined threshold ϵ 1 the module is validated. Hence for each frame I m , m = 1,…, M in our dataset the first computation is the maximal value MD m of the absolute pixel values difference between the two results dI m : MD m = max ðx,yÞ ðjOCV G ðI m ðx, yÞÞ -FPGA G ðI m ðx, yÞÞjÞ ð11Þ Here OCV G is the OpenCV cvSmooth function, FPGA G is the GFDG module.
Then the mean maximal absolute difference on the whole dataset MMD is computed: MMD ¼ 1 M X M m = 1 MD m ð12Þ with M is the number of video frames in the whole dataset. In the simulation of the Vivado HLS 2018. 328 shows the MMD between the GFDG module and the OpenCV 2. 4. 13. 7 cvSmooth function are smaller than ϵ 1 = 1e-5 in the whole bowl dataset.
| Comparison of the CPU implementations with FPGA implementations To illustrate the behaviour of our simplified SIFT detector implementation in FPGA and in comparison with OpenSIFT  12  we present the number of detected points on each frame of one sequence from our dataset in Figure  8  for both detectors. As we expected the FPGA implementation detects more keypoints. The video sequence in this experiment is the BowlPlace6Subject2 glass video. During this experiment the default parameters (Table  2 ) have been used. In Figure  9  an example is given which show the detection for one frame of a video for the seek of visual comparison both by OpenSIFT  12  and the FPGA implementations. One can see that the keypoint sets are quite similar. The difference is coming from the fact that  12  uses Taylor expansion in the keypoint localization step, and the FPGA implementation uses the Non-Maximum Suppression only, see Section 4.
| The FPGA keypoint detection module assessment Here we compare the overall results of detection of SIFT keypoints by our FPGA solution with detection by the reference software OpenSIFT. 12 We consider that two keypoints (FPGA-detected and OpenSIFT-detected) coincide if the Manhattan distance between them is less than a predefined threshold ϵ 2. As a threshold value we have taken 2. Hence, True Positive : x CPU 9x FPGA : ‖x CPU -x FPGA ‖ < ϵ 2 False negative : x CPU \ ðx FPGA = ;Þ False positive : x FPGA \ ðx CPU = ;Þ True negative : ; CPU and ; FPGA ð13Þ where x CPU is a keypoint in  12  and x FPGA is a keypoint in the FPGA implementation. True positive (TP) is the case when the distance between the x CPU and x FPGA is less than a predefined threshold ϵ 2. False negative (FN) is when no x FPGA points in a radius ϵ 2 circle in a center x CPU.
96 keypoints in average respectively) compared to the 1 st and 2 nd octaves. Thus, the behaviour of our SIFT detector implemented in FPGA is generally the same: it gives more points than reference OpenSIFT implementation in different octaves. These results showed that our FPGA and the full-software OpenSIFT  12  keypoints are almost equivalent. In that case equivalent means that the keypoints in the reference images are really close to each other and situated at a distance less than the predefined threshold of 2 pixels.
| Used resources The resource usage of the complete system is presented in Table  4. The implementation is based on the Vivado 2018. 3  28  software, which can report the hardware requirements and clock frequency of the circuit. Here the default parameters presented in Table  2  have been used. The results show that around 50% of the resources have been used on the Xilinx ZCU 102 FPGA board. Therefore there are enough resources to implement other steps of the prosthetic arm controlling algorithm. The system is implemented in Vivado HLS as a single IP block. The Vivado toolchain does not hold the details of this block's exact inner structure during the implementation step. Therefore, the area requirements of the submodules are summarised using the Vivado HLS area report as shown in Table  5.
The Intel Xeon E5-2620 CPU, FPGA, GPU, and CMOS Vision sensor are capable of real-time processing. The analog solutions can only process small input images, whereas the FPGA, GPU, and both of the CPUs can handle sufficiently high image resolution which is higher than Full HD. The FPGA solution is equivalent to the full software implementation OpenSIFT  12  in terms of proximity and number of detected keypoints. The disadvantage of the FPGA solution is that the input image size is small 480 px × 480 px, due to the limited 32. 1Mb internal memory of the Xilinx UltraScale+ ZCU102. The CPUs and the GPU's solutions can process higherresolution input. Nevertheless, for controlling the robotic arm this resolution is sufficient. Comparison of the power consumption, processing speed and input image size for the different architectures shows that the FPGA is the best choice for creating a wearable device as Table  6  shows.
Our proposed architecture can compute 135 frames per second, which is higher than both Doménech-Asensi et al. 15  and Vourvoulakis et al. 17, 18  and Shao et al. 20  In comparison, Chang et al. 19  solution can process 900 frames per second; however, it uses lower image resolution. Our implementation is a software/hardware Note: The Intel Xeon E5-2620 and ARM Cortex-A53 are used the OpenSIFT12 and the Xilinx UltraScale+ ZCU102 are used our FPGA implementation. All three platform which used OpenSIFT are used with the default parameters (Table  2 ). On the CMOS Vision Sensor only the time for Gaussian Pyramid calculation is reported. solution similarly to Rubio-Ib añez et al,  16  and Chang et al. 19  and Shao et al. 20  Other papers such as Doménech-Asensi et al. 15  and Vourvoulakis et al.
| CONCLUSION Hence in this paper a design and implementation of SIFT detector on FPGA was presented. The results show that a wearable device can be developed by using FPGA with 135 images per second processing rate, when the input image resolution is of 480 px × 480 px and it is running on Xilinx ZCU102 FPGA board. In our FPGA implementation a simplified keypoint localization method is proposed. Instead of computing Taylor expansion, keypoints too close to each other are filtered using NMS. Our FPGA implementation and the reference OpenSIFT 12 software implementation were compared in different ways. The experiments with real examples demonstrated that our FPGA SIFT implementation gives equivalent output to the reference software detector. Indeed, tests on real-world video in cluttered environments yielded the average precision up to 0.
ACKNOWLEDGMENTS The support of the  Széchenyi 2020 Program , of the  Human Resource Development Operational Program , of the  Program of Integrated Territorial Investments in Central-Hungary  (project numbers:  EFOP-3. 6. 2-16-2017-00013AND 3. 6. 3-vekop-16-2017-00002 )and of the European Structural and Investment funds,  CNRS  and  Balaton PHC  are gratefully acknowledged.
Examples of the Bowl subset video scenes. Top left is BowlPlace1Subject2, the center is BowlPlace4Subject2, the top right is BowlPlace5Subject2 the bottom left is BowlPlace6Subject2, and the bottom right is BowlPlace7Subject2 scene [Colour figure can be viewed at wileyonlinelibrary. com] T A B L E 1 Bowl subdataset overview Number Foldername Duration (s) Number of frames Bounding Box objects 1 BowlPlace1Subject1 8. 72 219 117 2 BowlPlace1Subject2 7. 12 179 165 3 BowlPlace1Subject3 5. 48 138 27 4 BowlPlace1Subject4 5. 64 142 59 5 BowlPlace4Subject1 7. 12 179 34 6 BowlPlace4Subject2 12. 48 313 188 7 BowlPlace4Subject3 12. 36 310 151 8 BowlPlace4Subject4 7. 84 197 107 9 BowlPlace5Subject1 5. 76 145 98 10 BowlPlace5Subject2 9 226 124 11 BowlPlace5Subject3 8. 16 205 154 12 BowlPlace6Subject1 11 276 144 13 BowlPlace6Subject2 8.
The environment modules (within DKE) contain the environmental disturbances that may affect the rotational and translational dynamics of the spacecraft. Both spacecraft have each one an associated environment module, since the dynamics depend on specific spacecraft properties. The chaser dynamics consider the flexible modes of the solar array, the modes of the propellant sloshing and the thruster-based propulsion and sensor/navigation units. Uncertainties are included in the FES as integrated functions of the SPACELAB toolbox, such as variation of the mass, center-of-mass and inertia, uncertainties of the thruster's minimum impulse bit, thruster's misalignment and delay, thrust noise, uncertainties in the frequency modes and damping factors of the solar arrays and propellant sloshing, uncertainties in the navigation unit (caused by sensor noise and misalignment), see Table  2  for the list of considered uncertainties.
Acknowledgements The authors gratefully acknowledge the financial support from  CONACyT  under grant  440473  ( CVU 594664 ). J. Dávila  gratefully acknowledges the financial support from  SIP-IPN  under grants  20200733  and  20210302. Alejandra Ferreira De Loza gratefully aknoledge Cátedras CONACYT ,  CVU 166403 , Project  1537.
pathogenic designation of synonymous and non-coding VUS. The approach provides a molecular diagnosis to approximately 10% of unsolved WES/WGS cases with a suspected mitochondrial disease  (Kremer et al. , 2017)  and up to 35% in other disease cohorts  (Cummings et al. , 2017 , Gonorazky et al. , 2019 , Fresard et al. , 2019). Proteomics has the added potential to capture the consequence of protein-destabilising missense variants, a variant class eluding detection by RNA-seq. Missense variants are one of the most frequently reported functional classes of P/LP variants in Mendelian disease according to the ClinVar database (Simple ClinVar, see online Resources), accounting for just over 25%. While proteomics has been used to elucidate underlying disease mechanism  (Lake et al. , 2017 , Borna et al.
Our matched genome, transcriptome, and proteome datasets together with protein expression outlier calls allowed us to investigate how aberrant RNA and protein expression relate to one another in the context of rare genetic variation, to our knowledge for the first time. After multiple-testing correction, we identified a median of two aberrantly expressed transcripts and six aberrantly expressed proteins per sample (Supplementary Fig. 2a ). The majority of RNA outliers resulted in protein outliers (77%) (Fig. 2a ). Those not resulting in a protein outlier may be explained by buffering mechanisms on the protein level  (Ishikawa et al. , 2017 , Battle et al. , 2015 , Vogel et al. , 2012) , artefact, or lack of power. The expression outliers were stratified into three classes: RNA-only, protein-only, and RNAand-protein outliers.
3h ), and impaired mtDNA repopulation (Fig. 3i ). The downstream functional consequence of the LIG3 variants was reflected by 63 additional protein outliers. Next, looking at the four cases solved by protein-only outliers, in all cases at least one of the responsible variants was missense or inframe indel in nature, variant classes with pathogenic consequence eluding detection by RNA-seq (Supplementary Table  3 ). Amongst these, was a hemizygous X-linked NDUFB11 missense variant resulting in aberrant protein underexpression (z-score -4. 52) and pathologically low abundance of respiratory chain complex (RCC) I (63%) with no rare variants within any other RCCI subunit. The reduction in RCCI was most pronounced in the ND4-module to which NDUFB11 belongs (44% remaining, lowest in dataset), in-keeping with a second confirmed NDUFB11 case (55% remaining, second lowest in dataset).
4a ). Our code is freely available (https://prokischlab. github. io/omicsDiagnostics/) and an interactive web interface allows the user to browse all results and could serve as a basis for developing future integrative multi-omics diagnostic interfaces. We used TMT-labelling, a proteomics technique quantifying the very same peptides for all samples of a batch. This greatly facilitates detection of underexpression outliers compared to conventional untargeted mass-spectrometry which suffers from widespread missing values in low intensity ranges. Though RNA-seq did not allow interpretation of missense variants, it provided independent cumulative evidence and guided the identification of causative splice variants in half of the solved cases. Moreover, RNA-seq has a deeper coverage of expressed genes in comparison to proteomics, capturing 50% more genes.
Variant annotation and handling Variant Effect Predictor  (McLaren et al. , 2016)  from Ensembl  (Zerbino et al. , 2018)  was used to annotate genetic variants with minor allele frequencies from the 1000 Genomes Project (1000 Genome  Consortium, 2015) , gnomAD  (Karczewski et al. , 2020) , and the UK Biobank  (Bycroft et al. , 2018) , location, deleteriousness scores and predicted consequence with the highest impact among all possible transcripts. Variants with minor allele frequency (MAF) less than 1% across all cohorts were considered as rare. Genes harbouring one rare allele were classified as rare, with two or more rare alleles -potentially biallelic. ACMG guidelines for variant classification were implemented for variants in known OMIM disease genes with the InterVar software  (Li and Wang, 2015).
Gene-phenotypic matching Phenotype similarity was calculated as a symmetric semantic similarity score with the R::PCAN package  (Godard and Page, 2016). We considered genes to match phenotypically if the symmetric semantic similarity between the gene and the case HPO annotations was ≥2  (Köhler et al. , 2009; Frésard et al. , 2019 ) (Supplementary Fig. 2d ). Affected organ systems were visualized with R::gganatogram (Maag 2018), based on the patients' HPO phenotypes corresponding to the third level of HPO ontology  (Köhler et al. , 2019).
Detection of aberrant RNA expression, aberrant splicing, and mono-allelic expression RNA-seq analysis was performed using DROP  (Yepez et al. , 2021) , an integrative workflow that integrates quality controls, expression outlier calling with OUTRIDER  (Brechtmann et al. , 2018) , splicing outlier calling with FRASER  (Mertes et al. , 2020) , and mono-allelic expression (MAE) with a negative binomial test  (Kremer et al. , 2017). We used as reference genome the GRCh37 primary assembly, release 29, of the GENCODE project  (Frankish et al. , 2019)  which contains 60,829 genes. RNA expression outliers were defined as those with a false-discovery rate ≤ 0. 1. Splicing outliers were defined as those with a gene-level false-discovery rate ≤ 0. 1 and a deviation of the observed percent-spliced-in or splicing efficiency from their expected value larger than 0.
Mass spectrometric sample preparation Proteomics was performed at the BayBioMS core facility at the Technical University Munich, Freising, Germany. Fibroblast cell pellets containing 0. 5 million cells were lysed under denaturing conditions in urea containing buffer and quantified using BCA Protein Assay Kit (Thermo Scientific). 15 µg of protein extract were further reduced, alkylated and the tryptic digest was performed using Trypsin Gold (Promega). Digests were acidified, desalted and TMT-labeling was performed according to  (Zecha et al. , 2019)  using TMT 10-plex labelling reagent (Thermo Fisher Scientific). Each TMT-batch consisted of 8 patient samples and 2 reference samples common to all batches to allow for data normalization between batches. Each TMT 10-plex peptide mix was fractionated using trimodal mixed-mode chromatography as described  (Yu et al.
, 2017). LC-MS measurements were conducted on a Fusion Lumos Tribrid mass spectrometer (Thermo Fisher Scientific) which was operated in data-dependent acquisition mode and multi-notch MS3 mode. Peptide identification was performed using MaxQuant version 1. 6. 3. 4  (Tyanova et al. , 2016)  and protein groups obtained. Missing values were imputed with the minimal value across the dataset.
Benchmark of PROTRIDER against alternative approaches As no method for outlier detection in proteomics data was established yet, we benchmarked our method against an approach that is based on limma  (Smyth 2005) , which was developed for differential expression analyses on microarray data and assesses statistical significance with a moderated t-statistic. We used recalibrated protein data which has been adjusted with respect to the two identical control samples in each MS-run as the input for limma and included the sex, batch and instrument annotation to adjust for confounding factors. To be able to use limma for outlier detection, we tested each sample against all other samples. Additionally, we compared PROTRIDER to regression of covariates (instrument, sample preparation batch, TMT batch, and gender) followed by z-score calculation on the residuals.