dataesr/scientific-paragraphs-categorization huggingface dataset:
C. 2 Positive/Negative Bag Sampling Formulations In the main paper, we described two approaches for sampling positive/negative MIL bags in Due to space constraints, the sampling mechanisms were described at a high-level. Here, we provide more precise definitions of each bag. In addition to the set notation below, we include in the code release, the loss implementation as a PyTorch  (Paszke et al. 2019 ) function in loss/loss. py, together with a sample input (loss/sample_inputs. pkl) comprising embedding outputs from the MLP for continuous and dictionary videos. As noted in the main paper, we do not have access to positive pairs because: (1) for the segments of videos in S that are annotated (i. e. (x k , v k ) ∈ M), we have a set of potential sign variations represented in the dictionary (annotated with the common label v k ), rather than a single unique sign; (2) since S provides only weak supervision, even when a word is mentioned in the subtitles we do not know where it appears in the continuous signing sequence (if it appears at all).
Datasets: We provide (i) the number of individual signing videos, (ii) the vocabulary size of the annotated signs, and (iii) the number of signers for several subsets of BSL-1K and is large in the number of annotated signs whereas BslDict is large in the vocabulary size. Note that BSL-1K is constructed differently depending on whether it is used for the task of recognition or retrieval: for retrieval, longer signing sequences are used around individual localised signs as described in Sect. 4. 1.
Acknowledgements This work was supported by  EPSRC  grant ExTol and a  Royal Society Research Professorship. The authors would to like thank  Abhishek Dutta ,  A. Sophia Koepke ,  Andrew Brown ,  Necati Cihan Camgöz ,  Neil Fox ,  Joon Son Chung ,  Bencie Woll , and  Hannah Bull  for their help. The authors are also grateful to  Daniel Mitchell  who made signbsl. comwebpage available. SA would like to thank  Z. Novak  and  S. Carlson  for enabling his contribution. In CVPR. Farhadi ,  A. ,  Forsyth ,  D. A. ,  & White , R. (2007). Transfer learning in sign language. In CVPR. Feng, Y. , Ma, L. , Liu, W. , Zhang, T. ,  & Luo , J. (2018). Video relocalization. In ECCV. Fenlon, J. , Cormier, K. , Rentelis,  R. , Schembri ,  A. , Rowley , K. , Adam,  R. ,  & Woll , B. (2014). BSL SignBank: a lexical database and dictionary of  British Sign Language  (first edition).
, 2021) , or commerce management with ship monitoring  (Cao et al. , 2019). Similarly, object detection and tracking contribute to military applications such as border protection or abnormal activity monitoring. For example, the work proposed by  Kirubarajan et al. (2000)  presents an approach to detect and tracks convoys in different scenarios such as road networks or open fields. While object tracking has dramatically improved during the last years, a significant amount of approaches solve problems that contain large training datasets and feature-rich targets, such as pedestrian tracking in surveillance cameras or city landscapes. Nevertheless, novel methods need to tackle application-related challenges such as small object tracking in remote sensing images and have to overcome challenges such as datasets with scarce and incomplete annotations.
This assumption simplifies the filter and allows it to be computationally efficient in comparison to other related filters at the cost of tracking single state instances instead of full target trajectories. In this paper, we propose an enhanced version of the PHD filter to propagate labels in time without compromising the filter's performance and also to discriminate surviving and appearing objects in each frame. This paper is divided into five sections. We discuss popular object detection and tracking approaches used in satellite images in Section 2. We discuss the proposed method in Section 3 where we present the object detection and object tracking approaches. We show results for a challenging dataset in Section 4 and we discuss the conclusion and future work in Section 5.
Static Image Object Detection Static image object detection methods rely on spatial information to extract features and obtain object segmentation masks or bounding boxes. Popular approaches include Faster-RCNN, proposed by  Ren et al. (2015) , YOLO, proposed by  Redmon et al. (2016) , Retina-Net, proposed by  Lin et al. (2017). Although these works obtain remarkable results across several benchmarks, their performance decreases significantly when tested with small objects or weakly labeled datasets such as in remote sensing images. In fact,  Acatay et al. (2018)  presented a comprehensive review and the drawbacks from using the base Faster-RCNN, YOLO, and Single Shot Detectors (SSD) on aerial images. Several researchers approached satellite object detection with modified appearance-based object detector approach for remote sensing images.
Spatio-Temporal Convolutional Neural Networks State-of-the-art methods aim to combine approaches from both appearance and motion to improve object detection. Generally, these methods use CNNs that take into account both motion and appearance information to extract object locations. For instance,  LaLonde et al. (2018)  proposed ClusterNet and FoveaNet, a two-stage approach for exploiting spatial and temporal data in small object detection. They use five consecutive frames as input to an under-sampling network to create clusters of object locations (ClusterNet), and then they use a region specialized network (FoveaNet) to refine the outputs of the first network. Also,  Canepa et al. (2021)  proposed T-Rex Net, a network that uses frame differencing as inputs to the network to improve small object detection performance.
Sommer et al. (2021)  proposed an appearance-based and motion-based object detector by combining two networks, one to estimate moving objects locations, and one to extract image features. These methods showed promising results for ultra high resolution datasets such as the WPAFB 2009 (AFRL (  2009 )) dataset which contains a resolution of up to 0. 25 cms/pixel; however, these approaches cannot be directly applied to lower resolution data such as at 1m/pixel as the target features are lost and performing undersampling could miss the small targets.
Experiment Set up For evaluation purposes, we use the CGSTL dataset, available at https://mall. charmingglobe. com. This dataset contains a video of the city of Valencia, Spain, recorded on 7 March 2017, by the Jilin-1 satellite. Its spatial resolution is 1 m/pixel and the video spans 12 kms 2 , with a size of 3,071 × 4,096 pixels. The video contains 580 frames and represents 29 s of video imaged at 20 frames per second. The labels were provided by  Ao et al. (2020)  and contain the (x, y) object center coordinates, the width, and height of the object bounding boxes. The provided ground truth contains strong labeling for only moving targets in three areas of interest (AoI) of size 500, ×, 500 pixels (shown in Figure  4 ). The approximate coordinate location for each area are AoI 1  [520, 1616] , AoI 2  [1074, 1895]   and AoI 3   [450, 2810]  with respect to the first frame.
Additionally, we performed image stabilization (ORB  (Rublee et al. (2011) ) to compensate for the satellite motion during the recorded video. Finally, only one every ten frames is labeled (58 total labeled frames), hence, we used the stabilization procedure and linear interpolation between frames to fill the label subsampling. The stabilization procedure has a significant impact on object detection, object tracking, and score evaluation across all 580 frames as these methods depend on linear object motion and static background. It is worth mentioning we improve the stabilization procedure over our previous work  (Aguilar et al. (2021) ) by using the Python OpenCV implementation of ORB  (Rublee et al. (2011) ); hence our 'true positive' distance criteria is set to five pixels rather than 20 pixels as in our previous work.
On the other hand, our proposed GM-PHD filter has a linear complexity of O(PM). Additionally, we present sample computational times using the default GM-PHD (O(PM)) filter and default GLMB (O(P 2 M)) filter implemented in Matlab by  Vo et al. (2017). Table  7  shows that the default GLMB filter is on average 4. 77 times slower than the default GM-PHD filter. While our implementation of the GM-PHD filter obtains slightly lower tracking scores, it presents a considerable advantage in terms of computational demands. This advantage is particularly important for onboard applications where robusts online tracking algorithms are preferred.
CONCLUSION AND FUTURE WORK In this paper, we presented an improved track-by-detection approach where we use motion information together with neural networks to detect small moving objects on satellite images. Additionally, we perform tracking by using a modified version of the GM-PHD filter. Our version of the GM-PHD uses a measurement-driven birth intensity approximation and a label propagation in time. We present results for three AoIs in a challenging dataset where our approaches do not only outperform competing detection and tracking algorithms, but also detect objects not labeled by the ground truth annotations. While our method performs detection and tracking, the method still requires several improvements. For example, our approach still misses several objects at sub-pixel level that appear and disappear.
Image analysis For growth analyses, the MorphoGraphX (MGX) 3D image analysis software was used (de  Reuille et al. , 2015). There are several guides to use the software such as  (de Reuille et al. , 2015; Stanislas et al. , 2017; Strauss et al. , 2019). To reveal the general growth patterns of the sepals as depicted in Figure  3 , the range of growth rates was set to 1-3 for all time points. Local variation of growth rate is defined here as the coefficient of variation (CoV = SD/mean*100%) in growth rate within a group of cells consisting of a seed cell in the center and all its contacting neighbors, as described in  (Uyttewaal et al. , 2012). For a given cell, its neighboring cells be found using the Heat map function in MGX (Heat map type: Walls; Heat map visualization: Geometry; Box Geometry is checked).
Sepal area measurements Sepals of stage 14 flowers  (Smyth et al. , 1990)  were dissected and placed as flat as possible on double-sided tape on top of a microscope slide. They were photographed on a black background using a binocular equipped with a camera. A Python program in Linux called SepalContour described in  (Hong et al. , 2016)  were used to extract sepal contours and morphological parameters such as sepal area, length, width, aspect ratio and circularity. Some users may find the installation of the tool difficult; therefore, a complete virtual machine (~ 9GB in compressed size) with the tool ready to run is available upon request.
Quantification of sepal shape variability Sepal contours extracted using the SepalContour tool described above were then used as input for shape analyses. The Contour Analysis program was written in Mathematica and described in  (Hong et al. , 2016). From the input contours, the program returns a graph of all original contours, a graph of contours normalized by their area and the average contour, and for each sepal contour, an S 2 value depicting the squared deviation of that contour from the average contour. These S 2 values were used to estimate sepal shape variability within and between genotypes  (Hong et al. , 2016).
ACKNOWLEDGEMENTS We thank  Richard S. Smith  ( Department of Computational and Systems Biology, John Innes Centre, Norwich, UK ) for help in using MorphoGraphX. We thank  Platim (Simone Bovio  and  Claire Lionnet ) for their help in using AFM and confocal microscopy. We are grateful to our collaborators ( Arezki Boudaoud ,  Adrienne Roeder ,  Richard Smith  and  Jan Traas ) for their comments and help in this project. This work is supported by the  European Research Council  ( ERC , grant agreement No  101019515 , "Musix"), by  CEFIPRA  (grant  6103-1 ), and by the  French National Research Agency  through a  European ERA-NET Coordinating Action in Plant Sciences (ERA-CAPS) grant  ( Grant  No. ANR-17-CAPS-0002-01 ).
Although the proteome of CLL B cells has been compared to non-malignant normal B cells 22-24   , a systematic description of the proteomic landscape of CLL integrating genome, transcriptome, and clinical datasets is currently missing. Here, we employed in-depth high-resolution isoelectric focusing liquid chromatography-mass spectrometry (HiRIEF LC-MS)  25  based proteomics to connect the proteomic landscape of a clinically well characterized and representative cohort of 68 CLL patients with genome, transcriptome, and drug perturbation profiling. Among the six identified proteome-based subgroups, we discovered a previously unknown subset of CLL patients with poor outcome. This subgroup was characterized by high abundance of spliceosomal proteins. For the first time, we could link spliceosomal protein abundances to aberrant splicing and poor outcome in CLL.
We performed an unsupervised multi-omics factor analysis (MOFA  30  ) to obtain an integrative view of covariations across multiple datasets. MOFA revealed eleven latent factors (LF) explaining at least 1. 5 % of variation each (Fig. 3a ). Only LF1, LF2 and LF9 were significantly associated with time to next treatment (TTNT, Fig. 3b ). LF1 and LF2 were active in all data layers (proteome, RNA and genetics) and were mainly driven by trisomy 12 and IGHV mutation status in the genetics data (Fig. 3c ). The proteins loaded onto LF1 and LF2 were not only significantly associated with TTNT in the discovery cohort (Fig. 3d ), but also showed a significant association with overall survival in our DIA proteomics validation cohort (Validation1_DIA; Fig. S3 ). Interestingly, LF9 was nearly exclusively active in the proteomics data and was apart from LF1 and LF2 the only factor significantly associated with clinical outcome.
Hence, MOFA revealed that proteomics profiling uncovers clinically relevant biology that was not detected with the other data layers. We discovered associations of the proteomics layer and clinical outcome (TTNT), which could not be found on the transcript or on the genetic level. Therefore, we decided to explore the proteomics layer in depth. To describe similarities and differences between protein profiles of patients, we performed consensus clustering of the protein dataset. This revealed six proteomics groups (PG; Fig. 4a, S4a, b ). T-distributed stochastic neighbor embedding (t-SNE) and principal component analysis supported the partition into these subgroups (Fig. S4c, d ).
Validation of ASB-CLL in independent cohorts To robustly detect ASB-CLL in DIA proteomics, we trained a k-top scoring pairs (kTSP) classifier on all BcR and spliceosomal proteins in the HiRIEF dataset. We applied the classifier to the Validation1_DIA cohort and identified 28 ASB-CLL patients (10 untreated, 15 pretreated, 3 unknown), corresponding to 17 % of patients. This proportion of ASB-CLL was similar to the training dataset. As expected, BcR proteins were down-and spliceosomal proteins were upregulated in ASB-CLL (Fig. 6b, c ; Wilcoxon signed-rank test p =p <3. 7x10 -8 , p <2. 3x10  -7  ). Additionally, ASB-CLL was characterized by upregulation of BCAA proteins and downregulation of proteasomal proteins, which is in accordance with the findings observed in the discovery cohort (Fig. S7a, b ; Wilcoxon signed-rank test p =0.
Our integrative multi-omics analysis of CLL provides the first comprehensive overview of the interplay between genetic alterations, the transcriptome, and the proteome, along with functional consequences for drug response and clinical outcome. The detailed analysis of our dataset improves our understanding of the biological heterogeneity of CLL and provides molecular phenotype-based subtypes that will improve patient stratification and personalized treatments. Through our web application we provide this comprehensive dataset as a valuable and easily accessible resource to the research community (https://www. imbi. uni-heidelberg. de/dietrichlab/CLL_Proteomics/).
Study design The purpose of this study was to investigate the relationships between the proteome, transcriptome, and genetic aberrations to clinical parameters (notably TTNT) and drug sensitivity in CLL. The proteome of CLL cells from 68 patient samples was analyzed by HiRIEF-LC-MS/MS, the transcriptome of 59 patients by RNA-sequencing and mutations of 68 samples by DNA panel sequencing. To minimize the introduction of biases all of these datasets were acquired from the same aliquot of cells. Drug sensitivity was assessed by microscopy based ex-vivo drug screening and clinical parameters (TTNT) were determined from patient records. The results obtained from this discovery cohort were validated in five independent CLL cohorts.
Discovery cohort: HiRIEF proteomics Cell pellets were lysed by 4 % SDS lysis buffer and prepared for mass spectrometry analysis using a modified version of the SP3 protein clean up and digestion protocol  42. Peptides were labelled with TMT10-plex reagent according to the manufacturer's protocol (Thermo Fisher Scientific) and separated by immobilized pH gradient -isoelectric focusing (IPG-IEF) on 3-10 strips as described previously  25. Extracted peptide fractions from the IPG-IEF were separated using an online 3000 RSLCnano system coupled to a Thermo Fisher Scientific Q Exactive-HF. MSGF+ and Percolator in the Galaxy platform were used to match MS spectra to the Ensembl92 human protein database  43. For acetylation and methylation variable modifications, we performed two separate searches; one where acetylation was allowed on lysines and one where monomethylation was allowed on lysines and arginines (see supplementary methods for additional details).
Discovery cohort: mRNA Sequencing Stranded mRNA sequencing, using a TruSeq Stranded Total RNA Library Preparation Kit, was performed on a Illumina NextSeq 500. Reads were aligned to GRCh 37. 75/hg 19 using STAR (v2. 6. 0c;  44 ) and counted with htseq-count  45. Library size normalization, variance stabilizing transformation and differential expression calling were performed using DESeq2 46. Discovery cohort: Ex-vivo drug sensitivity screen Drug response profiles were obtained for 68 leukemia samples and 43 drugs (Selleckchem) in 3 concentrations (Data SI4). Cells were thawed and seeded in DMEM medium (Thermo Fisher Scientific) containing 10 % human serum (Sigma Aldrich), 1 % penicillin/streptomycin (Thermo Fisher Scientific) and 1 % glutamine (Thermo Fisher Scientific) at 2x10 4 cells/well into a CellCarrier-384 Ultra Microplate (Perkin Elmer).
Validation1_DIA cohort: DIA proteomics For DIA based proteomics cell pellets were digested and cleaned as described above. Unlabeled peptides from individual samples were separated using an online 3000 RSLCnano system coupled to a Thermo Fisher Scientific Q Exactive-HF. Data independent acquisition (DIA) was employed using a variable window strategy. Spectronaut was used to analyze the spectral files using the Direct-DIA option and files were searched against the ENSEMBL database (see supplementary methods for additional details). In total 203 samples were analyzed by DIA, 36 from the original discovery cohort and 167 validation samples, The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD024544.
Validation3_RNA cohort: RNA Sequencing To validate the splicing signature of ASB-CLL we took advantage of a cohort of 169 CLL patients for who paired end sequencing, using an Illumina TruSeq RNA sample preparation kit v2, was performed on an Illumina HiSeq2000 with 300 bp insert size  47. No overlap between the samples of the discovery cohort, the Validation1_DIA cohort or the Validation3_RNA cohort existed. The data is available through the European Genome-Phenome Archive (EGA) under accession number EGAS00001001746.
For overlapping gene symbols and samples in transcriptomics and proteomics data (n = 6310, m = 50), we estimated mRNA -protein correlation for each gene i using the first coefficient in the following model: lm ( [scaled mRNA ] i ~ ( [scaled protein ] i +IGHV+ Trisomy12 + IGHV:Trisomy12). Significant correlated pathways were identified by two-sided t-tests between the mRNA-protein coefficients of leading edge genes (>=5) versus all genes at a Benjamini-Hochberg FDR level of 0. 01.
Multi-omics factor analysis The multi-omics factor analysis was performed on genetics, transcriptomics, and proteomics datasets using the MOFA R package  30. Only the 2000 proteins with the highest variance were used. The MOFA model was calculated 10 times and the model with the highest evidence lower bound (ELBO) was chosen. The convergence threshold was set to 0. 01. Only factors which explained at least 1. 5 % of variance were kept for further analysis (github code: "perfom_MOFA_analysis").
RNA level For the alternative splicing analysis of the discovery cohort, 59 single-read stranded total RNA-seq samples were processed. First, RNA-seq reads were aligned to the human reference genome (build hs37d5, based on NCBI GRCh37, hg19) using STAR (v2. 5. 2a) with standard parameters. Aligned RNA-seq data were then subjected to quality control by RNASeQC (v1. 1. 8) to ensure the integrity of the transcriptome dataset. The same procedure was applied to the Validation3_RNA cohort of 169 paired end RNA-seq samples. Our alternative splicing analyses largely rely on rMATS, which we employed to identify exon skipping events, mutually exclusive exons, alternative 5' and 3' splice sites, and intron retention in individual CLL patient RNA-seq samples. The rMATS tool was also used to quantify alternative splicing activity by calculating estimates for the percent spliced-in (PSI) values associated with each event in a given sample.
Protein-protein correlation For the protein core complex analysis protein-protein Pearson correlations were calculated for CORUM complex members and converted into a pairwise interaction matrix as previously described 15. For the generation of the protein correlation network proteins with a high standard deviation were selected and pairwise Pearson correlations were calculated (github code: "ppi_network"). The resulting network was visualized in Gephi 0. 9. 2 and nodes were filtered using a kcore setting of 3. Modularity clustering of the nodes was carried out with a resolution of 0. 8. Annotation of the modularity clusters was done by first extracting all proteins belonging to a cluster. Next, any protein in the full overlap dataset (n =7313) with a Pearson correlation above 0. 7 to any of the cluster members was included in the target gene set for that cluster.
Validation of ASB-CLL using DIA based proteomics To validate the existence of ASB-CLL we analysed 167 new patient samples from Sweden and Germany using DIA based proteomics (Validation1_DIA cohort) as well as 36 samples from the original discovery cohort. Four samples were excluded after quality control assessment due to either poor correlation to the in-depth data (2 samples, discovery cohort) or a low number of identifications (2 samples, Validation1_DIA). We trained a k-TSP based classifier on the relative MS2-level quantifications of all identified proteins from the KEGG "Spliceosome" and "B-cell receptor signaling" pathways which were identified by at least 3 spectral matches in the training set. After optimization 8 k-TSP pairs were chosen. 36 of the original 68 samples analyzed by HiRIEF were run on DIA and samples with a correlation between the DIA and HiRIEF data of >0.