dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Setup Datasets. We evaluate the performance of our approach on two widely-used benchmark datasets: BP4D  [Zhang et al. , 2014]  and DISFA  [Mavadati et al. , 2013]. BP4D recorded 328 videos (about 140,000 facial frames) from 41 young adults (23 females and 18 males) who were asked to respond to 8 emotion elicitation tasks. DISFA recorded 130, 815 frames from 27 subjects (12 females and 15 males) who were watching Youtube videos. Each frame in BP4D and DISFA is annotated with occurrence labels of multiple AUs. Implementation Details. For both datasets, we use MTCNN  [Yin and Liu, 2017]  to perform face detection and alignment for each frame and crop it to 224 × 224 as the input for backbones. We then follow the same protocol as previous studies  [Zhao et al. , 2016; Li et al. , 2018; Song et al.
, 2021c]  to conduct subject-independent three folds cross-validation for each dataset, and report the average results over 3 folds. During the training, we employ an AdamW optimizer with β 1 = 0. 9, β 2 = 0. 999 and weight decay of 5e -4. The number K for choosing nearest neighbors in the FGG is set to 3 and 4 for BP4D and DISFA, respectively. For the hyperparameter λ in Eq. 8, we set it to 0. 05 and 0. 01 for models based on ResNet and Swin Transformer, respectively. We totally train the proposed model for 40 epochs, including 20 epochs for the first stage (the initial learning rate of 1e -4 ) and 20 epochs for the second stage (the initial learning rate of 1e -6 ), with a batch size of 64. The cosine decay learning rate scheduler is also used. Both backbones are pre-trained on ImageNet  [Deng et al.
Results and Discussion Comparison to State-of-the-art Methods. This section compares our best systems of two backbones with several state-of-the-art methods on both datasets. Table  1  reports the occurrence recognition results of 12 AUs on BP4D. We additionally provide the AUC results in Appendix A. It can be observed that the proposed AU relationship modelling approach allows both backbones (ResNet-50 and Swin Transformer-Base (Swin-B)) to achieve superior overall F1 scores than all other listed with 0. 5% and 1. 3% average improvements over the state-of-the-art  [Jacob and Stenger, 2021]. Specifically, our approach allows both backbones to achieve the top three performances for 9 out of 12 AUs' recognition (e. g. , AU 4, AU 6, AU 7, AU 10, AU 12, AU 14, AU 15, AU 17, and AU 23) among all listed approaches.
Similar results were also achieved on DISFA. According to Table  2 , our approach helps both backbones to achieve the state-of-the-art average F1 scores over 8 AUs, which outperform the current state-of-the-art with 1. 6% and 0. 9% improvements, respectively. For fair comparisons, we only compare our approach with static face-based methods that did not remove any frame from the datasets. According to both tables, our ResNet-50-based system also clearly outperforms other graph-based AU recognition ap- proaches which also use CNNs (ResNet (UGN-B, HMP-PS) or VGG (SRERL)) as backbones. Since SRERL only uses a pre-computed adjacent matrix to describe the relationship between AUs for all faces, our system shows a large advantage over it, with 1. 8% and 7. 2% F1 score improvements for the average results on BP4D and DISFA, respectively.
Although UGN-B and HMP-PS assigned each facial display a unique adjacent matrix and achieved better performance than SRERL, they still use a single value to describe the relationship between each pair of AUs, without considering multiple relationship cues. Thus, our deep-learned task-specific multi-dimensional edge features lead our system to achieve more than 1. 3% and 2. 1% average F1 score improvements over UGN-B and HMP-PS on both datasets. Ablation Studies. Table  3  evaluates the influence of each component of our pipeline on the average AU recognition results. It can be observed that simply using the AFG block to specifically learn a representation for each AU enhanced the performance for both backbones, indicating that the relationship between each AU's activation and the full face representation is unique.
Conclusion This paper proposes to deep learn a graph that explicitly represents relationship cues between each pair of AUs for each facial display. These relationship cues are encoded in both node features and multi-dimensional edge features of the graph. The results demonstrate that the proposed node and edge feature learning methods extracted reliable task-specific relationship cues for AU recognition, i. e. , both CNN and transformer-based backbones have been largely enhanced, and achieved state-of-the-art results on two widely used datasets. Since our graph-based relationship modelling approach can be easily incorporated with standard CNN/transformer backbones, it can be directly applied to enhance the performance of multi-label tasks or tasks whose data contains multiple objects, by explicitly exploring the task-specific relationship cues among labels or objects.
DNA sequencing data continues to progress towards longer reads with increasingly lower sequencing error rates. We focus on the critical problem of mapping, or aligning, lowdivergence sequences from long reads (PacBio HiFi) to a reference genome, which poses challenges in terms of accuracy and computational resources when using cutting-edge read mapping approaches that are designed for all types of alignments. A natural idea would be to optimize efficiency with longer seeds to reduce the probability of extraneous matches; however, contiguous exact seeds quickly reach a sensitivity limit. We introduce mapquik, a novel strategy that creates accurate longer seeds by anchoring alignments through matches of k consecutivelysampled minimizers (k-min-mers) and only indexing k-min-mers that occur once in the reference genome, thereby unlocking ultra-fast mapping while retaining high sensitivity.
For long reads, there has been a recent breakthrough by sampling and indexing only a relatively small number of short potential seeds from the reference genome, which has led to faster and more accurate mapping tools, e. g. , minimap2  [24]  and Winnowmap2  [16]. Chaining consists of finding maximal subsets of seeds that all agree on a certain genomic location  [14] ; seeds often have spurious matches due to their short lengths. However, even the most recent long-read alignment tools are the bottlenecks in analysis pipelines. For instance, the popular minimap2 software requires 12 CPU hours to map a typical PacBio HiFi dataset to the human genome, and Winnowmap2 requires 15 CPU days, preventing both real-time analysis of sequencing data  [26]  and efficient reanalysis of previously-sequenced data collections  [8].
Other works have focused on improving the sensitivity and accuracy of minimap2, at the expense of speed. Winnowmap  [17]  and Winnowmap2  [16]  use weighted minimizer sampling and minimal confidently-alignable substrings to better align in highly-repetitive regions, e. g. , centromeres of chromosomes. Winnowmap2 is around 15× slower than minimap2 end-to-end, yet uses around 3× less memory. In recent years, many research groups focusing on low-level and/or hardware-specific acceleration have proposed ways to accelerate minimap2. mm2-fast  [18] , a CPU acceleration of minimap2 developed by Intel, achieved a 1. 5× acceleration over minimap2, end-to-end on HiFi reads. The bulk of the speed-up was obtained in the alignment phase, not in the seeding/chaining phase (Figure  1  in  [18] ). The domain-specific language Seq was developed to speed up genomic sequence analysis, and achieved two orders of magnitude improvement in the homology table reconstruction of the CORA read mapper  [44, 42].
Specifically, we formulate and verify the following two hypotheses: (1) Long exact k-mer seeds are inadequate for accurate long-read alignment due to lack of sensitivity, whereas (2) k-min-mers are adequate and also offer near-perfect specificity. An empirical analysis on simulated reads over the entire 2 human genome (Figure  1 ) justifies these observations. The following experiments were performed using Jellyfish  [27] , DSK  [36] , rust-mdbg  [9]  and mapquik. All code and data are available in the mapquik repository (experiments/figure-seeds/ folder). Average number of reference locations per read using unique k-min-mers as seeds Fig. 1 : Increased sensitivity and specificity of k-min-mers versus long k-mers. Both panels use the human reference genome CHM13v2. 0 and the HG002 DeepConsensus HiFi reads.
Datasets and mapping evaluation We used the complete human reference genome CHM13v2. 0 for our evaluations. We constructed a simulated dataset of long reads with 99% base-level accuracy and 24 Kbp mean length using pbsim  [34] , mimicking HiFi reads at 10× genome coverage. We also used real HiFi reads for the HG002 individual corrected using DeepConsensus  [3] , at 30× genome coverage. For maize, we simulated reads from the maize RefSeq genome (GCF 902167145. 1) at 30× coverage using the same protocol as the human simulated reads. mapquik was run with default parameters (k = 5, ℓ = 31, δ = 0. 01, β = 4, µ = 11, ε = 2000). All other tools were run with default parameters in HiFi mapping mode. Command lines and versions are given in the Appendix. For simulated reads, we assessed mapping accuracy using the mapeval command of the paftools software distributed in the minimap2 package.
A read is considered to be correctly mapped if the intersection between the true and mapped reference intervals is at least 10% of their union. For real reads, we evaluated the concordance between the alignments of minimap2 and mapquik using a custom script (experiments/intersect pafs. py in the GitHub repository), similar to mapeval. Mappers report a mapping quality metric for each read, indicating their confidence that the read is mapped at the right location, as an integer between 0 and 60 where 60 corresponds to the highest confidence. In our evaluation of both the simulated and real datasets, we focus on reads with the highest mapping quality (Q60), as reads with low mapping quality are less frequent and are often removed in downstream applications (e. g. , the popular variant calling pipeline GATK  [28]  filters out reads with mapQ ≤ 20 by default).
Furthermore on minimap2 results, mapeval only reported two mapping quality groups (Q0 and Q60), with no value inbetween. In mapquik, we report a mapQ of 60 if the output pseudo-chain has score ≥ µ or length ≥ β, and 0 otherwise. mapquik achieves faster and accurate mapping of HiFi reads to the human genome Table  1  shows the overall performance of mapquik and other evaluated methods (minimap2, mm2-fast, Winnowmap2, and concurrently-developed BLEND) on mapping simulated and real PacBio HiFi reads to the human genome and the maize genome. On the simulated human dataset, mapquik is 32× faster than minimap2. mapquik maps 97. 2% of reads at a mapQ score of 60 (Q60), indicating a high-confidence match, with 2 errors. In contrast, other mappers map 97. 4 -99. 0% of reads at Q60 with also no/almost no error.
On the real dataset, a similar trend is observed, and mapquik outperforms all mappers except Winnowmap2 in terms of percentage of reads mapped (96. 1%), as opposed to 92. 2 -97. 9% for the other tools. The concordance of minimap2 and mapquik mappings is 99. 8% on the Q60 reads mapped by minimap2. All mappers required less than 13 gigabytes of memory on the human genome (MashMap2 was not further evaluated as it took over 13 wall-clock hours on the simulated human dataset, and does not output mapping quality scores). A highlight of mapquik is a 350× mapping speed-up compared to minimap2 on the maize genome. Remarkably, this speed-up comes with no loss of sensitivity, as mapquik reports the second highest number of mapped reads at mapping quality 60 across all tools after Winnowmap2, with near-perfect precision.
Winnowmap2 is faster than its performance on the human genome and that of minimap2 on the maize genome. We further investigated why some reads were mapped at lower qualities than Q60 or were not mapped at all. Out of 42,198 reads from the simulated human dataset that were not mapped at Q60 by mapquik, 94. 4% of these reads intersected with centromeric/satellite regions of chromosomes, as reported by bedtools using the chm13v2. 0 censat v2. 0. bed annotation from the T2T consortium. Thus, the vast majority of reads not aligned at Q60 corresponds to challenging genomic regions that would likely have been masked in downstream analyses, making their lack of alignment potentially inconsequential. We hypothesize that similar conclusions hold on real data, but this cannot be ascertained as the true reference interval of each read is not known.
Software availability. https://github. com/ekimb/mapquik Acknowledgements. The authors thank Erik Garrison, Younhun Kim, Victoria Popic, and Rohit Singh for valuable feedback. In order to map a query sequence using the reference index (top right, nucleotide C in blue denotes a sequencing error), mappers compute the minimizers on the query sequence (c), and find matches between the minimizers of the query and those in the reference index. Once minimizer matches are found, minimap2 and Winnowmap2 perform a colinear chaining step to output a high-scoring set of matches, using dynamic programming (d). In contrast, mapquik (bottom) indexes reference sequences by generating k-min-mers, k consecutive, randomly-selected minimizers of length ℓ (k = 3, ℓ = 2) (e), and storing only the k-min-mers that appear exactly once in the reference (f).
This research is partially based upon work supported by the  National Science Foundation  under Grants  DBI-2138585  and  OAC-1931531  (to P. M). Research reported in this publication was also supported by the  National Institutes of Health  under Grants  NIH   R01HG010959  (for B. E. to B. B. ),  NIH   1R35GM141861  (to B. B. ), and  NIH   R01GM146462  (to P. M. ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institutes of Health.
We rely on the preexisting tool MaBoSS, which enables probabilistic simulations of cellular networks. A novel software layer is added to account for cell interactions and population dynamics, but without considering the spatial dimension. This modeling approach can be seen as an intermediate step towards more complex spatial descriptions. We illustrate our methodology by means of a case study dealing with TNF-induced cell death. Interestingly, the simulation of cell population dynamics with UPMaBoSS reveals a mechanism of resistance triggered by TNF treatment. Relatively easy to encode, UPMaBoSS simulations require only moderate computational power and execution time. To ease the reproduction of simulations, we provide several Jupyter notebooks that can be accessed within the CoLoMoTo Docker image, which contains all software and models used for this study.
Such explorations constitute a first step towards the use of multi-scale modeling for clinical applications  (Wolkenhauer et al. , 2014; Viceconti and Hunter, 2016). This modeling approach is illustrated with a simple toy model of cell-cell interaction, and with a case study dealing with the effect of the Tumor Necrosis Factor (TNF) on the cell fate decisions triggered by the engagement of death receptors. To foster the reproducibility of our results, we provide several notebooks in a dedicated GitHub at https://github. com/sysbiocurie/UPMaBoSS-docker, with examples of models in MaBoSS language, as well as in the more standard SBML-Qual format  (Chaouiya et al. , 2013). Noteworthy, published logical models of cellular networks  (Helikar et al. , 2012; Naldi et al. , 2018)  can be easily adapted and extended by adding cell death, division, and cell-cell interactions.
Constructing a Model in UPMaBoSS From an Influence Network UPMaBoSS relies on MaBoSS software and extends it by adding some important functionalities to model interacting cell populations  (Stoll et al. , 2012; Stoll et al. , 2017). Every population state is represented by a set of Boolean values associated with the network nodes (including the division node, the death node, the ligand and the receptors, cell type nodes, etc. ). The UPMaBoSS framework enables the computation of the timedependent probabilities of states, allowing an interpretation of the dynamics at the cell population level, considering that: (Number of cells in state S) (Number of cells) × (Probability of S) The definition of the model components assumes that each node of the network represents a gene, protein, complex, phenotype or cell type.
The UPMaBoSS model gathers the pathways and entities potentially active in the different cell types. It is derived from an influence network by applying the following procedure: • For every node, except receptors, two transition rates are defined, rate_up for activation and rate_down for inhibition, with triggering rules formulated in the language of MaBoSS (see the Results section for an example of the grammar used for writing logical rules and transition rates). • For receptors, the rates associated with the update of their state must contain term(s) that depend on the population state probabilities. For each time step, UPMaBoSS computes the relative population size (with respect to the initial size), and the distribution of state probabilities in the population (including the status of death, division, and cell type nodes) (Figure  2 ).
The second family of parameters corresponds to the number of trajectories to include in the computation, and the length of one MaBoSS run (ensuring that transitory behaviors are observable, see an example below). An exhaustive list of these parameters is provided in the Supplementary Section S1. 5, including default values and guidelines for choosing their values. In some cases, a sensitivity analysis may be needed to select the appropriate range of parameter values. We provide an example with a variation of the length of one MaBoSS run (max_time) and analyze its impact on the expected results in a python notebook included in the GitHub folder (https://github. com/ sysbio-curie/UPMaBoSS-docker/tree/master/CellFate).
Description of the Model We start with a model initially built to understand how the same signal can lead to three different cell phenotypes depending on the cellular context. The activation of the death receptors TNFR or Fas by their respective ligands can trigger a cascade of events leading to either survival with the activation of NFκB pathway, or to non-apoptotic cell death (referring to necrosis or to a programmed necrosis called necroptosis) with the loss of ATP, or to apoptosis with the cleavage of caspase 3  (Calzone et al. , 2010). This generic model was built on the basis of data collected from the literature, focusing on the main components influencing the cell fate decision between death and survival. The model can be found in the GINsim repository: http://ginsim. org/node/227.
Indeed, the chosen time window (i. e. , max_time) must be such that the population is in a transient state. The best value for the parameter max_time turned out to be around 1 h (just before the peak of activation of Figure  4B ). To simulate the population dynamics, we proceeded to compute 48 MaBoSS runs with max_time equal to one for each run. We further studied the sensitivity to this parameter and included online (Jupyter notebook TimeStepDependency. ipynb, https://github. com/sysbio-curie/ UPMaBoSS-docker/tree/master/CellFate). In the MaBoSS simulation, we noticed that non-apoptotic cell death first decreases, before increasing to reach a steady state solution after t = 15 h. This dynamics is due to the activity of ATP, itself dependent on that of RIP1. RIP1 increases until CASP8 is activated and able to inhibit it.
TNF Treatments in Mutant Conditions In the clinics, the various mutations found in patients may affect the efficacy of the response to treatments. To explore the potential roles of the different model components in the observed TNF resistance, we simulated the effects of all possible single mutants (Jupyter notebook CellFateModel_upmaboss at https://github. com/sysbio-curie/UPMaBoSS-docker/tree/master/CellFate). For each single mutant, we measured the population ratio at t = 96 h for four possible scenarios: "No TNF" + "No TNF" (control, dashed green curve), "No TNF" + "TNF" (late treatment, plain green curve), "Pulse of TNF" + "No TNF" (single early treatment, dashed red curve), and "Pulse of TNF" + "TNF" (consecutive treatments, plain red curve). Here, we focus on the results obtained for three genetic backgrounds: wild type, IKK knock-down, and RIP1K knockdown (Figure  6 ).
). This can be done by considering positive and negative controls, since what we are most interested in with this approach is to compute the change of probabilities between two experiments or 2 cell conditions rather than compare exactly the model probabilities with quantitative experimental measures. This comparison can be facilitated by normalizing experimental measures between 0 and 1. The probabilities of the network states could be validated using flow cytometry or microscopic images, using thresholds that separate active/inactive states. Single-cell transcriptomic (with CRISPR/Cas9 genetic engineering) and flow cytometry (on cell population markers), in principle, can be used to estimate the sizes of different cell sub-populations: once thresholds have been applied on the quantitative markers defining cell populations, the experimental data can be translated in terms of cell sub-populations.
Consequently, computational cost increases moderately (less than an order of magnitude) compared to prior MaBoSS models. Moreover, because UPMaBoSS can be run directly on a conda environment or within a simple pair of python and C++ scripts, simulations can be launched on High Performance Computers, with a natural parallelization for tasks such as parameter variations and gene mutations. Finally, on the practical side, the implementation of UPMaBoSS in a docker image and in a conda package should greatly facilitate its use, whereas the reproducibility of the analyses can be enforced by the use of Jupyter notebooks.
ACKNOWLEDGMENTS We would like to thank  I. Martins. GK is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR)-Projets blancs ;  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases ;  AMMICa US23/CNRS UMS3655 ;  Association pour la recherche sur le cancer (ARC) ;  Association "Le Cancer du Sein , Parlons-en!";  Cancéropôle Ile-de-France ;  Chancelerie des universités de Paris  ( Legs Poix ),  Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR) ;  Gustave Roussy Odyssea , the  European Union   Horizon 2020 Project Oncobiome ;  Fondation Carrefour ;  Institut National du Cancer (INCa) ;  Inserm (HTE) ;  Institut Universitaire de France ;  LeDucq Foundation ; the  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ); the  RHU Torino Lumière ; the  Seerave Foundation ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and the  SIRIC Cancer Research and Personalized Medicine (CARPEM).
FUNDING VN and LC were partially funded by ANR-FNR project "AlgoReCell" (ANR-16-CE12-0034) and VN was partially funded by the European Union's Horizon 2020 Programme under the PerMedCoE Project (www. permedcoe. eu), grant agreement n °951773. The project received financial support from Inserm Cancer (ModICeD, ITMO MIC 2020).