dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The research leading to these results was supported by the  European Union 's  Horizon 2020 MSCA-ITN program , funded by the  European Commission  under the grant agreement No  813120  ( INSPiRE-MED ). This research also received funding from the  Flemish Government (AI Research Program). Sabine Van Huffel ,  Frederik Maes , and  Pooya Ashtari  are affiliated to  Leuven. AI -KU Leuven  institute for AI, B-3000, Leuven,  Belgium.
• Shifted Window (SW) Matricize operation is introduced and combined with NMF to fully exploit local contexts. • Scalable interpretable U-shaped segmentation models based on NMF are proposed. • The proposed models achieve state-of-the-art results on the BraTS and ISLES'22 datasets. Notation. We denote vectors by boldface lower-case letters, e. g. , , matrices by boldface upper-case letters, e. g. , , and tensors by boldface calligraphic letters, e. g. , . Elements in a matrix (tensor) are denoted by [, ] ( [ 1 , … ,   ]). The th row and th column of a matrix is denoted by [, ∶] and [∶, ], respectively. A sequence of  vectors (a. k. a. tokens) is denoted by (  )  =1. We use [ 1 | … |  ] to denote a matrix  created by stacking   s along the columns. We show the inner product between matrices by ⟨, ⟩ = ∑ , [, ][, ] and  2 -norm of a matrix by ‖‖ = √ ⟨𝐗, 𝐗⟩.
2. Related work CNN-based segmentation models. Convolutional neural networks (CNNs) have dominated medical image segmentation. Particularly, following an encoder-decoder architecture with skip connections, U-Net (Ron- neberger et al. , 2015) has achieved state-of-the-art on various medical image datasets. The simplicity and effectiveness of a U-shaped archi- tecture have led to the emergence of numerous U-Net variants in the field. Çiçek et al. (2016) extended U-Net by replacing all 2D operations with their 3D counterparts. UNet++ (Zhou et al. , 2018) follows a Myronenko (2019) 2021)der-decoder network consisting of sub-networks connected through a series of nested, dense skip connections. nnU-Net(Isenseeetal. , 2021)proved effective in various medical image segmentation tasks by only making minor modifications to the standard 3D U-Net(Çiçek et al.
9G 76. 40 82. 06 89. 37 82. 61 8. 09 9. 41 10. 59 9. 56 Swin UNETR 62. 2M 1549. 8G 76. 50 82. 65 90. 10 83. 08 9. 69 8. 75 11. 04 9. 91 nnFormer 149. 4M 489. 9G 77. 71 83. 41 90. 05 83. 72 5. 42 7. 45 7. 68 6. 93 Global Factorizer 5. 9M 170. 0G 78. 20 82. 86 88. 65 83. 24 6. 67 9. 53 11. 69 9. 71 Local Factorizer 5. 9M 170. 0G 78. 67 82. 85 89. 30 83. 61 5. 29 7. 77 8. 91 7. 41 Swin Factorizer 5. 9M 174. 2G 79. 33 83. 14 90. 16 84. 21 4. 91 7. 31 8. 23 6. 89 *: p-value < 0. 05; **: p-value < 0. 01; ***: p-value < 0. 001; and ****: p-value < 0. 0001). 4. 3. 1. Brain tumor segmentation (BraTS)Quantitative evaluation. For all the experiments, we performed 5-fold cross-validation to estimate how capable our models are in generalizing to unseen data. The results on the BraTS dataset are reported in Table1and illustrated by box plots in Figs.
Nevertheless, this average template strategy assumes a stationary spiking activity over the course of the recording. Recent work have proposed a decentralized method  [27, 29]  to relax this assumption, which uses all the pairwise displacements between activity profiles, instead of using a single reference template, to directly estimate a motion signal to register the traces. We used the modular architecture of the sortingcomponents module of the SpikeInterface package  [6]  to factorize and benchmark all the steps in a modular manner. As shown in the bottom of Figure  1C , we have implemented three major algorithms to estimate the positions of the neurons (see Methods), two methods to estimate the motion given these positions (the one from Kilosort , termed "ìterative template" and the one from  [3]  termed "decentralized", see Methods), and three methods to interpolate the traces.
k (t) at known positions ⃗ p n = (x n , y n ), and to estimate the position of a given spike ⃗ w i (t) projected onto this basis. If the spatial resolution of this grid is finer than the one of the recording channels, one could expect to enhance the resolution of the localization estimates. First, the scalar products ω i,n = ⃗ w i (t) ⃗ f n (t) of the spike with all the templates in the basis are computed; then, the spike position Grid(i) is estimated as a weighted sum of the positions, with 1 the implementation was ported from pyKilosorthttps://github. com/int-brain-lab/pykilosort/.
Simulated datasets We used the MEArec simulator  [4]  (version 1. 9. 0) to generate 10-minute-long synthetic ground-truth recordings. MEArec uses biophysically detailed multicompartment models to simulate extracellular action potentials, or so-called "templates". For this study, we used 13 default cell models from layer 5 of a juvenile rat somatosensory cortex  [19, 23]  to simulate a dictionary of biologically plausible drifting • Zigzag (Non-rigid): This mode is similar to Zigzag (Rigid), but in this case the drift is nonrigid, leading to a non-homogeneous movement of all the cells as function of their depths. In this non-rigid case, the drift vector is modulated by a linear gradient, ranging from 0. 4 (12 µm maximum displacement) at the upper part of the probe to 1 (30 µm maximum displacement) at the bottom.
Benchmarking motion estimation To benchmark the performance of different motion estimation strategies, we generated several artificial recordings with 256 neurons and various drift cases (see Simulated datasets). In Figure  3 , we first show the results for a representative subset of six drift recordings (one per row). All the remaining cases can be found in Supplementary Figure  S2. The left column displays the raster maps with the overlaid drift signals at multiple depths, which give a qualitative view of the drift over the entire recording. The right part of the figure focuses on the errors of different peak localization + motion inference combinations: the left panels show the errors over time, the central panels the error distributions, and the right panels the errors over probe depth (left -bottom, right -top).
Global impact at the spike sorting level After evaluating the performance at the motion estimation and motion interpolation stage, we now investigate how a complete drift correction pipeline affects spike sorting results. From the previous section, we showed that IDW and kriging are the best interpolation options. For this analysis, we used kriging since it is also used by Kilosort 2. 5 and it therefore allowed us to perform a direct comparison. We ran spike sorting using Kilosort 2. 5 on three different datasets -ZigZag, ZigZag (non rigid), Bumps -all with uniform depth distributions and homogeneous firing rates (Figure  2A, C,  and E ). For each recording, we ran four spike sorting options, and note that the cell distributions and spiking activity for all three recordings is the same, resulting in the exact same static recordings: • Static -no interpolation: spike sorting on the static recording, turning off motion correction in Kilosort 2.
Discussion In this work, we have presented an exhaustive comparison of state-of-the-art methods to correct for motion in extracellular electrophysiology recordings. We generated a wide variety of simulated benchmark datasets using a Neuropixels 1. 0 probe design with varying level of complexity, using Zigzag and/or bumping drift signals, rigid or non-rigid motion, and uniform or non-uniform depth distributions and firing rates. Knowing the ground-truth motion and spiking activity, we were able to finely evaluate the two different steps involved in drift correction, i. e. motion estimation and motion interpolation. For motion estimation, we showed that the combination of monopolar triangulation (for peak localization) and decentralized registration generally outperforms all other options on the test datasets.
In this work, we used only simulated datasets on Neuropixels 1. 0-like probe designs as a benchmark. While clearly artificial data cannot fully substitute experimental ones, they provide a very controlled and customizable framework to test specific aspects of recordings, including drifts. A quantitative evaluation of drift correction methods on real data would in fact be very challenging due to the lack of ground truth. Even in the case of mechanical manipulation of the probe to inject drift  [24] , the relative movement of different layers of tissue with respect to the rigid probe movement would be hard to control. Furthermore, while here we focus on Neuropixels 1. 0 probes, commercialized in 2019 and already widely used in neuroscience research, further benchmarks following a similar approach could target different probe layouts, such as Neuropixels 2.
Finally, all the methods and drift correction options evaluated in this work are readily available to the electrophysiology community within the SpikeInterface package and can be immediately deployed with a few lines of code prior to any spike sorting process, as shown in Section "Drift correction with SpikeInterface ". S3 : Quantification of the interpolation on sorting accuracy with quasi optimal merging. ADG) Accuracy of all units (sorted by accuracy) in the simulated recordings when launching Kilosort 2. 5 in various conditions for the Zigzag rigid case (A), the Zigzag (non-rigid case) (D) or the bumps (non-rigid) case (G). All recordings have uniform distributions and homogeneous firing rates. BEH) Number of well detected/overmerged/redundant/false positive and bad units found by Kilosort , with various drift correction methods, for the recordings explained in A, D, G.
Words of Estimative Probability (WEP) are phrases used to express the plausibility of a statement. Examples include terms like probably, maybe, likely, doubt, unlikely, and impossible. Surveys have shown that human evaluators tend to agree when assigning numerical probability levels to these WEPs. For instance, the term highly likely equates to a median probability of 0. 90±0. 08 according to a survey by  Fagen-Ulmschneider (2015). In this study, our focus is to gauge the competency of neural language processing models in accurately capturing the consensual probability level associated with each WEP. Our first approach is utilizing the UNLI dataset (Chen et al. , 2020), which links premises and hypotheses with their perceived joint probability p. From this, we craft prompts in the form: "[PREMISE].
[WEP], [HYPOTHESIS]. " This allows us to evaluate whether language models can predict if the consensual probability level of a WEP aligns closely with p. In our second approach, we develop a dataset based on WEP-focused probabilistic reasoning to assess if language models can logically process WEP compositions. For example, given the prompt "[EVENTA] is likely. [EVENTB] is impossible. ", a wellfunctioning language model should not conclude that [EVENTA&B] is likely. Through our study, we observe that both tasks present challenges to out-of-the-box English language models. However, we also demonstrate that fine-tuning these models can lead to significant and transferable improvements.
We use the median of the participant answers to assign a consensual value to each WEP. Associated probabilities for the 19 WEP we use are available in Appendix A, table  2. Here, we assess whether neural language models learn the consensual probability judgment of WEP from language modeling pretraining. We develop datasets and a methodology to probe neural language model understanding of WEP. The first dataset leverages previously annotated probability scores between a premise and a hypothesis, in order to measure a language model's ability to capture the agreement between numerical probabilities and WEP-expressed probabilities. The second dataset is based on compositions of facts with WEP-expressed probabilities, and measures verbal probabilistic reasoning in language models. Our contributions are as follows: (i) two datasets and methods to measure understanding of WEP; and (ii) evaluation of the ability of neural language models (GPT2, RoBERTa-trained on MNLI) to tackle WEP-related problems, showing that offthe-shelf models are very little influenced by them, even though fine-tuning on our constructed datasets quickly leads to high accuracies.
, 2020). Fagen-Ulmschneider (2015)  proposed the largest survey up to date with 123 participants about generaldomain WEP perception. Logical and probabilistic reasoning Another strand of work probes NLP text encoders capabilities, notably reasoning abilities. Weston et al. (2015)  probed understanding of specific problems like negation, spatial and temporal reasoning with the bAbI dataset. Richardson et al. (2020)  probe understanding of first-order logic reasoning,  Sileo and Lernould (2023)  probe epistemic logic reasoning. Our work is the first to address probabilistic logic, alongside  Dries et al. (2017) ;  Suster et al. (2021)  who construct a dataset of natural language probability problems, e. g. , "A bag has 4 white and 8 blue marbles. You pull out one marble and it is blue. You pull out another marble, what is the probability of it being white?".
They also rely on the ProbLog solver  (De Raedt et al. , 2007) , but focus on numeric probability problems. By contrast, our work targets WEP, and textual probabilistic logical reasoning. Natural language inference, uncertainty, modality, evidentiality Uncertainty was also studied in the context of natural language inference tasks. Zhou et al. (2022)  study the disagreement across annotators when labeling entailment relationships. Zhang et al. (2017)  annotate graded entailment with 5 probability levels, and the UNLI dataset  (Chen et al. , 2020 ) go further by annotating numerical probabilities. Our work also pertains to the study of modality  (Palmer, 1992; Saurí et al. , 2006)  and more particularly evidentiality  (Su et al. , 2010) , but where previous work focused on WEP. 3 Probing WEP understanding.
WEP-UNLI: probability/WEP matching The UNLI dataset annotates (PREMISE, HYPOTH-ESIS) pairs from the SNLI dataset  (Bowman et al. , 2015)  with joint probability scores p, totaling 55k training examples, 3k/3k validation/test examples. We use these examples to generate WEPunderstanding dataset with verbalization validity prediction as shown in the previous subsection.
WEP-Reasoning: WEP compositions Here, our goal is to assess models' ability to reason over combinations of probabilistic statements. We construct synthetic (PREMISE, HYPOTHESIS, p) examples from random factoids extracted from the bAbI dataset  (Weston et al. , 2015). Figure  1  illustrates the construction of WEP-reasoning examples: We randomly sample initial facts and associated probability levels, and we verbalize them with the previously mentioned templates from Table  3  (Round 1). We further compose them with randomly sampled logical operators  (and, or, xor). We then generate a hypothesis with logical combinations of the previous round. Finally, we feed the constructed premise and hypothesis to a probabilistic soft reasoning engine in order to derive the likelihood of the hypothesis given the premise.
We rely on the ProbLog  (De Raedt et al. , 2007)  reasoner which implements  Dantsin (1992)  semantics. To evaluate different complexities of reasoning, we propose two variants: 2-hop reasoning, where facts in Round 2 combine facts from Round 1, and the final hypothesis combines facts from Round 2. and 1-hop reasoning where facts from the hypothesis combine Round 1 facts (Round 2 is skipped). Since we want to sample more than two facts and we cannot a priori use text from the UNLI dataset, because UNLI only provides entailment likelihood for specific pairs. Combining several sentences could cause unaccounted interference. Therefore, we sample subject/verb/object factoids from the bAbI  (Weston et al. , 2015)  datasets instead, which is built with handwritten arbitrary factoids such as John went to the kitchen.
To sample multiple factoids, we prevent any overlap of concepts  (verb, subject, object)  between any pair of facts to make the facts independent of one another. We sample probability levels from the list of medians of all WEP to prevent sampling the levels that too distant from a known WEP. When we assign a WEP to a probability level, we assume that the correct semantics is the consensual one, but humans differs slightly from this consensus. Still, when adding random perturbations of 20% to sampled p 1. 6 , the hypothesis probability is perturbed by less than 40% for 98% of examples. We generate 5k examples using the template depicted in Figure  1 , and use 10%/10% of the data for the validation/test splits. Appendix C shows the distribution of correct WEP for each dataset.
Zero-shot models We use off-the-shelf language models to assign likelihood scores to a context and its conclusion. We evaluate the rate at which valid verbalization is scored higher than invalid verbalization. We refine the scores by also considering the average likelihood per token  (Brown et al. , 2020; Schick and Schütze, 2021)  and calibrated scores  (Brown et al. , 2020; Zhao et al. , 2021)  where we divide the score of a PREMISE. T p (HYPOTHESIS). by the score of T p (HYPOTHESIS). We evaluate the normalized, length-normalized, and calibrated likelihood on the validation sets of each and select the most accurate method for each dataset and model. We also consider a pretrained natural language inference model, which is trained to predict entailment scores between a context and a conclusion.
GPT2 We use the pretrained GPT2 base version with 127M parameters  (Radford et al. , 2019) , which is a causal language model trained to estimate text likelihood. We concatenate the premise and hypothesis and compute their likelihood as a plausibility score. RoBERTa We also use the pretrained RoBERTa base model with 123M parameters  (Liu et al. , 2019)  to score the masked language modeling likelihood of the premise/hypothesis pair.
RoBERTa-MNLI We fine-tune RoBERTa on the MNLI entailment detection dataset  (Williams et al. , 2018)  with standard hyperparameters (see the following subsection). Human baseline To establish human baseline performance on the constructed dataset, we had two NLP researchers annotate 100 examples randomly sampled from the test set of each dataset, with a multiple-choice question answering setting. Overall inter-annotator agreement is relatively high, with a Fleiss's κ of 0. 70/0. 68/0. 71 for WEP Reasoning 1 hop, 2 hops and WEP-UNLI respectively.
Fine-tuning and transfer across probes We fine-tune RoBERTa-base models on our datasets, using standard  (Mosbach et al. , 2021)  hyperparameters 3  (3 epochs, sequence length of 256, learning rate of 2. 10 -5 batch size of 16. We use length-normalization with GPT2 likelihood and calibration with RoBERTa likelihood as they worked best on the validation sets. ). We use a multiplechoice-question answering setup (we predict logit scores for the valid and invalid verbalization, combine their score with a softmax, then optimize the likelihood of the valid verbalization). The same format is applied to all tasks, so we can also study the transfer of capacities acquired during fine-tuning of each probe, for instance, between probability matching and compositional reasoning.
Results and discussion Table  1  shows the results of our experiments. The very low accuracy of causal and masked language models (first two rows) demonstrates how challenging the WEP-understanding tasks are. RoBERTa fine-tuned on MNLI dataset performs better than chance for WEP-UNLI. MNLI contains 814 instances of probably in the MNLI dataset, but we found little to no evidence of WEP compositions among them, which can explain the results. Finally, fine-tuning on the dataset of a particular probe leads to high test accuracy on the associated test set. More surprisingly, fine-tuning on one dataset also causes substantial accuracy gain on other probes. This suggests that our datasets can be incorporated in text encoder training in order to improve WEP handling.
Procedure This study was conducted between November 2020 and December 2021. The researchers conducted data analysis and obtained the results after each round to implement insights into the following round. Researchers conducted FGDs and qualitative interviews with patients with a history of MI and HPs (eg, general practitioners, nurses, and cardiac specialists). Participants were people living in Australia who were aged ≥18 years. Eligibility criteria was as follows: the patients with a history of MI must have a history of hospitalization for MI and HPs must have experience treating patients with a history of MI. Participants were consecutively sampled with either web-based advertisements on various cardiology-focused Facebook pages and social media websites, or in person, with flyers delivered to cardiology clinics and assisted living villages in Victoria, Australia.
Data Analysis Our descriptive data analysis involved a flexible approach, which was found to be effective in obtaining rich data  [31]. The qualitative methods use the tenants of naturalistic inquiry, with a primary interest in studying humans in their natural state without the constraints of preexisting theoretical underpinnings  [32]. For all transcribed focus group and interview discussions, data were extracted into NVivo (version 20. 5; Lumivero). Here, participants were separated into "patients with a history of MI" and "HP" to establish initial codes based on the data. Once codes were created, categories that encompassed the codes for each patients with a history of MI and HP group were defined. Multiple iterations were performed to determine which groups of categories were the most representative of the data.
profile fitting is a quantitative, generally automatic, or semiautomatic, and often a faster approach, compared to the qualitative visual classification process. This is especially important for statistical approaches using the very large datasets we are expecting with missions such as Euclid in the near future. Euclid is a European Space Agency 1. 2 m space-based telescope mission, primarily designed to investigate dark energy and dark matter by mapping a large fraction of the visible sky  (Laureijs et al. 2011). In order to achieve this goal, Euclid will conduct a Wide Survey of around 1. 5 billion galaxies out to z ∼ 3 with relatively high spatial resolution wide-field optical and near-infrared (NIR) imaging, as well as low-resolution grism spectroscopy (R ∼ 250). These data will be provided by the VIS instrument, which features one broad optical band called I E , covering approximately 540 nm to 900 nm (i.
Euclid's nominal requirements are to image 15 000 deg 2 or 35% of the accessible sky down to at least a 10σ depth of magnitude I E = 24. 5 in the optical and down to a 5σ depth of magnitude 24. 3 at NIR wavelengths (Y E = 24. 3, J E = 24. 5, and H E = 24. 4). Observing strategies and initial tests of the instrument forecast higher sensitivity than the nominal requirements. In addition, the Euclid Deep Survey will provide images two magnitudes deeper in a smaller area of 40 deg 2 , as part of the deep fields. Euclid will thus provide an unprecedented number of high spatial resolution images for morphological measurements, which will be an extraordinary database for a range of legacy science questions including galaxy formation and evolution, as well as a plethora of follow-up projects. The Sérsic law  (Sérsic 1968 ) is a commonly used parametric model to describe galaxy radial profiles, which can describe a variety of shapes, from a disc or underlying smooth component of spiral galaxies  (Freeman 1970; Kormendy 1977) , to elliptical galaxies and bulges  (de Vaucouleurs 1948).