dataesr/scientific-paragraphs-categorization huggingface dataset:
The 193 top 15 significant uncorrelated descriptors that discriminate the compared conditions are then shown in 194 a MitoRadar plot and a slider is placed along a heat color scale, leading to the delineation of five distinct 195 categories (No effect, Limited / Substantial / Measurable or Large overall effect) (Fig. 1Design of the MITOMATICS workflow 199 Quantitative imaging of cellular structures and substructures like mitochondria requires rethinking low-200 throughput experimental procedures to include the use of multi-well plates (rather than tissue culture 201 petri dishes or small imaging chambers), live cell techniques (instead of fixed-cell imaging methods), 202 random imaging of multiple fields (rather than operator-dependent monitoring of only one chosen 203 microscope field encompassing multiple cells), databases of high-quality, classified images (rather than 204 production of a limited number of acquired images), automated digital image processing (instead of 205 manual counting) and rapid, massive data gathering, analysis and visualization (rather than slow 206 performance data analysis with suboptimal charts, graphs or histograms hindering easy and correct data 207 interpretation).
In and multidimensional data by creating unique radar plots in which sample datasets (e. g. from cells classified within a 'morphospace' of mitochondriome morphologies. It would be interesting to 412 439 466 particular, a growing body of literature points to mitochondria as a key organelle targeted by treated with chemicals) are compared to a reference dataset (i. e. , untreated or mock-treated cells). These determine whether specific signatures are linked to particular physicochemical properties of pesticides 413 440 467 environmental pollutants (3-5,48-53). Not only do these environmental pollutants (present in air, water information-rich representations natively come up with a variety of statistical charts and plots (t-tests, or to their toxicological profile. 414 441 468 and soil) disturb the mitochondrial machinery but their mitotoxicity may cause significant damage to PCA, LDA, violin distributions) and with a simple score (MitoScore), making it quick and easy to The combined effects of chemical mixtures, a rapidly emerging topic in environmental toxicology (62), 415 442 469 the epigenome and transgenerational inheritance of dysfunctional mitochondria (4).
Machine learning-based classification of THG signal The THG-signal channel of 3PM images were denoised as described above. Subsequently, both the raw images as well as the denoised images were interpolated in their z-dimension with a factor of 4. The image z stacks were processed with a bilinear interpolation in this step. Semantic segmentation with ilastik was performed on the upsampled stack of raw images, using the denoised images as a prediction mask to limit the amount of false positives caused by the noise. Ilastik "Autocontext" workflow with 2 training stages was chosen for its superior performance on noisy data  56. The THG signal was segmented into three classes of either vessel (1), myelin (2), or background (3). All color/intensity features, edge features and texture features were used up to a standard deviation of σ = 6.
Statistical analysis The results of quantifications were transferred to GraphPad Prism (GraphPad Software) or R to test the statistical significance with the appropriate tests as indicated in the figure legends, normality was tested using the Shapiro-Wilk test. Results were considered statistically significant if the P value was below 0. 05. Quantifications were done blinded by 2 independent investigators. Animal group sizes were as low as possible and empirically chosen and longitudinal measurements allowed a reduction of animal numbers by maintaining an adequate power. No statistical methods were used to predetermine sample size. In quantifications that were depicted as boxplots, the upper and the lower hinges correspond to the third and the first quartiles.
Extended Data Figures Extended Data Fig It is therefore critical that the cranial window in of the highest optical quality, placed over a region of the brain ensuring wide and deep access to the most accessible white matter structures, and that the glue is both secure and not obscuring or contaminating the imaging area. The longitudinal 2PM imaging of glioblastoma in the mouse brain is helpful in tracking the progress of tumor progression. Subsequently, the goal is then to optimize access to as much white matter as possible that yields desirable SNR properties. The inhomogeneous illumination in cortex and the corpus callosum also means that simply turning up the laser power is not advisable. One hemisphere can be privileged for wider or deeper access by moving the window off-center from the sinus midline towards the target hemisphere.
Acknowledgements V. V. received financial support from the  German Research Foundation  (DFG:  VE1373/2-1 ), the  Else Kröner-Fresenius-Stiftung  ( 2020-EKEA. 135 ), the  University of Heidelberg  (Physician- Scientist-Program , Krebs-und Scharlachstiftung) and the  Health + Life Science Alliance Heidelberg Mannheim. M. S. , E. R. and S. T. were supported by the  Deutsche Krebshilfe/German Cancer Aid  ( Mildred-Scheel-Scholarship for  MD students). W. W. , F. W. and V. V. were supported by a grant from the  German Research Foundation  (DFG:  SFB 1389 ). R. P. acknowledges support from the  European Commission  (grant no. 951991 , Brainiaqs), and the  Chan Zuckerberg Initiative  ( Deep Tissue Imaging  grant no. 2020-225346 ). R. P. , A. T. and E. M. were supported by the  EMBL. The  Northwestern Nervous System Tumor Bank  is supported by the  P50CA221747   SPORE for Translational Approaches  to Brain Cancer.
Code availability The indirect AO routines developed for integration into ScanImage32 are available at https://github. com/prevedel-lab/AO. git as previously described  22. AI denoising was performed using a customized approach as described above. Code can be found here: https://github. com/prevedel-lab/Deep3P-Denoising. Extended Data Fig. 8  Following myelin tracts and blood vessels over weeks in 3P. a, Arrow heads indicate myelin fibers, blood vessel is labeled in red (left). Post-processed with denoising.
17. 57 57 ms, repetition time = 1000 ms, acquisition matrix = 75x100, field-of-view = 15x20 mm2, slice thickness = 0. 5 mm. Diffusion tensor imaging analysis Fractional anisotropy (FA) maps were extracted from the respective DTI image stacks using open-source software MITK-Diffusion (https://github. com/MIC-DKFZ/MITK-Diffusion/) 59. Four different regions of interest (ROI) of the corpus callosum (CC) were manually drawn by J. S. (1 year of experience in neuroimaging) and F. T. K. (board-certified neuroradiologist with 11 years of experience in neuroimaging) using the open-source software 3D Slicer (https://www. slicer. org/) 60 at four different cross-sections of the CC moving cranially from the anterior forceps of the CC.
Interactive theorem provers are today increasingly used to certify mathematical theories. To formally prove a theorem, reasoning procedures called tactics are invoked successively on the proof states starting with the initial theorem statement, transforming them into subsequent intermediate goals, and ultimately discharging all proof obligations. In this work, we develop and experimentally evaluate approaches that predict the most likely tactics that will achieve particular desired transformations of proof states. First, we design several characterizations to efficiently capture the semantics of the proof transformations. Then we use them to create large datasets on which we train state-of-the-art random forests and language models. The trained models are evaluated experimentally, and we show that our best model is able to guess the right tactic for a given proof transformation in 74% of the cases.
A Coq proof state consists of a list of hypotheses and a goal that needs to be proven. Given a proof state before the tactic application, the tactic may either transform the before state to several after states or finish the proof. The semantic of a tactic is captured by the (usually infinite) set of proof state transformations that can potentially be generated by that tactic. In this work, we approximate that infinite set with a finite dataset of transformations that occur in real proofs written by Coq users. We then use machine learning models to gain an understanding of tactics using their approximated semantics. As an example, Fig. 2  presents the before and after states of the tactic rewrite add_comm at its position in Fig. 1. In this particular case, the hypotheses remain unchanged, but in the goal, the two sides of the addition are swapped.
Anti-unification Anti-unification, first proposed by Plotkin  [27]  and Reynolds  [29] , aims to calculate generalizations of the given objects. Since Coq is based on the Calculus of Inductive Constructions (CIC)  [25] , an appropriate anti-unification algorithm for Coq should be higher-order. However, higher-order anti-unification is undecidable  [26]. Therefore, we first convert Coq terms to first-order terms so that we can execute a decidable and efficient first-order anti-unification algorithm. To encode Coq terms into first-order logic, we transform them recursively following the AST. First-order applications and constants are encoded directly, other applications use the apply functor app and all other cases use special first-order functions (e. g. , a dependent product is encoded as a first-order function prod).
Each sub-forest is built on a random subset of the entire dataset. We choose a random forest implementation that has previously been used to predict tactics for Coq  [42]. The other used machine learning technique is the pre-trained language model GPT-2  [28]. GPT-2 is based on neural networks, which consist of many artificial neurons to learn from training data. The self-attention  [35]  technique is intensively applied in GPT-2 to differentially weigh every part of the input data. As a language model, GPT-2 predicts the probability distribution of the next word given a sequence of words as the input. GPT-2 is a pre-trained language model. The concept of pre-training imitates the learning process of humans. When humans encounter a new task, humans do not need to learn it from scratch. They will transfer and reuse their old knowledge to learn to solve it.
Similarly, GPT-2 is pre-trained on a large natural language dataset BooksCorpus  [43]. Afterward, GPT-2 can reuse the knowledge of natural language learned from pre-training to solve new tasks. To be adapted to a new task, we need to fine-tune GPT-2 on a relatively small dataset and slightly modify the weights learned from pre-training. We decide on GPT-2 because pre-trained language models have recently demonstrated outstanding achievements in natural language process (NLP)  [8]  and formal mathematics  [34, 39].
Experiments We perform the experiments on the dataset extracted from the Coq standard library. The dataset consists of 158, 494 states extracted from 11, 372 lemmas. We randomly split the dataset into three subsets for training, validation, and testing in an 80-10-10% ratio. First, we use 100 trees by default and optimize the Gini Impurity  [22]. Gini Impurity is a metric of the information gain. After the optimization, we set the Gini Impurity to its best value, try various numbers of trees and obtain the optimized number of trees. Finally, the best combination of Gini Impurity and the number of trees is determined for each characterization. The experiments with GPT-2 are based on the Hugging Face library  [38]. In particular, we employ the smallest GPT-2. The hyper-parameters are: eta = 3e -4, num_beams = 3, batch_size = 32.
During training, we apply a linear schedule with the first 20% training steps for warm-up. The remaining parameters are left as their default values. At most 50 tokens are predicted for a single tactic. We truncate the input on the left side if it is longer than the maximal length limitation of GPT-2 (1024 tokens). Language models have length limitations for efficiency. The attention mechanism used by them causes a quadratic usage of memory as the length of tokens scales. Every model is trained for 25 epochs on an NVIDIA V100 GPU, and the snapshot with the highest accuracy on the validation dataset is selected for testing. Table  1  depicts the results of our experiments. The accuracies of the combinations of before states with after states are significantly better than only relying on the before states in both random forests and GPT-2.
Shortening Proofs The results presented in the previous Sect. 4 focused on decomposed tactics. This means compound tactic expressions that perform several steps at once have been decomposed into individual tactic invocations. We apply the technique that is developed by  [5]  to decompose the tactics. Here, we utilize the same models; however, we focus on the original human-written tactics and try to shorten these (shortening expanded tactics would be unfair). For all tactic sequences of lengths two and three in the training dataset, we input their before and after states into the model. In our experiment, we can only consider the states in the training dataset since our model is trained on all present tactics. Compared to the validation dataset and testing dataset, our model should be able to give better predictions on proof shortening for the training dataset.
The amount of original tactics in the training dataset is 56,788. The model synthesizes 10 tactics for each sequence, and we execute them in Coq to verify that they perform the same transformation as the sequence modulo α-equivalence. 1 https://coq. discourse. group/t/how-to-avoid-awkward-assertions/1153/2. The results are presented in Table  4. We define the number of redundant tactics of ps 0 ⇒ t0 ps 1 ⇒ t1 ps 2. ⇒ tn ps n+1 as n. The shortening ratio is defined as the number of all discovered redundant tactics divided by the total number of occurrences of tactics in the training dataset. In this section, our method only applies to a tactic sequence that, besides the last tactic, every intermediate tactic produces a single after state. While in Sect. 4, our experiments apply to tactic applications that may produce several after states.
The reason is that it is difficult to calculate the number of redundant tactics if intermediate tactics produce several after states. The tactic sequence will become a tree of tactics, and each path consists of a sequence of tactics. We initially expected that the shortening ratios would not be very high because of the selected dataset. Indeed, the Coq standard library is written by Coq experts and has been edited and improved for decades, so we expected that there is not much room to improve. However, given the size of the dataset, the proposed technique can find a number of redundant tactics, which lets us conclude that taking the after states into consideration is useful for proof shortening. We discover many interesting cases, where proofs can be optimized. We present two examples of such proofs in Table  5.
Conclusion In this paper, we propose a new machine learning task, with which we aim to capture the semantics of tactics in formal mathematics. Based on a dataset of almost 160 thousand proof states we consider synthesizing a tactic that transforms a before state to the expected after states. We implement three novel characterizations to describe the transformation: feature difference, anti-unification, and tree difference. The results of the experiments confirm the effectiveness of our characterizations. Two applications of the task are discussed: tactic suggestion for declarative proofs and proof shortening. In the future, we will investigate if tactic embeddings can be used directly. We can also try to estimate the after states by calculating the embeddings of the before state and the tactic or align tactics between systems in a similar way to how concepts are already aligned between systems  [11].
Acknowledgements. This work was partially supported by the  ERC Starting Grant SMART  no. 714034 , the  ERC Consolidator  grant  AI4REASON  no. 649043 , the  European Regional Development Fund  under the  Czech  project  AI&Reasoning  no. CZ. 02. 1. 01/0. 0/0. 0/15_003/0000466 , the Cost action  CA20111   EuroProofNet , the  ERC-CZ  project  POSTMAN  no. LL1902 ,  Amazon Research Awards , and the  EU   ICT-48 2020  project  TAILOR  no. 952215.
In recent years, research in RDF archiving has gained traction due to the ever-growing nature of semantic data and the emergence of community-maintained knowledge bases. Several solutions have been proposed to manage the history of large RDF graphs, including approaches based on independent copies, time-based indexes, and change-based schemes. In particular, aggregated changesets have been shown to be relatively efficient at handling very large datasets. However, ingestion time can still become prohibitive as the revision history increases. To tackle this challenge, we propose a hybrid storage approach based on aggregated changesets, snapshots, and multiple delta chains. We evaluate different snapshot creation strategies on the BEAR benchmark for RDF archives, and show that our techniques can speed up ingestion time up to two orders of magnitude while keeping competitive performance for version materialization and delta queries.
I. INTRODUCTION The ever-growing nature of RDF data and the emergence of large collaborative knowledge graphs have propelled research in efficient techniques for RDF archiving  [FUPK19] ,  [PGH21] , which is the task of keeping track of an RDF graph's change history. RDF archiving is of great value to both maintainers and consumers of RDF data. To the former, archives are the basis of version control [ANR + 19], which opens the door not only to novel data processing tasks, e. g. , mining of temporal and correction patterns  [PTBS19] , but also to temporal data analytics [RCS + 15],  [Bru10]. For data consumers, archives are a way to query the past and study the evolution of a given domain of knowledge  [HBS13] ,  [TS19] ,  [AMH21]. From a technical point of view, building and maintaining RDF archives is a very challenging endeavor, primarily due to the massive size of current knowledge graphs.
As of April 2022, DBpedia accounts for 220M entities and 1. 45B facts 1 , and changes from one release to the next one can be in the order of millions  [PGH21]. However, large changesets are not the only issue that challenges state-of-the-art RDF archive systems. For instance, DBpedia Live 2 receives continuous updates with changes made by the Wikipedia community. This dynamicity makes DBpedia's revision history extremely long, and exacerbates the challenges of managing an archive for a dataset of that nature. As shown in our experimental section, existing approaches for RDF archiving cannot ingest long 1 https://www. dbpedia. org/resources/knowledge-graphs/ 2 https://www. dbpedia. org/resources/live/ histories on large datasets, even when the changes between revisions are small. We therefore propose an approach to ingest, store, and query long revision histories on very large RDF graphs.
Our techniques rely on a combination of dataset snapshots and sequences of aggregated changesets -called delta chains [TSH + 19] in the literature. We evaluate our approaches on the BEAR benchmark  [FUPK19]  and show that our techniques can ingest the BEAR-B instant dataset in no more than 2 hours -something that so far has been beyond reach. Moreover, our techniques exhibit competitive runtimes for most types of queries on RDF archives. We implemented our approach on top of OSTRICH [TSH + 19], a state-of-the-art engine for archiving large RDF graphs. The remainder of this paper is structured as follows. Section II elaborates on the background concepts and the state of the art in RDF archiving. Our storage techniques are detailed in Section III, while Section IV explains how to query archives with multiple snapshots and delta chains.
A. Experimental Setup We resort to the BEAR benchmark for RDF archives  [FUPK19]  for our evaluation. BEAR comes in three flavors: BEAR-A, BEAR-B, and BEAR-C, which comprise a representative selection of different RDF graphs and query loads. We omit BEAR-C from our experiments because its query load consists of full SPARQL queries and diverse constructs, which are not supported by our implementation, nor by any other RDF archiving approaches. Table  II  summarizes the characteristics of the experimental datasets and query loads. Due to the very long history of BEAR-B instant, OSTRICH could only ingest one third of the archive's history (7063 out 21046 revisions) after one month of execution. In a similar vibe, OSTRICH took one month to ingest the first 18 revisions (out of 58) of BEAR-A. Despite the dataset's short history, changesets in BEAR-A are in the order of millions of changes, which also makes ingestion intractable in practice.
On these grounds, the original OSTRICH paper [TSH + 19] omitted BEAR-B instant and included only the first 10 versions of BEAR-A. Multi-snapshot solutions, on the other hand, allow us to manage these datasets. All our experiments were run on a Linux server with a 16-core CPU (AMD EPYC 7281), 256 GB of RAM, and 8TB hard disk drive. We evaluate the different strategies for snapshot creation detailed in Section III-B along ingestion speed, storage size, and query runtime. Except for our baseline (OSTRICH), all our strategies are defined by parameters that we adjust according to the dataset: Periodic. This strategy is defined by the period d. We set d ∈ {2, 5} for BEAR-A, d ∈ {5, 10} for BEAR-B daily, d ∈ {50, 100} for BEAR-B hourly, and d ∈ {100, 500} for BEAR-B instant. Values of d were adjusted per dataset experimentally w.
r. t. the length of the revision history and the baseline ingestion time. High periodicity, i. e. , smaller values for d, lead to more and shorter delta chains. Change-ratio (CR). This strategy depends on a cumulative change-ratio budget threshold γ. We set γ ∈ {2. 0, 4. 0} for all the tested datasets. γ = 2. 0 yields 10 delta chains for BEAR-A, as well as 5, 23, and 151 delta chains for BEAR-B daily, hourly, and instant, respectively. For γ = 4. 0, we obtain instead 6 delta chains for BEAR-A, and 3, 16, and 98 for the BEAR-B alternatives. Time. This strategy depends on the ratio θ between the ingestion time of the new revision and the ingestion time of the first delta in the current delta chain. We set θ = 20 for all datasets. This produces 3, 26, and 293 delta chains for the daily, hourly, and instant variants of BEAR-B respectively, and 2 delta chains for BEAR-A.
B. Ingestion Time Table III depicts the total time to ingest the experimental datasets. Since we always test two different values of d for the periodic strategy on each dataset, in both Table  III  and IV, we refer to them as "high" and "low" periodicity. This is meant to abstract away the exact parameters, which vary for each dataset, so that we can focus instead on the effects of higher/lower periodicity. We remind the reader that the baseline (OSTRICH) cannot ingest BEAR-A and BEAR-B instant in a reasonable amount of time. This explains their absence in Table  III. But even when OSTRICH can ingest the entire history (in less than 26 hours), a multi-snapshot strategy still incurs a significant speed-up. This becomes more significant for long histories as observed for BEAR-B hourly, where the speed-up can reach two orders of magnitude.