dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , increase of 20% of the size of the tumor radius). B Tumor radius evolution computed on each virtual patient (Box 1) and the time to progression is deduced from RECIST criteria. C Tumor radius is followed for each patient of the virtual population (Box 1) and represented with a Kaplan-Meier visualization of the probability of tumor non-progression (orange). N is the number of patients in the comparator experimental dataset. VPOP: virtual population; TTP: time to progression. Created with BioRender. com. • published xenografted mice data (see Table  3 )  [36] [37] [38] [39]  to calibrate tumor growth in xenograft mice. In addition, we performed two additional calibration steps to increase the context of use of the ISELA model to humans They both focus on finding values of the parameters related to human neo-angiogenesis, immune system, and treatment-resistant clones to reproduce the time to progression (TTP; i.
• We reproduced the population-level clinical data deduced from the NEJ002 trial  46  , and deduced correlations between patient descriptors. The extraction of the list of time-to-events (for both PFS and OS) was realized using R package digitize  47  , using the input survival times from graph reading; and the reported number at risk. TTP was inferred based on the clinical trial PFS and OS, as detailed in ref. 22. Therefore, the NEJ002 TTP dataset was deduced from the lists of time-to-events corresponding to the PFS and OS of the Maemondo/NEJ002 trial. Under the hypothesis that patients who died before disease progression are characterized by the same time to event in the PFS and OS sets, we filtered out PFS events that correspond to patients' death, leaving only the time-to-events corresponding to disease progression.
• The dataset was neither used for building nor calibrating the ISELA model. These are reported in Table  4. To be able to compare the ISELA model TTP to the LUX-LUNG 7 dataset, the disease progression endpoint was similarly     Details from smoking status were retrieved from ref. 52  also reporting analyses from the Lux-Lung 7 trial. a One of the patients with L858R was reported as having an additional mutation in ref. 52  but not in ref. 23. For Vpop comparison this virtual patient was considered in the L858R category. b Former smokers are considered as light ex former smokers from the paper; while current smokers are considered as other current or former smokers from the paper. derived from clinical PFS and OS, as explained in ref. 22  and detailed in the calibration context. Therefore, the Lux-Lung 7 TTP dataset was deduced from the lists of times-to-event corresponding to the PFS and OS of the Lux-Lung 7 trial.
The comparison of OS, PFS, and TTP is provided on Fig. 10. In the absence of information about which patients did not display tumor progression (and died without detectable progression), we assume that the distribution of patients characteristics is not altered in the subsets of patients who displayed tumor progression. Virtual population generation and statistical analyses for validation. The protocol described in Table  5  was applied to compare the simulated output with the clinical data set. The data processing and analysis were performed within R Software, version 3. 6. 1 or above. In particular, we used the following packages: survival, survminer, tidyr, data. table, jsonlite, and ggplot2. Sensitivity analysis. We chose to perform sensitivity analysis based on a tornado approach. In a nutshell, a population of 5000 virtual patients was generated, based on the characteristics of the general population (Supplementary Information), a 50/50 proportion of EGFR mutations (exon 19 deletions and exon 21 L858R point mutation) was used.
For each patient characteristic, patients were split in two categories: low value (those with value lower than the median) and high value (those with value higher than the median) and the median output of interest was computed for each category. The value used for the comparison of all parameters is the difference between the median output of interest in the complete virtual population minus the median output of interest in each of these two categories. The resulting values are plotted in tornado plots. The advantage of such an analysis is that it does not rely on statistical hypotheses on the distribution of the impact of the parameter on the output of interest. Fig. 10  Overall survival (OS, gray), progression-free survival (PFS, light blue), and time to progression (TTP, dark blue curve) from the Lux-Lung 7 dataset.
TTP corresponds to the PFS curve after removal of dead and censored patients. Median OS (24. 4 months), PFS (10. 9 months), and TTP (9. 0 months) are represented with dotted lines highlighting the effect of data-process on time corresponding to the median probability. OS and PFS data were manually extracted from ref. 23  , processed and plotted in R, version 3. 6. 3, with the packages survival (version 3. 1-8) and survminer (version 0. 4. 8). Table  5. Summary of the 5-step validation protocol. Step name Process 1. Generation of the virtual population Known distributions of patient baseline characteristics are extracted from available data, indicated on Table  4. When information was only partial, some assumptions were made in order to obtain a well-defined distribution: • Standard deviation was estimated from quantiles when not available.
• We assume that the population only contains common EGFR mutations (exon 19 deletions and L858R point mutation). For patients and population characteristics not reported in ref. 23  (e. g. , preponderance of KRAS (standing for K isoform of rat associated sarcoma protein) mutation, proportion of patients with mutation co-occurring with EGFR mutations, exact TNM (tumor, lymph node, and metastatic) status), distributions and correlations identified on the general population (from refs. 27, [53] [54] [55] [56]  ) and from calibration steps 3 and 4 were used. The generated virtual population was 10 times bigger than the real population, to allow bootstrapping and estimation of a prediction interval (PI). For all virtual patients, duration of follow-up, which corresponds to the maximal simulated time, corresponds to the period reported in the experimental clinical dataset.
Computation of validation metrics The final validation metrics are the following: Raw coverage: The raw coverage is defined as the percentage of the Kaplan-Meier curves extracted from ref. 23  that lie within the prediction interval around the simulated curve in the Kaplan-Meier visualization. A validation threshold was set to a coverage of 80% of the observed curve by the PI. Bootstrapped log-rank test: The bootstrapped log-rank (LR) test consists of performing multiple LR tests, each comparing TTP distribution between the one from a sampled population taken from the Virtual population, namely bootstrapped population, with the TTP distribution of the experimental dataset. The virtual bootstrapped population size used for each test was equal to the size of the related real population. We computed 5000 LR tests as advised in ref.
Special honest verifier zero-knowledge (SHVZK): There exists a polynomial-time simulator that, given a statement x and a challenge chall, outputs a valid transcript (com, chall, resp) that is indistinguishable from a real transcript. Definition 5. A Σ-protocol (P 1 , P 2 , V) is computationally special honest verifier zero-knowledge if there exists a probabilistic polynomial time simulator Sim such that for all probabilistic polynomial time stateful adversaries A Pr   A(com, chall, resp) = 1 (x, w, chall) ← A(1 λ ); com ← P 1 (x, w); resp = P 2 (chall)   ≈ Pr A(com, chall, resp) = 1 (x, w, chall) ← A(1 λ ); (com, resp) ← Sim(x, chall). If the above indistinguishability holds statistically against all unbounded adversaries A, the protocol is said to be statistically SHVZK.
Let Sim Σ be a ZK simulator that simulates an accepting transcript for the underlying Σ-protocol (as described in the proof of ZK for Σ). We construct a ZK simulator Sim NIZK that simulates an accepting proof as follows: 1. Sim NIZK simulates the random oracle H as follows: it maintains a local table consisting of tuples of the form (x, y) ∈ {0, 1} * × {-1, 0, 1} N. On receiving a query x ∈ {0, 1} * from the adversary A, it looks up this table to check if an entry of the from (x, y) exists. If yes, it responds with y. Otherwise, it responds with a uniformly sampled y ← {-1, 0, 1} N , and programs the random oracle as H(x) := y by adding the entry (x, y) to the table. 2. For each i ∈ [N ], Sim NIZK internally invokes the simulator Sim Σ for the underlying Σ-protocol to obtain the i-th accepting transcript of the form ((com 2,i , com 3,i ), chall i , resp i ).
3. At this point, Sim NIZK aborts if the adversary A has already issued a random oracle query on the input x = (com 2,1 , com 3,1 ),. , (com 2,N , com 3,N ). 4. Otherwise, Sim NIZK programs the random oracle as H (com 2,1 , com 3,1 ),. , (com 2,N , com 3,N ) := (chall 1 ,. , chall N ), and outputs the simulated proof as Π = {(com 2,i , com 3,i , resp i )} i∈[N ]. We note that Sim NIZK runs in polynomial time as long as Sim Σ runs in polynomial time. Additionally, if Sim NIZK does not abort, it outputs a simulated proof that is distributed in a statistically indistinguishable manner from the distribution of a real proof, assuming that Sim Σ outputs a simulated accepting transcript with distribution statistically indistinguishable from a real accepting transcript for Σ. Finally, Sim NIZK aborts with only negligible probability, since the adversary A guesses ((com 2,i , com 3,i ), chall i , resp i ) for each i ∈ [n] with at most negligible probability.
The resulting timings are shown in Table  2. They demonstrate that the algorithm is highly practical and can realistically be used within a trusted setup protocol: Generating proofs of knowledge for all four base fields takes less than five core-minutes on a modern CPU. Note that these algorithms need to be run only once per contributor. Real-world deployment. We briefly discuss how we intend to deploy the trusted setup protocol proposed in Section 5. The goals of such a deployment include include a transparent setup that allows parties to trust the process, a low bar of entry to participate in the protocol, and a secure system that can withstand Sybil and Denial-of-Service (DoS) attacks. Firstly, we are releasing at https://github. com/trusted-isogenies/ a set of tools that participants can download and run to generate a valid addition to the trusted setup, and for ceremony orchestrators to validate protocol submissions on the server-side.
This work began at the  Banff International Research Station  workshop " Supersingular Isogeny Graphs in Cryptography " ( 21w5229 ). This research was funded in part by the  MIUR Excellence Department Project MatMod@TOV  awarded to the  Department of Mathematics, University of Rome Tor Vergata , the  Commonwealth Cyber Initiative , the  Academia Sinica Investigator  Award  AS-IA-109-M01 , the  Agence Nationale de la Recherche  under grant  ANR   MELODIA  ( ANR-20-CE40-0013 ), and the  France 2030 program  under grant agreement No. ANR-22-PETQ-0008 PQ-TLS. Date of this document:  2023-02-24.
Despite the wide availability of large motion databases, and recent advances in exploiting them to create tailored contents, artists still spend tremendous efforts in editing and adapting existing character animations to new contexts. This motion editing problem is a well-addressed research topic. Yet only a few approaches have considered the influence of the camera angle, and the resulting visual features it yields, as a mean to control and warp a character animation. This paper proposes the design of viewpoint-dependent motion warping units that perform subtle updates on animations through the specification of visual motion features such as visibility, or spatial extent. We express the problem as a specific case of visual servoing, where the warping of a given character motion is regulated by a number of visual features to enforce.
Implementation We implemented our technique using Unreal Engine. The approach runs at interactive frame-rates (>30fps on a computer equipped with an Intel Core i7-9850H CPU @ 2. 60 GHz, 32 GB of RAM, Nvidia Quadro T2000) and can be used in interactive and non-interactive contexts. Estimators (see Section 3. 2) were implemented using shaders, while the visual motion feature vector was computed through multiple rendering passes. The warping operators were built above Unreal control rigs (see Section 3. 3) which provide a direct access to the degrees of freedom of the skeletal structure of animated characters. Our virtual agent is based on a Meta-humans model. The baseline motions (unwarped) were either recorded using a motion capture system (Xsens suit) or taken from a public database (Mixamo).
Acknowledgements The authors wish to thank the participants of the experiment. This work received funding from the  European Union  under grant agreement No  856879  ( H2020 ICT-25  RIA  PRESENT  project), as well as the  Brittany Region , project  SAD-2019-REACTIVE , No 19023. The authors would like to thank  Alexander Oliva  for his help and guidance in implementing the Jacobian and visual servoing aspects of the work.
To evaluate it, we describe in Section 5 an approach to derive minimum-size contrastive explanations and present some empirical results showing that this approach can be used in practice. A folder containing a full-proof version of the paper, a more detailed description of the datasets, and the code used in our experiments is available online at http://www. cril. univ-artois. fr/expekctation/; this code is also part of our XAI library PyXAI (https://www. cril. univ-artois. fr/pyxai/).
Empirical evaluation We have run some experiments in order to assess to which extent minimum-size contrastive explanations can be computed in practice. Setting Our empirical protocol was as follows. We have focused on 20 datasets (some of them based on numerical, categorical, and Boolean attributes) for binary classification, which are standard benchmarks from the repositories Kaggle (www. kaggle. com), OpenML (www. openml. org), or UCI (archive. ics. uci. edu/ml/). In these datasets, the number of attributes (features) varies from 4 to 20000, and the number of instances from 170 to 32561. For each dataset, random forest classifiers F have been learned using the Scikit-Learn library (version 1. 0. 2)  [36]. Categorical features have been one-hot encoded. Numerical features, have been binarized on-the-fly by the random forest learning algorithm.
The domain theory Σ used was Σ(F ). All hyper-parameters of the learning algorithm have been set to their default value (in particular, 100 trees per forest and no depth bound on the trees). Indeed, the approach to XAI 1 If x is such that f (x) = 0, then consider f instead of f. we follow, though exact (we do not approximate the predictor F nor the explanations), is post-hoc, i. e. , it is intended to take place once F has been learned, whatever its accuracy. As it can be observed, using default parameters led to quite a good accuracy for almost all datasets under consideration (see Table  1 ), but not in every case (especially, for the breast Tumor dataset), and again, this is on purpose since our objective is to derive explanations suited to predictors as they are, and not as they could have been.
What we want to evaluate is the ability to derive explanations in practice even if the accuracy of the predictor is rather low. For every dataset, a 10-fold cross validation process has been achieved. For each dataset, each random forest F , and a pool of 10 instances x over C f drawn at random from the test set and satisfying Σ (leading to 100 instances per dataset), we have run our algorithm for computing a minimum-size contrastive explanation c for x given (F, Σ). For making the experiments, we took advantage of the PyXAI library (https://www. cril. univ-artois. fr/pyxai/) and used the openwbo (WEIGHTED) PARTIAL MAXSAT solver  [32]. For each dataset, we counted the number of instances (out of 100) for which a minimum-size contrastive explanation has been computed in due time (a time-out (TO) of 100s has been considered per instance).
For each instance for which the computation has been successful, we measured the time needed to get the result and the size of the resulting contrastive explanation. All the experiments have been conducted on a computer equipped with Intel(R) XEON E5-2637 CPU @ 3. 5 GHz and 128 Gib of memory. Results A synthesis of the results obtained is provided in Table  1. The columns give, from left to right, the name of the dataset, the number of attributes A its is based on, the mean number of Boolean variables from CF used in the random forests that have been generated, the mean number of attributes from A used in the random forests that have been generated, the mean accuracy of the random forests. The two remaining groups of columns are about the performance of our algorithm for computing minimum-size contrastive explanations c, in terms of run time and size of the explanations, respectively.
TOs were met frequently only for the largest datasets (mnist38, christine, gisette and dexter), involving hundreds attributes and leading to random forests based on more than 7500 Boolean conditions. When no TOs occurred (value 0 in column #TO of Table  1 ), each of the 100 contrastive explanations that have been derived for the dataset under consideration is guaranteed to be of minimal size. Seemingly, the accuracy of the predictor does not have any impact on the time needed by our algorithm for deriving a minimum-size explanation. Especially, for the datasets for which the computation always terminated in due time, minimum-size contrastive explanations have been derived in a short amount of time (in average, 13. 55s). We also ran additional experiments with a larger time-out (1200s per instance).
Acknowledgements The authors would like to thank the anonymous reviewers for their comments and insights. This work has benefited from the support of the  AI Chair EXPEKCTATION  ( ANR-19-CHIA-0005-01 ) of the  French National Research Agency (ANR). It was also partially supported by  TAILOR , a project funded by  EU Horizon 2020 research and innovation programme  under  GA  No  952215.
Current carbon map and model comparisons We compared our aboveground carbon-density maps (foliage + wood) derived from individual trees with current state-of-the-art maps (Fig. 2  and Extended Data Fig. 3 ) available at moderate spatial resolutions of 30-1,000 m. The temporal dynamics were assessed by low-frequency passive microwaves (L-VOD)  36, 37  , which has emerged as a tool for the assessment of carbon stock dynamics at the 25 × 25-km spatial scale (Extended Data Fig. 4 ). Moreover, we compared carbon-density maps and dynamics with dynamic ecosystem models from the TRENDY database with a 50 × 50-km grid cell size  23. None of these maps were designed specifically for drylands; most dynamic ecosystem models and satellite-based models are developed and trained for forest ecosystems and, in the case of the TRENDY models, used meteorological forcings and prescribed vegetation maps that contain further uncertainties for comparative purposes.
Nevertheless, the divergence between our results and previous assessments in higher-rainfall zones needs to be further investigated and our maps should be used with caution beyond 800 mm year -1 rainfall. The indirect inclusion of the tree height and the application of the same equation to all tree species are uncertainty factors that will be assessed in future versions of the dataset. Finally, the fact that larger trees shade out smaller trees in areas of dense tree cover makes the method based on individual tree counting less suited to more humid areas.
Satellite imagery We used 326,523 Maxar multispectral images from the QuickBird-2, GeoEye-1, WorldView-2 and WorldView-3 satellites collected from 2002 to 2020 from November to March from 9. 5° N to 24° N latitude within Universal Transverse Mercator (UTM) zones 28-37 for Africa (Extended Data Table  1a ). These images were obtained by NASA through the Next-View License from the National Geospatial-Intelligence Agency. Data were assembled over several years with a focus on later years to achieve a relatively recent and complete wall-to-wall coverage. When using satellite data from different satellites over several years, with varying sun-target-satellite angles, with varying radiometric calibration of satellite spectral bands and different atmospheric compositions through which the surface is imaged, there are two possibilities for using hundreds of thousands of satellite images together quantitatively.
We included trees viewed substantially off-nadir, trees collected under different aerosol optical thicknesses, trees collected under cirrus cloud conditions, trees viewed in the forward and backward scan directions, trees on sandy soils, trees on clay soils, trees on burn scars, trees in laterite areas and trees in riverine settings. Our training data were collected by one team member and are a carefully selected manual delineation of 89,899 individual trees under a range of atmospheric conditions, viewing perspectives and ecological settings. All multispectral and panchromatic bands associated with our Maxar images were orthorectified to a common mapping basis. We next pan-sharpened all multispectral bands to the 0. 5-m scale with the associated panchromatic band. The absolute locational uncertainty of pixels at the 0.
5-m scale from orbit is approximately ±11 m, considering the root-mean-square location errors among the QuickBird-2, GeoEye-1, WorldView-2 and WorldView-3 satellites (Extended Data Table  1 ). We formed the normalized difference vegetation index (NDVI)  39  from every image in the traditional way from the pan-sharpened red and near-infrared bands. We also associated the panchromatic band with the NDVI band and ensured that the panchromatic and NDVI bands were highly co-registered. The NDVI was used to distinguish tree crowns from non-vegetated background because the images were taken from a period when only woody plants were photosynthetically active in this area  36. Our training data were labelled on images from the early dry season when only trees have green leaves. Because most semi-arid savannah trees continue to photosynthesize in the early dry season after herbaceous vegetation senesces, green leaf tree crowns are easily mapped because of their higher NDVI values than their senescent herbaceous vegetation surroundings.
Allometry Very-high-resolution satellite images and deep learning have achieved mapping of individual trees over large areas  1. Each tree is georeferenced in the satellite data and defined by crown area. The challenge was to develop allometric equations for foliage, wood and root dry masses or carbon based on crown area regardless of species. This was met by reanalysing existing Sahelian and Sudanian woody plant data from destructive sampling. Overall, the seasonal maximum foliage, wood and root dry masses were measured on 900, 698 and 26 trees or shrubs from 27, 26 and 5 species, respectively, for which crown area was also measured. Several allometric regression models tested for foliage, wood or root masses are power functions and independent of species. All the regression outputs were inter-compared for fit indicators, by systematic estimates of prediction uncertainty and by root-to-wood ratios and foliage-to-wood ratios over the range of crown areas.