dataesr/scientific-paragraphs-categorization huggingface dataset:
they were selected to include typical target phrases, names, etc. , which occur with a minimum frequency. Evaluation data consists of 200 utterances (50 per language), and 36 queries (9 per language), selected using the same criteria. Participants were allowed to use any additional resources they might have available, as long as their use is documented. Participants received development audio data (documents and queries) as well as matches between queries and documents, and had five weeks to develop systems, before they also received the evaluation audio data. Results were due another five weeks later. There was no overlap between development and evaluation speakers, samples, and queries. Participants also attempted to detect occurrences of development queries on evaluation data, as well as evaluation queries on development data.
Data was provided as a "term-list" XML file, in which the "termid" corresponds to the file-name of the audio query. This information was distributed along with a modified version of the NIST 2006 Spoken Term Detection (STD) evaluation scoring software  [1]. The primary evaluation metric was ATWV (Average Term Weighted Value), computed with the default values for the 2006 STD evaluation with respect to density of search terms, etc. , but with "similarity" and "find" thresholds set to 20 s.
Phone Recognition -MUST Aiming at both speaker independence and robustness with respect to recognition errors in the spoken queries, we implemented a phonebased system. The main dataset used for acoustic modeling was 60 hours of spontaneous conversations in colloquial Hindi. There are 996 native Hindi speakers and all conversations range between 1 and 4 minutes in duration. All conversations are transcribed and a basic pronunciation dictionary is provided. A standard HMM-based recognizer was constructed using the Hindi data. The list of mono-phones was reduced from 62 to 21 units in order to work with a small set of broad but reliable classes, appropriate to the later scoring tasks. The speech data and transcriptions provided were cleaned using an aggressive form of garbage modeling, and the resulting data used to Maximum-A-Posteriori-adapt the Hindi acoustic models to the task domain (and languages).
The probability distribution function PDF of all uncertainty components are defined. Correlation between input quantities are taken onto account. For instance uncertainty due to the stray light correction is evaluated based on equation 6: ( ) Std True S D S 1 1 - + = i Std i True i E S CAL , , = i i Sun i Sun CAL S E , , = (6) (j=1 to N), where N is the number of wavelengths taken into account. The S Std,j elements are correlated as well the (1-D) i,j elements. The multivariate Gaussian algorithm  [1]  is used to evaluate the uncertainty associated to these correlated input quantities. A software using Matlab is under development to evaluate the uncertainty. The validation will be performed using data from the various tasks of WP2. A guideline will be issued to allow a proper use of the software.
MatrixExp ( , , ) ( , , ) Tr MatrixExp ( , , ) i c i i c H x D f x D H x D               (4) In the limit 0   , Equation (4) reduces appropriately to the generating functions derived in previous work for the corresponding systems  (Kaneyoshi et al. , 1993; Khater & Abou Ghantous, 2011; Abou Ghantous & Khater, 2011). The operation of Exp in Equation (  4 ) is defined in the Mathematica package as the code MatrixExp. To illustrate these generating functions, we give in the appendix the exact Van der Wearden identity for S = 2 and its characteristic functions as deduced from Equation (4), omitting the anisotropy therein for simplicity. The thermodynamic canonical averages in EFT concern the appropriate set of spin operators in the system. For the present calculation, these are the longitudinal and transverse magnetizations, namely m z = <S zi > and m x = <S xi >, the single site component correlations 2 zz z i q S  and 2 xx x i q S  , and the single site cross correlations zx zi xi zz xi zi q S S q S S    , where i denotes a given site on the core and periphery domains.
The computations of these variables are developed using the exact Van der Wearden identities. For spin S = 1, for example, this identity is 2 2 exp( ) cosh( ) sinh( ) 1 z z z z JS S J S J S        (5) where ∇ = ∂/∂x is the differential operator. In Equation (  5 ) the operators for the magnetizations S z and the single site spin correlations 2 z S are clearly identified under the identity. The EFT approach is general and may be applied to higher spin values with corresponding Van Der Wearden identities, but for these systems one needs to make appropriate decoupling approximations for the multiple-spin correlations as shown for the S = 2 system, for example, in the appendix. The developed Mathematica package that pilots the code to calculate Equation (  4 ) consists of a number of symbolic and numerical procedures.
We give here only those for m zp and q zxp as a sample representation 2 4 (0) (1) (2) (4) zp z p z p z p m u u m u m u m     (11) 2 4 (0) (1) (2) (4) zxp zp zp zp q p p m p m p m     (12) The coefficients a(n), e(n),. , and u(n), p(n),. , in Eqs. (9, 10,. ; 11, 12. ) are in general extensive polynomial functions of the temperature kT, core variable q zz , and system constants (Ω, J, D c , D p ). They are determined exactly from the numerical results obtained by applying the symbolic and numerical procedures of the Mathematica package code. It is in this sense that Equations (9, 10,. ; 11, 12. ) are accurate numerically generated results. The ensembles of the nonlinear polynomial equations are obtained directly in our procedure; however, they will not be detailed here because of their extensive symbolic and numerical character.
Summary and Conclusions In this work we present an Ising spin EFT model to investigate the consequences of an applied transverse field for the magnetic properties of a 2D mono-layer nano-island. A non-diagonal Ising Hamiltonian with nearest neighbor exchange, single-atom magnetic anisotropy, and a transverse magnetic field term, defines the ground state of the system. The choice of Ising spin S = 1 and S = 2 systems for the nano-island, permits the analysis of the effects of spin fluctuations via the single-atom spin correlations. To avoid approximations inherent to previous analytical treatments of the Ising spin Hamiltonian which presents diagonal and off-diagonal terms, a novel symbolic and numerical approach is developed. The procedure consists of using defined codes in Mathematica package for the density matrix over any given characteristic spin operator.
Data-driven and context-specific predictive models. Here, we take a unique modeling approach to construct context specific, de novo and predictive network models of signaling pathways from drug perturbation data (Figure  1 ). Here, de novo means that network inference is done without depending on known molecular interactions, e. g. , extracted from literature or pathway databases that are not curated to account for biological context. This approach also emphasizes context specificity since it relies on rich experimental data from a single biological context as its training set. The models are constructed through parameterization of a simple model equation, which has been used in other network modeling approaches  [13, 23, 24, 25, 26, 27]. The ansatz of the model equation yields parameters that are both mechanistically descriptive of system interactions (Supplemental Figure  S15 ) and computationally predictive of cell-type specific response to new drug perturbations and perturbation combinations.
Thus, quantitative models of protein signaling pathways are typically constructed on the basis of existing prior knowledge from literature searches  [16, 17]  and interaction databases  [19, 28, 29]. However, different cancer contexts have unique genetic and proteomic alterations to normal protein signaling. Moreover, signaling pathways have a highly nonlinear nature with negative feedback loops, non-overlapping pathways and cross-talk etc. For example, distinct mutations in effector proteins of the PI3K pathway confer oncogenicity in unique ways leading to highly context dependent functional consequences across different cancer types, subtypes and patients  [30]. The method we introduce in this document is capable of inferring parameterized network models with or without prior knowledge. We test our method without prior knowledge.
We demonstrated that the technology successfully captured already known interactions and predicted new ones in signaling pathways. We tested the quantitative descriptive power of the method by quantifying the ability to reproduce the response profiles from withheld experiments. We then used in silico simulations and to predict the effects of novel drug perturbations, particularly the inhibition of Polo-like Kinase 1 (PLK1). The success of BP depends on whether or not the simulations of the models taken from BP are quantitatively predictive of cellular response to drug combinations. It is also useful, however, to evaluate the overall performance of the BP algorithm on data sets engineered from completely known networks. With such toy datasets we (i) demonstrated that BP converges quickly and correctly, (ii) compared BP network models to the known data-generating network, and (iii) evaluate performance in biologically realistic conditions of noise and sparse perturbations.
To do this, one toy data generator was constructed for each of the ten different sizes from N=10 to N=100. The number of training patterns equaled the number of nodes for consistency of comparison, i. e. M=N. Both methods searched a very large parameter space of 41 possible parameter assignments with Ω = {-2,-1. 9,…,1. 9,2. 0}; thus for this toy dataset the search space of all configurations was of order 41 N. The first criterion of interest was time of convergence (Figure  3A ). For both methods, the time required for convergence increases as the size of the system increased, but MC was consistently about three orders of magnitude slower than BP. The speed advantage of BP is vital for our ability to scale up the size of de novo model construction to hundreds of proteins, phospho-proteins and cellular phenotypes.
The second criterion was accuracy. While we were interested primarily in the accuracy of predicting responses to new perturbations, we were also interested in the accuracy of the inferred interactions as an indicator of the models' explanatory and predictive power. In practice, we found that BP did no worse than MC on these datasets given reasonable termination conditions for MC (Supplemental Text-S5). A Pearson correlation coefficient between the set of non-zero parameters in the data generators and the average values inferred by BP (blue) was a reasonable measure of agreement between true and inferred parameters. BP resulted in a correlation of R=0. 7, which was quite high considering the relatively small number of training patterns used for each inference (M=N). As the number and quality of training pattern increases, the correlation approached 1 (Supplemental Text S1, Supplemental Figures  S2  and S3 ).
Complete knowledge of the perturbations and off-target effects was used for the noise-free results, while only knowledge of the main targets was used for the noisy data results thus simulating inference on drugs with unknown off-target effects. We simulated the system to steady state in response to each condition of 14 in silico drugs applied individually and in pairs. The steady state profiles were recorded and used as the training data, while those conditions that oscillated were excluded. Ultimately, the predictive power of the inferred models can only be assessed by explicit simulation of individual models and comparison with experiment. However, the average value of the BP inferred probability distributions can be used in a descriptive sense and either guide human intuitive understanding of biological pathways or be compared to prior knowledge.
For each experimental condition, i. e. , for each perturbation, three biological replicates were measured. The (phospho)-protein levels were quantified from spot intensities on the protein arrays and log normalized with respect to the corresponding protein levels from non-perturbed control experiments (response=log 2 (perturbed/unperturbed) ). Viability of the cells after drug perturbation was measured using a resazurin assay 72 hours after the cells were perturbed. Overall, log ratios for 16 protein intensity readouts and the cell viability phenotype in 44 perturbation conditions constitute the training data for de novo network inference, from a series of experiments using all combinations of 8 drugs (28 experiments) and 2 drug concentrations for single drug perturbations (16 experiments), (Figure  5 ).
Cell viability measurements at 72hrs were used to ensure the phenotypic responses reached to steady state and significant phenotypic response was observed as a consequence of changes in relatively early proteomic responses to drug perturbations. Indeed, analysis of cell viability changes at 0, 24, 72 and 120 hrs after drug perturbations revealed no significant changes in cell viability at 72 and 120 hrs, whereas the cell viability change at 24hrs was not at steady state (data not shown).
Leave--k--out tests of predictive power of network models In order to test the accuracy and the predictive power of the models, we used a leave-kout validation strategy. For each leave-k-out test, we withheld data from k experiments, inferred network models on the remaining experimental data, predicted molecular responses corresponding to those in withheld experiments through numerical simulations and compared the predictions with the experimental observations in the withheld experiments. Specifically, for the SKMEL-133 cell line, network models were inferred using a set of perturbation experiments that includes paired combinations of 7 drugs + 1 drug applied as a single agent in two different doses so that 7 unique combinations of the single agent are withheld. For each partial dataset, we generated 1000 network models with the BP guided decimation algorithm.
Overall, eight sets of network models were generated by withholding experimental information for all possible combinations with one drug at a time, but using data from all single drug experiments. The network models were numerically simulated using the model equation 1 and virtual perturbations mimicking the perturbations in withheld experiments to predict the proteomic and phenotypic responses to drug combinations. Response trajectories in time were generated for each read-out until the system reaches steady state and the predicted steady state response was computed by averaging the simulated steady state values over network models, using even weights for all 100 models with lowest error, for simplicity. Comparison of the predicted steady state response profiles for each condition with the values in withheld experiments indicated good agreement between the experimental and predicted response profiles (Figure  6 ).
Furthermore, since BP performed as well or better than MC, which employed neither assumption 3 nor 4, we suspect by process of elimination that assumption 2 is the largest contributor to the observed limitations of our current method. For real biological data, the quality of BP's performance depends on the applicability of the model equations and the richness and quality of the perturbation data used for training. We have previously published an MCF7 breast cancer cell line dataset, for which network models were inferred with a nested Monte Carlo search algorithm (CoPIA)  [94]. Reassuringly, many of the strongest interactions from CoPIA are recovered by the BP method (Supplementary Text-S2, Figure  S10 ). We have also modeled the same SKMEL-133 dataset with Gaussian Graphical Models, a popular probabilistic model that goes beyond pairwise correlations to distinguish between direct and indirect correlations.
Drug specificity also affects the predictive power of the resulting models. All effects from the AKT inhibitor, for example, are assigned to single knockdown of the aAKT node, even if other targets of the AKT inhibitor are partially responsible for the measured outcomes. Therefore, any simulation-based prediction regarding perturbation of the aAKT node will be tethered to the off-target effects of the AKT inhibitor used in training. The complete solution of the drug specificity problem requires a more comprehensive and systematic analysis to determine the effect of off-target effects on quality and predictive power of inferred models. Failed predictions and optimal design of experiment. The detailed analysis of the predictions reveals few outlier points, where the prediction of the response profiles from incomplete datasets fails.
A straightforward and powerful extension is the systematic use of prior information, in the form of quantitatively expressed network models adapted from the current scientific literature or pathway databases. Such prior information is easily incorporated as a set of additional constraints on the probability distributions over W in the cost function (Equation  3 ). We are also actively pursuing a method for the systematic inclusion of prior knowledge interactions from curated databases. On the experimental side, measurement of richer phenotypic attributes of cells, such as apoptosis or cell cycle arrest, as well as markers of differentiation states would much increase the predictive power by providing more links between molecular and phenotypic quantities. The network pharmacology approach described here provides a strong tool for a system level description of signaling events in cancer cells and moreover, it presents a step forward in quantitative prediction of responses of cancer cells to drug perturbations.
To reach the desired order of connectivity, these two coefficients are adjusted using a series of systematic belief propagation (BP) calculations with β and λ in the interval (0, 5. 0]. In each BP calculation, the most probable value observed in probabilities is assigned as the edge value, for each interaction; this allows the calculation of the total number of edges with non-zero values and thus of the average connectivity. As a result, β and λ values are chosen such that the desired level of connectivity is ~1. 5 edges per node. These values (β=2, λ=5) are used for the computation of the instantiated solutions in subsequent decimation calculations. For each node, is taken as 1 and is estimated from the dynamic range of each proteomic measurement sampled in the biological dataset. The and parameters are further optimized with a gradient descent algorithm.
For example, fixing a particular xi value to 0 accounts for a homozygous deletion in the corresponding gene. One may also fix the value of xi to a positive value to model the impact of amplification of a gene product (e. g. , DNA copy number change). RNAi perturbations can be modeled similarly to targeted drug perturbations. The perturbations We subsequently evaluated recall and precision (Figure  S2B ,C respectively) of the BP models trained to the two data sets. The BP models from the extended data set reach a higher recall and do so faster than for the pair perturbation data. Similarly, the precision curve for the extended data set lies consistently much higher than for the pair perturbation set. Finally, as we increase the number of experiments from the pair perturbation data set (by allowing additional nodes to be targeted) we definitely see that we get new information about the true interactions since the recall curve goes up.
However, the novel information per additional experiment is fairly low and yields networks with increasing numbers of interactions. These three results confirm that using BP against more informative data produces superior network model, and that different strategies of perturbing the system yield training patterns with different information. Although we see similar correlations between pair perturbation datasets in biological experiments (data not shown), it is not clear if the alternative strategy proposed here that works on the synthetic data generator would hold in the biological setting due to unexpected drug-drug interactions. Furthermore, we were interested in looking at BP results when Assumption 2 is not at play. There are many assumptions in the BP method described in the main manuscript.
We study the strong normalization of a new Curry-Howard correspondence for HA+EM 1 , constructive Heyting Arithmetic with the excluded middle on Σ 0 1 -formulas. The proof-term language of HA + EM 1 consists in the lambda calculus plus an operator a which represents, from the viewpoint of programming, an exception operator with a delimited scope, and from the viewpoint of logic, a restricted version of the excluded middle. We give a strong normalization proof for the system based on a technique of "non-deterministic immersion".
Plan of the Paper This is the plan of the paper. In §2 we introduce a type theoretical version of intuitionistic arithmetic HA extended with EM 1. In §3, we present the system HA + EM 1 and prove that it can simulate the reduction rules of HA + EM 1. In §4 we define reducibility for HA + EM 1 and in §5 we prove its main properties. Finally, in §6 we prove that this reducibility is sound for HA + EM 1. As a corollary, we deduce that HA + EM 1 is strongly normalizing. 2 The System HA + EM 1 In this section we formalize intuitionistic Arithmetic HA, and we add an operator a formalizing EM 1. We start with the language of formulas. Definition 1 (Language of HA + EM 1 ) The language L of HA + EM 1 is defined as follows. 1. The terms of L are inductively defined as either variables α, β ,. or 0 or S(t) with t ∈ L.
* This work was supported by the  LABEX MILYON  ( ANR-10-LABX-0070 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR)  Γ P[t 1 /α] Γ eq(t 1 ,t 2 ) Γ P[t 2 /α].
Gavrinis The raising of digital stones Abstract -The Tumulus of Gavrinis, which is located in Brittany, is one of the best known monuments of Neolithic art because of its extraordinary engravings. A joint team of archaeologists and architects began recording the monument using digital multiscalar technology in 2011. The scanning was complicated by the narrowness of the inner spaces, uncomfortable conditions, granulometry of the stone, and extensive vegetation coverage. A huge database was created by using a combination of photogrammetric technology and low-tech scanning methods. A lot of post-production work was needed to turn the cloud of data into a clean 3D model, since the original tumulus is irregular and lacks any repetitive patterns or predictive geometry. The goal was to obtain a detailed geometric analysis of the tumulus in order to develop a better understanding of its architecture and engravings, which are sometimes less than a millimeter deep.
A. Collecting the initial data The overall process started with a raw Sketchup model based on C. -T. le Roux's and E. Shee-Twohig's renderings. As Gavrinis is situated on an island, which is only accessible by boat, and has a poor electricity supply, everything had to be carefully prepared. The main problem that emerged was the narrowness of the interior spaces. It forced us to imagine the scanning process as a progression from outside to inside, with a large overlap. The survey involved recording geolocational data with different scales of acquisition. Thus the whole cairn and surrounding ground were scanned using a Leica Geosystems C10 which is a time-of-flight system. Then, the walls of the tomb and the upper face of the cover slab were recorded with a Nikon Krypton K610 scanner with infra-millimetric resolution associated with Kube software.