dataesr/scientific-paragraphs-categorization huggingface dataset:
Hsp27 knock-out targets The differences in muscle protein abundance between HspB1-null mice (n=5) and their controls (n=5) were assessed by analysis of variance (ANOVA) using XLSTAT Software  [19]. The effects tested in the model included muscle (M), genotype (G), and muscleXgenotype interaction (MXG). Results are expressed as the LS-mean ± standard error of mean (SEM). A difference between groups was considered significant when P<0. 05.
Results The first step of our study was to build a network of the Hsp27 nearest neighbours (direct interactors) using the Pathway Studio software according to the information stored and shared in bioinformatics databases of mammalian experiments. As shown in Table  2 , the network comprised 34 proteins predicted as direct interactors of Hsp27, but was not a hub in the tenderness network  [9]. A gene ontology analysis indicated that these proteins belonged to different biological processes such as the response to heat, apoptotic process, and response to oxidative stress. As the initial Hsp27 network was built independently of tenderness, we performed an intersection between both networks to keep the Hsp27 interactors potentially linked to beef tenderness. Thus we compared the list of the Hsp27 neighbours with the 330 proteins of the tenderness network.
Data The altimeter data used in this study are provided by the DU-ACS system  (Dibarboure et al. , 2011)  and operated by the Sea Level Thematic Assembly Center (SLTAC) of the My-Ocean project (http://www. myocean. eu. org/). These products are obtained from the Level 2 altimeter data (i. e. , Geophysical Data Record, GDR), available under three forms: Fast Delivery product and Intermediate product, both used in operational processing, and Delayed Time product, which is a reprocessed product. Although each altimeter mission is processed separately, consistency between the different missions is ensured by a homogenization process that consists of applying the most recent corrections, models and references recommended by agencies or expert groups. The up-to-date list of corrections and standards are given in the SSALTO/DUACS user handbook (2011).
EKE = 1 2 (u 2 g + v 2 g ) with u g = - g f ∂SLA ∂y and v g = + g f ∂SLA ∂x , ( 1 ) where g is the gravity and f the Coriolis coefficient. Maps of absolute dynamic topography (MADT) will also be used in the following to analyze surface circulation patterns. The MADT are obtained by adding the mean dynamic topography (MDT) to the MSLA (Eq. 2). MADT = MSLA + MDT (2) The MDT used in Sect. 3 is the CNES-CLS09 MDT  (Rio et al. , 2011) , whereas the MDT used in Sect. 4 is a regional MDT  (Rio et al. , 2007)  dedicated to the Mediterranean Sea. Both of them are computed using a combination of in situ data (surface drifting buoys, ARGO floats, CTD and XBT casts) and altimeter data. SLTAC produces global 1/4 • gridded maps of the global ocean as well as 1/8 • gridded maps for regional seas as the Mediterranean Sea, the Black Sea and the Arctic Sea.
ENSO analysis with sea surface temperature As the ocean state linked to ENSO causes strong social and economical damages, several monitorings of the tropical Pacific have been already established (e. g. , the TOGA/TRITON moored buoy array). UNESCO/IOC calculates weekly the Oceanic Niño Index (ONI) with the Reynolds OIv2  (Reynolds et al. , 2002)  sea surface temperature (SST) analysis, made available through the International Research Institute Data Library (http://iridl. ldeo. columbia. edu/index. html). The anomaly is calculated relative to a climatological seasonal cycle based on the years 1982-2005. For the same purpose, the National Oceanic and Atmospheric Administration (NOAA) analyses three-month running means of Extended Reconstructed SST (ERSST. v2) anomalies  (Smith et al. , 2004)  in the Niño 3.
The altimetry-based El Ni ño ocean indicator Altimetric sea level anomalies are a good candidate for providing information on El Niño. Since this area is already well studied, we first focus on the feasibility of using altimetry to build an El Niño ocean indicator before checking the relevancy of altimeter-based indicators for other areas. In order to compute an altimeter indicator for the El Niño/La Niña events, we first extracted the Niño 3. 4 area from the 1/3 • global altimeter maps. The Delayed Time (DT) product is used when available since it is a consistent database using state-of-the-art recommendations from the altimetry community. However, there might be a lag of 6 months to a year between the availability of this dataset and present. The difference between the indices calculated from the two datasets is primarily a negative bias of the NRT compared to DT of the order of 1 cm or 0.
1 in index value, when the same reference is used (i. e. , MSLA from DT-UPD from 1993-2010, since a consistent NRT dataset over a long period is not available due to continuous upgrade of the NRT system without reanalysis). Near Real Time (NRT) products are thus used to complete the time series. In order to focus on the interannual signal, the biannual and annual cycles are removed from the data. Furthermore, a 60-day non-geophysical period is also removed in the altimeter products due to aliasing of tides, orbit and radiometer at the nearly 10-day repeat cycle from TOPEX and JASON. As mentioned earlier, the SLA delivered by the SLTAC are referenced to a 7 yr time period  (1993) (1994) (1995) (1996) (1997) (1998) (1999). This is short compared to the SST availability period. We tested the impact of the reference period length on the results by using either a 7 or 14 yr reference period.
2 ). The comparison between the NOAA ONI and the SST mainly shows the impact of the reference period on the index, and the two collocated time series show a good 0. 99 correlation. The SLA index compares well with the NOAA ONI (correlation of 0. 88) and the SST index (correlation of 0. 89). We conclude that the SLA index is an indicator of the ENSO as precise as the SST-based indicator (Fig. 1 ). Moreover, as SLA data reveal both steric effects in the whole water column and changes in the eustatic component of the sea level, the SLA-based index can therefore identify changes better than the SST-based index in areas where the subsurface steric effects or the eustatic component are assumed to dominate the variability.
The resulting raising of isopycnes subsequently generates an anticyclonic shear in the upper layer  (Borzelli et al. , 2009). Conversely, after 1997 the outflowing Adriatic dense water set up a bottom pressure gradient able to sustain a stationary anticyclonic shear in the bottom layer and a cyclonic one in the upper layer. In the following sections, nineteen years of SLTAC/DUACS altimeter data have been used to investigate the low-frequency changes in the Ionian general surface circulation, in order to define an efficient oceanic indicator of the different IoSC phases.
Conclusion and perspective In this paper, new ocean state indicators have been derived from the SLTAC/DUACS altimeter dataset. They enable monitoring three well-known phenomena, the ENSO events in the tropical Pacific, the Kuroshio Current Extension state in the northwestern Pacific and the Ionian surface circulation reversal in the Mediterranean Sea. These indicators were obtained by averaging into specific boxes the altimeter sea level anomaly or derived eddy kinetic energy information. Except for the ENSO indicator, for which the Niño 3. 4 box is used (initially defined for computing SST-based indices), the relevant boxes have been defined in order to best reproduce the principal component time series of the EOF variability mode corresponding to the dynamical phenomena for monitoring. The indicator developed in the Kuroshio area clearly allows the elongated states periods  (1993-94, 2002-2004 and 2010-2011)  to be distinguished from the contracted states periods  (1997-2001 and 2009).
Acknowledgements. The altimeter products were produced by  Ssalto/Duacs  and distributed by Aviso with support from  CNES. Aviso staff is gratefully acknowledged for the services provided. The authors would like to express their thanks to  B. Qiu  and  G. Larnicol  for fruitful discussions. Helpful comments from the reviewers were appreciated. Edited by:  J. A. Johannessen.
Based on the above ideas and theories we propose a new architecture combining Natural Actor-Critic (NAC) and a CPG network to achieve a "learning to walk" task on a humanoid. This is the so-called Natural CPG-Actor-Critic. The natural actor-critic has been proposed by  Kakade (2002)  and further improved and used by Peters in the field of supervised motor learning  (Peters and Schaal, 2006, 2008). This particular RL algorithm uses natural policy gradient methods which may achieve very efficient exploration and fast convergence of learning. Based on their ideas,  Nakamura et al. (2007)  proposed a natural CPG-Actor-Critic approach and implemented it with a 2D 1  -simulated stick walker in MATLAB. At the present time, the natural CPG-Actor-Critic has not been implemented on a humanoid platform.
Natural CPG-actor-critic model Natural CPG-Actor-Critic is an autonomous RL learning framework used for CPG network based on Actor-Critic learning with the natural policy gradient. It was proposed by Nakamura in 2007 and successfully implemented on Taga's stick walker in Matlab simulation  (Taga, 1998; Nakamura et al. , 2007). We adopted his approach but with an entirely different CPG architecture, learning schema, and basic RL algorithm (for details, refer to discussion). Since the output of our CPG model is based on the input of PF layer and the states of sensory feedback and posture control terms, a CPG is an adaptive controller whose output is dependent on all these inputs. As a matter of fact, the layered architecture proposed in our work can be viewed as a feed-forward neural network (Figure  3 ) where the posture control works as a bias.
Robotic platform and the neural controller Figure  4  shows the robot and the neural network used to implement learning. We use the popular commercialized robot NAO. The advantages of using the NAO robot are summarized as: (1) There are locomotion-relevant sensors mounted on the NAO robot, such as gyro sensors which can detect acceleration of the body center in 3D space, joint sensors which can measure angle values, and foot pressure sensors which can sense ground contact of feet. All these sensors are useful for learning a proper walking gait. (2) Nao has a good firmware called Naoqi which is convenient for users to program and organize modules working together. The layered CPG network (Figure  4  left) is used to control the NAO robot. Each output sends out position trajectories to each corresponding joint of NAO.
Software In this work, we use a simulated environment in the Webots simulator. Webots is an ODE (Open Dynamics Engine) based simulator in which users can not only simulate physics close to the real world but also move robots or objects and even change the environment. This is why there is a typical feature of Webots for simulating batch learning processes  (Michel, 2004). There are three main modules working together in the Naoqi of Webots. The supervisor module is in charge of restarting the simulation every episode by putting the robot in the initial position, changing the angle of the ground, measuring the distance the robot walks for each episode. The learning module is the main process where the CPG architecture and the learning algorithm are implemented. The stability indicator is a module working only for obtaining necessary sensory information from the supervisor module and the robot as well as calculating the immediate reward.
8 to +9. 8 m/s 2. This part is implemented based on the inspiration of a vestibular system in the inner-ear mechanism for keeping body balance. It senses "falling" of the body by detecting the accelerations in 3D space  (Thomas et al. , 2009). Here, as we aim to study walking on the ground, the perpendicular acceleration is ignored. Twenty-five and 50 are the boundary values for the robot to fall. The even cos function is used to indicate this oscillatory motion of the walking in negative and positive directions of each axis. r distance = 2S and S is the walking distance detected by the supervisor module in Webots. After each episode, two kinds of average reward are acquired. One is the average reward (AR) for each episode equal to. If AR > GAR, the updating rule is satisfied. Otherwise, the episode is regarded as a failure.
The criterion can be considered part of the family of General Information Criteria (see for instance  Bai, Rao and Wu, 1999 , whose criterion presents some analogy to slope heuristics). Section 2 of this paper presents the mixture model framework and the model selection paradigm. In Section 3 we describe and prove our main result, the oracle inequality. Section 4 examines the practical aspect of our procedure, which was implemented in the stand-alone software MixMoGenD (Mixture Model using Genotypic Data) that was first introduced in  Toussile and Gassiat (2009). Simulated experimental results are presented in Section 5, including a comparison of our proposed criterion with classical BIC and AIC, considering both the selection of the true model and the density estimation. Examples of applications to real datasets can be found in Section 6.
Practical application In real datasets, the number A l of all possible modalities for each variable X l is not necessarily known. However, the observed number can be used instead. In fact, the MLE estimator selects a density with null weight on non-observed alleles. Then, in each model M (K, S) , an approximated ML-estimator can be computed thanks to the Expectation-Maximization (EM) algorithm of  Dempster, Lairdsand and Rubin (1977). We use the same EM strategy as  Toussile and Gassiat (2009)  to avoid a local maximization of the likelihood: We run a certain number (15 by default) of iterations in the EM algorithm from several (10 by default) randomly chosen parameter points and perform a long EM run of the best candidate in terms of likelihood. Two other points that have to be addressed before obtaining the final estimator P ( Kn, Sn) concern the choice of the penalty function and the sub-collection of models among which to select the optimal model.
Simulations Our proposed procedure is implemented in the software MixMoGenD (Mixture Model for Genotypic Data), which already offers a selection procedure based on the asymptotic criteria BIC and AIC  (Toussile and Gassiat, 2009). Numerical experiments with simulated datasets are performed to assess the performance of the new non-asymptotic criterion with respect to BIC, AIC, and the Integrated Completed Likelihood (ICL)  (Biernacki, Celeux and Govaert, 2000). We set up two series of experiments to simulate multilocus genotypic data from diploid organisms (Case 2). Consistency behaviors of the competing criteria are then evaluated based on the first series, which examines how the main   loci 1, 2, 3, 4, 5  and 6  BIC ICL AIC Cte x Dim q q q q q q q q q q 0 200 400 600 800 1000 0. 0 0. 2 0.
Consistency behaviors We consider a setting of L = 10 variables with 10 categories each. Each dataset is simulated as a mixture of K 0 = 5 populations in equal proportions. The simulation parameters are chosen so that the differentiation between populations, as measured by a population genetics parameter F st (a measure of genetic differentiation), decreases with the variable rank. Populations are distinctly separated for the first 6 variables; for the next 2 variables populations are poorly differentiated; the last 2 variables follow the uniform distribution for all populations. The complete parameter is available at http://www. math. u-psud. fr/ ~toussile/. The overall differentiation occurs in a range considered difficult for clustering of such data  (Latch et al. , 2006). We examine different values of the sample size n in [100, 1000], and 30 datasets are simulated for each value.
As expected, the data-driven calibration of the penalty function globally improves the performance of the selection procedure and consequently provides an answer to the question "Which penalty for which sample size?" Small variations in the results obtained for small sample sizes may occur from one run to another. In fact, the EM algorithm may fail to identify the global maximum for such sample sizes, in particular for models of larger dimensions. This is probably the case for some datasets of size n ≤ 300; the number of free parameters in our simulated model is ≥ 310.
Oracle performance As previously mentioned, the new criterion is designed in a density estimation framework. The following section compares the risks of the selected estimators. Our simulations consist of 101 datasets with L = 6 variables, 3 categories for each variable, and K 0 = 3 components in equal proportions. The simulation parameters are chosen in such a way that the differentiation between the components is significant for the first 3 variables and very small for the 4 th and 5 th variables, whereas the 6 th variable follows the uniform distribution for all components. Thus, the true model is defined by K 0 = 3 and S 0 = {1, 2, 3, 4, 5}. The complete parameter is available at http://www. math. u-psud. fr/ ~toussile/. q q q q q q q q AIC BIC ICL Cte*Dim 0. 00 0. 05 0. 10 0. 15 Critera Kullback q q q q q mean Mean of smallest Kullback 95% CI    AIC 2.
2e-16 4. 4e-10  CteDim 2. 2e-16 2. 2e-16 0. 0031  The Kullback risk is estimated using a Monte Carlo procedure for 100 simulated datasets, each with a sample size of 600. Our results are summarized in Fig. 3  and Table  1. Unsurprisingly, concerning the Kullback risk, the least favorable behavior originates from ICL, followed by BIC. In fact, these criteria are not designed to retrieve the minimal risk estimator. In addition, ICL and BIC are based on asymptotic approximations and may require large sample sizes. In contrast, the new criterion with a data-driven calibration of the penalty function performes significantly better (see Table  1 ). As stated previously, both AIC and the new criterion are designed to find the minimizer of the Kullback risk. Yet, similarly to BIC and ICL, AIC is based on asymptotic approximations.
U. S. Congress voting data The data set entitled "1984 United States Congressional Voting Records Database" includes votes of the U. S. House of Representatives Congressmen on 16 key issues (disability, religion, immigration, army, education,. ) identified by the Congressional Quarterly Almanac (CQA) in Asuncion and  Newman (2007). This data set has n = 435 instances (267 Democrats and 168 Republicans). For each vote, three possible responses are taken into account: for, against, and abstention. The model selection procedure with calibration of the penalty function is applied to these data. The maximum number of clusters is set to K max = 10. The selected number of clusters is K n = 6, and the selected subset of relevant variables does not include votes on disability and army issues. The confusion matrix comparing the obtained partition and the Democrat/Republican bi-partition is given in Table  2.
Breeds of chicken We consider a collection of 27-locus genotypes from 600 individuals representing 20 chicken breeds (30 individuals per breed). These data have been described in  Rosenberg et al. (2001)  in the context of a clustering method evaluation of multilocus genotypes. Of the 27 loci, we consider 15 that have no missing data. The data illustrate a very common difficulty with biologic datasets: the dimensions of the considered models are very large with respect to the number of individuals (note, however, that the dimensions of the competing models are large because we have 15 variables resulting in 600x2x15 = 18 000 individual measures). Nevertheless, our procedure resulted in an interesting classification: 17 clusters correspond mostly to the initial breeds, and three of the clusters contain 2 breeds each.
LASSO logistic regression is introduced to help us make variable selection from which we are capable to highlight the importance of different features in predicting the future price jump. In order to get rid of the intraday data seasonality, the analysis is based on two separated datasets: morning dataset and afternoon dataset. Based on an analysis on forty largest French stocks of CAC40, we find that trade sign and market order size as well as the liquidity on the best bid (best ask) are consistently informative for predicting the incoming price jump.
In a limit order book, as shown in Figure  1 , only a certain number of best buy/sell limit orders are available for public. We denote the number of available bid/ask limit prices by L. In this study, for simplicity, we focus on limit order arrival events, limit order cancellation events and market order arrival events, see Figure  2. The number of visible limit order levels is chosen to be five L = 5. Our dataset is provided by NATIXIS via Thomson Reuter's "Reuters Tick Capture Engine" and comprises of trades and limit order activities of the 40 member stocks of index CAC40 between April 1st 2011 and April 30th 2011. In order to get rid of open hour and close hour, we extract the data from 09h05 to 17h25. Every transaction and every limit order book modification are recorded in milliseconds.
i lasso p N i i j i Y             X X     1 j       The constraint on 1 p j j   makes the solutions nonlinear in the i and there is no closed form expression as in ridge regression. Because of the nature of constraint, making y  sufficiently large will cause some of the coefficients to be exactly zero. [29]  gives the uniform consistency and a functional central limit theorem for the LASSO regularization path for the general linear model. For the implementation, we use the package glmnet in R which implements the fast algorithm via coordinate descent. This algorithm select the smallest value max  for which the entire parameter vector 0   then constructs a sequences of K values of  decreasing from max  to max   on the log scale where K and  are some constants, see more details in  [30].
Results We apply the LASSO logistic regression on three datasets: morning dataset (between 09h05 and 13h15), afternoon dataset (between 13h15 and 17h25) and allday dataset (between 09h05 and 17h25). For each dataset, one third is used to estimate the parameters and two thirds is applied to evaluate the prediction quality of the estimator. The size of datasets is given in Table  2. In this work, we choose , and 5 L  5 m  5 n  , then the dimension of the profile vector of a limit order book becomes   1 2 1 6 7 p m L n      5 n 6. The choice of L, m, n can be arbitrary, nevertheless, following the result shown in  [31]  on the long memory of order flow, it makes sense to choose  which represents an order flow correlation larger than 6%. The parameter  in LASSO is estimated by crossvalidation, then we calculate AUC value (area under ROC curve) to measure the prediction quality.
A ROC (receiver operating characteristic) curve is a graphical plot of the true positive rate vs. false positive rate. The area under the ROC curve is a good measure to measuring the model prediction quality. The AUC value is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. We show the out-of-sample AUC value in Figure  6. The stocks are sorted in alphabetic order. We see that for each prediction task, the median AUC values lie around 0. 70 and they are consistently high over all datasets and all stocks of CAC40. Moreover, we remark that the mean of all mixed AUC values obtained from three datasets are not significantly different in Table  3. It confirms that the predictability of order book information is not timevariant.