dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions A minimum data set to adequately demonstrate that a test compound operates via the known MoA of CAR activation can be drawn from a currently available toolbox of in vivo and in vitro assays (illustrated in Tables  2  and 3 ). A typical data set includes a short-term (1-28 days) MoA study in the same strain of mouse/rat as the cancer study, as well as various endpoints to demonstrate KE1 (CAR activation), KE2 (altered gene expression secondary to CAR activation) and KE3 (cell proliferation). Studies in genetically modified mice/rats are not required, however, they can be helpful; for example, experiments in CAR/PXR KO mice that show either an absence or significant attenuation of some key events is one way to demonstrate that CAR activation (KE1) is operative. Measurements should be included in the short-term MoA study to disprove alternative MOAs (e.
g. , that AhR, PPARα and cytotoxicity followed by regenerative hyperplasia are not the primary causal events). In vitro studies using primary cultures of hepatocytes (rodent, human) are not required, but based on current practice and regulatory feedback, most registrants may want to include these. They should measure 1) cell proliferation (KE3), and 2) some measure to show increased markers of CAR activation such as Cyp2b isoform mRNA levels, or PROD and/or BROD activities. There are other ways to show KE1, KE2 and the associative event AE1 (increased Cyp2b and Cyp3a enzyme activity and/or protein) are operative in a MoA data set, including in vitro CAR reporter assays with mouse, rat and human CAR. Extensive data exist with known CAR-activating compounds that show a fundamental qualitative difference between the species, and indicate that the key events leading to tumors in mice or rats are not operative in humans.
Acknowledgements This manuscript is a review of prior published studies and typical study designs, so no funding of new research was involved. The authors would like thank reviewers at  ECHA  for their helpful feedback on this manuscript prior to its publication. Additional thanks go to  Lauren K. Markell  ( FMC Corporations ),  Trent Stevens  and  Phil Botham  ( Syngenta Crop Protection, LLC ) for review and editing of the manuscript.
(Salamon and Bello, 2017)  proposed a model which comprised of 3 convolutional layers interleaved with 2 pooling operations, followed by 2 fully connected layers. Most works, including the above-mentioned networks, in auditory event recognition follow similar strategies, where signals lasting from tens to hundreds of ms are modeled first. (Takahashi et al. , 2018)  proposed a 9 layer CNN networks which inspired by VGG Net  (Simonyan and Zisserman, 2014)  with signals lasting multiple seconds handled as a single input. Furthermore, the labeled data is one of the main barriers to training deep neural networks, and available datasets of environmental sound recordings are still very limited. Therefore, the above-mentioned literature all use data augmentation to acquire more training database. In  (Salamon and Bello, 2017) , the experiment result also proved that the classifying accuracy of CNN will be better when data augmentation was applied.
Meanwhile, fully connected layers and dropout function are abandoned as well. Experiment result showed that with the layers of model gained, the performance gained. Computational modeling of auditory awareness plays a major role in a large variety of artificial intelligence applications. To be specific, a bottom-up attention mechanism specifically designed for efficient auditory surveillance demonstrated powerful detection of alarming sound events such as gunshots and screams in natural scenes  (Hu et al. , 2010). However, due to the insufficiency of the database, current deep model-based methods commonly use single sounds or artificially synthesized mixtures to train the networks rather than the real environmental sounds. Meanwhile, manually-selected has been proved that the loss of information cannot be avoided, but use raw waveforms as input need not only more layers but also large receptive fields in the first layer which demand a high quality of hardware.
Existing models are proposed for specific tasks and the performance will be greatly reduced when they are used to detect other kinds of sound. Another important problem is the lack of a universal environment sound database. Although there is some sound database, most of the units are just simple sounds. Furthermore, the current research works showed that the accuracy could be improved but the computational complexity will raise as well. This means that the existing method could not detect the salient sounds in real time. Therefore, the application of acoustic perception models on the artificial intelligence for understanding the real environment is still severely constrained. The novelty detection approaches show a possible way of applying DL in auditory saliency detection. Novelty detection aims at recognizing situations in which unusual events occur.
We feed the collected data into a database, and by analyzing the data in real-time we can identify patterns in the data to predict frost events. Because of the heavy machinery that moves inside the orchard, using cables to interconnect the sensors is not an option. The main challenge is to deploy a system that provides both high end-to-end reliability and long lifetime, without using cables. We use SmartMesh IP off-the-shelf, the low-power wireless mesh solution from Analog Devices. The sensor devices are battery-powered and equipped with a radio. They form a multi-hop mesh topology, and collaborate to route the data generated by the devices (called "motes") to a gateway. This gateway is connected to the Internet, and forwards the gathered data to the servers in Paris, France. Data appears on the web interface of the servers seconds after it was gathered by the sensor network.
This article extends this work and offers analysis over a 3-month period, and contrasts and compares the performance of SmartMesh IP in the Smart Agriculture deployment with that in a (new) Smart Building deployment. The remainder of this article is organized as follows. Section 2 lists the related work on real-world deployments. Section 3 gives details about TSCH and explains why we chose SmartMesh IP Section 4 describes the types of network statistics, and the dataset of statistics collected over each 3 month period. Section 5 presents results that confirm assumptions about what we can expect for a real-world TSCH SmartMesh IP deployment. Section 6 presents not so intuitive results about link symmetry and network stability. Finally, Section 7 concludes this article and discusses further improvements.
Smart Building applications face external interference, as the low-power wireless mesh networks are co-located with other technologies sharing the same frequency bands. For example, SmartMesh IP operates in the same 2. 4 GHz band as WiFi and Bluetooth. On top of the requirements for Smart Agriculture applications, a solution deployed in a building must be resilient to external interference. Deploying IoT-based solutions in real-world application is complex, and involves elements well beyond the low-power wireless network. Barrenetxea et al. [5]  survey experimental studies and discuss best practices from system conception to data analysis. The Smart Agriculture and Smart Building deployments described in this article use IEEE802. 15. 4 technology. In the 2015 amendment of IEEE802. 15. 4  [6] , the IEEE has introduced enhancements to the medium access control sub-layer of the standard.
On the gateway side, we develop software to gather the network statistics automatically generated by the motes every 5 min, and provide network health monitoring tools. After   Each device in the network produces both sensor data and network statistics. Network statistics can be separated in Events and Health Reports messages. Event messages are non-periodic notifications the network generates when a network event happens (e. g. a mote joins/leaves the network, a link is created/deleted). Health Report (HR) messages are sent periodically by each mote; they contain counters and statistics about that mote. HRs are used to assess the health and performance of the network. Table  1  summarizes the number of events and HRs gathered during the 3 month periods of both deployments: mote create. Each node in a SmartMesh IP network periodically sends beacons to announce the presence of the network.
End-to-End Reliability We expect the SmartMesh IP network to offer wire-like reliability. Table  2  confirms that this is the case. It presents statistics gathered over the 15-25 July 2016 period in the Smart Agriculture deployment, and over the 12 November 2016 -12 February 2017 period in the Smart Building deployment. Both sensor data and network statistics are taken into account. It shows that, as none of the 693,844 and 431,193 packets generated in the networks was lost, the end-to-end reliability is 100%. The average PDR over all links is very high (95% and 87%), indicating that the nodes are deployed close Figure  6 : In the Smart Building deployment, external interference from nearby WiFi devices causes some retries (the PDR is lower) without impacting end-toend reliability which stays at 100% (see Section 5.
The dataset contains 411,132 HR NEIGHBORS messages received from 14 DC9003 nodes (same hardware). During that period, 21 links are active with at least 250 transmissions for each link. For each of these links, we compute the difference between the average RSSI in each direction. Results are presented in Fig. 7. In 99. 6% of the cases, the difference does not exceed 7 dB. Looking at Fig. 5 , this translates into only a handful of percentage points difference in PDR. This means the links can be considered symmetric. This result is in-line with the physical phenomenon that the signal traveling from A to B undergoes the same attenuation as that from B to A. This result would not hold if the neighbor radios had a different transmit power or sensitivity. That being said, discussions on link (a)symmetry at the routing layer is largely artificial, as virtually all state-of-the-art medium access control (MAC) protocols uses link-layer acknowledgments, thereby naturally filtering out assymetric links.
Acknowledgments This work was partially supported by the  STIC-AmSud program  through the  PEACH  project, the  European Commission  through the  H2020 F-Interop  and  H2020  ARMOUR projects, Inria through the REALMS and DIVERSITY associate teams, and the  Spanish Ministry of Economy  and the  FEDER regional development fund  through the  SINERGIA  project ( TEC2015-71303-R ). The authors thank the  Dust Networks Product Group at Analog Devices  for their excellent technical support, and  Felipe Morán  for his contributions in the interference analysis.
Unlike previous works  [1] [2] [3] 6]  we do not want to provide an ad hoc implementation of these two steps but rather taking advantage of well established and robust procedures. We use probabilistic computations in Bayesian networks for the E-step  [11] , and classical survival analysis methods for the M-step  [12]. The paper is organized as follows: Section "Methods" contains the main contribution of this paper which includes the model formulation, the EM-framework and the detailed PLOS 2/20 E-and M-steps. Then, Section "Validation on Simulated Datasets" presents several simulation analyses that validate the method while Section "Application to the THA" applies the proposed method to THA families from different origins (French, Portuguese, and Swedish). Finally, some conclusions are drawn in Section "Discussion".
M-step Once the weights w i have been computed (at the E-step), the model components can be updated by maximizing Eq. (  1 ) which is simply a weighted survival log-likelihood function where each individual observation receives the weight w i. Since most statistical softwares allow for weighted observations, we can therefore rely on well-established existing survival tools for performing our M-step. Using the programming software R [13], we can for example take advantage of the robust survival package  [12, 25]  which provides non-parametric Kaplan-Meier estimation of the survival through the survfit() function. Note that the coxph() can also be combined with survfit() to provide non-parametric Nelson-Aalen survival estimates taking into account proportional hazard effects. In addition, using full parametric survival estimation procedures, such as the survreg() function, allows the method to provide alternative classical survival estimation (namely Weibull, exponential, Gaussian, logistic, log-normal, log-logistic) with no additional development costs.
Validation on Simulated Datasets For validation purposes we first consider the application of our method on simulated datasets. In order to simulate realistic pedigree structure (parental relationships and individual genders), we use 64 French and Portugese THA families from  [2]  totalizing 1, 095 individuals. These 64 families were replicated three times resulting in a dataset of n = 3, 285 individuals in 192 families. Genotypes were assigned using the Hardy-Weinberg distribution for the founders and respecting the Mendelian transmission for the non-founders. We have used an allele frequency of f = 0. 20 in order to obtain enough informative families (without simulating any ascertainment process). The gender of the transmitting parent was not taken into account in this work (no distinction between X = 01 and X = 10).
The final Cox fitting gives that the β parameter was estimated by β = -0. 59 (p-value < 0. 01) which is very close to the true value β = -0. 6, and one can see on Figure  1  (right) the survival estimates for the two classes. Like for the simpler case with no covariates, the estimations are quite consistent with the ground truth. Again, increasing the sample size or the number of affected individuals leads to sharper confidence intervals (data not shown). Now that the method appears to be validated on simulated datasets, we can consider real datasets.
The drawback of this procedure is that the survival function can be significantly overestimated if the ascertainment process is not taken into account  [27]. PLOS 9/20 Therefore, an adjustment for the ascertainment bias is required. Different adjustments for ascertainment bias have already been proposed in order to provide valid risk estimates of a genetic disease (see for instance  [1, 3, 6] ). In these applications, the ascertainment bias was corrected by a classical method that consists in simply removing the phenotypic information of the individual (called proband ) who allowed his family to be selected. This ascertainment correction is a well-known (and validated) preprocessing technique whose relevance is not discussed here. Here we considered three datasets (see Table  2 ): the French dataset totalized 46 families from French descent with as many as 12 different pathogenic TTR variants including the MET30 in 22; the Portuguese dataset included 33 MET30 families from Portugal; the 3rd dataset enrolled 77 MET30 kindreds from Northern Sweden.
The frequency of mutated allele was set to f = 0. 001  [6, 28]. This parameter is generally unknown in practice. In addition, it has been shown in  [6]  that the survival estimations are not highly sensitive to this parameter. For each dataset, we provide a semi-parametric survival estimation with a gender proportional hazard effect. We provide p-values for the gender effect through Cox's (partial-) likelihood ratio tests. For each dataset, the results are compared to previously published analyses. Figure  2  shows the survival estimates by gender for the three datasets. These observations are highly consistent with the previously published analyses  [2, 29]. In the previous stratified analysis, the gender difference was found lower and not significant in the whole French dataset. This difference can be explained by the additional power provided by the proportional hazard model used here.
Discussion In the present article we introduced a flexible and robust framework to estimate survival function from familial data in cases of age-dependent genetic diseases. Our new method provides a unifying way to simply implement both previously published methods (parametric Weibull-based) as well as new interesting extension such as the PLOS 10/20 non-parametric or semi-parametric extensions. In order to tackle the challenging problem of the unknown genotypes in the family data, our method relies on the EM algorithm and decomposes the problem into two steps: the E-step which uses belief propagation in Bayesian networks to compute marginal individual posterior carrier distribution, and the M-step which estimates survival using weighted observations. The key feature of our approach is that these two steps are handled by robust and validated implementations: the bped command-line program for the belief propagation, and the survival package (statistical software R  [13] ) for the survival estimates.
Figure2shows the survival estimates by gender for the three datasets. For the French dataset (top-left Figure2), one observes a later disease onset (median around 70) than in the Portuguese sample (Figure2, top-right) showing a median around age 45 years. A significantly higher instantaneous risk is observed for men compared to women in both the French (RH 1. 7, Cox's p-value 0. 03) and the Portuguese (RH 1. 57, Cox's p-value 0. 033) datasets. In contrast, we found no gender effect in the Swedish dataset (Figure2, bottom-left, Cox's p-value 0. 42) and hence present the estimate without gender effect in Figure2(bottom-right). The disease onset appears to be much later in the Swedish population in comparison with the French and Portugese populations.
Cluster analysis is widely used in the areas of machine learning and data mining. Fuzzy clustering is a particular method that considers that a data point can belong to more than one cluster. Fuzzy clustering helps obtain flexible clusters, as needed in such applications as text categorization. The performance of a clustering algorithm critically depends on the number of clusters, and estimating the optimal number of clusters is a challenging task. Quality indices help estimate the optimal number of clusters. However, there is no quality index that can obtain an accurate number of clusters for different datasets. Thence, in this paper, we propose a new cluster quality index associated with a visual, graph-based solution that helps choose the optimal number of clusters in fuzzy partitions. Moreover, we validate our theoretical results through extensive comparison experiments against state-of-the-art quality indices on a variety of numerical real-world and artificial datasets.
Clustering algorithms behave differently for different reasons. The first reason relates to dataset features such as geometry and the density distribution of clusters. The second reason is the choice of input parameters such as the fuzziness coefficient m (m = 1 indicating that clustering is crisp and m > 1 that clustering becomes fuzzy). These parameters all affect the quality of clustering. To study how the choice of parameters impacts clustering quality, we need a quality criterion. For instance, when the dataset is well separated and has only two variables, a scatter plot can help determine the number of clusters. However, when the dataset has more than two variables, a good quality index is needed to compare various cluster configurations and choose the appropriate number of clusters.
Hence, in this paper, we first review existing quality indices that are wellsuited to fuzzy clustering, such as  [3, 4, 5, 6, 7, 8]. Then, we propose an innovative, visual quality index for the well-known Fuzzy C-Means (FCM) method. Moreover, we compare our proposal with state-of-the-art quality indices from the literature on several numerical real-world and artificial datasets. The remainder of this paper is organized as follows. Section 2 recalls the principles of fuzzy clustering. Section 3 surveys quality indices for fuzzy clustering. Section 4 details our visual quality index. Section 5 reports on the experimental comparison of our quality index against existing ones on different datasets. Finally, we conclude this paper and provide research perspectives in Section 6.
x = 1 n n i=1 x i (4) FCM is a common method for fuzzy clustering that adapts the principle of the K-Means algorithm  [9]. FCM, proposed by  [10]  and extended by  [11] , applies on numerical data. Since numerical data are the most common case, we choose to experiment our proposals with FCM. The aim of the FCM algorithm is to minimize F W. It starts by choosing K data points as initial centroids of the clusters. Then, membership matrix values u ik (Equation  5 ) are assigned to each data point in the dataset. Centroids of clusters c k are updated based on Equation 6 until a termination criterion is reached successfully. In FCM, this criterion can be a fixed number of iterations t, e. g. , t = 100. Alternatively, a threshold can be used, e. g. , = 0. 0001. Then, the algorithm stops when F W K+1 / | F W K+1 -F W K | <.
This change represents the optimal number of clusters K. To construct an algebraic determination, let i K being the index value for K clusters. The variation of i K before K and after K are compared. In case of a positive Elbow, the second difference min K ((i K+1 -i K ) -(i K -i K-1 )) is minimized. Yet, since the values before K and after K are used for calculation, the Elbow Rule can be applied to more than two clusters only. Among all the above-stated quality indices, there is no single quality index that gives the best result for any dataset. Thus, there is room for a new quality index that is specifically tailored for fuzzy validation and helps the user choose the value of K.
Datasets Quality indices are compared on ten real-life datasets (Table  1 ; IDs 1-10) from the UCI Machine Learning Repository 1  and seven artificial datasets (Table  1 ;. In real-life datasets, the true number of clusters is assimilated to the number of labels. Although using the number of labels as the number of clusters is debatable, it is acceptable if the set of descriptive variables explains the labels well. In artificial datasets, the number of clusters is known by construction. Moreover, we created new artificial datasets by introducing overlapping and noise to some of the existing datasets, such as E1071-3  [16] , Ruspini  [1]  and E1071-5  [16]  (Table  1 ; IDs 12-14). To create a new dataset, new data points are introduced, and each must be labeled. To obtain a dataset with overlapping, we modify the construction of the E1071 artificial datasets  [16].
In the original datasets, there are three or five clusters of equal size (50). Cluster i is generated according to a Gaussian distribution N (i; 0. 3). To increase overlapping in the three clusters while retaining the same cluster size, we only change the standard deviation from 0. 3 to 0. 4. Then, there is no labeling problem. To introduce noise in a dataset, we add in each cluster noisy points generated by a Gaussian variable around each label gravity center. Noisy data are often generated by distributions with positive skewness. For example, in a two-dimensional dataset, for each label, we add points that are far away from the corresponding gravity center, especially on the right-hand side, which generally contains the most points. Then, we draw a random number r between 0 and 1. If r ≤ 0.