dataesr/scientific-paragraphs-categorization huggingface dataset:
Data were collected in two sessions, one for each cue validity. In each session, 100 neutral trials and 250 nonneutral trials for each condition were tested. Each participant performed 1400 trials in total. Data processing. Oculomotor parameters were extracted by using ad hoc software in Matlab. Recorded horizontal and vertical gaze positions were low-pass filtered with a Butterworth (acausal) filter of order 2 with a 30-Hz cutoff frequency and then numerically differentiated to obtain velocity measurements. An automatic conjoint acceleration and velocity threshold method was used to detect saccades  49. Aberrant trials, without recorded saccades (e. g. , due to a long blink), were excluded (less than 3% of all saccades). In each trial, we considered a "regular saccade" the first detected saccade with a latency (with respect to target onset) longer than 80 ms  50, 51  and shorter than 500 ms (~ 95% of the first detected saccades overall), and an amplitude larger than 2 degrees (40% of the entire eccentricity).
So, only phylogenetic analyses that take into account morphological characters can incorporate both fossil and extant species. Herein we present an unprecedented morphological dataset for a vast group of glirid rodents, to which different phylogenetic methodologies have been applied. We have compared the tree topologies resulting from traditional parsimony and Bayesian phylogenetic approaches and calculate stratigraphic congruence indices for each. Bayesian tip-dated clock methods seem to outperform parsimony with our dataset. The strict consensus tree recovered by tip-dating invalidates the classic classification and allow to propose dates for the divergence and origin of the different clades.
However, two of the subfamilies (Dryomyinae and Myomiminae) proposed by them belong in fact to the Leithiinae (for discussion about the validity of the subfamilies and their synonymies see  Wilson and Reeder's, 2005 and Mac Kenna and Bell, 1997)  and the fossil taxa that should be assigned to these groups are still matter of discussion  (Freudenthal and Martín Suárez, 2013). Here, we use as empirical data a morphological dataset of Myomiminae and Dryomyinae species, two controversial groups of glirid rodents and apply parsimony and model-based approaches to test the performance of these approaches in a morphological dataset of understudied fossils. Moreover, this work allow providing the first divergence date estimates for different fossil lineages belonging to the largest groups of glirids.
Material We have chosen rodents as study material because their teeth have a high morphological diversity and outstanding preservation in the fossil record. Moreover, rodent teeth can be readily characterized by discrete characters, providing an ideal morphological dataset to be analysed. Amongst rodents, we have turned our attention to Gliridae because it is one of the oldest families of living rodents. The systematic study presented below is based on the examination of original specimens and data from the literature listed in (Appendix I).
Parsimony analysis: The data matrix has been built using Mesquite 3. 04  (Maddison and Maddison, 2009)  and the analysis ran in TNT  (Goloboff et al. , 2008)  v. 1. 135 with the 'traditional search' option (using TBR). The analysis has been performed applying implied weighting as there is evidence that it improves the results of the phylogenetical analyses when working with morphological dataset  (Goloboff et al. , 2008). K values larger than the default (3. 0) are more accurate to perform this kind of analyses  (Goloboff et al. , 2017) , so K = 10 (weak implied weighting) and collapsing all branches with support = 0 has been used. Branch support has been estimated through the complementary index Relative Bremer Support  (Goloboff and Farris, 2001). Owing to the lack of a priori information, all characters were unordered.
Bayesian analyses: Bayesian undated and dated analyses have been carried out with Mr. Bayes v. 3. 2. 6  (Ronquist et al. , 2012)  using the CIPRES Science Gateway v. 3. 3  (Miller et al. , 2010). The morphological dataset has been analysed with the MkV model  (Lewis, 2001)  under the γ model, 30 million generations and four independent runs. The prior on the gamma shape parameter is an exponential distribution. Following  Simões et al. (2020) , the fossilized birthdeath tree model with sampled ancestors (FBD-SA)  (Stadler, 2010) , under relaxed-clock models has been applied. In order to put the clock rate prior the median value for tree height Median (TL) from previous non-clock analysis (TL=9. 77) divided by the age of the tree has been used. The age of the root is calculated by dividing the age of Eogliravus (45.
S1 available on Dryad). The strict consensus tree recovered by tip-dating has significantly higher stratigraphic congruence than that resulting from undated Bayesian and parsimony analyses. Taking into account that the topology obtained by Parsimony shows some anomalous results (e. g. basal position of younger taxa such as Tempestia) and that time-scaling these topologies (Fig. 1 ) results in improbable long ghost lineages for a large number taxa (e. g. Dryomys spp. ), the topology obtained via the tip-dating Bayesian analyses (Fig. 2 ) is most likely to be more accurate with this kind of morphological dataset (fossil rodent teeth). This is in agreement with the hypothesis of López-Antoñanzas and Peláez-Campomanes (2022). Moreover, the topology obtained by applying Bayesian methodology is congruent with the results of molecular analyses  (Montgelard et al.
Nodes are designed by numbers 65-124. Chronology after  Vandenberghe et al. , 2012 and Hilgen et al. , 2012. Biochronological data from Daams, 1999; NOW Database, Peláez-Campomanes, 1995; García-Paredes et al. 2016; Prieto et al. 2018 , Prieto and Rummel, 2016 and Ünay, 1994.
Acknowledgments. We sincerely thank the instructors  T. Simões  ( Harvard University, Cambridge, USA ) and  O. Vernygora  ( University of Kentucky, Lexington, USA ) of the  Transmitting Science Course "Morphological Phylogenetics" and the team Palaeontology of the University of Montpellier. Thanks are also due to  Michael Pittman  ( The Chinese University of Hong Kong, Hong Kong SAR, China ) and two anonymous reviewers, whose comments have helped to improve this work.
Purpose: KEYNOTE-224 cohort 1 demonstrated that pembrolizumab was efficacious and tolerable in patients with advanced hepatocellular carcinoma (HCC) previously treated with sorafenib. We report results from KEYNOTE-224 (NCT02702414) cohort 2, which enrolled patients with advanced HCC and no prior systemic therapy. Patients and Methods: KEYNOTE-224 was an open-label, multicountry phase II trial. Eligible patients in cohort 2 had advanced HCC not amenable or refractory to locoregional therapy and not previously treated with systemic therapy. Patients received pembrolizumab 200 mg intravenously every 3 weeks for ≤2 years. Primary endpoint was objective response rate (ORR) by central imaging review per RECIST v1. 1. Secondary endpoints included duration of response (DOR), disease control rate (DCR), time to progression (TTP), progression-free survival (PFS), overall survival (OS), and safety/tolerability.
Statistical analysis considerations A sample size of $50 patients was chosen to provide acceptable precision for the assessment of ORR with the expected 95% CI of approximately AE 11% around the ORR estimate if the observed ORR was $17%. No interim analysis was planned for cohort 2. Efficacy and safety were assessed in patients who received at least one dose of study treatment. DOR was assessed in responders (patients who had confirmed partial or complete response). Because this was an open-label, single-arm cohort, there was no hypothesis testing. The estimates and 95% CIs for ORR and DCR were based on Clopper-Pearson method. The Kaplan-Meier method was used to estimate OS, PFS, TTP, and DOR. Patients without evaluable response data were counted as nonresponders for ORR assessment and as patients whose disease was not under control for DCR assessment.
no), and extrahepatic disease (yes vs. no) to assess the consistency of the primary endpoint across various subgroups. Hepatitis B seropositivity was defined as hepatitis B surface antigen-positive and/or hepatitis B virus DNA detectable and/or anti-hepatitis B core positive, and hepatitis C seropositivity was defined as hepatitis C virus RNA detectable and/or anti-hepatitis C virus-positive. This study is registered with ClinicalTrials. gov, NCT02702414. The study is ongoing for follow-up but is no longer enrolling patients.
Data-sharing statement Merck Sharp & Dohme Corp. (MSD), a subsidiary of Merck & Co. , Inc. , Kenilworth, NJ, is committed to providing qualified scientific researchers access to anonymized data and clinical study reports from the company's clinical trials for the purpose of conducting legitimate scientific research. MSD is also obligated to protect the rights and privacy of trial participants and, as such, has a procedure in place for evaluating and fulfilling requests for sharing company clinical trial data with qualified external scientific researchers. The MSD data sharing website (available at http://engagezone. msd. com/ds_documen tation. php) outlines the process and requirements for submitting a data request. Applications will be promptly assessed for completeness and policy compliance.
Patients Of the 70 patients screened for cohort 2, 51 met the eligibility criteria and were allocated between September 4, 2018 and February 20, 2019. All allocated patients received at least one dose of the study drug and were included in the efficacy and safety analyses. The median time from the first dose to the data cutoff (January 19, 2021) was 27 months (range,  [23] [24] [25] [26] [27] [28] [29]. At the time of data cutoff, 6 patients completed the treatment with pembrolizumab, 1 patient remained on treatment, and 44 patients discontinued treatment. The most common reasons for discontinuation were progressive disease in 27 (53%) patients and AEs in 8 (16%) patients (Supplementary Fig. S1 ). Baseline characteristics are shown in Table  1.
Subgroup analyses were also performed in small patient numbers, although confirmed responses were observed across various patient baseline characteristics, such as disease etiology. Recent data suggest that nonalcoholic steatohepatitis as an etiology leading to HCC may be important to collect  (23, 24). Our study did not collect this etiology, but future studies should consider doing so. Furthermore, in this study, the value of subgroup analyses is limited, although both viral and nonviral etiology patients benefited from pembrolizumab. Other ongoing phase III trials investigating pembrolizumab-based regimens in HCC that may inform treatment selection in various settings include KEYNOTE-937 of pembrolizumab as adjuvant therapy (NCT03867084), and LEAP-012 of the combination of pembrolizumab and lenvatinib, in combination with transarterial chemoembolization for intermediate-stage HCC (NCT04246177).
Acknowledgments The authors thank the patients and their families and all investigators and site personnel;  Scot W. Ebbinghaus ,  Olga Kuznetsova , and  Leonid Dubrovsky  from  Merck & Co. , Inc. , Kenilworth, NJ, for their input and discussions on data analysis. Medical writing assistance was provided by  Yue Liu  and  Ina Nikolaeva  of  Merck & Co. , Inc. , Kenilworth, NJ. This work was supported by  Merck Sharp & Dohme Corp. , a subsidiary of  Merck & Co. , Inc. , Kenilworth, NJ. The funder had a role in study design, collection, analysis, and interpretation of data, and medical writing support. All authors had full access to the data and approved the final version of the manuscript for submission for publication. The costs of publication of this article were defrayed in part by the payment of page charges.
INTRODUCTION Scientific research data infrastructures bring together facilitates, resources, research data and services used by scientific communities to conduct datacentric research and establish best practice for science, and foster innovation. In the environmental and earth sciences domain, there are various existing research data infrastructures in operation, e. g. , EPOS 1 of solid earth and ACTRIS 2 of atmosphere. Among them, Euro- enabling applications with critical business values such as realtime early warning. Monitoring the service performance and detecting abnormal system behavior is a meaningful way to diagnose the system status and assure service quality. Anomaly detection, a. k. a. outlier detection or novelty detection, is referred to as the process of detecting data instances that have significantly different characteristics from the majority of data instances.
Unlike traditional distance-based, one-class classification, probabilistic or cluster-based methods, there are mainly three categories in deep anomaly detection: feature extraction, learning feature representations of normality, and endto-end anomaly scoring. Recently, deep anomaly detection approaches have continuously pushed the boundaries from different aspects for detecting challenging real-world anomalies. Supervised deep anomaly detection still suffers from limited available high-quality labeled datasets. Generative Adversarial Networks (GAN)  [4]  framework has shown to be wildly successful in generating realisticlooking images, real-valued time series sequences, or even melodious polyphonic music. Thus GANbased anomaly detection has attracted much attention, and several GAN-based methods have been proposed recently.
• Exploit end-to-end one-class discrimination and the reconstruction error of anomalies by developing a novel anomaly score function. The Computer Journal, Vol. ??, No. ??, ???? Effectively Detecting Operational Anomalies in Large-scale IoT Data Infrastructures by using a GAN-base • Improve the computational efficiency of the anomaly score by introducing a BiGAN-based structure. The rest of this paper is organized as follows. Section 2 presents a brief review of the related works. Section 3 introduces the proposed HTA-GAN architecture and corresponding anomaly score function. In Section 4, we introduce seven test datasets, including real operational logs from the Euro-Argo infrastructure, anomaly bench-marks, and synthetic datasets, and then show the experimental results of the proposed HTA-GAN with other seven state-of-the-art methods on these test datasets.
Local Outlier Factor (LOF)  [12]  is another example of distance-based methods. Most linear or distancebased models are only effective for highly correlated data and require the data to comply with some assumptions, such as following multivariate Gaussian distribution  [13]. The probabilistic-based methods were proposed as improvements of distance-based methods by paying more attention to the data distributions. For example, the Angle-Based Outlier Detection (ABOD)  [14]  and Isolation Forest (IF)  [15]  deal with data by considering variable correlations. However, these methods are unable to take into consideration the temporal correlation along with time steps and thus do not work well for complicated datasets. One-Class Support Vector Machine (OC-SVM)  [16]  does not make any assumptions about the data distribution.
Next, the predictability-based model, which learns feature representations by predicting the current data instances using the representations of the previous instances within a temporal window as the context, is widely used for sequence representation The Computer Journal, Vol. ??, No. ??, ???? P. Chen et al. learning and anomaly detection  [30, 31, 32, 33]. The predictability-based methods should capture the temporal or sequential dependence within a given time window or sequence length to achieve accurate predictions. Specifically, GAN-based  [4]  anomaly detection methods emerge quickly after their early use in  [34]  due to GAN's capability to capture the deep representation of real data through a mini-max game and achieved state-of-the-art performance in a variety of applications. The majority of GANbased methods fall into this group as the GAN-based generative methods.
Problem Formulation Given a dataset X = {x 1 , x 2 ,. , x N } , x i ∈ R D with unobservable labels Y = {y 1 , y 2 ,. , y N } , y i ∈ {0, 1}, let Z ∈ R K (K N) be a feature space, goal of deep anomaly detection is to learn a feature mapping function φ(•) : X → Z or an anomaly scoring function τ (•) : X → R in order that anomalies can be easily distinguished from normality in the space yielded by the φ or τ function, where both φ and τ are neural networks with H ∈ N hidden layers with weight matrix Θ = M 1 , M 2 ,. , M H. Specifically for the feature mapping φ (•), an additional step is required to calculate the anomaly score of each data instance in the new representation space. As an anomaly scoring function, τ (•) can directly infer the anomaly scores with raw data inputs. More significant τ outputs indicate a greater degree of being anomalous.
Therefore, deep anomaly detection can be formulated as: For φ(•) : X → Z, there is: {Θ * , W * } = argmin Θ, W x∈χ (ψ(φ(x; Θ); W)) (1) S x = f (x, φΘ * , ψW * ) (2) While τ (•) : X → R, there is: Θ * = argmin Θ x∈χ (τ (x; Θ)) (3) S x = τ (x; Θ * ) (4) Where φ maps the original data to the feature space Z, ψ is parameterized by W is a learning task that operates on the feature space Z and is dedicated to learning normal data's regularities and distributions, is a model relevant loss function, and f is a scoring function that utilizes φ and ψ to calculate the anomaly score s. As in most existing GAN-based generative methods, the parameters of generator G and discriminator D are updated based on the outputs of D until Nash Equilibrium. On the one hand, train the discriminator The Computer Journal, Vol.
??, No. ??, ???? Effectively Detecting Operational Anomalies in Large-scale IoT Data Infrastructures by using a GAN-base D to be as sensitive as possible to assign correct labels to either real or fake data instances. On the other hand, simultaneously turn the generator G to be as ingenious as possible to fool the discriminator after sufficient iterations. As can be imagined, the generator G will have a better understanding of datasets' hidden distributions of the training dataset and be able to generate realistic samples. Theoretically, in a typical GAN model, the generator, which takes randomly generated noises as input, can directly generate informative potential anomalous instances that occur far from the real normal data through the guide of the discriminator. An anomaly score could be adopted inversely for such fake anomaly generation.
As a result, the discriminator can identify anomalies based on the anomaly score by describing a division boundary separating potential anomalies from the normal instances. There is an assumption that the class distributions of the datasets should be unbalanced between anomalies and normal data. This assumption is widely used for anomaly detection. In addition, as we mainly focus on point anomaly detection for time series, we only consider generating point anomaly, i. e. , single anomalous point in time series, via the GAN model. To be more concrete, the fundamental intuition of this GAN-based generative approach is that, given any data instance x, it aims at an instance z from latent feature space of the generative network G so that the corresponding generated instance G(z) and x are as similar as possible.
Since the latent space is enforced to learn the training dataset's underlying key feature, anomalies are expected to have less similarity to generated counterparts than normal instances. Specifically, a GAN is first trained with the following conventional objective: min Θ G max Θ D V (D, G) = E x∼px [log[D(x)]+E x∼pz [log[1-D(G(z))] (5) Where G and D are respectively parameterized by Θ G and Θ D , and V is the value function of the twoplayer minimax game. After that, for each x, to find its best z, two loss functions -residual loss and discrimination loss -are used to guide the search. The residual loss is defined as: R (X, Z y ) = X -G (Z y ) 1 (6) While the discrimination loss is defined based on the feature matching: f m (X, Z y ) = h(x) -h (G (Z y )) 1 ( 7 ) Where γ is the index of the search iteration step, and h is a feature mapping from an intermediate layer of D.
To perform effective anomaly detection in the real-time operation of data infrastructure, we should handle them reasonably. Moreover, the engineering problem discussed in this paper is originated from the service optimization of Euro-Argo. To enable Euro-Argo data infrastructures to adapt to real-time operation and dynamic provisioning, the maximum duration for analyzing the integrated data should be less than 10 minutes, including data preprocessing, training the model in a sliding window, and calculating anomaly scores. The 10 minutes maximum time window to log analysis is defined by Euro-Argo based on both business value and real-world operation. If it is too long, anomaly detection is not effective enough for real-time operation. However, if a short interval, like less than 1 minute, infrastructure provisioning like VM initialization may not even finish.
HTA-GAN Architecture To address the first key problem, i. e. , how to exploit a single GAN's generator and discriminator architecture to combine the two models' advantages, we need first to look into the differences between the GANbased generative methods and the end-to-end oneclass classification approaches. In brief, there are The Computer Journal, Vol. ??, No. ??, ???? FIGURE 2. HTA-GAN architecture overview. Z is the latent space while X is the original data space. two main differences: i) the GAN-based methods aim at learning generative characteristics to be maximally similar to the real data distribution, obtaining a generative model that nicely captures the normality of the training data instances; while the end-to-end oneclass classification based approaches aim to optimize a discriminative model to separate normal instances from generated anomalous instances.
Therefore, the anomaly scoring function would be appropriately defined by a combination of binary crossentropy from the discriminator D and the anomaly reconstruction error from generator G, which, according to  (8) , can be formulated as follows: s x = α OC (X) -(1 -α) AR (X) (10) The Computer Journal, Vol. ??, No. ??, ???? Effectively Detecting Operational Anomalies in Large-scale IoT Data Infrastructures by using a GAN-base Where OC is the one-class classification error, which can be obtained from binary cross-entropy of the discriminator D. However, the anomaly reconstruction error AR could not be calculated directly, which is the second key problem to be handled.
Datasets To verify the proposed anomaly detection method, we conduct experiments on three different kinds of datasets, covering both real-world and synthetic datasets. Euro-Argo Integrated Data Service log: The Euro-Argo scientific research data infrastructure is the European contribution to the global Argo program, which currently has more than 3500 autonomous float instruments globally deployed over the world ocean to measure and report temperature, salinity, and other properties of the oceans. The collected raw data from deployed floats, massive but lowdimensional, is processed into scientific research data, which are key assets for conducting environmental and interdisciplinary scientific research. They are then made available via the Euro-Argo data portal, and research communities can access them from various methods.