nils-herrmann/clean_scirepeval huggingface dataset:
Polymer Schwarzschild black hole: An effective metric

We consider the modified Einstein equations obtained in the framework of effective spherically symmetric polymer models inspired by loop quantum gravity. When one takes into account the anomaly free pointwise holonomy quantum corrections, the modification of Einstein equations is parametrized by a function f(x) of one phase space variable. We solve explicitly these equations for a static interior black-hole geometry and find the effective metric describing the trapped region, inside the black hole, for any f(x). This general resolution allows to take into account a standard ambiguity inherent to the polymer regularization: namely the choice of the spin j labelling the SU(2)-representation of the holonomy corrections. When , the function f(x) is the usual sine function used in the polymer litterature. For this simple case, the effective exterior metric remains the classical Schwarzschild's one but acquires modifications inside the hole. The interior metric describes a regular trapped region and presents strong similarities with the Reissner-Nordström metric, with a new inner horizon generated by quantum effects. We discuss the gluing of our interior solution to the exterior Schwarzschild metric and the challenge to extend the solution outside the trapped region due to covariance requirement. By starting from the anomaly free polymer regularization for inhomogeneous spherically symmetric geometry, and then reducing to the homogeneous interior problem, we provide an alternative treatment to existing polymer interior black-hole models which focus directly on the interior geometry, ignoring the covariance issue when introducing the polymer regularization.
Precision Light Curves from TESS Full-frame Images: A Different Imaging Approach

The Transiting Exoplanet Survey Satellite (TESS) will observe ∼150 million stars brighter than , with photometric precision from 60 ppm to 3%, enabling an array of exoplanet and stellar astrophysics investigations. While light curves will be provided for ∼400,000 targets observed at 2 minute cadence, observations of most stars will only be provided as full-frame images (FFIs) at 30 minute cadence. The TESS image scale of ∼21″/pix is highly susceptible to crowding, blending, and source confusion, and the highly spatially variable point-spread function (PSF) will challenge traditional techniques, such as aperture and Gaussian-kernel PSF photometry. We use official “End-to-End 6” TESS simulated FFIs to demonstrate a difference image analysis pipeline, using a δ-function kernel, that achieves the mission specification noise floor of 60 ppm hr−1/2. We show that the pipeline performance does not depend on position across the field, and only ∼2% of stars appear to exhibit residual systematics at the level of ∼5 ppt. We also demonstrate recoverability of planet transits, eclipsing binaries, and other variables. We provide the pipeline as an open-source tool at https://github.com/ryanoelkers/DIA in both IDL and PYTHON. We intend to extract light curves for all point sources in the TESS FFIs as soon as they become publicly available, and will provide the light curves through the Filtergraph data visualization service. An example data portal based on the simulated FFIs is available for inspection at https://filtergraph.com/tess_ffi.
Precision and Recall for Time Series

Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time. Yet, many real-world anomalies are range-based, meaning they occur over a period of time. Motivated by this observation, we present a new mathematical model to evaluate the accuracy of time series classification algorithms. Our model expands the well-known Precision and Recall metrics to measure ranges, while simultaneously enabling customization support for domain-specific preferences.
Metrics for Evaluating Quality of Embeddings for Ontological Concepts

Although there is an emerging trend towards generating embeddings for primarily unstructured data and, recently, for structured data, no systematic suite for measuring the quality of embeddings has been proposed yet. This deficiency is further sensed with respect to embeddings generated for structured data because there are no concrete evaluation metrics measuring the quality of the encoded structure as well as semantic patterns in the embedding space. In this paper, we introduce a framework containing three distinct tasks concerned with the individual aspects of ontological concepts: (i) the categorization aspect, (ii) the hierarchical aspect, and (iii) the relational aspect. Then, in the scope of each task, a number of intrinsic metrics are proposed for evaluating the quality of the embeddings. Furthermore, w.r.t. this framework, multiple experimental studies were run to compare the quality of the available embedding models. Employing this framework in future research can reduce misjudgment and provide greater insight about quality comparisons of embeddings for ontological concepts. We positioned our sampled data and code at this https URL under GNU General Public License v3.0.
FEVER: a Large-scale Dataset for Fact Extraction and VERification

In this paper we introduce a new publicly available dataset for verification against textual sources,  Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo by annotators achieving 0.6841 in Fleiss kappa. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles. The best accuracy we achieve on labeling a claim accompanied by the correct evidence is 31.87%, while if we ignore the evidence we achieve 50.91%. Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.
Datalog: Bag Semantics via Set Semantics

Duplicates in data management are common and problematic. In this work, we present a translation of Datalog under bag semantics into a well-behaved extension of Datalog (the so-called warded Datalog+-) under set semantics. From a theoretical point of view, this allows us to reason on bag semantics by making use of the well-established theoretical foundations of set semantics. From a practical point of view, this allows us to handle the bag semantics of Datalog by powerful, existing query engines for the required extension of Datalog. Moreover, this translation has the potential for further extensions -- above all to capture the bag semantics of the semantic web query language SPARQL.
Dynamic State Model for Analysis of Traffic States using Dirichlet Process Mixture Model

Appropriate modeling of a surveillance scene is essential for detection of anomalies in road traffic. Learning usual paths can provide valuable insight of road traffic conditions and thus can help in identifying abnormal routes taken by commuters/vehicles. If usual traffic paths are learned in a nonparametric way, manual interventions for marking roads can be avoided. We propose an unsupervised and nonparametric method to learn frequently used paths from the tracks of moving objects in $\Theta(kn)$ time, where $k$ is the number of paths and $n$ represents the number of tracks. In the proposed method, temporal dependencies of the moving objects are taken into consideration to make the clustering meaningful using Temporally Incremental Gravity Model (TIGM)-Dynamic State Model (DSM). In addition, the distance-based scene learning makes it realistically intuitive to estimate the model parameters. Experimental validation reveals that the proposed method can learn a scene quickly without prior knowledge about the number of paths ($k$). We have compared the results with state-of-the-art methods. We also highlight the advantages of the proposed method over these techniques, especially for traffic monitoring applications. Further, we extend the model to represent notable traffic dynamics of a scene, that can be used for administrative decision making to control traffic at junctions or crowded places. We have also applied the proposed model to understand its effectiveness in clustering network traffic data.
Exploiting symmetry in complex network analysis

Virtually all network analyses involve structural measures or metrics between pairs of vertices, or of the vertices themselves. The large amount of redundancy present in real-world complex networks is inherited by such measures, and this has practical consequences which have not yet been explored in full generality, nor systematically exploited by network practitioners. Here we present a complete theory for the study of symmetry in empirical networks and their effects on arbitrary network measures, and show how this can be exploited in practice in a number of ways, from redundancy compression, to computational reduction. We also uncover the spectral signatures of symmetry for an arbitrary network measure such as the graph Laplacian. Computing and decomposing network symmetries is very efficient in practice, and we test real-world examples up to several million nodes. We illustrate our methods with some popular network measures. Our results are widely applicable, and place previous work on network symmetry in a common framework.
Bayesian Design of Experiments for Intractable Likelihood Models Using Coupled Auxiliary Models and Multivariate Emulation

A Bayesian design is given by maximising the expected utility over the design space. The utility is chosen to represent the aim of the experiment and its expectation is taken with respect to all unknowns: responses, parameters and/or models. Although straightforward in principle, there are several challenges to finding Bayesian designs in practice. Firstly, the expected utility is rarely available in closed form and requires approximation. Secondly, the expected utility needs to be maximised over a, potentially, high-dimensional design space. In the case of intractable likelihood models, these problems are compounded by the fact that the likelihood function, whose evaluation is required to approximate the expected utility, is not available in closed form. A strategy is proposed to find Bayesian designs for intractable likelihood models. It relies on the development of new methodology involving auxiliary modelling to approximate the expected utility, under an intractable likelihood model, applied with the latest approaches to maximising approximated expected utilities.
Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up the Baselines

We compile baselines, along with dataset split, for multimodal sentiment analysis. In this paper, we explore three different deep-learning-based architectures for multimodal sentiment classification, each improving upon the previous. Further, we evaluate these architectures with multiple datasets with fixed train/test partition. We also discuss some major issues, frequently ignored in multimodal sentiment analysis research, e.g., the role of speaker-exclusive models, the importance of different modalities, and generalizability. This framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field.
CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks

3D LiDARs and 2D cameras are increasingly being used alongside each other in sensor rigs for perception tasks. Before these sensors can be used to gather meaningful data, however, their extrinsics (and intrinsics) need to be accurately calibrated, as the performance of the sensor rig is extremely sensitive to these calibration parameters. A vast majority of existing calibration techniques require significant amounts of data and/or calibration targets and human effort, severely impacting their applicability in large-scale production systems. We address this gap with CalibNet: a geometrically supervised deep network capable of automatically estimating the 6-DoF rigid body transformation between a 3D LiDAR and a 2D camera in real-time. CalibNet alleviates the need for calibration targets, thereby resulting in significant savings in calibration efforts. During training, the network only takes as input a LiDAR point cloud, the corresponding monocular image, and the camera calibration matrix K. At train time, we do not impose direct supervision (i.e., we do not directly regress to the calibration parameters, for example). Instead, we train the network to predict calibration parameters that maximize the geometric and photometric consistency of the input images and point clouds. CalibNet learns to iteratively solve the underlying geometric problem and accurately predicts extrinsic calibration parameters for a wide range of mis-calibrations, without requiring retraining or domain adaptation. The project page is hosted at
Deep Learning using Rectified Linear Units (ReLU)

We introduce the use of rectified linear units (ReLU) as the classification function in a deep neural network (DNN). Conventionally, ReLU is used as an activation function in DNNs, with Softmax function as their classification function. However, there have been several studies on using a classification function other than Softmax, and this study is an addition to those. We accomplish this by taking the activation of the penultimate layer $h_{n - 1}$ in a neural network, then multiply it by weight parameters $\theta$ to get the raw scores $o_{i}$. Afterwards, we threshold the raw scores $o_{i}$ by $0$, i.e. $f(o) = \max(0, o_{i})$, where $f(o)$ is the ReLU function. We provide class predictions $\hat{y}$ through argmax function, i.e. argmax $f(x)$.
The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings

We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture. Dimensions induced by word differences (rich – poor) in these spaces correspond to dimensions of cultural meaning, and the projection of words onto these dimensions reflects widely shared associations, which we validate with surveys. Analyzing text from millions of books published over 100 years, we show that the markers of class continuously shifted amidst the economic transformations of the twentieth century, yet the basic cultural dimensions of class remained remarkably stable. The notable exception is education, which became tightly linked to affluence independent of its association with cultivated taste.
English verb regularization in books and tweets

The English language has evolved dramatically throughout its lifespan, to the extent that a modern speaker of Old English would be incomprehensible without translation. One concrete indicator of this process is the movement from irregular to regular (-ed) forms for the past tense of verbs. In this study we quantify the extent of verb regularization using two vastly disparate datasets: (1) Six years of published books scanned by Google (2003–2008), and (2) A decade of social media messages posted to Twitter (2008–2017). We find that the extent of verb regularization is greater on Twitter, taken as a whole, than in English Fiction books. Regularization is also greater for tweets geotagged in the United States relative to American English books, but the opposite is true for tweets geotagged in the United Kingdom relative to British English books. We also find interesting regional variations in regularization across counties in the United States. However, once differences in population are accounted for, we do not identify strong correlations with socio-demographic variables such as education or income.
Forward-Backward Reinforcement Learning

Goals for reinforcement learning problems are typically defined through hand-specified rewards. To design such problems, developers of learning algorithms must inherently be aware of what the task goals are, yet we often require agents to discover them on their own without any supervision beyond these sparse rewards. While much of the power of reinforcement learning derives from the concept that agents can learn with little guidance, this requirement greatly burdens the training process. If we relax this one restriction and endow the agent with knowledge of the reward function, and in particular of the goal, we can leverage backwards induction to accelerate training. To achieve this, we propose training a model to learn to take imagined reversal steps from known goal states. Rather than training an agent exclusively to determine how to reach a goal while moving forwards in time, our approach travels backwards to jointly predict how we got there. We evaluate our work in Gridworld and Towers of Hanoi and empirically demonstrate that it yields better performance than standard DDQN.
Cosmological bounds on dark matter-photon coupling

We investigate an extension of the $\Lambda$CDM model where the dark matter (DM) is coupled to photons. In addition, we consider the presence of massive neutrinos with the effective number of species $N_{\rm eff}$ as a free parameter. The effects of the DM-photon coupling on the cosmic microwave background (CMB) and matter power spectra are analyzed. We derive the observational constraints on the model parameters by using the data from CMB, baryonic acoustic oscillations (BAO) measurements, the recently measured new local value of Hubble constant from the Hubble Space Telescope (HST), and large scale structure (LSS) information from the abundance of galaxy clusters. The DM-photon coupling parameter $\Gamma_{\gamma }$ is constrained to $\Gamma_{\gamma } \leq 1.3 \times10^{-5}$ (at 95\% CL) from the joint analysis carried out by using all the mentioned data sets. The neutrino mass scale $\sum m_{\nu}$ upper bounds at 95\% CL are obtained as $\sum m_{\nu} \sim 0.4$ eV and $\sum m_{\nu} \sim 0.9$ eV with and without the LSS data, respectively. We observe that the DM-photon coupling causes significant changes in the best fit value of $N_{\rm eff}$ but yields statistical ranges of $N_{\rm eff}$ compatible with the standard predictions, and we do not find any evidence of dark radiation. Due to non-conservation of photons in our model, we also evaluate and analyze the effects on the BAO acoustic scale at the drag epoch. The DM-photon coupling model yields high values of Hubble constant consistent with the local measurement, and thus alleviates the tension on this parameter.
Safe end-to-end imitation learning for model predictive control

We propose the use of Bayesian networks, which provide both a mean value and an uncertainty estimate as output, to enhance the safety of learned control policies under circumstances in which a test-time input differs significantly from the training set. Our algorithm combines reinforcement learning and end-to-end imitation learning to simultaneously learn a control policy as well as a threshold over the predictive uncertainty of the learned model, with no hand-tuning required. Corrective action, such as a return of control to the model predictive controller or human expert, is taken when the uncertainty threshold is exceeded. We validate our method on fully-observable and vision-based partially-observable systems using cart-pole and autonomous driving simulations using deep convolutional Bayesian neural networks. We demonstrate that our method is robust to uncertainty resulting from varying system dynamics as well as from partial state observability.
Incremental Training of Deep Convolutional Neural Networks

We propose an incremental training method that partitions the original network into sub-networks, which are then gradually incorporated in the running network during the training process. To allow for a smooth dynamic growth of the network, we introduce a look-ahead initialization that outperforms the random initialization. We demonstrate that our incremental approach reaches the reference network baseline accuracy. Additionally, it allows to identify smaller partitions of the original state-of-the-art network, that deliver the same final accuracy, by using only a fraction of the global number of parameters. This allows for a potential speedup of the training time of several factors. We report training results on CIFAR-10 for ResNet and VGGNet.
Dark matter in the standard model?

We critically reexamine two possible Dark Matter candidate within the Standard Model. First, we consider the $uuddss$ exa-quark. Its QCD binding energy could be large enough to make it (quasi) stable. We show that the cosmological Dark Matter abundance is reproduced thermally if its mass is 1.2 GeV. However, we also find that such mass is excluded by the stability of Oxygen nuclei. Second, we consider the possibility that the instability in the Higgs potential leads to the formation of primordial black holes while avoiding vacuum decay during inflation. We show that the non-minimal Higgs coupling to gravity must be as small as allowed by quantum corrections, $|\xi_H| < 0.01$. Even so, one must assume that the Universe survived in $e^{120}$ independent regions to fluctuations that lead to vacuum decay with probability 1/2 each.
Disease-Atlas: Navigating Disease Trajectories with Deep Learning

Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. While there are many advantages to joint modeling, the standard forms suffer from limitations that arise from a fixed model specification, and computational difficulties when applied to high-dimensional datasets. In this paper, we propose a deep learning approach to address these limitations, enhancing existing methods with the inherent flexibility and scalability of deep neural networks, while retaining the benefits of joint modeling. Using longitudinal data from two real-world medical datasets, we demonstrate improvements in performance and scalability, as well as robustness in the presence of irregularly sampled data.
Canonical Correlation Analysis of Datasets With a Common Source Graph

Canonical correlation analysis (CCA) is a powerful technique for discovering whether or not hidden sources are commonly present in two (or more) datasets. Its well-appreciated merits include dimensionality reduction, clustering, classification, feature selection, and data fusion. The standard CCA, however, does not exploit the geometry of the common sources, which may be available from the given data or can be deduced from (cross-) correlations. In this paper, this extra information provided by the common sources generating the data is encoded in a graph, and is invoked as a graph regularizer. This leads to a novel graph-regularized CCA approach, that is termed graph (g) CCA. The novel gCCA accounts for the graph-induced knowledge of common sources, while minimizing the distance between the wanted canonical variables. Tailored for diverse practical settings where the number of data is smaller than the data vector dimensions, the dual formulation of gCCA is developed too. One such setting includes kernels that are incorporated to account for nonlinear data dependencies. The resultant graph-kernel CCA is also obtained in closed form. Finally, corroborating image classification tests over several real datasets are presented to showcase the merits of the novel linear, dual, and kernel approaches relative to competing alternatives.