nils-herrmann/clean_scirepeval huggingface dataset:
Jointly Embedding the Local and Global Relations of Heterogeneous Graph for Rumor Detection

The development of social media has revolutionized the way people communicate, share information and make decisions, but it also provides an ideal platform for publishing and spreading rumors. Existing rumor detection methods focus on finding clues from text content, user profiles, and propagation patterns. However, the local semantic relation and global structural information in the message propagation graph have not been well utilized by previous works. In this paper, we present a novel global-local attention network (GLAN) for rumor detection, which jointly encodes the local semantic and global structural information. We first generate a better integrated representation for each source tweet by fusing the semantic information of related retweets with the attention mechanism. Then, we model the global relationships among all source tweets, retweets, and users as a heterogeneous graph to capture the rich structural information for rumor detection. We conduct experiments on three real-world datasets, and the results demonstrate that GLAN significantly outperforms the state-of-the-art models in both rumor detection and early detection scenarios.
Chargrid-OCR: End-to-end Trainable Optical Character Recognition for Printed Documents using Instance Segmentation.

We present an end-to-end trainable approach for Optical Character Recognition (OCR) on printed documents. Specifically, we propose a model that predicts a) a two-dimensional character grid (\emph{chargrid}) representation of a document image as a semantic segmentation task and b) character boxes for delineating character instances as an object detection task. For training the model, we build two large-scale datasets without resorting to any manual annotation - synthetic documents with clean labels and real documents with noisy labels. We demonstrate experimentally that our method, trained on the combination of these datasets, (i) outperforms previous state-of-the-art approaches in accuracy (ii) is easily parallelizable on GPU and is, therefore, significantly faster and (iii) is easy to train and adapt to a new domain.
Data Science in Biomedicine.

We highlight the role of Data Science in Biomedicine. Our manuscript goes from the general to the particular, presenting a global definition of Data Science and showing the trend for this discipline together with the terms of cloud computing and big data. In addition, since Data Science is mostly related to areas like economy or business, we describe its importance in biomedicine. Biomedical Data Science (BDS) presents the challenge of dealing with data coming from a range of biological and medical research, focusing on methodologies to advance the biomedical science discoveries, in an interdisciplinary context.
Countering Language Drift via Visual Grounding

Emergent multi-agent communication protocols are very different from natural language and not easily interpretable by humans. We find that agents that were initially pretrained to produce natural language can also experience detrimental language drift: when a non-linguistic reward is used in a goal-based task, e.g. some scalar success metric, the communication protocol may easily and radically diverge from natural language. We recast translation as a multi-agent communication game and examine auxiliary training constraints for their effectiveness in mitigating language drift. We show that a combination of syntactic (language model likelihood) and semantic (visual grounding) constraints gives the best communication performance, allowing pre-trained agents to retain English syntax while learning to accurately convey the intended meaning.
ArduCode: Predictive Framework for Automation Engineering

Automation engineering is the task of integrating, via software, various sensors, actuators, and controls to automate a real-world process. Today, automation engineering is supported by a suite of software tools, including integrated development environments (IDEs), hardware configurators, compilers, and runtimes. These tools focus on the automation code itself but leave the automation engineer unassisted in their decision-making. This can lead to longer software development cycles due to the imperfections in the decision-making, which arise when integrating software and hardware. To address this problem, this article addresses multiple challenges often faced in automation engineering and proposes machine learning-based solutions to assist engineers tackle these challenges. We show that machine learning can be leveraged to assist the automation engineer in classifying automation code, finding similar code snippets, and reasoning about the hardware selection of sensors and actuators. We validate our architecture on two real data sets consisting of 2927 Arduino projects and 683 programmable logic controller (PLC) projects. Our results show that paragraph embedding techniques can be utilized to classify automation using code snippets with precision close to human annotation, giving an $F_{1}$ -score of 72%. Furthermore, we show that such embedding techniques can help us find similar code snippets with high accuracy. Finally, we use autoencoder models for hardware recommendation and achieve a $p\text{@}3$ of 0.79 and $p\text{@}5$ of 0.95. We also present the implementation of ArduCode in a proof-of-concept user interface integrated into an existing automation engineering system platform. Note to Practitioners—This article is motivated by the use of artificial intelligence methods to improve the efficiency and quality of the automation engineering software development process. Our goal is to develop and integrate intelligent assistants in existing automation engineering development tools to minimally disrupt existing workflows. Practitioners should be able to adapt our framework to other tools and data. Our contributions address important practical problems: 1) we address the lack of realistic data sets in automation engineering with two publicly available data sources; 2) we make the reference implementation of our algorithms publicly available on GitHub for other practitioners to have a starting point for future research; and 3) we demonstrate the integration of our framework as an add-on to an existing automation engineering toolchain.
Group-Testing on Hypergraphs with Variable-Cost Tests: A Power Systems Case Study

Assessing risk of cascading failure in an electrical grid requires identifying many small "defective" subsets of the N elements in a power system, such that the simultaneous failure of all elements in a defective set triggers a large cascading blackout. Most supersets of defective sets will also initiate cascades. This property was leveraged by Random Chemistry (RC), a state-of-the-art algorithm for efficiently finding minimal defective sets. While not previously acknowledged as such, RC is a group-testing algorithm, in that it tests pools of candidate solutions. RC then reduces the size of defective pools until a minimal defective set is found. Using a synthetic model of the Western United States power grid, we show that run times are minimized with a much smaller initial pool size than previously recommended, nearly doubling the speed of RC. We compare RC to a proposed new alternative group-testing algorithm (SIGHT). Both algorithms have their advantages; while SIGHT is shown to require fewer total tests than RC, RC requires fewer tests of defective sets. Thus, the relative speed of each algorithm depends on the relative cost of defective vs. non defective tests, a factor not previously considered in the group-testing literature. In the power systems application, tests of defective sets are much more computationally costly, so RC is faster. However, with only a single control parameter (compared to many in RC), SIGHT is easier to optimize, and in applications where the cost ratio of defective:non-defective tests is low enough, SIGHT will be faster than RC.
Is culture a contributing factor of strong science?

Many factors such as economy size, capital resources, and size of national publication market seem to be related to the scientific performance of nations. In this paper we link the national culture values with scientific performance of 53 nations. We focus on the year 2010. Our study uses three datasets: 1) Hofstede's data on national culture, 2) data on migration share of societies, and 3) citation impact data. We found that four dimensions of culture (i.e. individualism, power distance, uncertainty avoidance, and indulgence) correlate practically and statistically significantly with scientific impact of nations. The findings are discussed in mirror of cultural theories.
Skin cancer detection based on deep learning and entropy to detect outlier samples

We describe our methods that achieved the 3rd and 4th places in tasks 1 and 2, respectively, at ISIC challenge 2019. The goal of this challenge is to provide the diagnostic for skin cancer using images and meta-data. There are nine classes in the dataset, nonetheless, one of them is an outlier and is not present on it. To tackle the challenge, we apply an ensemble of classifiers, which has 13 convolutional neural networks (CNN), we develop two approaches to handle the outlier class and we propose a straightforward method to use the meta-data along with the images. Throughout this report, we detail each methodology and parameters to make it easy to replicate our work. The results obtained are in accordance with the previous challenges and the approaches to detect the outlier class and to address the meta-data seem to be work properly.
What do Deep Networks Like to Read?

Recent research towards understanding neural networks probes models in a top-down manner, but is only able to identify model tendencies that are known a priori. We propose Susceptibility Identification through Fine-Tuning (SIFT), a novel abstractive method that uncovers a model's preferences without imposing any prior. By fine-tuning an autoencoder with the gradients from a fixed classifier, we are able to extract propensities that characterize different kinds of classifiers in a bottom-up manner. We further leverage the SIFT architecture to rephrase sentences in order to predict the opposing class of the ground truth label, uncovering potential artifacts encoded in the fixed classification model. We evaluate our method on three diverse tasks with four different models. We contrast the propensities of the models as well as reproduce artifacts reported in the literature.
BINOCULARS for efficient, nonmyopic sequential experimental design

Finite-horizon sequential experimental design (SED) arises naturally in many contexts, including hyperparameter tuning in machine learning among more traditional settings. Computing the optimal policy for such problems requires solving Bellman equations, which are generally intractable. Most existing work resorts to severely myopic approximations by limiting the decision horizon to only a single time-step, which can underweight exploration in favor of exploitation. We present BINOCULARS: Batch-Informed NOnmyopic Choices, Using Long-horizons for Adaptive, Rapid SED, a general framework for deriving efficient, nonmyopic approximations to the optimal experimental policy. Our key idea is simple and surprisingly effective: we first compute a one-step optimal batch of experiments, then select a single point from this batch to evaluate. We realize BINOCULARS for Bayesian optimization and Bayesian quadrature -- two notable SED problems with radically different objectives -- and demonstrate that BINOCULARS significantly outperforms myopic alternatives in real-world scenarios.
Road Mapping in Lidar Images Using a Joint-Task Dense Dilated Convolutions Merging Network

It is important, but challenging, for the forest industry to accurately map roads which are used for timber transport by trucks. In this work, we propose a Dense Dilated Convolutions Merging Network (DDCM-Net) to detect these roads in lidar images. The DDCM-Net can effectively recognize multi-scale and complex shaped roads with similar texture and colors, and also is shown to have superior performance over existing methods. To further improve its ability to accurately infer categories of roads, we propose the use of a joint-task learning strategy that utilizes two auxiliary output branches, i.e, multi-class classification and binary segmentation, joined with the main output of full-class segmentation. This pushes the network towards learning more robust representations that are expected to boost the ultimate performance of the main task. In addition, we introduce an iterative-random-weighting method to automatically weigh the joint losses for auxiliary tasks. This can avoid the difficult and expensive process of tuning the weights of each task’s loss by hand. The experiments demonstrate that our proposed jointtask DDCM-Net can achieve better performance with fewer parameters and higher computational efficiency than previous state-of-the-art approaches.
Modeling User Reputation in Online Social Networks: The Role of Costs, Benefits, and Reciprocity

We analyze an agent-based model to estimate how the costs and benefits of users in an online social network (OSN) impact the robustness of the OSN. Benefits are measured in terms of relative reputation that users receive from their followers. They can be increased by direct and indirect reciprocity in following each other, which leads to a core-periphery structure of the OSN. Costs relate to the effort to login, to maintain the profile, etc. and are assumed as constant for all users. The robustness of the OSN depends on the entry and exit of users over time. Intuitively, one would expect that higher costs lead to more users leaving and hence to a less robust OSN. We demonstrate that an optimal cost level exists, which maximizes both the performance of the OSN, measured by means of the long-term average benefit of its users, and the robustness of the OSN, measured by means of the lifetime of the core of the OSN. Our mathematical and computational analyses unfold how changes in the cost level impact reciprocity and subsequently the core-periphery structure of the OSN, to explain the optimal cost level.
Prediction of Overall Survival of Brain Tumor Patients

Automated brain tumor segmentation plays an important role in the diagnosis and prognosis of the patient. In addition, features from the tumorous brain help in predicting patients' overall survival. The main focus of this paper is to segment tumor from BRATS 2018 benchmark dataset and use age, shape and volumetric features to predict overall survival of patients. The random forest classifier achieves overall survival accuracy of 59% on the test dataset and 67% on the dataset with resection status as gross total resection. The proposed approach uses fewer features but achieves better accuracy than state-of-the-art methods.
Properties of central stars of planetary nebulae with distances in Gaia DR2

We have compiled a catalogue of central stars of planetary nebulae (CSPN) with reliable distances and positions obtained from Gaia Data Release 2 (DR2) astrometry. Distances derived from parallaxes allow us to analyse the galactic distribution and estimate other parameters such as sizes, kinematical ages, bolometric magnitudes, and luminosities.
Aims. Our objective is to analyse the information regarding distances together with other available literature data about photometric properties, nebular kinematics, and stellar effective temperatures to throw new light on this rapid and rather unknown evolutionary phase. We seek to understand how Gaia distances compare with other indirect methods commonly used and, in particular, with those derived from non-local thermodynamic equilibrium (non-LTE) models; how many planetary nebulae (PNe) populate the Galaxy; and how are they spatially distributed. We also aim to comprehend their intrinsic luminosities, range of physical sizes of the nebulae; how to derive the values for their kinematical ages; and whether those ages are compatible with those derived from evolutionary models.
Methods. We considered all PNe listed in catalogues from different authors and in Hong Kong/AAO/Strasbourg/Hα (HASH) database. By X-matching their positions with Gaia DR2 astrometry we were able to identify 1571 objects in Gaia second archive, for which we assumed distances calculated upon a Bayesian statistical approach. From those objects, we selected a sample of PNe with good quality parallax measurements and distance derivations, we which refer to as our Golden Astrometry PNe sample (GAPN), and obtained literature values of their apparent sizes, radial and expansion velocities, visual magnitudes, interstellar reddening, and effective temperatures.
Results. We found that the distances derived from DR2 parallaxes compare well with previous astrometric derivations of the United States Naval Observatory and Hubble Space Telescope, but that distances inferred from non-LTE model fitting are overestimated and need to be carefully reviewed. From literature apparent sizes, we calculated the physical radii for a subsample of nebulae that we used to derive the so-called kinematical ages, taking into account literature expansion velocities. Luminosities calculated with DR2 distances were combined with literature central stars Teff values in a Hertzsprung–Russell (HR) diagram to infer information on the evolutionary status of the nebulae. We compared their positions with updated evolutionary tracks finding a rather consistent picture. Stars with the smallest associated nebular radii are located in the flat luminosity region of the HR diagram, while those with the largest radii correspond to objects in a later stage, getting dimmer on their way to become a white dwarf. Finally, we commented on the completeness of our catalogue and calculated an approximate value for the total number of PNe in the Galaxy.
Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping

This article develops a Bayesian continuous 3D semantic occupancy map from noisy point clouds by generalizing the Bayesian kernel inference model for building occupancy maps, a binary problem, to semantic maps, a multi-class problem. The proposed method provides a unified probabilistic model for both occupancy and semantic probabilities and nicely reverts to the original occupancy mapping framework when only one occupied class exists in obtained measurements. The Bayesian spatial kernel inference relaxes the independent grid assumption and brings smoothness and continuity to the map inference, enabling to exploit local correlations present in the environment and increasing the performance. The accompanying software uses multi-threading and vectorization, and runs at about 2 $\mathrm{Hz}$ on a laptop CPU. Evaluations using multiple sequences of stereo camera and LiDAR datasets show that the proposed method consistently outperforms current baselines. We also present a qualitative evaluation using data collected with a bipedal robot platform on the University of Michigan - North Campus.
Water Vapor and Clouds on the Habitable-zone Sub-Neptune Exoplanet K2-18b

Results from the Kepler mission indicate that the occurrence rate of small planets (<3 R⊕) in the habitable zone of nearby low-mass stars may be as high as 80%. Despite this abundance, probing the conditions and atmospheric properties on any habitable-zone planet is extremely difficult and has remained elusive to date. Here, we report the detection of water vapor and the likely presence of liquid and icy water clouds in the atmosphere of the 2.6 R⊕ habitable-zone planet K2-18b. The simultaneous detection of water vapor and clouds in the mid-atmosphere of K2-18b is particularly intriguing because K2-18b receives virtually the same amount of total insolation from its host star ( 1368 − 107 + 114 W m−2) as the Earth receives from the Sun (1361 W m−2), resulting in the right conditions for water vapor to condense and explain the detected clouds. In this study we observed nine transits of K2-18b using Hubble Space Telescope/WFC3 in order to achieve the necessary sensitivity to detect the water vapor, and we supplement this data set with Spitzer and K2 observations to obtain a broader wavelength coverage. While the thick hydrogen-dominated envelope we detect on K2-18b means that the planet is not a true Earth analog, our observations demonstrate that low-mass habitable-zone planets with the right conditions for liquid water are accessible with state-of-the-art telescopes.
The Study of Dynamic Caching via State Transition Field—the Case of Time-Varying Popularity

In the second part of this two-part paper, we extend the study of dynamic caching via state transition field (STF) to the case of time-varying content popularity. The objective of this part is to investigate the impact of time-varying content popularity on the STF and how such impact accumulates to affect the performance of a replacement scheme. Unlike the case in the first part, the STF is no longer static over time, and we introduce instantaneous STF to model it. Moreover, we demonstrate that many metrics, such as instantaneous state caching probability and average cache hit probability over an arbitrary sequence of requests, can be found using the instantaneous STF. As a steady state may not exist under time-varying content popularity, we characterize the performance of replacement schemes based on how the instantaneous STF of a replacement scheme after a content request impacts on its cache hit probability at the next request. From this characterization, insights regarding the relations between the pattern of change in the content popularity, the knowledge of content popularity exploited by the replacement schemes, and the effectiveness of these schemes under time-varying popularity are revealed. In the simulations, different patterns of time-varying popularity, including the shot noise model, are experimented. The effectiveness of example replacement schemes under time-varying popularity is demonstrated, and the numerical results support the observations from the analytic results.
The ultra-diffuse dwarf galaxies NGC 1052-DF2 and 1052-DF4 are in conflict with standard cosmology

Recently van Dokkum et al. reported that the galaxy NGC 1052-DF2 (DF2) lacks dark matter if located at 20 Mpc from Earth. In contrast, DF2 is a dark-matter-dominated dwarf galaxy with a normal globular cluster population if it has a much shorter distance near 10 Mpc. However, DF2 then has a high peculiar velocity wrt. the cosmic microwave background of 886 $\rm {km\, s^{-1}}$, which differs from that of the Local Group (LG) velocity vector by 1298 $\rm {km\, s^{-1}}$ with an angle of $117 \, ^{\circ }$. Taking into account the dynamical M/L ratio, the stellar mass, half-light radius, peculiar velocity, motion relative to the LG, and the luminosities of the globular clusters, we show that the probability of finding DF2-like galaxies in the lambda cold dark matter (ΛCDM) TNG100-1 simulation is at most 1.0 × 10−4 at 11.5 Mpc and is 4.8 × 10−7 at 20.0 Mpc. At 11.5 Mpc, the peculiar velocity is in significant tension in the TNG100-1, TNG300-1, and Millennium simulations, but naturally in a Milgromian cosmology. At 20.0 Mpc, the unusual globular cluster population would challenge any cosmological model. Estimating that precise measurements of the internal velocity dispersion, stellar mass, and distance exist for 100 galaxies, DF2 is in 2.6σ (11.5 Mpc) and 4.1σ (20.0 Mpc) tension with standard cosmology. Adopting the former distance for DF2 and assuming that NGC 1052-DF4 is at 20.0 Mpc, the existence of both is in tension at ≥4.8σ with the ΛCDM model. If both galaxies are at 20.0 Mpc the ΛCDM cosmology has to be rejected by ≥5.8σ.