nils-herrmann/clean_scirepeval huggingface dataset:
The physical origin of the cosmological constant: continuum limit and the analogy with the Casimir effect

In this paper we continue the investigations in (Viaggiu 2018  215011) concerning the origin of the cosmological constant. First of all, we generalize the results in (Viaggiu 2018  215011, Buchdahl 1970 Mon. Not.  by considering a continuum approximation for a radiation field in a cosmological background. In this way, we clearly show that the bare cosmological constant is obtained with wanishing temperature T and that the specific heat C is zero at the decoherence scale LD. Moreover, we address the issue to fix the parameters present in our model. In particular, we push forward the analogy between our expression for Λ L at a given proper scale L and the one extrapolated by the Casimir effect. As a consequence, we can fix the decoherence scale LD at which we have the crossover to classicality to be of the order of ∼ 10 − 5 m. This implies that the actual observed value of the cosmological constant is fixed (frozen) at this new physical scale.
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods

Abuse on the Internet represents an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse on online platforms. The psychological effects of such abuse on individuals can be profound and lasting. Consequently, over the past few years, there has been a substantial research effort towards automated abuse detection in the field of natural language processing (NLP). In this paper, we present a comprehensive survey of the methods that have been proposed to date, thus providing a platform for further development of this area. We describe the existing datasets and review the computational approaches to abuse detection, analyzing their strengths and limitations. We discuss the main trends that emerge, highlight the challenges that remain, outline possible solutions, and propose guidelines for ethics and explainability
Needles in Haystacks: On Classifying Tiny Objects in Large Images

In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images. However, most Convolutional Neural Networks (CNNs) for image classification were developed using biased datasets that contain large objects, in mostly central image positions. To assess whether classical CNN architectures work well for tiny object classification we build a comprehensive testbed containing two datasets: one derived from MNIST digits and one from histopathology images. This testbed allows controlled experiments to stress-test CNN architectures with a broad spectrum of signal-to-noise ratios. Our observations indicate that: (1) There exists a limit to signal-to-noise below which CNNs fail to generalize and that this limit is affected by dataset size - more data leading to better performances; however, the amount of training data required for the model to generalize scales rapidly with the inverse of the object-to-image ratio (2) in general, higher capacity models exhibit better generalization; (3) when knowing the approximate object sizes, adapting receptive field is beneficial; and (4) for very small signal-to-noise ratio the choice of global pooling operation affects optimization, whereas for relatively large signal-to-noise values, all tested global pooling operations exhibit similar performance.
Few-shot Text Classification with Distributional Signatures

In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly applying this approach to text is challenging--lexical features highly informative for one task may be insignificant for another. Thus, rather than learning solely from words, our model also leverages their distributional signatures, which encode pertinent word occurrence patterns. Our model is trained within a meta-learning framework to map these signatures into attention scores, which are then used to weight the lexical representations of words. We demonstrate that our model consistently outperforms prototypical networks learned on lexical knowledge (Snell et al., 2017) in both few-shot text classification and relation classification by a significant margin across six benchmark datasets (20.0% on average in 1-shot classification).
Performing Deep Recurrent Double Q-Learning for Atari Games

Currently, many applications in Machine Learning are based on defining new models to extract more information about data, In this case Deep Reinforcement Learning with the most common application in video games like Atari, Mario, and others causes an impact in how to computers can learning by himself with only information called rewards obtained from any action. There is a lot of algorithms modeled and implemented based on Deep Recurrent Q-Learning proposed by DeepMind used in AlphaZero and Go. In this document, we proposed deep recurrent double Q-learning that is an improvement of the algorithms Double Q-Learning algorithms and Recurrent Networks like LSTM and DRQN.
Cosmological inference using gravitational wave standard sirens: A mock data analysis

The observation of binary neutron star merger GW170817, along with its optical counterpart, provided the first constraint on the Hubble constant ${H}_{0}$ using gravitational wave standard sirens. When no counterpart is identified, a galaxy catalog can be used to provide the necessary redshift information. However, the true host might not be contained in a catalog which is not complete out to the limit of gravitational-wave detectability. These electromagnetic and gravitational-wave selection effects must be accounted for. We describe and implement a method to estimate ${H}_{0}$ using both the counterpart and the galaxy catalog standard siren methods. We perform a series of mock data analyses using binary neutron star mergers to confirm our ability to recover an unbiased estimate of ${H}_{0}$. Our simulations used a simplified universe with no redshift uncertainties or galaxy clustering, but with different magnitude-limited catalogs and assumed host galaxy properties, to test our treatment of both selection effects. We explore how the incompleteness of catalogs affects the final measurement of ${H}_{0}$, as well as the effect of weighting each galaxy's likelihood of being a host by its luminosity. In our most realistic simulation, where the simulated catalog is about three times denser than the density of galaxies in the local universe, we find that a 4.4% measurement precision can be reached using galaxy catalogs with 50% completeness and $\ensuremath{\sim}250$ binary neutron star detections with sensitivity similar to that of Advanced LIGO's second observing run.
Generalized group-based epidemic model for spreading processes on networks: GgroupEM.

We develop a generalized group-based epidemic model (GgroupEM) framework for any compartmental epidemic model (for example; susceptible-infected-susceptible, susceptible-infected-recovered, susceptible-exposed-infected-recovered). Here, a group consists of a collection of individual nodes. This model can be used to understand the important dynamic characteristics of a stochastic epidemic spreading over very large complex networks, being informative about the state of groups. Aggregating nodes by groups, the state space becomes smaller than the individual-based approach at the cost of aggregation error, which is strongly bounded by the isoperimetric inequality. We also develop a mean-field approximation of this framework to further reduce the state-space size. Finally, we extend the GgroupEM to multilayer networks. Since the group-based framework is computationally less expensive and faster than an individual-based framework, then this framework is useful when the simulation time is important.
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training

We propose Unicoder-VL, a universal encoder that aims to learn joint representations of vision and language in a pre-training manner. Borrow ideas from cross-lingual pre-trained models, such as XLM (Lample and Conneau 2019) and Unicoder (Huang et al. 2019), both visual and linguistic contents are fed into a multi-layer Transformer (Vaswani et al. 2017) for the cross-modal pre-training, where three pre-trained tasks are employed, including Masked Language Modeling(MLM), Masked Object Classification(MOC) and Visual-linguistic Matching(VLM). The first two tasks learn context-aware representations for input tokens based on linguistic and visual contents jointly. The last task tries to predict whether an image and a text describe each other. After pretraining on large-scale image-caption pairs, we transfer Unicoder-VL to caption-based image-text retrieval and visual commonsense reasoning, with just one additional output layer. We achieve state-of-the-art or comparable results on both two tasks and show the powerful ability of the cross-modal pre-training.
A practical and consistent parametrization of dark matter self-interactions

Self-interacting dark matter has been proposed to explain the apparent mass deficit in astrophysical small-scale halos, while observations from galaxy clusters suggest that the corresponding cross section depends on the velocity. Accounting for this is often believed to be highly model-dependent with studies mostly focusing on scenarios with light mediators. Based on the effective-range formalism, in this work we point out a model-independent approach which accurately approximates the velocity dependence of the self-interaction cross section with only two parameters, in addition to the dark matter mass. We illustrate how this parameterization can be simultaneously interpreted in various well-motivated scenarios, including self-interactions induced by Yukawa forces, Breit-Wigner resonances and bound states. We investigate the astrophysical implications and discuss how the approximation can be improved in certain special regimes where it works poorly.
Analyzing the fine structure of distributions

One aim of data mining is the identification of interesting structures in data. For better analytical results, the basic properties of an empirical distribution, such as skewness and eventual clipping, i.e. hard limits in value ranges, need to be assessed. Of particular interest is the question of whether the data originate from one process or contain subsets related to different states of the data producing process. Data visualization tools should deliver a clear picture of the univariate probability density distribution (PDF) for each feature. Visualization tools for PDFs typically use kernel density estimates and include both the classical histogram, as well as the modern tools like ridgeline plots, bean plots and violin plots. If density estimation parameters remain in a default setting, conventional methods pose several problems when visualizing the PDF of uniform, multimodal, skewed distributions and distributions with clipped data, For that reason, a new visualization tool called the mirrored density plot (MD plot), which is specifically designed to discover interesting structures in continuous features, is proposed. The MD plot does not require adjusting any parameters of density estimation, which is what may make the use of this plot compelling particularly to non-experts. The visualization tools in question are evaluated against statistical tests with regard to typical challenges of explorative distribution analysis. The results of the evaluation are presented using bimodal Gaussian, skewed distributions and several features with already published PDFs. In an exploratory data analysis of 12 features describing quarterly financial statements, when statistical testing poses a great difficulty, only the MD plots can identify the structure of their PDFs. In sum, the MD plot outperforms the above mentioned methods.
Shallow Domain Adaptive Embeddings for Sentiment Analysis

This paper proposes a way to improve the performance of existing algorithms for text classification in domains with strong language semantics. A proposed domain adaptation layer learns weights to combine a generic and a domain specific (DS) word embedding into a domain adapted (DA) embedding. The DA word embeddings are then used as inputs to a generic encoder + classifier framework to perform a downstream task such as classification. This adaptation layer is particularly suited to data sets that are modest in size, and which are, therefore, not ideal candidates for (re)training a deep neural network architecture. Results on binary and multi-class classification tasks using popular encoder architectures, including current state-of-the-art methods (with and without the shallow adaptation layer) show the effectiveness of the proposed approach.
Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack

The detection of offensive language in the context of a dialogue has become an increasingly important application of natural language processing. The detection of trolls in public forums (Galán-García et al., 2016), and the deployment of chatbots in the public domain (Wolf et al., 2017) are two examples that show the necessity of guarding against adversarially offensive behavior on the part of humans. In this work, we develop a training scheme for a model to become robust to such human attacks by an iterative build it, break it, fix it scheme with humans and models in the loop. In detailed experiments we show this approach is considerably more robust than previous systems. Further, we show that offensive language used within a conversation critically depends on the dialogue context, and cannot be viewed as a single sentence offensive detection task as in most previous work. Our newly collected tasks and methods are all made open source and publicly available.
A Reliable IoT-Based Embedded Health Care System for Diabetic Patients

This paper introduces a reliable health care system for diabetic patients based on the Internet of Things technology. A diabetic health care system with a hardware implementation is presented. The proposed work employs Alaris 8100 infusion pump, Keil LPC-1768 board, and IoT-cloud to monitor the diabetic patients. The security of diabetic data over the cloud and the communication channel between health care system components are considered as part of the main contributions of this work. Moreover, an easy way to control and monitor the diabetic insulin pump is implemented. The \mbox{patient\textquotesingle s} records are stored in the cloud using the Keil board that is connected to the infusion pump. The reliability of the proposed scheme is accomplished by testing the system for five performance characteristics (availability, confidentiality, integrity, authentication, and authorization). The Kiel board is embedded with Ethernet port and Cortex-M3 micro-controller that controls the insulin infusion pump. The secure hash algorithm and secure socket shell are employed to achieve the reliability components of the proposed scheme. The results show that the proposed design is reliable, secure and authentic according to different test experiments and a case study of the Markov model. Moreover, a 99.3\% availability probability has been achieved after analyzing the case study.
Batch 1: Definition of several Weather & Climate Dwarfs

This document is one of the deliverable reports created for the ESCAPE project. ESCAPE stands for Energy-efficient Scalable Algorithms for Weather Prediction at Exascale. The project develops world-class, extreme-scale computing capabilities for European operational numerical weather prediction and future climate models. This is done by identifying weather & climate dwarfs which are key patterns in terms of computation and communication (in the spirit of the Berkeley dwarfs). These dwarfs are then optimised for different hardware architectures (single and multi-node) and alternative algorithms are explored. Performance portability is addressed through the use of domain specific languages. 
This deliverable contains the description of the characteristics of the weather & climate dwarfs that form key functional components of prediction models in terms of the science that they encapsulate and in terms of computational cost they impose on the forecast production. The ESCAPE work flow between work packages centres on these dwarfs and hence their selection, their performance assessment, code adaptation and optimization is crucial for the success of the project. At this stage of ESCAPE, a selection of established and new dwarfs has been made, their documentation been compiled and the software been made available on the software exchange platform. The selection of dwarfs will be extended throughout the course of the project (see Deliverable D1.2). 
The current selection includes the spectral transforms, the cloud microphysics scheme, two and three-dimensional elliptic solvers, a bi-Fourier spectral transform, an interpolation needed for the semi-Lagrangian advection scheme and a first version of the semi-Lagrangian advection scheme itself. This deliverable includes their scientific description and the guidance for installation, execution and testing.
Gravitational Waves, CMB Polarization, and the Hubble Tension.

The discrepancy between the Hubble parameter inferred from local measurements and that from the cosmic microwave background (CMB) has motivated careful scrutiny of the assumptions that enter both analyses. Here we point out that the location of the recombination peak in the CMB B-mode power spectrum is determined by the light horizon at the surface of last scatter and thus provides an alternative early-Universe standard ruler. It can thus be used as a cross-check for the standard ruler inferred from the acoustic peaks in the CMB temperature power spectrum and to test various explanations for the Hubble tension. The measurement can potentially be carried out with a precision of ≲2% with stage-IV B-mode experiments. The measurement can also be used to measure the propagation speed of gravitational waves in the early Universe.
The impact of stars stripped in binaries on the integrated spectra of stellar populations

Stars stripped of their envelopes from interaction with a binary companion emit a significant fraction of their radiation as ionizing photons. They are potentially important stellar sources of ionizing radiation, however, they are still often neglected in spectral synthesis simulations or simulations of stellar feedback. In anticipating the large datasets of galaxy spectra from the upcoming James Webb Space Telescope, we modeled the radiative contribution from stripped stars by using detailed evolutionary and spectral models. We estimated their impact on the integrated spectra and specifically on the emission rates of H I-, He I-, and He II-ionizing photons from stellar populations. We find that stripped stars have the largest impact on the ionizing spectrum of a population in which star formation halted several Myr ago. In such stellar populations, stripped stars dominate the emission of ionizing photons, mimicking a younger stellar population in which massive stars are still present. Our models also suggest that stripped stars have harder ionizing spectra than massive stars. The additional ionizing radiation, with which stripped stars contribute affects observable properties that are related to the emission of ionizing photons from stellar populations. In co-eval stellar populations, the ionizing radiation from stripped stars increases the ionization parameter and the production efficiency of hydrogen ionizing photons. They also cause high values for these parameters for about ten times longer than what is predicted for massive stars. The effect on properties related to non-ionizing wavelengths is less pronounced, such as on the ultraviolet continuum slope or stellar contribution to emission lines. However, the hard ionizing radiation from stripped stars likely introduces a characteristic ionization structure of the nebula, which leads to the emission of highly ionized elements such as O2+ and C3+. We, therefore, expect that the presence of stripped stars affects the location in the BPT diagram and the diagnostic ratio of O III to O II nebular emission lines. Our models are publicly available through CDS database and on the STARBURST99 website.
Rydberg-Mediated Entanglement in a Two-Dimensional Neutral Atom Qubit Array.

We demonstrate high fidelity two-qubit Rydberg blockade and entanglement on a pair of sites in a large two-dimensional qubit array. The qubit array is defined by a grid of blue detuned lines of light with 121 sites for trapping atomic qubits. Improved experimental methods have increased the observed Bell state fidelity to F_{Bell}=0.86(2). Accounting for errors in state preparation and measurement we infer a fidelity of F_{Bell}^{-SPAM}=0.88. Accounting for errors in single qubit operations we infer that a Bell state created with the Rydberg mediated C_{Z} gate has a fidelity of F_{Bell}^{C_{Z}}=0.89. Comparison with a detailed error model based on quantum process matrices indicates that finite atom temperature and laser noise are the dominant error sources contributing to the observed gate infidelity.
The Intrinsic Scatter of the Radial Acceleration Relation

We present a detailed Monte Carlo model of observational errors in observed galaxy scaling relations to recover the intrinsic (cosmic) scatter driven by galaxy formation and evolution processes. We apply our method to the stellar radial acceleration relation (RAR), which compares the local observed radial acceleration to the local Newtonian radial acceleration computed from the stellar mass distribution. The stellar and baryonic RAR are known to exhibit similar scatter. Lelli+2017 () studied the baryonic RAR using a sample of 153 spiral galaxies and inferred a negligible intrinsic scatter. If true, a small scatter might challenge the ΛCDM galaxy formation paradigm, possibly favoring a modified Newtonian dynamics interpretation. The intrinsic scatter of the baryonic RAR is predicted by modern ΛCDM simulations to be ∼0.06–0.08 dex, contrasting with the null value reported by . We have assembled a catalog of structural properties with over 2500 spiral galaxies from six deep imaging and spectroscopic surveys (called the “Photometry and Rotation curve OBservations from Extragalactic Surveys”) to quantify the intrinsic scatter of the stellar RAR and other scaling relations. The stellar RAR for our full sample has a median observed scatter of 0.17 dex. We use our Monte Carlo method, which accounts for all major sources of measurement uncertainty, to infer a contribution of 0.12 dex from the observational errors. The intrinsic scatter of the stellar RAR is thus estimated to be 0.11 ± 0.02 dex, in agreement with, though slightly greater than, current ΛCDM predictions.