nils-herrmann/clean_scirepeval huggingface dataset:
Algebra of conserved quantities in curved spacetime

In General Relativity (GR) finding out geodesics of a given spacetime manifold is an important task because it determines what are the classical processes which are dynamically forbidden. Conserved quantities play an important role in solving geodesic equations of a general spacetime manifold. Furthermore, knowing all possible conserved quantities of a system tells about the hidden symmetries of a system which is not apparent, since conserved quantities are deeply connected with symmetry properties of the system, which are very important in their own right. This connection is more transparent due to Noether's theorem with the development of the Lagrangian and Hamiltonian formulations of Newtonian mechanics. Conserved quantities are also useful to capture certain features of spacetime manifold for an asymptotic observer. In this report, we have found out the possible methods of finding conserved quantities and their algebra in the dynamics of an object in curved spacetime both in presence and absence of spin of that object which will help in analyzing dynamics of objects in curved spacetime in future run of advanced LIGO with the help of gravitational memory effect and gravitational lensing effect using principles of electromagnetism and GR.
From English to Code-Switching: Transfer Learning with Strong Morphological Clues

Linguistic Code-switching (CS) is still an understudied phenomenon in natural language processing. The NLP community has mostly focused on monolingual and multi-lingual scenarios, but little attention has been given to CS in particular. This is partly because of the lack of resources and annotated data, despite its increasing occurrence in social media platforms. In this paper, we aim at adapting monolingual models to code-switched text in various tasks. Specifically, we transfer English knowledge from a pre-trained ELMo model to different code-switched language pairs (i.e., Nepali-English, Spanish-English, and Hindi-English) using the task of language identification. Our method, CS-ELMo, is an extension of ELMo with a simple yet effective position-aware attention mechanism inside its character convolutions. We show the effectiveness of this transfer learning step by outperforming multilingual BERT and homologous CS-unaware ELMo models and establishing a new state of the art in CS tasks, such as NER and POS tagging. Our technique can be expanded to more English-paired code-switched languages, providing more resources to the CS community.
Trust and Cognitive Load During Human-Robot Interaction

This paper presents an exploratory study to understand the relationship between a humans' cognitive load, trust, and anthropomorphism during human-robot interaction. To understand the relationship, we created a \say{Matching the Pair} game that participants could play collaboratively with one of two robot types, Husky or Pepper. The goal was to understand if humans would trust the robot as a teammate while being in the game-playing situation that demanded a high level of cognitive load. Using a humanoid vs. a technical robot, we also investigated the impact of physical anthropomorphism and we furthermore tested the impact of robot error rate on subsequent judgments and behavior. Our results showed that there was an inversely proportional relationship between trust and cognitive load, suggesting that as the amount of cognitive load increased in the participants, their ratings of trust decreased. We also found a triple interaction impact between robot-type, error-rate and participant's ratings of trust. We found that participants perceived Pepper to be more trustworthy in comparison with the Husky robot after playing the game with both robots under high error-rate condition. On the contrary, Husky was perceived as more trustworthy than Pepper when it was depicted as featuring a low error-rate. Our results are interesting and call further investigation of the impact of physical anthropomorphism in combination with variable error-rates of the robot.
Dependency-Aware Named Entity Recognition with Relative and Global Attentions

Named entity recognition is one of the core tasks in NLP. Although many improvements have been made on this task during the last years, the state-of-the-art systems do not explicitly take into account the recursive nature of language. Instead of only treating the text as a plain sequence of words, we incorporate a linguistically-inspired way to recognize entities based on syntax and tree structures. Our model exploits syntactic relationships among words using a Tree-LSTM guided by dependency trees. Then, we enhance these features by applying relative and global attention mechanisms. On the one hand, the relative attention detects the most informative words in the sentence with respect to the word being evaluated. On the other hand, the global attention spots the most relevant words in the sequence. Lastly, we linearly project the weighted vectors into the tagging space so that a conditional random field classifier predicts the entity labels. Our findings show that the model detects words that disclose the entity types based on their syntactic roles in a sentence (e.g., verbs such as speak and write are attended when the entity type is PERSON, whereas meet and travel strongly relate to LOCATION). We confirm our findings and establish a new state of the art on two datasets.
FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency

Machine learning algorithms can take important decisions, sometimes legally binding, about our everyday life. In most cases, however, these systems and decisions are neither regulated nor certified. Given the potential harm that these algorithms can cause, qualities such as fairness, accountability and transparency of predictive systems are of paramount importance. Recent literature suggested voluntary self-reporting on these aspects of predictive systems -- e.g., data sheets for data sets -- but their scope is often limited to a single component of a machine learning pipeline, and producing them requires manual labour. To resolve this impasse and ensure high-quality, fair, transparent and reliable machine learning systems, we developed an open source toolbox that can inspect selected fairness, accountability and transparency aspects of these systems to automatically and objectively report them back to their engineers and users. We describe design, scope and usage examples of this Python toolbox in this paper. The toolbox provides functionality for inspecting fairness, accountability and transparency of all aspects of the machine learning process: data (and their features), models and predictions. It is available to the public under the BSD 3-Clause open source licence.
Intercellular communication induces glycolytic synchronization waves between individually oscillating cells

Many organs have densely packed cells divided in subpopulations that display coherent and spatially differentiated behavior. Using yeast cells packed in a microfluidic device, constructed to mimic an organ structure with peripheral blood flow, we show that cells coupled via diffusion of metabolites can lead to spatially differentiated subpopulations with temporally synchronized behavior. A detailed mathematical model for each yeast cell simulated in its precise architectural location within the microfluidic system correctly predicts the cells’ behavior as a function of an external stress solution. Our results provide us with a mechanistic understanding of this system and indicate how a relatively simple mechanism can underlie a yet poorly understood cellular differentiation process. Many organs have internal structures with spatially differentiated and sometimes temporally synchronized groups of cells. The mechanisms leading to such differentiation and coordination are not well understood. Here we design a diffusion-limited microfluidic system to mimic a multicellular organ structure with peripheral blood flow and test whether a group of individually oscillating yeast cells could form subpopulations of spatially differentiated and temporally synchronized cells. Upon substrate addition, the dynamic response at single-cell level shows glycolytic oscillations, leading to wave fronts traveling through the monolayered population and to synchronized communities at well-defined positions in the cell chamber. A detailed mechanistic model with the architectural structure of the flow chamber incorporated successfully predicts the spatial-temporal experimental data, and allows for a molecular understanding of the observed phenomena. The intricate interplay of intracellular biochemical reaction networks leading to the oscillations, combined with intercellular communication via metabolic intermediates and fluid dynamics of the reaction chamber, is responsible for the generation of the subpopulations of synchronized cells. This mechanism, as analyzed from the model simulations, is experimentally tested using different concentrations of cyanide stress solutions. The results are reproducible and stable, despite cellular heterogeneity, and the spontaneous community development is reminiscent of a zoned cell differentiation often observed in multicellular organs.
ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia

Algorithmic systems -- from rule-based bots to machine learning classifiers -- have a long history of supporting the essential work of content moderation and other curation work in peer production projects. From counter-vandalism to task routing, basic machine prediction has allowed open knowledge projects like Wikipedia to scale to the largest encyclopedia in the world, while maintaining quality and consistency. However, conversations about how quality control should work and what role algorithms should play have generally been led by the expert engineers who have the skills and resources to develop and modify these complex algorithmic systems. In this paper, we describe ORES: an algorithmic scoring service that supports real-time scoring of wiki edits using multiple independent classifiers trained on different datasets. ORES decouples several activities that have typically all been performed by engineers: choosing or curating training data, building models to serve predictions, auditing predictions, and developing interfaces or automated agents that act on those predictions. This meta-algorithmic system was designed to open up socio-technical conversations about algorithmic systems in Wikipedia to a broader set of participants. In this paper, we discuss the theoretical mechanisms of social change ORES enables and detail case studies in participatory machine learning around ORES from the 4 years since its deployment.
Event Representation Learning Enhanced with External Commonsense Knowledge

Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction. On the other hand, events extracted from raw texts lacks of commonsense knowledge, such as the intents and emotions of the event participants, which are useful for distinguishing event pairs when there are only subtle differences in their surface realizations. To address this issue, this paper proposes to leverage external commonsense knowledge about the intent and sentiment of the event. Experiments on three event-related tasks, i.e., event similarity, script event prediction and stock market prediction, show that our model obtains much better event embeddings for the tasks, achieving 78% improvements on hard similarity task, yielding more precise inferences on subsequent events under given contexts, and better accuracies in predicting the volatilities of the stock market.
The Longer the Better? The Interplay Between Review Length and Line of Argumentation in Online Consumer Reviews

Review helpfulness serves as focal point in understanding customers' purchase decision-making process on online retailer platforms. An overwhelming majority of previous works find longer reviews to be more helpful than short reviews. In this paper, we propose that longer reviews should not be assumed to be uniformly more helpful; instead, we argue that the effect depends on the line of argumentation in the review text. To test this idea, we use a large dataset of customer reviews from Amazon in combination with a state-of-the-art approach from natural language processing that allows us to study argumentation lines at sentence level. Our empirical analysis suggests that the frequency of argumentation changes moderates the effect of review length on helpfulness. Altogether, we disprove the prevailing narrative that longer reviews are uniformly perceived as more helpful. Our findings allow retailer platforms to improve their customer feedback systems and to feature more useful product reviews.
Autism spectrum disorder: a neuro-immunometabolic hypothesis of the developmental origins.

Fetal neuroinflammation and prenatal stress (PS) may contribute to lifelong neurological disabilities. Astrocytes and microglia play a pivotal role, but the mechanisms are poorly understood. Here, we test the hypothesis that via gene-environment interactions, fetal neuroinflammation and PS may reprogram glial immunometabolic phenotypes which impact neurodevelopment and neurobehavior. This glial-neuronal interplay increases the risk for clinical manifestation of autism spectrum disorder (ASD) in at-risk children. Drawing on genomic data from the recently published series of ovine and rodent glial transcriptome analyses with fetuses exposed to neuroinflammation or PS, we conducted a secondary analysis against the Simons Foundation Autism Research Initiative (SFARI) Gene database. We confirmed 21 gene hits. Using unsupervised statistical network analysis, we then identified six clusters of probable protein-protein interactions mapping onto the immunometabolic and stress response networks and epigenetic memory. These findings support our hypothesis. We discuss the implications for ASD etiology, early detection, and novel therapeutic approaches.
Reconstructing continuous distributions of 3D protein structure from cryo-EM images

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of proteins and other macromolecular complexes at near-atomic resolution. In single particle cryo-EM, the central problem is to reconstruct the 3D structure of a macromolecule from $10^{4-7}$ noisy and randomly oriented 2D projection images. However, the imaged protein complexes may exhibit structural variability, which complicates reconstruction and is typically addressed using discrete clustering approaches that fail to capture the full range of protein dynamics. Here, we introduce a novel method for cryo-EM reconstruction that extends naturally to modeling continuous generative factors of structural heterogeneity. This method encodes structures in Fourier space using coordinate-based deep neural networks, and trains these networks from unlabeled 2D cryo-EM images by combining exact inference over image orientation with variational inference for structural heterogeneity. We demonstrate that the proposed method, termed cryoDRGN, can perform ab-initio reconstruction of 3D protein complexes from simulated and real 2D cryo-EM image data. To our knowledge, cryoDRGN is the first neural network-based approach for cryo-EM reconstruction and the first end-to-end method for directly reconstructing continuous ensembles of protein structures from cryo-EM images.
On Memory Mechanism in Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environment. Consequently, algorithms for solving MARL problems incorporate various extensions beyond traditional RL methods, such as a learned communication protocol between cooperative agents that enables exchange of private information or adaptive modeling of opponents in competitive settings. One popular algorithmic construct is a memory mechanism such that an agent's decisions can depend not only upon the current state but also upon the history of observed states and actions. In this paper, we study how a memory mechanism can be useful in environments with different properties, such as observability, internality and presence of a communication channel. Using both prior work and new experiments, we show that a memory mechanism is helpful when learning agents need to model other agents and/or when communication is constrained in some way; however we must to be cautious of agents achieving effective memoryfulness through other means.
Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics

We study low dimensional complier parameters that are identiﬁed using a binary instrumental variable Z , which is valid conditional on a possibly high dimensional vector of covariates X . We characterize the doubly robust moment function for the entire class of complier parameters deﬁned by Abadie (2003) by combining two classic formulations: the Wald formula and the κ weight. In particular, we reinterpret the κ weight as the Riesz representer to the Wald formula, which appears to be a new insight. The main result includes new cases such as average complier characteristics. We use the main result to propose a hypothesis test, free of functional form restrictions, to evaluate (i) whether two diﬀerent instruments induce compliers with the same observable characteristics on average, and (ii) whether compliers have observable characteristics that are the same as the full population on average. By developing this hypothesis test, we equip empirical researchers with a new robustness check.
Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems

Self-attentional models are a new paradigm for sequence modelling tasks which differ from common sequence modelling methods, such as recurrence-based and convolution-based sequence learning, in the way that their architecture is only based on the attention mechanism. Self-attentional models have been used in the creation of the state-of-the-art models in many NLP task such as neural machine translation, but their usage has not been explored for the task of training end-to-end task-oriented dialogue generation systems yet. In this study, we apply these models on the DSTC2 dataset for training task-oriented chatbots. Our finding shows that self-attentional models can be exploited to create end-to-end task-oriented chatbots which not only achieve higher evaluation scores compared to recurrence-based models, but also do so more efficiently.
NODE: Extreme Low Light Raw Image Denoising using a Noise Decomposition Network

Denoising extreme low light images is a challenging task due to the high noise level. When the illumination is low, digital cameras increase the ISO (electronic gain) to amplify the brightness of captured data. However, this in turn amplifies the noise, arising from read, shot, and defective pixel sources. In the raw domain, read and shot noise are effectively modelled using Gaussian and Poisson distributions respectively, whereas defective pixels can be modeled with impulsive noise. In extreme low light imaging, noise removal becomes a critical challenge to produce a high quality, detailed image with low noise. In this paper, we propose a multi-task deep neural network called Noise Decomposition (NODE) that explicitly and separately estimates defective pixel noise, in conjunction with Gaussian and Poisson noise, to denoise an extreme low light image. Our network is purposely designed to work with raw data, for which the noise is more easily modeled before going through non-linear transformations in the image signal processing (ISP) pipeline. Quantitative and qualitative evaluation show the proposed method to be more effective at denoising real raw images than state-of-the-art techniques.
Radial velocity confirmation of K2-100b: a young, highly irradiated, and low-density transiting hot Neptune

We present a detailed analysis of HARPS-N radial velocity observations of K2-100, a young and active star in the Praesepe cluster, which hosts a transiting planet with a period of 1.7 d. We model the activity-induced radial velocity variations of the host star with a multidimensional Gaussian Process framework and detect a planetary signal of 10.6 ± 3.0 ${\rm m\, s^{-1}}$, which matches the transit ephemeris, and translates to a planet mass of 21.8 ± 6.2 M⊕. We perform a suite of validation tests to confirm that our detected signal is genuine. This is the first mass measurement for a transiting planet in a young open cluster. The relatively low density of the planet, $2.04 _{ - 0.61 } ^ { + 0.66 }$ ${\rm g\, cm^{-3}}$, implies that K2-100b retains a significant volatile envelope. We estimate that the planet is losing its atmosphere at a rate of $10^{11}\!-\!10^{12}\, {\rm g\, s^{-1}}$ due to the high level of radiation it receives from its host star.
Towards string theory expectations for photon couplings to axionlike particles

ALP-photon couplings are modeled in large ensembles of string vacua and random matrix theories. In all cases, the effective coupling increases polynomially in the number of ALPs, of which hundreds or thousands are expected in the string ensembles, many of which are ultralight. The expected value of the couplings $g_{a\gamma\gamma}\simeq 10^{-12}\text{GeV}^{-1} - 10^{-10}\text{GeV}^{-1}$ provide viable targets for future x-ray telescopes and axion helioscopes, and in some cases are already in tension with existing data.
Extreme ionised outflows are more common when the radio emission is compact in AGN host galaxies

Using a sample of 2922 z <  0.2, spectroscopically identified active galactic nuclei (AGN), we explore the relationship between radio size and the prevalence of extreme ionised outflows, as traced using broad [O III] emission-line profiles in spectra obtained by the Sloan Digital Sky Survey (SDSS). To classify radio sources as compact or extended, we combined a machine-learning technique for morphological classification with size measurements from two-dimensional Gaussian models to data from all-sky radio surveys. We find that the two populations have statistically different [O III] emission-line profiles; the compact sources tend to have the most extreme gas kinematics. When the radio emission is confined within 3″ (i.e. within the spectroscopic fibre or ≲5 kpc at the median redshift), the chance of observing broad [O III] emission-line components, which are indicative of very high velocity outflows and have a full width at half-maximum > 1000 km s−1, is twice as high. This difference is greatest for the highest radio luminosity bin of log[L1.4 GHz/W Hz−1] = 23.5−24.5 where the AGN dominate the radio emission; specifically, > 1000 km s−1 components are almost four times as likely to occur when the radio emission is compact in this subsample. Our follow-up ≈0.3″–1″ resolution radio observations for a subset of targets in this luminosity range reveal that radio jets and lobes are prevalent, and suggest that compact jets might be responsible for the stronger outflows in the wider sample. Our results are limited by the available relatively shallow all-sky radio surveys, but forthcoming surveys will provide a more complete picture of the connection between radio emission and outflows. Overall, our results add to the growing body of evidence that ionised outflows and compact radio emission in highly accreting “radiative” AGN are closely connected, possibly as a result of young or weak radio jets.