nils-herrmann/clean_scirepeval huggingface dataset:
Location, orbit, and energy of a meteoroid impacting the Moon during the lunar eclipse of 2019 January 21

During lunar eclipse of 2019 January 21, a meteoroid impacted the Moon producing a visible light flash. The impact was witnessed by casual observers offering an opportunity to study the phenomenon from multiple geographical locations. We use images and videos collected by observers in seven countries to estimate the location, impact parameters (speed and incoming direction), and energy of the meteoroid. Using parallax, we achieve determining the impact location at lat.$-29.43^{+0.30}_{-0.21}$, lon.$-67.89^{+0.07}_{-0.09}$, and geocentric distance as 356 553 km. After devising and applying a photometric procedure for measuring flash standard magnitudes in multiple RGB images having different exposure times, we found that the flash, had an average G-magnitude 〈G〉 = 6.7 ± 0.3. We use gravitational ray tracing (GRT) to estimate the orbital properties and likely radiant of the impactor. We find that the meteoroid impacted the moon with a speed of $14^{+7}_{-6}$ km s−1 (70 per cent C.L.) and at a shallow angle, θ < 38.2 deg. Assuming a normal error for our estimated flash brightness, educated priors for the luminous efficiency and object density, and using the GRT-computed probability distributions of impact speed and incoming directions, we calculate posterior probability distributions for the kinetic energy (median Kmed = 0.8 kton), body mass (Mmed = 27 kg) and diameter (dmed = 29 cm), and crater size (Dmed = 9 m). If our assumptions are correct, the crater left by the impact could be detectable by prospecting lunar probes. These results arose from a timely collaboration between professional and amateur astronomers that highlight the potential importance of citizen science in astronomy.
A Simple Method to Reduce Off-chip Memory Accesses on Convolutional Neural Networks

For convolutional neural networks, a simple algorithm to reduce off-chip memory accesses is proposed by maximally utilizing on-chip memory in a neural process unit. Especially, the algorithm provides an effective way to process a module which consists of multiple branches and a merge layer. For Inception-V3 on Samsung's NPU in Exynos, our evaluation shows that the proposed algorithm makes off-chip memory accesses reduced by 1/50, and accordingly achieves 97.59 % reduction in the amount of feature-map data to be transferred from/to off-chip memory.
Tipping point dynamics: a universal formula

Sudden and unexpected disruptive phenomena like current French "gilets jaunes" movement, Arab springs, Trump and Brexit victories, put at stake the feasibility of their eventual forecasting. Here we claim that such unpredictable social events are indeed predictable, provided one can build the opinion landscape to identify the underlying dynamics. We derive a universal mathematical formula for the temporal evolution of opinion distribution among a heterogeneous population. It allows identifying the various tipping points and attractors triggering opinion flows thus unveiling the hidden mechanisms behind sudden upheavals like minority spreading and majority collapse. With our formula, the final outcome of an opinion dynamics can be predicted with substantial flexibility in the parameters evaluations, if the proper identification of relevant attractors are made. This opens the path to a large spectrum of real applications including voting outcomes, market shares and societal trends. This positioning provides a new ground to predict opinion dynamics outcomes and envision competing strategies to win a public debate.
Will a Large Economy Be Stable?

We study networks of firms with Constant Elasticity of Substitution (CES) production functions. Relying on results from Random Matrix Theory, we argue that such networks generically become unstable when their size increases, when the heterogeneity in productivities/connectivities becomes too strong or when substitutability is reduced. At marginal stability and for large heterogeneities, we find that the distribution of firm sizes develops a power-law tail, as observed empirically. Crises can be triggered by small idiosyncratic shocks, which lead to "avalanches" of defaults characterized by a power-law distribution of total output losses. We conjecture that evolutionary and behavioural forces conspire to keep the economy close to marginal stability. This scenario would naturally explain the well-known "small shocks, large business cycles" puzzle, as anticipated long ago by Bak, Chen, Scheinkman and Woodford.
Fake News Detection via NLP is Vulnerable to Adversarial Attacks

News plays a significant role in shaping people's beliefs and opinions. Fake news has always been a problem, which wasn't exposed to the mass public until the past election cycle for the 45th President of the United States. While quite a few detection methods have been proposed to combat fake news since 2015, they focus mainly on linguistic aspects of an article without any fact checking. In this paper, we argue that these models have the potential to misclassify fact-tampering fake news as well as under-written real news. Through experiments on Fakebox, a state-of-the-art fake news detector, we show that fact tampering attacks can be effective. To address these weaknesses, we argue that fact checking should be adopted in conjunction with linguistic characteristics analysis, so as to truly separate fake news from real news. A crowdsourced knowledge graph is proposed as a straw man solution to collecting timely facts about news events.
Personalized Dialogue Generation with Diversified Traits

Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. 
To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. 
Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.
Optimized Deformed Laplacian for Spectrum-based Community Detection in Sparse Heterogeneous Graphs

Spectral clustering is one of the most popular, yet still incompletely understood, methods for community detection on graphs. In this article we study spectral clustering based on the deformed Laplacian matrix $D-rA$, for sparse heterogeneous graphs (following a two-class degree-corrected stochastic block model). For a specific value $r = \zeta$, we show that, unlike competing methods such as the Bethe Hessian or non-backtracking operator approaches, clustering is insensitive to the graph heterogeneity. Based on heuristic arguments, we study the behavior of the informative eigenvector of $D-\zeta A$ and, as a result, we accurately predict the clustering accuracy. Via extensive simulations and application to real networks, the resulting clustering algorithm is validated and observed to systematically outperform state-of-the-art competing methods.
OWA aggregation of multi-criteria with mixed uncertain fuzzy satisfactions

We apply the Ordered Weighted Averaging (OWA) operator in multi-criteria decision-making. To satisfy different kinds of uncertainty, measure based dominance has been presented to gain the order of different criterion. However, this idea has not been applied in fuzzy system until now. In this paper, we focus on the situation where the linguistic satisfactions are fuzzy measures instead of the exact values. We review the concept of OWA operator and discuss the order mechanism of fuzzy number. Then we combine with measure-based dominance to give an overall score of each alternatives. An example is illustrated to show the whole procedure.
Evaluating word embedding models: methods and experimental results

Extensive evaluation on a large number of word embedding models for language processing applications is conducted in this work. First, we introduce popular word embedding models and discuss desired properties of word models and evaluation methods (or evaluators). Then, we categorize evaluators into intrinsic and extrinsic two types. Intrinsic evaluators test the quality of a representation independent of specific natural language processing tasks while extrinsic evaluators use word embeddings as input features to a downstream task and measure changes in performance metrics specific to that task. We report experimental results of intrinsic and extrinsic evaluators on six word embedding models. It is shown that different evaluators focus on different aspects of word models, and some are more correlated with natural language processing tasks. Finally, we adopt correlation analysis to study performance consistency of extrinsic and intrinsic evaluators.
Analogies Explained: Towards Understanding Word Embeddings

Word embeddings generated by neural network methods such as word2vec (W2V) are well known to exhibit seemingly linear behaviour, e.g. the embeddings of analogy "woman is to queen as man is to king" approximately describe a parallelogram. This property is particularly intriguing since the embeddings are not trained to achieve it. Several explanations have been proposed, but each introduces assumptions that do not hold in practice. We derive a probabilistically grounded definition of paraphrasing that we re-interpret as word transformation, a mathematical description of "$w_x$ is to $w_y$". From these concepts we prove existence of linear relationships between W2V-type embeddings that underlie the analogical phenomenon, identifying explicit error terms.
Stable resolutions of multi-parameter persistence modules.

The theory of persistence, which arises from topological data analysis, has been intensively studied in the one-parameter case both theoretically and in its applications. However, its extension to the multi-parameter case raises numerous difficulties. Indeed, it has been shown that there exists no complete discrete invariant for persistence modules with many parameters, such as so-called barcodes in the one-parameter case. To tackle this problem, some new algebraic invariants have been proposed to study multi-parameter persistence modules, adapting classical ideas from commutative algebra and algebraic geometry to this context. Nevertheless, the crucial question of the stability of these invariants has raised few attention so far, and many of the proposed invariants do not satisfy a naive form of stability. In this paper, we equip the homotopy and the derived category of multi-parameter persistence modules with an appropriate interleaving distance. We prove that resolution functors are always isometric with respect to this distance, hence opening the door to performing homological algebra operations while "keeping track" of stability. This approach, we believe, can lead to the definition of new stable invariants for multi-parameter persistence, and to new computable lower bounds for the interleaving distance (which has been recently shown to be NP-hard to compute in the case with more than one parameter).
Visualizing and Understanding Generative Adversarial Networks (Extended Abstract)

Generative Adversarial Networks (GANs) have achieved impressive results for many real-world applications. As an active research topic, many GAN variants have emerged with improvements in sample quality and training stability. However, visualization and understanding of GANs is largely missing. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models. In this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to concepts with a segmentation-based network dissection method. We quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. Finally, we examine the contextual relationship between these units and their surrounding by inserting the discovered concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in the scene. We will open source our interactive tools to help researchers and practitioners better understand their models.
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System

The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. The model updates are sent back and aggregated on the server to update the master model then redistributed to the clients. In this paradigm, the user data never leaves the client, greatly enhancing the user' privacy, in contrast to the traditional paradigm of collecting, storing and processing user data on a backend server beyond the user's control. In this paper we introduce, as far as we are aware, the first federated implementation of a Collaborative Filter. The federated updates to the model are based on a stochastic gradient approach. As a classical case study in machine learning, we explore a personalized recommendation system based on users' implicit feedback and demonstrate the method's applicability to both the MovieLens and an in-house dataset. Empirical validation confirms a collaborative filter can be federated without a loss of accuracy compared to a standard implementation, hence enhancing the user's privacy in a widely used recommender application while maintaining recommender performance.
Few-shot Learning with Meta Metric Learners

Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approaches are limited in handling diverse domains with various number of labels. The meta-learning approaches train a meta learner to predict weights of homogeneous-structured task-specific networks, requiring a uniform number of classes across tasks. The metric-learning approaches learn one task-invariant metric for all the tasks, and they fail if the tasks diverge. We propose to deal with these limitations with meta metric learning. Our meta metric learning approach consists of task-specific learners, that exploit metric learning to handle flexible labels, and a meta learner, that discovers good parameters and gradient decent to specify the metrics in task-specific learners. Thus the proposed model is able to handle unbalanced classes as well as to generate task-specific metrics. We test our approach in the `$k$-shot $N$-way' few-shot learning setting used in previous work and new realistic few-shot setting with diverse multi-domain tasks and flexible label numbers. Experiments show that our approach attains superior performances in both settings.
See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification

Data augmentation is usually adopted to increase the amount of training data, prevent overfitting and improve the performance of deep models. However, in practice, random data augmentation, such as random image cropping, is low-efficiency and might introduce many uncontrolled background noises. In this paper, we propose Weakly Supervised Data Augmentation Network (WS-DAN) to explore the potential of data augmentation. Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning. Next, we augment the image guided by these attention maps, including attention cropping and attention dropping. The proposed WS-DAN improves the classification accuracy in two folds. In the first stage, images can be seen better since more discriminative parts' features will be extracted. In the second stage, attention regions provide accurate location of object, which ensures our model to look at the object closer and further improve the performance. Comprehensive experiments in common fine-grained visual classification datasets show that our WS-DAN surpasses the state-of-the-art methods, which demonstrates its effectiveness.
Modularization of End-to-End Learning: Case Study in Arcade Games

Complex environments and tasks pose a difficult problem for holistic end-to-end learning approaches. Decomposition of an environment into interacting controllable and non-controllable objects allows supervised learning for non-controllable objects and universal value function approximator learning for controllable objects. Such decomposition should lead to a shorter learning time and better generalisation capability. Here, we consider arcade-game environments as sets of interacting objects (controllable, non-controllable) and propose a set of functional modules that are specialized on mastering different types of interactions in a broad range of environments. The modules utilize regression, supervised learning, and reinforcement learning algorithms. Results of this case study in different Atari games suggest that human-level performance can be achieved by a learning agent within a human amount of game experience (10-15 minutes game time) when a proper decomposition of an environment or a task is provided. However, automatization of such decomposition remains a challenging problem. This case study shows how a model of a causal structure underlying an environment or a task can benefit learning time and generalization capability of the agent, and argues in favor of exploiting modular structure in contrast to using pure end-to-end learning approaches.
First study of reionization in tilted flat and untilted non-flat dynamical dark energy inflation models

We examine the effects of dark energy dynamics and spatial curvature on cosmic reionization by studying reionization in tilted spatially flat and untilted non-flat XCDM and ϕCDM dynamical dark energy inflation models that best fit the Planck 2015 cosmic microwave background (CMB) anisotropy and a large compilation of non-CMB data. We carry out a detailed statistical study, based on a principal component analysis and a Markov chain Monte Carlo analysis of a compilation of lower-redshift reionization data to estimate the uncertainties in the cosmological model reionization histories. We find that, irrespective of the nature of dark energy, there are significant differences between the reionization histories of the spatially flat and non-flat models. Although both the flat and non-flat models can accurately match the low-redshift (z ≲ 6) reionization observations, there is a clear discrepancy between high-redshift (z > 7) Lyman-α emitter data and the predictions from non-flat models. This is solely due to the fact that the non-flat models have a significantly larger electron scattering optical depth, τel, compared to the flat models, which requires an extended and much earlier reionization scenario supported by more high-redshift ionizing sources in the non-flat models. Non-flat models also require strong redshift evolution in the photon escape fraction, which can become unrealistically high (≳1) at some redshifts. However, τel is about 0.9-σ lower in the tilted flat ΛCDM model when the new Planck 2018 data are used and this reduction will partially alleviate the tension between the non-flat model predictions and the data.
Short-term Variability of Evolved Massive Stars with TESS

We present the first results from a study of Transiting Exoplanet Survey Satellite (TESS) Sector 1 and 2 light curves for eight evolved massive stars in the LMC: six yellow supergiants (YSGs) and two luminous blue variables (LBVs), including S Doradus. We use an iterative prewhitening procedure to characterize the short-timescale variability in all eight stars. The periodogram of one of the YSGs, HD 269953, displays multiple strong peaks at higher frequencies than its fellows. While the field surrounding HD 269953 is quite crowded, it is the brightest star in the region, and has infrared colors indicating it is dusty. We suggest HD 269953 may be in a post-red supergiant evolutionary phase. We find a signal with a period of ∼5 days for the LBV HD 269582. The periodogram of S Doradus shows a complicated structure, with peaks below frequencies of 1.5 cycles per day. We fit the shape of the background noise of all eight light curves, and find a red noise component in all of them. However, the power-law slope of the red noise and the timescale over which coherent structures arise changes from star to star. Our results highlight the potential for studying evolved massive stars with TESS.
Planetary giant impacts: convergence of high-resolution simulations using efficient spherical initial conditions and swift

We perform simulations of giant impacts on to the young Uranus using smoothed particle hydrodynamics (SPH) with over 100 million particles. This 100–1000 × improvement in particle number reveals that simulations with below 107 particles fail to converge on even bulk properties such as the post-impact rotation period, or on the detailed erosion of the atmosphere. Higher resolutions appear to determine these large-scale results reliably, but even 108 particles may not be sufficient to study the detailed composition of the debris – finding that almost an order of magnitude more rock is ejected beyond the Roche radius than with 105 particles. We present two software developments that enable this increase in the feasible number of particles. First, we present an algorithm to place any number of particles in a spherical shell such that they all have an SPH density within 1 per cent of the desired value. Particles in model planets built from these nested shells have a root-mean-squared velocity below 1 per cent of the escape speed, which avoids the need for long precursor simulations to produce relaxed initial conditions. Secondly, we develop the hydrodynamics code sph with interdependent fine-grained tasking(swift) for planetary simulations. swift uses task-based parallelism and other modern algorithmic approaches to take full advantage of contemporary supercomputer architectures. Both the particle placement code and swift are publicly released.