nils-herrmann/clean_scirepeval huggingface dataset:
The Hunt for Primordial Interactions in the Large-Scale Structures of the Universe

The understanding of the primordial mechanism that seeded the cosmic structures we observe today in the sky is one of the major goals in cosmology. The leading paradigm for such a mechanism is provided by the inflationary scenario, a period of violent accelerated expansion in the very early stages of evolution of the universe. While our current knowledge of the physics of inflation is limited to phenomenological models which fit observations, an exquisite understanding of the particle content and interactions taking place during inflation would provide breakthroughs in our understanding of fundamental physics at high energies. In this review, we summarize recent theoretical progress in the modeling of the imprint of primordial interactions in the large-scale structures of the universe. We focus specifically on the effects of such interactions on the statistical distribution of dark-matter halos, providing a consistent treatment of the steps required to connect the correlations generated among fields during inflation all the way to the late-time correlations of halos.
Anticipatory Thinking: A Metacognitive Capability

Anticipatory thinking is a complex cognitive process for assessing and managing risk in many contexts. Humans use anticipatory thinking to identify potential future issues and proactively take actions to manage their risks. In this paper we define a cognitive systems approach to anticipatory thinking as a metacognitive goal reasoning mechanism. The contributions of this paper include (1) defining anticipatory thinking in the MIDCA cognitive architecture, (2) operationalizing anticipatory thinking as a three step process for managing risk in plans, and (3) a numeric risk assessment calculating an expected cost-benefit ratio for modifying a plan with anticipatory actions.
Weighing the Sun with five photographs

Using only five photographs of the Sun at different dates, we show that the mass of the Sun can be calculated using a telescope, a camera, and Kepler’s third law. With these photographs we are able to calculate the distance between the Sun and the Earth at different dates over a period of time of about three months. These distances allow us to obtain the correct elliptical orbit of Earth, proving Kepler’s first law. Analysis of the data extracted from the photographs is performed using an analytical optimization approach that allows us to find the parameters of the elliptical orbit. Also, it is shown that the five data points fit an ellipse using a geometrical scheme. The obtained parameters are in very good agreement with the ones for Earth’s orbit, allowing us to foresee the future positions of Earth along its trajectory. The parameters for the orbit are used to calculate the Sun’s mass by applying Kepler’s third law and Newton’s law for gravitation. This method gives a result which is in excellent agreement with the correct value for the Sun’s mass. Thus, over a time span of about three months, any student is capable to calculate the mass of the Sun with only five photographs, a telescope and a camera.
Widening the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts

The transformer is a state-of-the-art neural translation model that uses attention to iteratively refine lexical representations with information drawn from the surrounding context. Lexical features are fed into the first layer and propagated through a deep network of hidden layers. We argue that the need to represent and propagate lexical features in each layer limits the model’s capacity for learning and representing other information relevant to the task. To alleviate this bottleneck, we introduce gated shortcut connections between the embedding layer and each subsequent layer within the encoder and decoder. This enables the model to access relevant lexical content dynamically, without expending limited resources on storing it within intermediate states. We show that the proposed modification yields consistent improvements over a baseline transformer on standard WMT translation tasks in 5 translation directions (0.9 BLEU on average) and reduces the amount of lexical information passed along the hidden layers. We furthermore evaluate different ways to integrate lexical connections into the transformer architecture and present ablation experiments exploring the effect of proposed shortcuts on model behavior.
PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows

As 3D point clouds become the representation of choice for multiple vision and graphics applications, the ability to synthesize or reconstruct high-resolution, high-fidelity point clouds becomes crucial. Despite the recent success of deep learning models in discriminative tasks of point clouds, generating point clouds remains challenging. This paper proposes a principled probabilistic framework to generate 3D point clouds by modeling them as a distribution of distributions. Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape. This formulation allows us to both sample shapes and sample an arbitrary number of points from a shape. Our generative model, named PointFlow, learns each level of the distribution with a continuous normalizing flow. The invertibility of normalizing flows enables the computation of the likelihood during training and allows us to train our model in the variational inference framework. Empirically, we demonstrate that PointFlow achieves state-of-the-art performance in point cloud generation. We additionally show that our model can faithfully reconstruct point clouds and learn useful representations in an unsupervised manner. The code is available at https://github.com/stevenygd/PointFlow.
Modeling echo chambers and polarization dynamics in social networks

Echo chambers and opinion polarization recently quantified in several sociopolitical contexts and across different social media raise concerns on their potential impact on the spread of misinformation and on the openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena remain unclear. We propose a model that introduces the dynamics of radicalization as a reinforcing mechanism driving the evolution to extreme opinions from moderate initial conditions. Inspired by empirical findings on social interaction dynamics, we consider agents characterized by heterogeneous activities and homophily. We show that the transition between a global consensus and emerging radicalized states is mostly governed by social influence and by the controversialness of the topic discussed. Compared with empirical data of polarized debates on Twitter, the model qualitatively reproduces the observed relation between users' engagement and opinions, as well as opinion segregation in the interaction network. Our findings shed light on the mechanisms that may lie at the core of the emergence of echo chambers and polarization in social media.
Anomaly Detection with Joint Representation Learning of Content and Connection

Social media sites are becoming a key factor in politics. These platforms are easy to manipulate for the purpose of distorting information space to confuse and distract voters. Past works to identify disruptive patterns are mostly focused on analyzing the content of tweets. In this study, we jointly embed the information from both user posted content as well as a user's follower network, to detect groups of densely connected users in an unsupervised fashion. We then investigate these dense sub-blocks of users to flag anomalous behavior. In our experiments, we study the tweets related to the upcoming 2019 Canadian Elections, and observe a set of densely-connected users engaging in local politics in different provinces, and exhibiting troll-like behavior.
K-Core Maximization through Edge Additions

A popular model to measure the stability of a network is k-core - the maximal induced subgraph in which every vertex has at least k neighbors. Many studies maximize the number of vertices in k-core to improve the stability of a network. In this paper, we study the edge k-core problem: Given a graph G, an integer k and a budget b, add b edges to non-adjacent vertex pairs in G such that the k-core is maximized. We prove the problem is NP-hard and APX-hard. A heuristic algorithm is proposed on general graphs with effective optimization techniques. Comprehensive experiments on 9 real-life datasets demonstrate the effectiveness and the efficiency of our proposed methods.
Split Q Learning: Reinforcement Learning with Two-Stream Rewards

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.
Testing gravity with interstellar precursor missions

We consider how the trajectory of an interstellar precursor mission would be affected by the gravity of the Sun in Newtonian and Milgromian dynamics (MOND). The Solar gravity is ${\approx 50\%}$ stronger in MOND beyond a distance of ${\approx 7000}$ astronomical units, the MOND radius of the Sun. A spacecraft travelling at $0.01 \, c$ reaches this distance after 11.1 years. We show that the extra gravity in MOND causes an anomalous deceleration that reduces its radial velocity by ${\approx 3}$ cm/s and the two-way light travel time from the inner Solar System by ${\approx 0.1}$ seconds after 20 years. A distinctive signature of MOND is that the gravity from the Sun is not directly towards it. This is due to the non-linear nature of MOND and the external gravitational field from the rest of the Galaxy, which we self-consistently include in our calculations. As a result, the sky position of the spacecraft would deviate by up to 0.2 mas over 20 years. This deviation is always in the plane containing the spacecraft trajectory and the direction towards the Galactic Centre. By launching spacecraft in different directions, it is possible to test the characteristic pattern of angular deviations expected in MOND. This would minimize the chance that any detected anomalies are caused by other processes like drag from the interstellar medium. Such confounding factors could also be mitigated using an onboard accelerometer to measure non-gravitational forces. We briefly discuss how the gravity theories could be conclusively distinguished using a Cavendish-style active gravitational experiment beyond the Sun's MOND radius.
Metastable nuclear isomers as dark matter accelerators

Inelastic dark matter and strongly interacting dark matter are poorly constrained by direct detection experiments since they both require the scattering event to deliver energy from the nucleus into the dark matter in order to have observable effects. We propose to test these scenarios by searching for the collisional de-excitation of meta-stable nuclear isomers by the dark matter particles. The longevity of these isomers is related to a strong suppression of $\gamma$- and $\beta$-transitions, typically inhibited by a large difference in the angular momentum for the nuclear transition. The collisional de-excitation by dark matter is possible since heavy dark matter particles can have a momentum exchange with the nucleus comparable to the inverse nuclear size, hence lifting tremendous angular momentum suppression of the nuclear transition. This de-excitation can be observed either by searching for the direct effects of the decaying isomer, or through the re-scattering or decay of excited dark matter states in a nearby conventional dark matter detector setup. Existing nuclear isomer sources such as naturally occurring $^{180m}$Ta, $^{137m}$Ba produced in decaying Cesium in nuclear waste, $^{177m}$Lu from medical waste, and $^{178m}$Hf from the Department of Energy storage can be combined with current dark matter detector technology to search for this class of dark matter.
The MOSDEF survey: direct-method metallicities and ISM conditions at z ∼ 1.5–3.5

We present detections of [O iii] λ4363 and direct-method metallicities for star-forming galaxies at z = 1.7–3.6. We combine new measurements from the MOSFIRE Deep Evolution Field (MOSDEF) survey with literature sources to construct a sample of 18 galaxies with direct-method metallicities at z > 1, spanning 7.5 < 12+log(O/H) < 8.2 and log(M*/M⊙) = 7–10. We find that strong-line calibrations based on local analogues of high-redshift galaxies reliably reproduce the metallicity of the z > 1 sample on average. We construct the first mass–metallicity relation at z > 1 based purely on direct-method O/H, finding a slope that is consistent with strong-line results. Direct-method O/H evolves by ≲0.1 dex at fixed M* and star formation rate from z ∼ 0 to 2.2. We employ photoionization models to constrain the ionization parameter and ionizing spectrum in the high-redshift sample. Stellar models with supersolar O/Fe and binary evolution of massive stars are required to reproduce the observed strong-line ratios. We find that the z > 1 sample falls on the z ∼ 0 relation between ionization parameter and O/H, suggesting no evolution of this relation from z ∼ 0 to z ∼ 2. These results suggest that the offset of the strong-line ratios of this sample from local excitation sequences is driven primarily by a harder ionizing spectrum at fixed nebular metallicity compared to what is typical at z ∼ 0, naturally explained by supersolar O/Fe at high redshift caused by rapid formation time-scales. Given the extreme nature of our z > 1 sample, the implications for representative z ∼ 2 galaxy samples at ∼1010 M⊙ are unclear, but similarities to z > 6 galaxies suggest that these conclusions can be extended to galaxies in the epoch of reionization.
Predicting the LISA white dwarf binary population in the Milky Way with cosmological simulations

White dwarf binaries with orbital periods below 1 h will be the most numerous sources for the space-based gravitational wave detector Laser Interferometer Space Antenna (LISA). Based on thousands of individually resolved systems, we will be able to constrain binary evolution and provide a new map of the Milky Way and its close surroundings. In this paper we predict the main properties of populations of different types of detached white dwarf binaries detected by LISA over time. For the first time, we combine a high-resolution cosmological simulation of a Milky Way-mass galaxy (taken from the FIRE project) with a binary population synthesis model for low- and intermediate-mass stars. Our Galaxy model therefore provides a cosmologically realistic star formation and metallicity history for the Galaxy and naturally produces its different components such as the thin and thick disc, the bulge, the stellar halo, and satellite galaxies and streams. Thanks to the simulation, we show how different Galactic components contribute differently to the gravitational wave signal, mostly due to their typical age and distance distributions. We find that the dominant LISA sources will be He–He double white dwarfs (DWDs) and He–CO DWDs with important contributions from the thick disc and bulge. The resulting sky map of the sources is different from previous models, with important consequences for the searches for electromagnetic counterparts and data analysis. We also emphasize that much of the science-enabling information regarding white dwarf binaries, such as the chirp mass and the sky localization, becomes increasingly rich with long observations, including an extended mission up to 8 yr.
Dark matter heating of gas accreting onto Sgr A*

We study effects of heating by dark matter (DM) annihilation on black hole gas accretion. We observe that, for reasonable assumptions about DM densities in spikes around supermassive black holes, as well as DM masses and annihilation cross-sections within the standard WIMP model, heating by DM annihilation may have an appreciable effect on the accretion on to Sgr A* in the Galactic Centre. Motivated by this observation we study the effects of such heating on Bondi accretion, i.e. spherically symmetric, steady-state Newtonian accretion on to a black hole. We consider different adiabatic indices for the gas, and different power-law exponents for the DM density profile. We find that typical transonic solutions with heating have a significantly reduced accretion rate. However, for many plausible parameters, transonic solutions do not exist, suggesting a breakdown of the underlying assumptions of steady-state Bondi accretion. Our findings indicate that heating by DM annihilation may play an important role in the accretion onto supermassive black holes at the centre of galaxies, and may help explain the low accretion rate observed for Sgr A*.
Training individually fair ML models with sensitive subspace robustness

We propose an approach to training machine learning models that are fair in the sense that their performance is invariant under certain perturbations to the features. For example, the performance of a resume screening system should be invariant under changes to the name of the applicant. We formalize this intuitive notion of fairness by connecting it to the original notion of individual fairness put forth by Dwork et al and show that the proposed approach achieves this notion of fairness. We also demonstrate the effectiveness of the approach on two machine learning tasks that are susceptible to gender and racial biases.
Angular separability of data clusters or network communities in geometrical space and its relevance to hyperbolic embedding

Analysis of 'big data' characterized by high-dimensionality such as word vectors and complex networks requires often their representation in a geometrical space by embedding. Recent developments in machine learning and network geometry have pointed out the hyperbolic space as a useful framework for the representation of this data derived by real complex physical systems. In the hyperbolic space, the radial coordinate of the nodes characterizes their hierarchy, whereas the angular distance between them represents their similarity. Several studies have highlighted the relationship between the angular coordinates of the nodes embedded in the hyperbolic space and the community metadata available. However, such analyses have been often limited to a visual or qualitative assessment. Here, we introduce the angular separation index (ASI), to quantitatively evaluate the separation of node network communities or data clusters over the angular coordinates of a geometrical space. ASI is particularly useful in the hyperbolic space - where it is extensively tested along this study - but can be used in general for any assessment of angular separation regardless of the adopted geometry. ASI is proposed together with an exact test statistic based on a uniformly random null model to assess the statistical significance of the separation. We show that ASI allows to discover two significant phenomena in network geometry. The first is that the increase of temperature in 2D hyperbolic network generative models, not only reduces the network clustering but also induces a 'dimensionality jump' of the network to dimensions higher than two. The second is that ASI can be successfully applied to detect the intrinsic dimensionality of network structures that grow in a hidden geometrical space.
Benefits of Overparameterization in Single-Layer Latent Variable Generative Models

One of the most surprising and exciting discoveries in supervising learning was the benefit of overparametrization (i.e. training a very large model) to improving the optimization landscape of a problem, with minimal effect on statistical performance (i.e. generalization). In contrast, unsupervised settings have been under-explored, despite the fact that it has been observed that overparameterization can be helpful as early as Dasgupta & Schulman (2007). In this paper, we perform an exhaustive study of different aspects of overparameterization in unsupervised learning via synthetic and semi-synthetic experiments. We discuss benefits to different metrics of success (held-out log-likelihood, recovering the parameters of the ground-truth model), sensitivity to variations of the training algorithm, and behavior as the amount of overparameterization increases. We find that, when learning using methods such as variational inference, larger models can significantly increase the number of ground truth latent variables recovered.
Algorithms for weighted independent transversals and strong colouring

An
 independent transversal
 (IT) in a graph with a given vertex partition is an independent set consisting of one vertex in each partition class. Several sufficient conditions are known for the existence of an IT in a given graph and vertex partition, which have been used over the years to solve many combinatorial problems. Some of these IT existence theorems have algorithmic proofs, but there remains a gap between the best existential bounds and the bounds obtainable by efficient algorithms.
 
 Recently, Graf and Haxell (2018) described a new (deterministic) algorithm that asymptotically closes this gap, but there are limitations on its applicability. In this article, we develop a randomized algorithm that is much more widely applicable, and demonstrate its use by giving efficient algorithms for two problems concerning the strong chromatic number of graphs.
The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation

We tested in a live setting the use of active learning for selecting text sentences for human annotations used in training a Thai segmentation machine learning model. In our study, two concurrent annotated samples were constructed, one through random sampling of sentences from a text corpus, and the other through model-based scoring and ranking of sentences from the same corpus. In the course of the experiment, we observed the effect of significant changes to the learning environment which are likely to occur in real-world learning tasks. We describe how our active learning strategy interacted with these events and discuss other practical challenges encountered in using active learning in the live setting.