nils-herrmann/clean_scirepeval huggingface dataset:
Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings

Agents are systems that optimize an objective function in an environment. Together, the goal and the environment induce secondary objectives, incentives. Modeling the agent-environment interaction in graphical models called influence diagrams, we can answer two fundamental questions about an agent's incentives directly from the graph: (1) which nodes is the agent incentivized to observe, and (2) which nodes is the agent incentivized to influence? The answers tell us which information and influence points need extra protection. For example, we may want a classifier for job applications to not use the ethnicity of the candidate, and a reinforcement learning agent not to take direct control of its reward mechanism. Different algorithms and training paradigms can lead to different influence diagrams, so our method can be used to identify algorithms with problematic incentives and help in designing algorithms with better incentives.
Fully Distributed Bayesian Optimization with Stochastic Policies

Bayesian optimization has become a popular method for applications, like the design of computer experiments or hyperparameter tuning of expensive models, where sample efficiency is mandatory. These situations or high-throughput computing, where distributed and scalable architectures are a necessity. However, Bayesian optimization is mostly sequential. Even parallel variants require certain computations between samples, limiting the parallelization bandwidth. Thompson sampling has been previously applied for distributed Bayesian optimization. But, when compared with other acquisition functions in the sequential setting, Thompson sampling is known to perform suboptimally. In this paper, we present a new method for fully distributed Bayesian optimization, which can be combined with any acquisition function. Our approach considers Bayesian optimization as a partially observable Markov decision process. In this context, stochastic policies, such as the Boltzmann policy, have some interesting properties which can also be studied for Bayesian optimization. Furthermore, the Boltzmann policy trivially allows a distributed Bayesian optimization implementation with high level of parallelism and scalability. We present results in several benchmarks and applications that shows the performance of our method.
Grammar Based Directed Testing of Machine Learning Systems

The massive progress of machine learning has seen its application over a variety of domains in the past decade. But how do we develop a systematic, scalable and modular strategy to validate machine-learning systems? We present, to the best of our knowledge, the first approach, which provides a systematic test framework for machine-learning systems that accepts grammar-based inputs. Our Ogma approach automatically discovers erroneous behaviours in classifiers and leverages these erroneous behaviours to improve the respective models. Ogma leverages inherent robustness properties present in any well trained machine-learning model to direct test generation and thus, implementing a scalable test generation methodology. To evaluate our Ogma approach, we have tested it on three real world natural language processing (NLP) classifiers. We have found thousands of erroneous behaviours in these systems. We also compare Ogma with a random test generation approach and observe that Ogma is more effective than such random test generation by up to 489 percent.
The GALAH survey and Gaia DR2: Linking ridges, arches, and vertical waves in the kinematics of the Milky Way

Gaia DR2 has revealed new small-scale and large-scale patterns in the phase-space distribution of stars in the Milky Way. In cylindrical Galactic coordinates $(R,\phi ,z)$, ridge-like structures can be seen in the $(R,V_\phi)$ plane and asymmetric arch-like structures in the $(V_R,V_\phi)$ plane. We show that the ridges are also clearly present when the third dimension of the $(R,V_\phi)$ plane is represented by $\langle z \rangle$, $\langle V_z \rangle$, $\langle V_R \rangle$, $\langle$[Fe/H]$\rangle$, and $\langle [\alpha /{\rm Fe}]\rangle$. The maps suggest that stars along the ridges lie preferentially close to the Galactic mid-plane ($|z|\lt 0.2$ kpc), and have metallicity and $\alpha$ elemental abundance similar to that of the Sun. We show that phase mixing of disrupting spiral arms can generate both the ridges and the arches. It also generates discrete groupings in orbital energy – the ridges and arches are simply surfaces of constant energy. We identify eight distinct ridges in the Gaia DR2 data: six of them have constant energy while two have constant angular momentum. Given that the signature is strongest for stars close to the plane, the presence of ridges in $\langle z \rangle$ and $\langle V_z \rangle$ suggests a coupling between planar and vertical directions. We demonstrate, using N-body simulations that such coupling can be generated both in isolated discs and in discs perturbed by an orbiting satellite like the Sagittarius dwarf galaxy.
Joint Bayesian analysis of large angular scale CMB temperature anomalies

Cosmic microwave background measurements show an agreement with the concordance cosmology model except for a few notable anomalies: Power Suppression, the lack of large scale power in the temperature data compared to what is expected in the concordance model, and Cosmic Hemispherical Asymmetry, a dipolar breakdown of statistical isotropy. An expansion of the CMB covariance in Bipolar Spherical Harmonics naturally parametrizes both these large-scale anomalies, allowing us to perform an exhaustive, fully Bayesian joint analysis of the power spectrum and violations of statistical isotropy up to the dipole level. Our analysis sheds light on the scale dependence of the Cosmic Hemispherical Asymmetry. Assuming a scale-dependent dipole modulation model with a two-parameter power law form, we explore the posterior pdf of amplitude A(l = 16) and the power law index α and find the maximum a posteriori values A*(l = 16) = 0.064 ± 0.022 and α* = −0.92 ± 0.22. The maximum a posteriori direction associated with the Cosmic Hemispherical Asymmetry is (l,b) = (247.8o, −19.6o) in Galactic coordinates, consistent with previous analyses. We evaluate the Bayes factor BSI−DM to compare the Cosmic Hemispherical Asymmetry model with the isotropic model. The data prefer but do not substantially favor the anisotropic model (BSI−DM=0.4). We consider several priors and find that this evidence ratio is robust to prior choice. The large-scale power suppression does not soften when jointly inferring both the isotropic power spectrum and the parameters of the asymmetric model, indicating no evidence that these anomalies are coupled.
The phenomenology of beyond Horndeski gravity

We study the phenomenology of the beyond Horndeski class of scalar-tensor theories of gravity, which on cosmological scales can be characterised in terms of one extra function of time, βH, as well as the usual four Horndeski set of free functions. We show that βH can be directly related to the damping of the matter power spectrum on both large and small scales. We also find that the temperature power spectrum of the cosmic microwave background (CMB) is enhanced at low multipoles and the lensing potential is decreased, as a function of βH. For a particular choice of time dependence we find constraints on βH of order 𝒪(1) using measurements of the temperature and polarisation of the CMB, as well as the lensing potential derived from it, combined with large scale structure data. We find that redshift space distortion measurements can play a significant role in constraining these theories. Finally, we comment on the recent constraints from the observation of an electromagnetic counterpart to a gravitational wave signal; we find that these constraints reduce the number of free parameters of the model but do not significantly change the constraints on the remaining parameters.
Constraints on the interacting vacuum–geodesic CDM scenario

We investigate an interacting dark sector scenario in which the vacuum energy is free to interact with cold dark matter (CDM), which itself is assumed to cluster under the sole action of gravity, i.e. it is in freefall (geodesic), as in ΛCDM. The interaction is characterized by a dimensionless coupling qV(z), in general a function of redshift. Aiming to reconstruct the evolution of the coupling, we use cosmic microwave background data from Planck 2015, along with baryon acoustic oscillation, redshift space distortion, and Type Ia supernova measurements to constrain various parametrizations of qV(z). We present the full linear perturbation theory of this interacting scenario and use Monte Carlo Markov Chains (MCMC) sampling to study five different cases: two cases in which we have ΛCDM evolution in the distant past, until a set redshift ztrans, below which the interaction switches on and qV is the single-sampled parameter, with ztrans fixed at ztrans = 3000 and 0.9, respectively; a case where we allow this transition redshift to vary along with qV; a case in which the vacuum energy is zero for z > ztrans and then begins to grow once the interaction switches on; and the final case in which we bin qV(z) in four redshift bins to investigate the possibility of a dynamical interaction, reconstructing the redshift evolution of the function using Gaussian processes. We find that, in all cases where the high-redshift evolution is not modified, the results are compatible with a vanishing coupling, thus finding no significant deviation from ΛCDM.
Real-time tree search with pessimistic scenarios

Autonomous agents need to make decisions in a sequential manner, under partially observable environment, and in consideration of how other agents behave. In critical situations, such decisions need to be made in real time for example to avoid collisions and recover to safe conditions. We propose a technique of tree search where a deterministic and pessimistic scenario is used after a specified depth. Because there is no branching with the deterministic scenario, the proposed technique allows us to take into account the events that can occur far ahead in the future. The effectiveness of the proposed technique is demonstrated in Pommerman, a multi-agent environment used in a NeurIPS 2018 competition, where the agents that implement the proposed technique have won the first and third places.
Bayesian Mendelian Randomization identifies disease causing proteins via pedigree data, partially observed exposures and correlated instruments

In a study performed on multiplex Multiple Sclerosis (MS) Sardinian families to identify disease causing plasma proteins, application of Mendelian Randomization (MR) methods encounters difficulties due to relatedness of individuals, correlation between finely mapped genotype instrumental variables (IVs) and presence of missing exposures. 
 
We specialize the method of Berzuini et al (2018) to deal with these difficulties. The proposed method allows pedigree structure to enter the specification of the outcome distribution via kinship matrix, and treating missing exposures as additional parameters to be estimated from the data. It also acknowledges possible correlation between instruments by replacing the originally proposed independence prior for IV-specific pleiotropic effect with a g-prior. Based on correlated (r2< 0.2) IVs, we analysed the data of four candidate MS-causing proteins by using both the independence and the g-prior. 
 
95% credible intervals for causal effect for proteins IL12A and STAT4 lay within the strictly negative real semiaxis, in both analyses, suggesting potential causality. Those instruments whose estimated pleiotropic effect exceeded 85% of total effect on outcome were found to act in trans. Analysis via frequentist MR gave inconsistent results. Replacing the independence with a g-prior led to smaller credible intervals for causal effect. 
 
Bayesian MR may be a good way to study disease causation at a protein level based on family data and moderately correlated instruments.
Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning

Injecting human knowledge is an effective way to accelerate reinforcement learning (RL). However, these methods are underexplored. This article presents our discovery that an abstract forward model [thought-game (TG)] combined with transfer learning is an effective way. We take StarCraft II as our study environment. With the help of a designed TG, the agent can learn a 99% win-rate on a 64×64 map against the Level-7 built-in AI, using only 1.08 h in a single commercial machine. We also show that the TG method is not as restrictive as it was thought to be. It can work with roughly designed TGs, and can also be useful when the environment changes. Comparing with previous model-based RL, we show TG is more effective. We also present a TG hypothesis that gives the influence of different fidelity levels of TG. For real games that have unequal state and action spaces, we proposed a novel XfrNet of which usefulness is validated while achieving a 90% win-rate against the cheating Level-10 AI. We argue that the TG method might shed light on further studies of efficient RL with human knowledge.
End-to-end Driving Deploying through Uncertainty-Aware Imitation Learning and Stochastic Visual Domain Adaptation

End-to-end visual-based imitation learning has been widely applied in autonomous driving. When deploying the trained visual-based driving policy, a deterministic command is usually directly applied without considering the uncertainty of the input data. Such kind of policies may bring dramatical damage when applied in the real world. In this paper, we follow the recent real-to-sim pipeline by translating the testing world image back to the training domain when using the trained policy. In the translating process, a stochastic generator is used to generate various images stylized under the training domain randomly or directionally. Based on those translated images, the trained uncertainty-aware imitation learning policy would output both the predicted action and the data uncertainty motivated by the aleatoric loss function. Through the uncertainty-aware imitation learning policy, we can easily choose the safest one with the lowest uncertainty among the generated images. Experiments in the Carla navigation benchmark show that our strategy outperforms previous methods, especially in dynamic environments.
PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things

We propose PanopticFusion, a novel online volumetric semantic mapping system at the level of stuff and things. In contrast to previous semantic mapping systems, PanopticFusion is able to densely predict class labels of a background region (stuff) and individually segment arbitrary foreground objects (things). In addition, our system has the capability to reconstruct a large-scale scene and extract a labeled mesh thanks to its use of a spatially hashed volumetric map representation. Our system first predicts pixel-wise panoptic labels (class labels for stuff regions and instance IDs for thing regions) for incoming RGB frames by fusing 2D semantic and instance segmentation outputs. The predicted panoptic labels are integrated into the volumetric map together with depth measurements while keeping the consistency of the instance IDs, which could vary frame to frame, by referring to the 3D map at that moment. In addition, we construct a fully connected conditional random field (CRF) model with respect to panoptic labels for map regularization. For online CRF inference, we propose a novel unary potential approximation and a map division strategy. We evaluated the performance of our system on the ScanNet (v2) dataset. PanopticFusion outperformed or compared with state-of-the-art offline 3D DNN methods in both semantic and instance segmentation benchmarks. Also, we demonstrate a promising augmented reality application using a 3D panoptic map generated by the proposed system.
Analyzing the Dynamics of a Yo-Yo Using a Smartphone Gyroscope Sensor

The dynamics of a traditional toy, the yoyo, is investigated theoretically and experimentally using smartphone' sensors. In particular, using the gyroscope the angular velocity is measured. The experimental results are complemented thanks to a digital video analysis. The concordance between theoretical and experimental results is discussed. As the yoyo is a ubiquitous, simple and traditional toy this simple proposal could encourage students to experiment with everyday objects and modern technologies.
Evolution of cosmic ray electron spectra in magnetohydrodynamical simulations

Cosmic ray (CR) electrons reveal key insights into the non-thermal physics of the interstellar medium (ISM), galaxies, galaxy clusters, and active galactic nuclei by means of their inverse Compton (IC) γ-ray emission and synchrotron emission in magnetic fields. While magnetohydrodynamical (MHD) simulations with CR protons capture their dynamical impact on these systems, only few computational studies include CR electron physics because of the short cooling time-scales and complex hysteresis effects, which require a numerically expensive, high-resolution spectral treatment. Since CR electrons produce important non-thermal observational signatures, such a spectral CR electron treatment is important to link MHD simulations to observations. We present an efficient post-processing code for Cosmic Ray Electron Spectra that are evolved in Time (crest) on Lagrangian tracer particles. The CR electron spectra are very accurately evolved on comparably large MHD time-steps owing to an innovative hybrid numerical-analytical scheme. crest is coupled to the cosmological MHD code arepo and treats all important aspects of spectral CR electron evolution such as adiabatic expansion and compression, Coulomb losses, radiative losses in form of IC, bremsstrahlung and synchrotron processes, diffusive shock acceleration and reacceleration, Fermi-II reacceleration, and secondary electron injection. After showing various code validations of idealized one-zone simulations, we study the coupling of crest to MHD simulations. We demonstrate that the CR electron spectra are efficiently and accurately evolved in shock-tube and Sedov–Taylor blast wave simulations. This opens up the possibility to produce self-consistent synthetic observables of non-thermal emission processes in various astrophysical environments.
The Lottery Ticket Hypothesis at Scale

Recent work on the lottery ticket hypothesis proposes that randomly-initialized, dense neural networks contain much smaller, fortuitously initialized subnetworks (winning tickets) capable of training to similar accuracy as the original network at a similar speed. While strong evidence exists for the hypothesis across many settings, it has not yet been evaluated on large, state-of-theart networks and there is even evidence against the hypothesis on deeper networks. We modify the lottery ticket pruning procedure to make it possible to identify winning tickets on deeper networks. Rather than set the weights of a winning ticket to their original initializations, we set them to the weights obtained after a small number of training iterations (late resetting). Using late resetting, we identify the first winning tickets for Resnet-50 on Imagenet To understand the efficacy of late resetting, we study the stability of neural network training to pruning, which we define as the consistency of the optimization trajectories followed by a winning ticket when it is trained in isolation and as part of the larger network. We find that later resetting produces stabler winning tickets and that improved stability correlates with higher winning ticket accuracy. This analysis offers new insights into the lottery ticket hypothesis and the dynamics of neural network learning.
Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal

In this work, we propose a novel method termed Frustum ConvNet (F-ConvNet) for amodal 3D object detection from point clouds. Given 2D region proposals in an RGB image, our method first generates a sequence of frustums for each region proposal, and uses the obtained frustums to group local points. F-ConvNet aggregates point-wise features as frustum-level feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN), which spatially fuses frustum-level features and supports an end-to-end and continuous estimation of oriented boxes in the 3D space. We also propose component variants of F-ConvNet, including an FCN variant that extracts multi-resolution frustum features, and a refined use of F-ConvNet over a reduced 3D space. Careful ablation studies verify the efficacy of these component variants. F-ConvNet assumes no prior knowledge of the working 3D environment and is thus dataset-agnostic. We present experiments on both the indoor SUN-RGBD and outdoor KITTI datasets. F-ConvNet outperforms all existing methods on SUN-RGBD, and at the time of submission it outperforms all published works on the KITTI benchmark. Code has been made available at: .
Implementing Microservices through Microtasks

Microtask programming is a form of crowdsourcing for programming in which implementation work is decomposed into short, self-contained microtasks. Each microtask offers a specific goal (e.g., write a unit test) as well as all of the required context and environment support necessary to accomplish this goal. Key to microtasking is the choice of workflow, which delineates the microtasks developers may complete and how contributions from each are aggregated to generate the final software product. Existing approaches either rely on a single developer to manually generate all microtasks, limiting their potential scalability, or impose coordination requirements which limit their effectiveness. Inspired by behavior-driven development, we describe a novel workflow for decomposing programming into microtasks in which each microtask involves identifying, testing, implementing, and debugging an individual behavior within a single function. We apply this approach to the implementation of microservices, demonstrating the first approach for implementing a microservice through microtasks. To evaluate our approach, we conducted a user study in which a small crowd worked to implement a simple microservice and test suite. We found that the crowd was able to use a behavior-driven microtask workflow to successfully complete 350 microtasks and implement 13 functions, quickly onboard and submit their first microtask in less than 24 minutes, contribute new behaviors in less than 5 minutes, and together implement a functioning microservice with only four defects. We discuss these findings and their implications for incorporating microtask work into open source projects.