nils-herrmann/clean_scirepeval huggingface dataset:
Semi-Implicit Generative Model

To combine explicit and implicit generative models, we introduce semi-implicit generator (SIG) as a flexible hierarchical model that can be trained in the maximum likelihood framework. Both theoretically and experimentally, we demonstrate that SIG can generate high quality samples especially when dealing with multi-modality. By introducing SIG as an unbiased regularizer to the generative adversarial network (GAN), we show the interplay between maximum likelihood and adversarial learning can stabilize the adversarial training, resist the notorious mode collapsing problem of GANs, and improve the diversity of generated random samples.
Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators

Generative adversarial networks (GANs) have shown great success in applications such as image generation and inpainting. However, they typically require large datasets, which are often not available, especially in the context of prediction tasks such as image segmentation that require labels. Therefore, methods such as the CycleGAN use more easily available unlabelled data, but do not offer a way to leverage additional labelled data for improved performance. To address this shortcoming, we show how to factorise the joint data distribution into a set of lower-dimensional distributions along with their dependencies. This allows splitting the discriminator in a GAN into multiple "sub-discriminators" that can be independently trained from incomplete observations. Their outputs can be combined to estimate the density ratio between the joint real and the generator distribution, which enables training generators as in the original GAN framework. We apply our method to image generation, image segmentation and audio source separation, and obtain improved performance over a standard GAN when additional incomplete training examples are available. For the Cityscapes segmentation task in particular, our method also improves accuracy by an absolute 14.9% over CycleGAN while using only 25 additional paired examples.
Reflected Light Phase Curves in the TESS Era

The reflected light signal from a planet throughout its orbit is a powerful probe of a planet’s atmospheric properties. There are a number of planets that are amenable to reflected light phase curve studies with present and future space-based instrumentation and here we assess our ability to characterize these worlds. Using simulated Transiting Exoplanet Survey Satellite (TESS) populations we identify the Nine, a set of archetypal exoplanets with the potential to be bright in reflected light, because of their radii and proximity to their star, while still being cool enough to have minimal thermal contamination at optical wavelengths. For each planet we compute albedo spectra for several cloud and atmosphere assumptions (e.g., thermochemical equilibrium, solar composition). We find that in the TESS bandpass the estimated contrast at optical wavelengths is typically <10 ppm except for the brightest, largest, or closest in planets with the highest lofted clouds where contrast can reach a few tens of parts per million. Meanwhile, in a bluer bandpass (0.3–0.5 μm) the estimated contrast can be as high as 150 ppm but typically 10–50 ppm. In the temperature range of interest, planets with the highest, most extensive cloud decks are generally darker at bluer wavelengths than cloudless planets because of the low single scattering albedos of their primary condensate constituents. Our models suggest that Neptune-sized planets with relatively low insolation and small semimajor axes are the most conducive to reflected light phase curve studies in TESS.
Graph Learning Network: A Structure Learning Algorithm

Recently, graph neural networks (GNNs) have proved to be suitable in tasks on unstructured data. Particularly in tasks as community detection, node classification, and link prediction. However, most GNN models still operate with static relationships. We propose the Graph Learning Network (GLN), a simple yet effective process to learn node embeddings and structure prediction functions. Our model uses graph convolutions to propose expected node features, and predict the best structure based on them. We repeat these steps recursively to enhance the prediction and the embeddings.
Evaluating structure learning algorithms with a balanced scoring function

Several structure learning algorithms have been proposed towards discovering causal or Bayesian Network (BN) graphs, which is a particularly challenging problem in AI. The performance of these algorithms is evaluated based on the relationship the learned graph has with respect to the ground truth graph. However, there is no agreed scoring function to determine this relationship. Moreover, this paper shows that the commonly used metrics tend to be biased in favour of graphs that minimise the number of edges. The evaluation bias is inconsistent and may lead to evaluating graphs with no edges as superior to graphs with varying numbers of correct and incorrect edges; implying that graphs that minimise edges are often favoured over more complex graphs due to bias rather than overall accuracy. While graphs that are less complex are often desirable, the current metrics encourage algorithms to optimise for simplicity, and to discover graphs with a limited number of edges that do not enable full propagation of evidence. This paper proposes a Balanced Scoring Function (BSF) that eliminates this bias by adjusting the reward function based on the difficulty of discovering an edge, or no edge, proportional to their occurrence rate in the ground truth graph. The BSF score can be used in conjunction with other traditional metrics to provide an alternative and unbiased assessment about the capability of structure learning algorithms in discovering causal or BN graphs.
Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes

We propose a new class of structured methods for Monte Carlo (MC) sampling, called DPPMC, designed for high-dimensional nonisotropic distributions where samples are correlated to reduce the variance of the estimator via determinantal point processes. We successfully apply DPPMCs to problems involving nonisotropic distributions arising in guided evolution strategy (GES) methods for RL, CMA-ES techniques and trust region algorithms for blackbox optimization, improving state-of-the-art in all these settings. In particular, we show that DPPMCs drastically improve exploration profiles of the existing evolution strategy algorithms. We further confirm our results, analyzing random feature map estimators for Gaussian mixture kernels. We provide theoretical justification of our empirical results, showing a connection between DPPMCs and structured orthogonal MC methods for isotropic distributions.
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems

Restless bandit problems are instances of non-stationary multi-armed bandits. These problems have been studied well from the optimization perspective, where the goal is to efficiently find a near-optimal policy when system parameters are known. However, very few papers adopt a learning perspective, where the parameters are unknown. In this paper, we analyze the performance of Thompson sampling in episodic restless bandits with unknown parameters. We consider a general policy map to define our competitor and prove an $\tilde{\mathcal{O}}(\sqrt{T})$ Bayesian regret bound. Our competitor is flexible enough to represent various benchmarks including the best fixed action policy, the optimal policy, the Whittle index policy, or the myopic policy. We also present empirical results that support our theoretical findings.
The (Non-)Utility of Structural Features in BiLSTM-based Dependency Parsers

Classical non-neural dependency parsers put considerable effort on the design of feature functions. Especially, they benefit from information coming from structural features, such as features drawn from neighboring tokens in the dependency tree. In contrast, their BiLSTM-based successors achieve state-of-the-art performance without explicit information about the structural context. In this paper we aim to answer the question: How much structural context are the BiLSTM representations able to capture implicitly? We show that features drawn from partial subtrees become redundant when the BiLSTMs are used. We provide a deep insight into information flow in transition- and graph-based neural architectures to demonstrate where the implicit information comes from when the parsers make their decisions. Finally, with model ablations we demonstrate that the structural context is not only present in the models, but it significantly influences their performance.
What Makes Training Multi-Modal Classification Networks Hard?

Consider end-to-end training of a multi-modal vs. a uni-modal network on a task with multiple input modalities: the multi-modal network receives more information, so it should match or outperform its uni-modal counterpart. In our experiments, however, we observe the opposite: the best uni-modal network can outperform the multi-modal network. This observation is consistent across different combinations of modalities and on different tasks and benchmarks for video classifications. This paper identifies two main causes for this performance drop: first, multi-modal networks are often prone to overfitting due to increased capacity. Second, different modalities overfit and generalize at different rates, so training them jointly with a single optimization strategy is sub-optimal. We address these two problems with a technique we call Gradient-Blending, which computes an optimal blending of modalities based on their overfitting behaviors. We demonstrate that Gradient Blending outperforms widely-used baselines for avoiding overfitting and achieves state-of-the-art accuracy on various tasks including human action recognition, ego-centric action recognition, and acoustic event detection.
Mean-dependent nonstationary spatial models

Nonstationarity is a major challenge in analyzing spatial data. For example, daily precipitation measurements may have increased variability and decreased spatial smoothness in areas with high mean rainfall. Common nonstationary covariance models introduce parameters specific to each location, giving a highly-parameterized model which is difficult to fit. We develop a nonstationary spatial model that uses the mean to determine the covariance in a region, resulting in a far simpler, albeit more specialized, model. We explore inferential and predictive properties of the model under various simulated data situations. We show that this model in certain circumstances improves predictions compared to a standard stationary spatial model. We further propose a computationally efficient approximation that has comparable predictive accuracy. We also develop a test for nonstationary data and show it reliably identifies nonstationarity. We apply these methods to daily precipitation in Puerto Rico.
Choosing Transfer Languages for Cross-Lingual Learning

Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages. However, given a particular task language, it is not clear which language to transfer from, and the standard strategy is to select languages based on ad hoc criteria, usually the intuition of the experimenter. Since a large number of features contribute to the success of cross-lingual transfer (including phylogenetic similarity, typological properties, lexical overlap, or size of available data), even the most enlightened experimenter rarely considers all these factors for the particular task at hand. In this paper, we consider this task of automatically selecting optimal transfer languages as a ranking problem, and build models that consider the aforementioned features to perform this prediction. In experiments on representative NLP tasks, we demonstrate that our model predicts good transfer languages much better than ad hoc baselines considering single features in isolation, and glean insights on what features are most informative for each different NLP tasks, which may inform future ad hoc selection even without use of our method.
Vector-Valued Graph Trend Filtering With Non-Convex Penalties

This article studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued. We extend the graph trend filtering framework to denoising vector-valued graph signals with a family of non-convex regularizers, which exhibit superior recovery performance over existing convex regularizers. Using an oracle inequality, we establish the statistical error rates of first-order stationary points of the proposed non-convex method for generic graphs. Furthermore, we present an ADMM-based algorithm to solve the proposed method and establish its convergence. Numerical experiments are conducted on both synthetic and real-world data for denoising, support recovery, event detection, and semi-supervised classification.
An [O iii] search for extended emission around AGN with H i mapping: a distant cloud ionized by Mkn 1

Motivated by the discovery of large ionized clouds around AGN, and particularly the large fraction of those that are consistent with photoionized gaseous tidal debris, we searched for [O iii] emission around Seyfert galaxies previously mapped in H i, many with extended gas features. Of 26 Seyfert galaxies, we find one spatially extended emission feature, a discrete cloud projected ≈12 kpc SW from the centre of Mkn 1 and spanning a transverse extent of 8 kpc. Optical spectroscopy (Kast/Lick and SCORPIO/BTA) of this cloud confirms its association with the Mkn 1–NGC 451 galaxy pair, closely matching the kinematics of nearby H i structures, and reveals emission-line ratios requiring photoionization by the AGN at roughly the direct observed luminosity of the nucleus. For the entire sample, the full opening angle of the ionization cones (bicones) must be <20° if the AGNs are continuously bright for scales longer than the light-traveltimes to the H i structures. Since typical AGN ionization cones are observed to be much broader than this, our low detection fraction may add to evidence for the ubiquity of strong variations in AGN luminosity on scales 104–105 yr.
Tweeting your Destiny: Profiling Users in the Twitter Landscape around an Online Game

Social media has become a major communication channel for communities centered around video games. Consequently, social media offers a rich data source to study online communities and the discussions evolving around games. Towards this end, we explore a large-scale dataset consisting of over 1 million tweets related to the online multiplayer shooter Destiny and spanning a time period of about 14 months using unsupervised clustering and topic modelling. Furthermore, we correlate Twitter activity of over 3,000 players with their playtime. Our results contribute to the understanding of online player communities by identifying distinct player groups with respect to their Twitter characteristics, describing subgroups within the Destiny community, and uncovering broad topics of community interest.
Building Trust for Continuous Variable Quantum States

We first introduce heterodyne quantum state tomography, a reliable method for continuous variable quantum state certification which directly yields the elements of the density matrix of the state considered and analytical confidence intervals, using heterodyne detection. This method neither needs mathematical reconstruction of the data, nor discrete binning of the sample space, and uses a single Gaussian measurement setting. Beyond quantum state tomography and without its identical copies assumption, we also derive a general protocol for verifying continuous variable pure quantum states with Gaussian measurements against fully malicious adversaries. In particular, we make use of a De Finetti reduction for infinite-dimensional systems. As an application, we consider verified universal continuous variable quantum computing, with a computational power restricted to Gaussian operations and an untrusted non-Gaussian states source. These results are obtained using a new analytical estimator for the expected value of any operator acting on a continuous variable quantum state with bounded support over Fock basis, computed with samples from heterodyne detection of the state.
Fallout: Reading Kernel Writes From User Space

Recently, out-of-order execution, an important performance optimization in modern high-end processors, has been revealed to pose a significant security threat, allowing information leaks across security domains. In particular, the Meltdown attack leaks information from the operating system kernel to user space, completely eroding the security of the system. To address this and similar attacks, without incurring the performance costs of software countermeasures, Intel includes hardware-based defenses in its recent Coffee Lake R processors. 
In this work, we show that the recent hardware defenses are not sufficient. Specifically, we present Fallout, a new transient execution attack that leaks information from a previously unexplored microarchitectural component called the store buffer. We show how unprivileged user processes can exploit Fallout to reconstruct privileged information recently written by the kernel. We further show how Fallout can be used to bypass kernel address space randomization. Finally, we identify and explore microcode assists as a hitherto ignored cause of transient execution. 
Fallout affects all processor generations we have tested. However, we notice a worrying regression, where the newer Coffee Lake R processors are more vulnerable to Fallout than older generations.
Robust measurement of innovation performances in Europe with a hierarchy of interacting composite indicators

For long time the measurement of innovation has been in the forefront of policy makers' and researchers' agenda worldwide. Therefore, there is an ongoing debate about which indicators should be used to measure innovation. Recent approaches have favoured the use of composite innovation indicators. However, there is no consensus about the appropriate methodology to aggregate the varying dimensions of innovation into a single summary indicator. One of the best known examples of composite innovation indicators is the European Innovation Scoreboard (EIS). It is a relevant tool for benchmarking innovation in Europe. Still, the EIS lacks a proper scheme for weighting the included indicators according to their relative importance. In this context, we propose an appraisal methodology permitting to take into consideration the interaction of criteria and robustness concerns related to the elicitation of the weights assigned to the elementary indicators. With this aim, we apply the hierarchical-SMAA-Choquet integral approach. This integrated multicriteria decision making (MCDM) method helps the users to rank and benchmark countries' innovation performance taking into account the importance and interaction of criteria assigned by themselves, rather than equal weights or weights exogenously fixed by external experts.
Heterogeneous causal effects with imperfect compliance: A Bayesian machine learning approach

This paper introduces an innovative Bayesian machine learning algorithm to draw interpretable inference on heterogeneous causal effects in the presence of imperfect compliance (e.g., under an irregular assignment mechanism). We show, through Monte Carlo simulations, that the proposed Bayesian Causal Forest with Instrumental Variable (BCF-IV) methodology outperforms other machine learning techniques tailored for causal inference in discovering and estimating the heterogeneous causal effects while controlling for the familywise error rate (or – less stringently – for the false discovery rate) at leaves’ level. BCF-IV sheds a light on the heterogeneity of causal effects in instrumental variable scenarios and, in turn, provides the policy-makers with a relevant tool for targeted policies. Its empirical application evaluates the effects of additional funding on students’ performances. The results indicate that BCF-IV could be used to enhance the effectiveness of school funding on students’ performance.
Dynamic Traffic Scene Classification with Space-Time Coherence

This paper examines the problem of dynamic traffic scene classification under space-time variations in viewpoint that arise from video captured on-board a moving vehicle. Solutions to this problem are important for realization of effective driving assistance technologies required to interpret or predict road user behavior. Currently, dynamic traffic scene classification has not been adequately addressed due to a lack of benchmark datasets that consider spatiotemporal evolution of traffic scenes resulting from a vehicle’s ego-motion. This paper has three main contributions. First, an annotated dataset is released to enable dynamic scene classification that includes 80 hours of diverse high quality driving video data clips collected in the San Francisco Bay area. The dataset includes temporal annotations for road places, road types, weather, and road surface conditions. Second, we introduce novel and baseline algorithms that utilize semantic context and temporal nature of the dataset for dynamic classification of road scenes. Finally, we showcase algorithms and experimental results that highlight how extracted features from scene classification serve as strong priors and help with tactical driver behavior understanding. The results show significant improvement from previously reported driving behavior detection baselines in the literature.
A Cooling Anomaly of High-mass White Dwarfs

Recently, the power of Gaia data has revealed an enhancement of high-mass white dwarfs (WDs) on the Hertzsprung–Russell diagram, called the Q branch. This branch is located at the high-mass end of the recently identified crystallization branch. Investigating its properties, we find that the number density and velocity distribution on the Q branch cannot be explained by the cooling delay of crystallization alone, suggesting the existence of an extra cooling delay. To quantify this delay, we statistically compare two age indicators—the dynamical age inferred from transverse velocity, and the photometric isochrone age—for more than one thousand high-mass WDs (1.08–1.23 M⊙) selected from Gaia Data Release 2. We show that about 6% of the high-mass WDs must experience an 8 Gyr extra cooling delay on the Q branch, in addition to the crystallization and merger delays. This cooling anomaly is a challenge for WD cooling models. We point out that 22Ne settling in C/O-core WDs could account for this extra cooling delay.
Path-Augmented Graph Transformer Network

Much of the recent work on learning molecular representations has been based on Graph Convolution Networks (GCN). These models rely on local aggregation operations and can therefore miss higher-order graph properties. To remedy this, we propose Path-Augmented Graph Transformer Networks (PAGTN) that are explicitly built on longer-range dependencies in graphstructured data. Specifically, we use path features in molecular graphs to create global attention layers. We compare our PAGTN model against the GCN model and show that our model consistentlyoutperforms GCNs on molecular property prediction datasets including quantum chemistry (QM7, QM8, QM9), physical chemistry (ESOL, Lipophilictiy) and biochemistry (BACE, BBBP)2.