nils-herrmann/clean_scirepeval huggingface dataset:
Global comparison of core-collapse supernova simulations in spherical symmetry

We present a comparison between several simulation codes designed to study the core-collapse supernova mechanism. We pay close attention to controlling the initial conditions and input physics in order to ensure a meaningful and informative comparison. Our goal is three-fold. First, we aim to demonstrate the current level of agreement between various groups studying the core-collapse supernova central engine. Second, we desire to form a strong basis for future simulation codes and methods to compare to. Lastly, we want this work to be a stepping stone for future work exploring more complex simulations of core-collapse supernovae, i.e., simulations in multiple dimensions and simulations with modern neutrino and nuclear physics. We compare the early (first ~500ms after core bounce) spherically-symmetric evolution of a 20 solar mass progenitor star from six different core-collapse supernovae codes: 3DnSNe-IDSA, AGILE-BOLTZTRAN, FLASH, F{\sc{ornax}}, GR1D, and PROMETHEUS-VERTEX. Given the diversity of neutrino transport and hydrodynamic methods employed, we find excellent agreement in many critical quantities, including the shock radius evolution and the amount of neutrino heating. Our results provide an excellent starting point from which to extend this comparison to higher dimensions and compare the development of hydrodynamic instabilities that are crucial to the supernova explosion mechanism, such as turbulence and convection.
Universality of the stochastic block model

Mesoscopic pattern extraction (MPE) is the problem of finding a partition of the nodes of a complex network that maximizes some objective function. Many well-known network inference problems fall in this category, including for instance: community detection, core-periphery identification, imperfect graph colouring. In this paper, we show that the most popular algorithms designed to solve MPE problems can in fact be understood as special cases of the maximum likelihood formulation of the stochastic block model, or one of its direct generalizations. These equivalence relations show that the SBM is nearly universal with respect to MPE problems.
PAC-Bayes Control: Synthesizing Controllers that Provably Generalize to Novel Environments

Our goal is to synthesize controllers for robots that provably generalize well to novel environments given a dataset of example environments. The key technical idea behind our approach is to leverage tools from generalization theory in machine learning by exploiting a precise analogy (which we present in the form of a reduction) between robustness of controllers to novel environments and generalization of hypotheses in supervised learning. In particular, we utilize the Probably Approximately Correct (PAC)-Bayes framework, which allows us to obtain upper bounds (that hold with high probability) on the expected cost of (stochastic) controllers across novel environments. We propose control synthesis algorithms that explicitly seek to minimize this upper bound. The corresponding optimization problem can be solved using convex optimization (Relative Entropy Programming in particular) in the setting where we are optimizing over a finite control policy space. In the more general setting of continuously parameterized controllers, we minimize this upper bound using stochastic gradient descent. We present examples of our approach in the context of obstacle avoidance control with depth measurements. Our simulated examples demonstrate the potential of our approach to provide strong generalization guarantees on controllers for robotic systems with continuous state and action spaces, complicated (e.g., nonlinear) dynamics, and rich sensory inputs (e.g., depth measurements).
Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks

Artificial neural networks tend to learn only what they need for a task. A manipulation of the training data can counter this phenomenon. In this paper, we study the effect of different alterations of the training data, which limit the amount and position of information that is available for the decision making. We analyze the accuracy and robustness against semantic and black box attacks on the networks that were trained on different training data modifications for the particular example of morphing attacks. A morphing attack is an attack on a biometric facial recognition system where the system is fooled to match two different individuals with the same synthetic face image. Such a synthetic image can be created by aligning and blending images of the two individuals that should be matched with this image.
MUSE crowded field 3D spectroscopy in NGC 300

As a new approach to the study of resolved stellar populations in nearby galaxies, our goal is to demonstrate with a pilot study in NGC 300 that integral field spectroscopy with high spatial resolution and excellent seeing conditions reaches an unprecedented depth in severely crowded fields.
Methods. Observations by MUSE with seven pointings in NGC 300 have resulted in data cubes that are analyzed in four ways: (1) Point spread function-fitting 3D spectroscopy with PampelMUSE, as already successfully pioneered in globular clusters, yields de-blended spectra of individually distinguishable stars, thus providing a complete inventory of blue and red supergiants, and asymptotic giant branch (AGB) stars of type M and C. The technique is also applicable to emission line point sources and provides samples of planetary nebulae (PNe) that are complete down to m5007 = 28. (2) Pseudo-monochromatic images, created at the wavelengths of the most important emission lines and corrected for continuum light with the P3D visualization tool, provide maps of H II regions, supernova remnants (SNR), and the diffuse interstellar medium (ISM) at a high level of sensitivity, where also faint point sources stand out and allow for the discovery of PNe, Wolf–Rayet (WR) stars, etc. (3) The use of the P3D line-fitting tool yields emission line fluxes, surface brightness, and kinematic information for gaseous objects, corrected for absorption line profiles of the underlying stellar population in the case of Hα. (4) Visual inspection of the data cubes by browsing through the row-stacked spectra image in P3D is demonstrated to be efficient for data mining and the discovery of background galaxies and unusual objects.
Results. We present a catalog of luminous stars, rare stars such as WR, and other emission line stars, carbon stars, symbiotic star candidates, PNe, H II regions, SNR, giant shells, peculiar diffuse and filamentary emission line objects, and background galaxies, along with their spectra.
Conclusions. The technique of crowded-field 3D spectroscopy, using the PampelMUSE code, is capable of deblending individual bright stars, the unresolved background of faint stars, gaseous nebulae, and the diffuse component of the ISM, resulting in unprecedented legacy value for observations of nearby galaxies with MUSE.
Embedding Text in Hyperbolic Spaces

Natural language text exhibits hierarchical structure in a variety of respects. Ideally, we could incorporate our prior knowledge of this hierarchical structure into unsupervised learning algorithms that work on text data. Recent work by Nickel and Kiela (2017) proposed using hyperbolic instead of Euclidean embedding spaces to represent hierarchical data and demonstrated encouraging results when embedding graphs. In this work, we extend their method with a re-parameterization technique that allows us to learn hyperbolic embeddings of arbitrarily parameterized objects. We apply this framework to learn word and sentence embeddings in hyperbolic space in an unsupervised manner from text corpora. The resulting embeddings seem to encode certain intuitive notions of hierarchy, such as word-context frequency and phrase constituency. However, the implicit continuous hierarchy in the learned hyperbolic space makes interrogating the model’s learned hierarchies more difficult than for models that learn explicit edges between items. The learned hyperbolic embeddings show improvements over Euclidean embeddings in some – but not all – downstream tasks, suggesting that hierarchical organization is more useful for some tasks than others.
Combining Model-Free Q-Ensembles and Model-Based Approaches for Informed Exploration

Q-Ensembles are a model-free approach where input images are fed into different Q-networks and exploration is driven by the assumption that uncertainty is proportional to the variance of the output Q-values obtained. They have been shown to perform relatively well compared to other exploration strategies. Further, model-based approaches, such as encoder-decoder models have been used successfully for next frame prediction given previous frames. This paper proposes to integrate the model-free Q-ensembles and model-based approaches with the hope of compounding the benefits of both and achieving superior exploration as a result. Results show that a model-based trajectory memory approach when combined with Q-ensembles produces superior performance when compared to only using Q-ensembles.
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen during training. Our system consists of three independently trained components: (1) a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech from thousands of speakers without transcripts, to generate a fixed-dimensional embedding vector from seconds of reference speech from a target speaker; (2) a sequence-to-sequence synthesis network based on Tacotron 2, which generates a mel spectrogram from text, conditioned on the speaker embedding; (3) an auto-regressive WaveNet-based vocoder that converts the mel spectrogram into a sequence of time domain waveform samples. We demonstrate that the proposed model is able to transfer the knowledge of speaker variability learned by the discriminatively-trained speaker encoder to the new task, and is able to synthesize natural speech from speakers that were not seen during training. We quantify the importance of training the speaker encoder on a large and diverse speaker set in order to obtain the best generalization performance. Finally, we show that randomly sampled speaker embeddings can be used to synthesize speech in the voice of novel speakers dissimilar from those used in training, indicating that the model has learned a high quality speaker representation.
Social Networks through the Prism of Cognition

Human relations are driven by social events - people interact, exchange information, share knowledge and emotions, or gather news from mass media. These events leave traces in human memory. The initial strength of a trace depends on cognitive factors such as emotions or attention span. Each trace continuously weakens over time unless another related event activity strengthens it. Here, we introduce a novel Cognition-driven Social Network (CogSNet) model that accounts for cognitive aspects of social perception and explicitly represents human memory dynamics. For validation, we apply our model to NetSense data on social interactions among university students. The results show that CogSNet significantly improves quality of modeling of human interactions in social networks.
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making

We draw attention to an important, yet largely overlooked aspect of evaluating fairness for automated decision making systems---namely risk and welfare considerations. Our proposed family of measures corresponds to the long-established formulations of cardinal social welfare in economics, and is justified by the Rawlsian conception of fairness behind a veil of ignorance. The convex formulation of our welfare-based measures of fairness allows us to integrate them as a constraint into any convex loss minimization pipeline. Our empirical analysis reveals interesting trade-offs between our proposal and (a) prediction accuracy, (b) group discrimination, and (c) Dwork et al.'s notion of individual fairness. Furthermore and perhaps most importantly, our work provides both heuristic justification and empirical evidence suggesting that a lower-bound on our measures often leads to bounded inequality in algorithmic outcomes; hence presenting the first computationally feasible mechanism for bounding individual-level inequality.
Hierarchical interpretations for neural network predictions

Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inability to effectively visualize these relationships has led to DNNs being characterized as black boxes and consequently limited their applications. To ameliorate this problem, we introduce the use of hierarchical interpretations to explain DNN predictions through our proposed method, agglomerative contextual decomposition (ACD). Given a prediction from a trained DNN, ACD produces a hierarchical clustering of the input features, along with the contribution of each cluster to the final prediction. This hierarchy is optimized to identify clusters of features that the DNN learned are predictive. Using examples from Stanford Sentiment Treebank and ImageNet, we show that ACD is effective at diagnosing incorrect predictions and identifying dataset bias. Through human experiments, we demonstrate that ACD enables users both to identify the more accurate of two DNNs and to better trust a DNN's outputs. We also find that ACD's hierarchy is largely robust to adversarial perturbations, implying that it captures fundamental aspects of the input and ignores spurious noise.
Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing

Model interpretability and systematic, targeted model adaptation present central tenets in machine learning for addressing limited or biased datasets. In this paper, we introduce neural stethoscopes as a framework for quantifying the degree of importance of specific factors of influence in deep networks as well as for actively promoting and suppressing information as appropriate. In doing so we unify concepts from multitask learning as well as training with auxiliary and adversarial losses. We showcase the efficacy of neural stethoscopes in an intuitive physics domain. Specifically, we investigate the challenge of visually predicting stability of block towers and demonstrate that the network uses visual cues which makes it susceptible to biases in the dataset. Through the use of stethoscopes we interrogate the accessibility of specific information throughout the network stack and show that we are able to actively de-bias network predictions as well as enhance performance via suitable auxiliary and adversarial stethoscope losses.
Detecting Statistically Significant Communities

Community detection is a key data analysis problem across different fields. During the past decades, numerous algorithms have been proposed to address this issue. However, most work on community detection does not address the issue of statistical significance. Although some research efforts have been made towards mining statistically significant communities, deriving an analytical solution of <inline-formula><tex-math notation="LaTeX">$p$</tex-math><alternatives><mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="he-ieq1-3015667.gif"/></alternatives></inline-formula>-value for one community under the configuration model is still a challenging mission that remains unsolved. The configuration model is a widely used random graph model in community detection, in which the degree of each node is preserved in the generated random networks. To partially fulfill this void, we present a tight upper bound on the <inline-formula><tex-math notation="LaTeX">$p$</tex-math><alternatives><mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="he-ieq2-3015667.gif"/></alternatives></inline-formula>-value of a single community under the configuration model, which can be used for quantifying the statistical significance of each community analytically. Meanwhile, we present a local search method to detect statistically significant communities in an iterative manner. Experimental results demonstrate that our method is comparable with the competing methods on detecting statistically significant communities.
Efficient and Modular Coalgebraic Partition Refinement

We present a generic partition refinement algorithm that quotients coalgebraic systems by behavioural equivalence, an important task in system analysis and verification. Coalgebraic generality allows us to cover not only classical relational systems but also, e.g. various forms of weighted systems and furthermore to flexibly combine existing system types. Under assumptions on the type functor that allow representing its finite coalgebras in terms of nodes and edges, our algorithm runs in time $\mathcal{O}(m\cdot \log n)$ where $n$ and $m$ are the numbers of nodes and edges, respectively. The generic complexity result and the possibility of combining system types yields a toolbox for efficient partition refinement algorithms. Instances of our generic algorithm match the run-time of the best known algorithms for unlabelled transition systems, Markov chains, deterministic automata (with fixed alphabets), Segala systems, and for color refinement.
Surprising Negative Results for Generative Adversarial Tree Search

While many recent advances in deep reinforcement learning (RL) rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment model. In this work, we provide an extensive study on the design of deep generative models for RL environments and propose a sample efficient and robust method to learn the model of Atari environments. We deploy this model and propose generative adversarial tree search (GATS) a deep RL algorithm that learns the environment model and implements Monte Carlo tree search (MCTS) on the learned model for planning. While MCTS on the learned model is computationally expensive, similar to AlphaGo, GATS follows depth limited MCTS. GATS employs deep Q network (DQN) and learns a Q-function to assign values to the leaves of the tree in MCTS. We theoretical analyze GATS vis-a-vis the bias-variance trade-off and show GATS is able to mitigate the worst-case error in the Q-estimate. While we were expecting GATS to enjoy a better sample complexity and faster converges to better policies, surprisingly, GATS fails to outperform DQN. We provide a study on which we show why depth limited MCTS fails to perform desirably.
A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina

Photoreceptors in the retina are coupled by electrical synapses called "gap junctions". It has long been established that gap junctions increase the signal-to-noise ratio of photoreceptors. Inspired by electrically coupled photoreceptors, we introduced a simple filter, the PR-filter, with only one variable. On BSD68 dataset, PR-filter showed outstanding performance in SSIM during blind denoising tasks. It also significantly improved the performance of state-of-the-art convolutional neural network blind denosing on non-Gaussian noise. The performance of keeping more details might be attributed to small receptive field of the photoreceptors.
Non-Negative Networks Against Adversarial Attacks

Adversarial attacks against neural networks are a problem of considerable importance, for which effective defenses are not yet readily available. We make progress toward this problem by showing that non-negative weight constraints can be used to improve resistance in specific scenarios. In particular, we show that they can provide an effective defense for binary classification problems with asymmetric cost, such as malware or spam detection. We also show the potential for non-negativity to be helpful to non-binary problems by applying it to image classification.
Multimodal Grounding for Language Processing

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.
Banach Wasserstein GAN

Wasserstein Generative Adversarial Networks (WGANs) can be used to generate realistic samples from complicated image distributions. The Wasserstein metric used in WGANs is based on a notion of distance between individual images, which induces a notion of distance between probability distributions of images. So far the community has considered $\ell^2$ as the underlying distance. We generalize the theory of WGAN with gradient penalty to Banach spaces, allowing practitioners to select the features to emphasize in the generator. We further discuss the effect of some particular choices of underlying norms, focusing on Sobolev norms. Finally, we demonstrate a boost in performance for an appropriate choice of norm on CIFAR-10 and CelebA.
Data-Driven Decentralized Optimal Power Flow

The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information. Collectively, all local controllers closely match the centralized OPF solution, providing near-optimal performance and satisfaction of system constraints. A rate distortion framework facilitates the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. Our methodology provides a natural extension to decide what buses a DER should communicate with to improve the reconstruction of its individual policy. The method is applied on both single- and three-phase test feeder networks using data from real loads and distributed generators. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to active distribution networks.
Neural Ordinary Differential Equations

We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a black-box differential equation solver. These continuous-depth models have constant memory cost, adapt their evaluation strategy to each input, and can explicitly trade numerical precision for speed. We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions. For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. This allows end-to-end training of ODEs within larger models.