nils-herrmann/clean_scirepeval huggingface dataset:
Gravitational waves from peaks

We discuss a novel mechanism to generate gravitational waves in the early universe. A standard way to produce primordial black holes is to enhance at small-scales the overdensity perturbations generated during inflation. The latter, upon horizon re-entry, collapse into black holes. They must be sizeable enough and are therefore associated to rare peaks. There are however less sizeable and much less rare overdensity peaks which do not end up forming primordial black holes and have a non-spherical shape. Upon collapse, they possess a time-dependent non-vanishing mass quadrupole which gives rise to the generation of gravitational waves. By their nature, such gravitational waves are complementary to those sourced at second-order by the very same scalar perturbations responsible for the formation of the primordial black holes. Their amplitude is nevertheless typically about two orders of magnitude smaller and therefore hardly measurable.
Neural Markov Logic Networks

We introduce Neural Markov Logic Networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov Logic Networks (MLNs), NMLNs are an exponential-family model for modelling distributions over possible worlds, but unlike MLNs, they do not rely on explicitly specified first-order logic rules. Instead, NMLNs learn an implicit representation of such rules as a neural network that acts as a potential function on fragments of the relational structure. Interestingly, any MLN can be represented as an NMLN. Similarly to recently proposed Neural theorem provers (NTPs) [Rockt\"aschel and Riedel, 2017], NMLNs can exploit embeddings of constants but, unlike NTPs, NMLNs work well also in their absence. This is extremely important for predicting in settings other than the transductive one. We showcase the potential of NMLNs on knowledge-base completion tasks and on generation of molecular (graph) data.
Joint Representation of Multiple Geometric Priors via a Shape Decomposition Model for Single Monocular 3D Pose Estimation

In this paper, we aim to recover the 3D human pose from 2D body joints of a single image. The major challenge in this task is the depth ambiguity since different 3D poses may produce similar 2D poses. Although many recent advances in this problem are found in both unsupervised and supervised learning approaches, the performances of most of these approaches are greatly affected by insufficient diversities and richness of training data. To alleviate this issue, we propose an unsupervised learning approach, which is capable of estimating various complex poses well under limited available training data. Specifically, we propose a Shape Decomposition Model (SDM) in which a 3D pose is considered as the superposition of two parts which are global structure together with some deformations. Based on SDM, we estimate these two parts explicitly by solving two sets of different distributed combination coefficients of geometric priors. In addition, to obtain geometric priors, a joint dictionary learning algorithm is proposed to extract both coarse and fine pose clues simultaneously from limited training data. Quantitative evaluations on several widely used datasets demonstrate that our approach yields better performances over other competitive approaches. Especially, on some categories with more complex deformations, significant improvements are achieved by our approach. Furthermore, qualitative experiments conducted on in-the-wild images also show the effectiveness of the proposed approach.
Attention Is (not) All You Need for Commonsense Reasoning

The recently introduced BERT model exhibits strong performance on several language understanding benchmarks. In this paper, we describe a simple re-implementation of BERT for commonsense reasoning. We show that the attentions produced by BERT can be directly utilized for tasks such as the Pronoun Disambiguation Problem and Winograd Schema Challenge. Our proposed attention-guided commonsense reasoning method is conceptually simple yet empirically powerful. Experimental analysis on multiple datasets demonstrates that our proposed system performs remarkably well on all cases while outperforming the previously reported state of the art by a margin. While results suggest that BERT seems to implicitly learn to establish complex relationships between entities, solving commonsense reasoning tasks might require more than unsupervised models learned from huge text corpora.
Probing gaseous halos of galaxies with radio jets

Gaseous halos play a key role in understanding inflow, feedback, and the overall baryon budget in galaxies. Literature models predict transitions of the state of the gaseous halo between cold and hot accretion, winds, fountains, and hydrostatic halos at certain galaxy masses. Since luminosities of radio AGN are sensitive to halo densities, any significant transition would be expected to show up in the radio luminosities of large samples of galaxies. The LOw Frequency ARray (LOFAR) Two-Metre Sky Survey (LoTSS) has identified a galaxy stellar mass scale, 1011 M⊙, above which the radio luminosities increase disproportionately.
Aims. We investigate if radio luminosities of galaxies, especially the marked rise at galaxy masses around 1011 M⊙, can be explained with standard assumptions regarding jet powers, scaling between black hole mass and galaxy mass, and gaseous halos.
 Based on observational data and theoretical constraints, we developed models for the radio luminosity of radio AGN in halos under infall, galactic wind, and hydrostatic conditions. We compared these models to LoTSS data for a large sample of galaxies in the mass range between 108.5 M⊙ and 1012 M⊙.
Results. Under the assumption that the same characteristic upper limit to jet powers known from high galaxy masses holds at all masses, we find the maximum radio luminosities for the hydrostatic gas halos to lie close to the upper envelope of the distribution of the LOFAR data. The marked rise in radio luminosity at 1011 M⊙ is matched in our model and is related to a significant change in halo gas density around this galaxy mass, which is a consequence of lower cooling rates at a higher virial temperature. Wind and infall models overpredict the radio luminosities for small galaxy masses and have no particular steepening of the run of the radio luminosities predicted at any galaxy mass.
Conclusions. Radio AGN could have the same characteristic Eddington-scaled upper limit to jet powers in galaxies of all masses in the sample if the galaxies have hydrostatic gas halos in phases when radio AGN are active. We find no evidence of a change of the type of galaxy halo with the galaxy mass. Galactic winds and quasi-spherical cosmological inflow phases cannot frequently occur at the same time as powerful jet episodes unless the jet properties in these phases are significantly different from what we assumed in our model.
Foundations of Digital Arch{\ae}oludology

Digital Archæoludology (DAL) is a new field of study involving the analysis and reconstruction of ancient games from incomplete descriptions and archaeological evidence using modern computational techniques. The aim is to provide digital tools and methods to help game historians and other researchers better understand traditional games, their development throughout recorded human history, and their relationship to the development of human culture and mathematical knowledge. This work is being explored in the ERC-funded Digital Ludeme Project. The aim of this inaugural international research meeting on DAL is to gather together leading experts in relevant disciplines – computer science, artificial intelligence, machine learning, computational phylogenetics, mathematics, history, archaeology, anthropology, etc. – to discuss the key themes and establish the foundations for this new field of research, so that it may continue beyond the lifetime of its initiating project. Seminar 10.–12. April, 2019 – https://www.dagstuhl.de/19153 1998
Chaos and complexity in quantum mechanics

We propose a new diagnostic for quantum chaos. We show that the time evolution of complexity for a particular type of target state can provide equivalent information about the classical Lyapunov exponent and scrambling time as out-of-time-order correlators. Moreover, for systems that can be switched from a regular to unstable (chaotic) regime by a tuning of the coupling constant of the interaction Hamiltonian, we find that the complexity defines a new time scale. We interpret this time scale as recording when the system makes the transition from regular to chaotic behavior.
Unsupervised Object Segmentation by Redrawing

Object segmentation is a crucial problem that is usually solved by using supervised learning approaches over very large datasets composed of both images and corresponding object masks. Since the masks have to be provided at pixel level, building such a dataset for any new domain can be very time-consuming. We present ReDO, a new model able to extract objects from images without any annotation in an unsupervised way. It relies on the idea that it should be possible to change the textures or colors of the objects without changing the overall distribution of the dataset. Following this assumption, our approach is based on an adversarial architecture where the generator is guided by an input sample: given an image, it extracts the object mask, then redraws a new object at the same location. The generator is controlled by a discriminator that ensures that the distribution of generated images is aligned to the original one. We experiment with this method on different datasets and demonstrate the good quality of extracted masks.
Asymptotic states for stationary Unruh-DeWitt detectors

We study the late-time asymptotic state of a stationary Unruh-DeWitt detector interacting with a field in a thermal state. We work in an open system framework, where the field plays the role of an environment for the detector. The long-time interaction between the detector and the field is modelled with the aid of a one-parameter family of switching functions that turn on and off the interaction Hamiltonian between the two subsystems, such that the long-time interaction limit is reached as the family parameter goes to infinity. In such limit, we show that if the field is in a KMS state and the detector is stationary with respect to the notion of positive frequency of the field, in the Born-Markov approximation, the asymptotic state of the detector is a Gibbs state at the KMS temperature. We then relax the KMS condition for the field state, and require only that a frequency-dependent version of the detailed balance condition for the Wightman function pulled back to the detector worldline hold, in the sense that the inverse temperature appearing in the detailed balance relation need not be constant. In this setting, we show that the late-time asymptotic state of the detector has the form of a thermal density matrix, but with a frequency-dependent temperature. We present examples of these results, which include the classical Unruh effect and idealised Hawking radiation (for fields in the HHI state), and also the study of the late-time behaviour of detectors following stationary "cusped" and circular motions in Minkowski space interacting with a massless Klein-Gordon field in the Minkowski vacuum. In the cusped motion case, a frequency-dependent, effective temperature for the asymptotic late-time detector state is obtained analytically. In the circular motion case, such effective temperature is obtained numerically.
High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks

We investigate the relationship between the frequency spectrum of image data and the generalization behavior of convolutional neural networks (CNN). We first notice CNN's ability in capturing the high-frequency components of images. These high-frequency components are almost imperceptible to a human. Thus the observation leads to multiple hypotheses that are related to the generalization behaviors of CNN, including a potential explanation for adversarial examples, a discussion of CNN's trade-off between robustness and accuracy, and some evidence in understanding training heuristics.
Evaluating Artificial Systems for Pairwise Ranking Tasks Sensitive to Individual Differences

Owing to the advancement of deep learning, artificial systems are now rival to humans in several pattern recognition tasks, such as visual recognition of object categories. However, this is only the case with the tasks for which correct answers exist independent of human perception. There is another type of tasks for which what to predict is human perception itself, in which there are often individual differences. Then, there are no longer single "correct" answers to predict, which makes evaluation of artificial systems difficult. In this paper, focusing on pairwise ranking tasks sensitive to individual differences, we propose an evaluation method. Given a ranking result for multiple item pairs that is generated by an artificial system, our method quantifies the probability that the same ranking result will be generated by humans, and judges if it is distinguishable from human-generated results. We introduce a probabilistic model of human ranking behavior, and present an efficient computation method for the judgment. To estimate model parameters accurately from small-size samples, we present a method that uses confidence scores given by annotators for ranking each item pair. Taking as an example a task of ranking image pairs according to material attributes of objects, we demonstrate how the proposed method works.
Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework

Although reinforcement learning (RL) can provide reliable solutions in many settings, practitioners are often wary of the discrepancies between the RL solution and their status quo procedures. Therefore, they may be reluctant to adapt to the novel way of executing tasks proposed by RL. On the other hand, many real-world problems require relatively small adjustments from the status quo policies to achieve improved performance. Therefore, we propose a student-teacher RL mechanism in which the RL (the "student") learns to maximize its reward, subject to a constraint that bounds the difference between the RL policy and the "teacher" policy. The teacher can be another RL policy (e.g., trained under a slightly different setting), the status quo policy, or any other exogenous policy. We formulate this problem using a stochastic optimization model and solve it using a primal-dual policy gradient algorithm. We prove that the policy is asymptotically optimal. However, a naive implementation suffers from high variance and convergence to a stochastic optimal policy. With a few practical adjustments to address these issues, our numerical experiments confirm the effectiveness of our proposed method in multiple GridWorld scenarios.
Matrix factorizations for self-orthogonal categories of modules

For a commutative ring  see text] and self-orthogonal subcategory  see text] of  see text], we consider matrix factorizations whose modules belong to . Let  see text] be a regular element. If  see text] is  see text], we show there is a natural embedding of the homotopy category of  see text] into a corresponding homotopy category of totally acyclic complexes. Moreover, we prove this is an equivalence if  see text] is the category of projective or flat-cotorsion  Dually, using divisibility in place of regularity, we observe there is a parallel equivalence when  see text] is the category of injective
Using Natural Language Processing to Develop an Automated Orthodontic Diagnostic System

We work on the task of automatically designing a treatment plan from the findings included in the medical certificate written by the dentist. To develop an artificial intelligence system that deals with free-form certificates written by dentists, we annotate the findings and utilized the natural language processing approach. As a result of the experiment using 990 certificates, 0.585 F1-score was achieved for the task of extracting orthodontic problems from findings, and 0.584 correlation coefficient with the human ranking was achieved for the treatment prioritization task.
Subspace Networks for Few-shot Classification

We propose subspace networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class. Subspace networks learn an embedding space in which classification can be performed by computing distances of embedded points to subspace representations of each class. The class subspaces are spanned by examples belonging to the same class, transformed by a learnable embedding function. Similarly to recent approaches for few-shot learning, subspace networks reflect a simple inductive bias that is beneficial in this limited-data regime and they achieve excellent results. In particular, our proposed method shows consistently better performance than other state-of-the-art few-shot distance-metric learning methods when the embedding function is deep or when training and testing domains are shifted.
Scaffold-based molecular design with a graph generative model†

Searching for new molecules in areas like drug discovery often starts from the core structures of known molecules. Such a method has called for a strategy of designing derivative compounds retaining a particular scaffold as a substructure. On this account, our present work proposes a graph generative model that targets its use in scaffold-based molecular design. Our model accepts a molecular scaffold as input and extends it by sequentially adding atoms and bonds. The generated molecules are then guaranteed to contain the scaffold with certainty, and their properties can be controlled by conditioning the generation process on desired properties. The learned rule of extending molecules can well generalize to arbitrary kinds of scaffolds, including those unseen during learning. In the conditional generation of molecules, our model can simultaneously control multiple chemical properties despite the search space constrained by fixing the substructure. As a demonstration, we applied our model to designing inhibitors of the epidermal growth factor receptor and show that our model can employ a simple semi-supervised extension to broaden its applicability to situations where only a small amount of data is available.
Resolving New Keynesian Anomalies with Wealth in the Utility Function

At the zero lower bound, the New Keynesian model predicts that output and inflation collapse to implausibly low levels and that government spending and forward guidance have implausibly large effects. To resolve these anomalies, we introduce wealth into the utility function; the justification is that wealth is a marker of social status, and people value status. Since people partly save to accrue social status, the Euler equation is modified. As a result, when the marginal utility of wealth is sufficiently large, the dynamical system representing the zero-lower-bound equilibrium transforms from a saddle to a source, which resolves all the anomalies.
The Network Effect in Credit Concentration Risk.

Measurement and management of credit concentration risk is critical for banks and relevant for micro-prudential requirements. While several methods exist for measuring credit concentration risk within institutions, the systemic effect of different institutions' exposures to the same counterparties has been less explored so far. In this paper, we propose a measure of the systemic credit concentration risk that arises because of common exposures between different institutions within a financial system. This approach is based on a network model that describes the effect of overlapping portfolios. This network metric is applied to synthetic and real world data to illustrate that the effect of common exposures is not fully reflected in single portfolio concentration measures. It also allows to quantify several aspects of the interplay between interconnectedness and credit risk. Using this network measure, we formulate an analytical approximation for the additional capital requirement corresponding to the systemic risk arising from credit concentration interconnectedness. Our methodology also avoids double counting between the granularity adjustment and the common exposure adjustment. Although approximated, this common exposure adjustment is able to capture, with only two parameters, an aspect of systemic risk that can extend single portfolios view to a system-wide one.
Improved memory in recurrent neural networks with sequential non-normal dynamics

Training recurrent neural networks (RNNs) is a hard problem due to degeneracies in the optimization landscape, a problem also known as vanishing/exploding gradients. Short of designing new RNN architectures, previous methods for dealing with this problem usually boil down to orthogonalization of the recurrent dynamics, either at initialization or during the entire training period. The basic motivation behind these methods is that orthogonal transformations are isometries of the Euclidean space, hence they preserve (Euclidean) norms and effectively deal with vanishing/exploding gradients. However, this ignores the crucial effects of non-linearity and noise. In the presence of a non-linearity, orthogonal transformations no longer preserve norms, suggesting that alternative transformations might be better suited to non-linear networks. Moreover, in the presence of noise, norm preservation itself ceases to be the ideal objective. A more sensible objective is maximizing the signal-to-noise ratio (SNR) of the propagated signal instead. Previous work has shown that in the linear case, recurrent networks that maximize the SNR display strongly non-normal, sequential dynamics and orthogonal networks are highly suboptimal by this measure. Motivated by this finding, here we investigate the potential of non-normal RNNs, i.e. RNNs with a non-normal recurrent connectivity matrix, in sequential processing tasks. Our experimental results show that non-normal RNNs outperform their orthogonal counterparts in a diverse range of benchmarks. We also find evidence for increased non-normality and hidden chain-like feedforward motifs in trained RNNs initialized with orthogonal recurrent connectivity matrices.
Functional Adversarial Attacks

We propose functional adversarial attacks, a novel class of threat models for crafting adversarial examples to fool machine learning models. Unlike a standard $\ell_p$-ball threat model, a functional adversarial threat model allows only a single function to be used to perturb input features to produce an adversarial example. For example, a functional adversarial attack applied on colors of an image can change all red pixels simultaneously to light red. Such global uniform changes in images can be less perceptible than perturbing pixels of the image individually. For simplicity, we refer to functional adversarial attacks on image colors as ReColorAdv, which is the main focus of our experiments. We show that functional threat models can be combined with existing additive ($\ell_p$) threat models to generate stronger threat models that allow both small, individual perturbations and large, uniform changes to an input. Moreover, we prove that such combinations encompass perturbations that would not be allowed in either constituent threat model. In practice, ReColorAdv can significantly reduce the accuracy of a ResNet-32 trained on CIFAR-10. Furthermore, to the best of our knowledge, combining ReColorAdv with other attacks leads to the strongest existing attack even after adversarial training. An implementation of ReColorAdv is available at this https URL .