nils-herrmann/clean_scirepeval huggingface dataset:
Local alliances and rivalries shape near-repeat terror activity of al-Qaeda, ISIS, and insurgents

We examine near-repeat activity patterns of al-Qaeda, ISIS, and local insurgents, whereby a first terrorist attack temporarily increases the likelihood of a second one by the same group. We observe heightened near-repeat activity for all organizations in six geographic clusters and quantify the effect to persist within 20 km and 4 to 10 weeks after the first event. Near-reaction patterns, where two distinct groups react to each other’s activities, depend on the adversarial, neutral, or collaborative relationship between parties at the local level. We find no evidence of outbidding, whereas terrorist and state activities mutually reinforce one another. Our results may be useful for counterterrorism decision making and strategic resource allocation; near-repeat patterns may offer insight into local power structures. We study the spatiotemporal correlation of terrorist attacks by al-Qaeda, the Islamic State of Iraq and Syria (ISIS), and local insurgents, in six geographical areas identified via k-means clustering applied to the Global Terrorism Database. All surveyed organizations exhibit near-repeat activity whereby a prior attack increases the likelihood of a subsequent one by the same group within 20 km and on average 4 (al-Qaeda) to 10 (ISIS) weeks. Near-response activity, whereby an attack by a given organization elicits further attacks from a different one, is found to depend on the adversarial, neutral, or collaborative relationship between the two. When in conflict, local insurgents respond quickly to attacks by global terror groups while global terror groups delay their responses to local insurgents, leading to an asymmetric dynamic. When neutral or allied, attacks by one group enhance the response likelihood of the other, regardless of hierarchy. These trends arise consistently in all clusters for which data are available. Government intervention and spillover effects are also discussed; we find no evidence of outbidding. Understanding the regional dynamics of terrorism may be greatly beneficial in policy making and intervention design.
Visual Understanding of Multiple Attributes Learning Model of X-Ray Scattering Images

This extended abstract presents a visualization system, which is designed for domain scientists to visually understand their deep learning model of extracting multiple attributes in x-ray scattering images. The system focuses on studying the model behaviors related to multiple structural attributes. It allows users to explore the images in the feature space, the classification output of different attributes, with respect to the actual attributes labelled by domain scientists. Abundant interactions allow users to flexibly select instance images, their clusters, and compare them visually in details. Two preliminary case studies demonstrate its functionalities and usefulness.
Asking Easy Questions: A User-Friendly Approach to Active Reward Learning

Robots can learn the right reward function by querying a human expert. Existing approaches attempt to choose questions where the robot is most uncertain about the human's response; however, they do not consider how easy it will be for the human to answer! In this paper we explore an information gain formulation for optimally selecting questions that naturally account for the human's ability to answer. Our approach identifies questions that optimize the trade-off between robot and human uncertainty, and determines when these questions become redundant or costly. Simulations and a user study show our method not only produces easy questions, but also ultimately results in faster reward learning.
Estimating Transfer Entropy via Copula Entropy

Causal inference is a fundemental problem in statistics and has wide applications in different fields. Transfer Entropy (TE) is a important notion defined for measuring causality, which is essentially conditional Mutual Information (MI). Copula Entropy (CE) is a theory on measurement of statistical independence and is equivalent to MI. In this paper, we prove that TE can be represented with only CE and then propose a non-parametric method for estimating TE via CE. The proposed method was applied to analyze the Beijing PM2.5 data in the experiments. Experimental results show that the proposed method can infer causality relationships from data effectively and hence help to understand the data better.
RLCard: A Toolkit for Reinforcement Learning in Card Games

RLCard is an open-source toolkit for reinforcement learning research in card games. It supports various card environments with easy-to-use interfaces, including Blackjack, Leduc Hold'em, Texas Hold'em, UNO, Dou Dizhu and Mahjong. The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward. In this paper, we provide an overview of the key components in RLCard, a discussion of the design principles, a brief introduction of the interfaces, and comprehensive evaluations of the environments. The codes and documents are available at this https URL
Monadic stability and growth rates of ω$\omega$ ‐categorical structures

For M$M$ ω$\omega$ ‐categorical and stable, we investigate the growth rate of M$M$ , that is, the number of orbits of Aut(M)$Aut(M)$ on n$n$ ‐sets, or equivalently the number of n$n$ ‐substructures of M$M$ after performing quantifier elimination. We show that monadic stability corresponds to a gap in the spectrum of growth rates, from slower than exponential to faster than exponential. This allows us to give a nearly complete description of the spectrum of slower than exponential growth rates (without the assumption of stability), confirming some longstanding conjectures of Cameron and Macpherson and proving the existence of gaps not previously recognized.
Causality and deceit: Do androids watch action movies?

We seek causes through science, religion, and in everyday life. We get excited when a big rock causes a big splash, and we get scared when it tumbles without a cause. But our causal cognition is usually biased. The 'why' is influenced by the 'who'. It is influenced by the 'self', and by 'others'. We share rituals, we watch action movies, and we influence each other to believe in the same causes. Human mind is packed with subjectivity because shared cognitive biases bring us together. But they also make us vulnerable. 
An artificial mind is deemed to be more objective than the human mind. After many years of science-fiction fantasies about even-minded androids, they are now sold as personal or expert assistants, as brand advocates, as policy or candidate supporters, as network influencers. Artificial agents have been stunningly successful in disseminating artificial causal beliefs among humans. As malicious artificial agents continue to manipulate human cognitive biases, and deceive human communities into ostensive but expansive causal illusions, the hope for defending us has been vested into developing benevolent artificial agents, tasked with preventing and mitigating cognitive distortions inflicted upon us by their malicious cousins. Can the distortions of human causal cognition be corrected on a more solid foundation of artificial causal cognition? 
In the present paper, we study a simple model of causal cognition, viewed as a quest for causal models. We show that, under very mild and hard to avoid assumptions, there are always self-confirming causal models, which perpetrate self-deception, and seem to preclude a royal road to objectivity.
Learning Only from Relevant Keywords and Unlabeled Documents

We consider a document classification problem where document labels are absent but only relevant keywords of a target class and unlabeled documents are given. Although heuristic methods based on pseudo-labeling have been considered, theoretical understanding of this problem has still been limited. Moreover, previous methods cannot easily incorporate well-developed techniques in supervised text classification. In this paper, we propose a theoretically guaranteed learning framework that is simple to implement and has flexible choices of models, e.g., linear models or neural networks. We demonstrate how to optimize the area under the receiver operating characteristic curve (AUC) effectively and also discuss how to adjust it to optimize other well-known evaluation metrics such as the accuracy and F1-measure. Finally, we show the effectiveness of our framework using benchmark datasets.
Dialog on a canvas with a machine

We propose a new form of human-machine interaction. It is a pictorial game consisting of interactive rounds of creation between artists and a machine. They repetitively paint one after the other. At its rounds, the computer partially completes the drawing using machine learning algorithms, and projects its additions directly on the canvas, which the artists are free to insert or modify. Alongside fostering creativity, the process is designed to question the growing interaction between humans and machines.
Controllable Sentence Simplification: Employing Syntactic and Lexical Constraints

Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with sequence-to-sequence models which have been developed assuming homogeneous target audiences. In this paper we argue that different users have different simplification needs (e.g. dyslexics vs. non-native speakers), and propose CROSS, ContROllable Sentence Simplification model, which allows to control both the level of simplicity and the type of the simplification. We achieve this by enriching a Transformer-based architecture with syntactic and lexical constraints (which can be set or learned from data). Empirical results on two benchmark datasets show that constraints are key to successful simplification, offering flexible generation output.
First Order Ambisonics Domain Spatial Augmentation for DNN-based Direction of Arrival Estimation

In this paper, we propose a novel data augmentation method for training neural networks for Direction of Arrival (DOA) estimation. This method focuses on expanding the representation of the DOA subspace of a dataset. Given some input data, it applies a transformation to it in order to change its DOA information and simulate new potentially unseen one. Such transformation, in general, is a combination of a rotation and a reflection. It is possible to apply such transformation due to a well-known property of First Order Ambisonics (FOA). The same transformation is applied also to the labels, in order to maintain consistency between input data and target labels. Three methods with different level of generality are proposed for applying this augmentation principle. Experiments are conducted on two different DOA networks. Results of both experiments demonstrate the effectiveness of the novel augmentation strategy by improving the DOA error by around 40%.
Learning from Indirect Observations

Weakly-supervised learning is a paradigm for alleviating the scarcity of labeled data by leveraging lower-quality but larger-scale supervision signals. While existing work mainly focuses on utilizing a certain type of weak supervision, we present a probabilistic framework, learning from indirect observations, for learning from a wide range of weak supervision in real-world problems, e.g., noisy labels, complementary labels and coarse-grained labels. We propose a general method based on the maximum likelihood principle, which has desirable theoretical properties and can be straightforwardly implemented for deep neural networks. Concretely, a discriminative model for the true target is used for modeling the indirect observation, which is a random variable entirely depending on the true target stochastically or deterministically. Then, maximizing the likelihood given indirect observations leads to an estimator of the true target implicitly. Comprehensive experiments for two novel problem settings --- learning from multiclass label proportions and learning from coarse-grained labels, illustrate practical usefulness of our method and demonstrate how to integrate various sources of weak supervision.
On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention

Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. While there have been great advances in STR methods, current methods which convert two-dimensional (2D) image to one-dimensional (1D) feature map still fail to recognize texts in arbitrary shapes, such as heavily curved, rotated or vertically aligned texts, which are abundant in daily life (e.g. restaurant signs, product labels, company logos, etc). This paper introduces an architecture to recognize texts of arbitrary shapes, named Self-Attention Text Recognition Network (SATRN). SATRN utilizes the self-attention mechanism, which is originally proposed to capture the dependency between word tokens in a sentence, to describe 2D spatial dependencies of characters in a scene text image. Exploiting the full-graph propagation of self-attention, SATRN can recognize texts with arbitrary arrangements and large inter-character spacing. As a result, our model outperforms all existing STR models by a large margin of 4.5pp on average in "irregular text" benchmarks and also achieved state-of-the-art performance in two "regular text" benchmarks. We provide empirical analyses that illustrate the inner mechanisms and the extent to which the model is applicable (e.g. rotated and multi-line text). We will open- source the
The HH 212 Interstellar Laboratory: Astrochemistry as a Tool To Reveal Protostellar Disks on Solar System Scales around a Rising Sun

The investigation of star forming regions have enormously benefited from the recent advent of the ALMA interferometer. More specifically, the unprecedented combination of high-sensitivity and high-angular resolution provided by ALMA allows one to shed light on the jet/disk systems associated with a Sun-like mass protostar. Also astrochemistry enjoyed the possibility to analyze complex spectra obtained using large bandwidths: several interstellar Complex Organic Molecules (iCOMs; C-bearing species with at least 6 atoms) have been imaged around protostars. This in turn boosted the study of the astrochemistry at work during the earliest phases of star formation paving the way to the chemical complexity in planetary systems where Life could emerge. There is mounting evidence that the observations of iCOMs can be used as unique tool to shed light, on Solar System scales (< 50 au), on the molecular content of protostellar disk. The increase of iCOMs abundances occur only under very selective physical conditions, such as those associated low-velocity shocks found where the infalling envelope is impacting the rotating accretion disk. The imaging of these regions with simpler molecules such as CO or CS is indeed paradoxically hampered by their high abundances and consequently high line opacities which do not allow the observers to disentangle all the emitting components at these small scales. In this respect, we review the state-of-the art of the ALMA analysis about the standard Sun-like star forming region in Orion named HH 212. We show (i) how all the physical components involved in the formation of a Sun-like star can be revealed only by observing different molecular tracers, and (ii) how the observation of iCOMs emission, observed to infer the chemical composition of star forming regions, can be used also as unique tracer to image protostellar disks on Solar System scales.
Breathing deformation model - application to multi-resolution abdominal MRI

Dynamic MRI is a technique of acquiring a series of images continuously to follow the physiological changes over time. However, such fast imaging results in low resolution images. In this work, abdominal deformation model computed from dynamic low resolution images have been applied to high resolution image, acquired previously, to generate dynamic high resolution MRI. Dynamic low resolution images were simulated into different breathing phases (inhale and exhale). Then, the image registration between breathing time points was performed using the B-spline SyN deformable model and using cross-correlation as a similarity metric. The deformation model between different breathing phases were estimated from highly undersampled data. This deformation model was then applied to the high resolution images to obtain high resolution images of different breathing phases. The results indicated that the deformation model could be computed from relatively very low resolution images.
Still No Free Lunches: The Price to Pay for Tighter PAC-Bayes Bounds

“No free lunch” results state the impossibility of obtaining meaningful bounds on the error of a learning algorithm without prior assumptions and modelling, which is more or less realistic for a given problem. Some models are “expensive” (strong assumptions, such as sub-Gaussian tails), others are “cheap” (simply finite variance). As it is well known, the more you pay, the more you get: in other words, the most expensive models yield the more interesting bounds. Recent advances in robust statistics have investigated procedures to obtain tight bounds while keeping the cost of assumptions minimal. The present paper explores and exhibits what the limits are for obtaining tight probably approximately correct (PAC)-Bayes bounds in a robust setting for cheap models.
Searching for a Robust Neural Architecture in Four GPU Hours

Conventional neural architecture search (NAS) approaches are usually based on reinforcement learning or evolutionary strategy, which take more than 1000 GPU hours to find a good model on CIFAR-10. We propose an efficient NAS approach, which learns the searching approach by gradient descent. Our approach represents the search space as a directed acyclic graph (DAG). This DAG contains thousands of sub-graphs, each of which indicates a kind of neural architecture. To avoid traversing all the possibilities of the sub-graphs, we develop a differentiable sampler over the DAG. This sampler is learnable and optimized by the validation loss after training the sampled architecture. In this way, our approach can be trained in an end-to-end fashion by gradient descent, named Gradient-based search using Differentiable Architecture Sampler (GDAS). In experiments, we can finish one searching procedure in four GPU hours on CIFAR-10, and the discovered model obtains a test error of 2.82% with only 2.5M parameters, which is on par with the state-of-the-art.
Local phase transitions in a model of multiplex networks with heterogeneous degrees and inter-layer coupling

Multilayer networks represent multiple types of connections between the same set of nodes. Clearly, a multilayer description of a system adds value only if the multiplex does not merely consist of independent layers, i.e. if the inter-layer overlap is nontrivial. On real-world multiplexes, it is expected that the observed overlap may partly result from spurious correlations arising from the heterogeneity of nodes and partly from true interdependencies. However, no rigorous way to disentangle these two effects has been developed. In this paper we introduce an unbiased maximum-entropy model of multiplexes with controllable node degrees and controllable overlap. The model can be mapped to a generalized Ising model where the combination of node heterogeneity and inter-layer coupling leads to the possibility of local phase transitions. In particular, we find that an increased heterogeneity in the network results in different critical points for different pairs of nodes, which in turn leads to local phase transitions that may ultimately increase the overlap. The model allows us to quantify how the overlap can be increased by either increasing the heterogeneity of the network (spurious correlation) or the strength of the inter-layer coupling (true correlation), thereby disentangling the two effects. As an application, we show that the empirical overlap in the International Trade Multiplex is not merely a spurious result of the correlation between node degrees across different layers, but requires a non-zero inter-layer coupling in its modeling.
$A_4$ model of Magic neutrino mass matrix with broken $\mu-\tau$ symmetry and Leptogenesis

We investigate baryogenesis via leptogenesis in $A_4$ flavor model within the paradigm of type-I and II seesaw mechanism resulting in magic neutrino mass matrix with broken $\mu-\tau$ symmetry in a minimal scenario with two right-handed neutrinos(2RHN). Z_3$ cyclic symmetry is employed to constrain the Yukawa structure of model. In general, the type-II seesaw terms play crucial role in generating non-degenerate neutrino masses and non-zero $\theta_{13}$, and contribute in baryogenesis. In particular, after the spontaneous symmetry breaking, the Yukawa coupling $y_{\Delta_1}$ at dimension-four is responsible for the breaking of $\mu-\tau$ symmetry. We, also, study the implication of the model for successful leptogenesis using approximated solution of the Boltzmann equations. We find that the observed baryon asymmetry of the universe requires the lightest right-handed neutrino mass to be $(2.36-2.40)\times 10^{9} $GeV for NH ($(2.15-2.17)\times10^{9}$GeV for IH) which is consistent with the Davidson-Ibarra bound of $10^{9}$GeV.
Image Super-Resolution via Attention Based Back Projection Networks

Deep learning based image Super-Resolution (SR) has shown rapid development due to its ability of big data digestion. Generally, deeper and wider networks can extract richer feature maps and generate SR images with remarkable quality. However, the more complex network we have, the more time consumption is required for practical applications. It is important to have a simplified network for efficient image SR. In this paper, we propose an Attention based Back Projection Network (ABPN) for image super-resolution. Similar to some recent works, we believe that the back projection mechanism can be further developed for SR. Enhanced back projection blocks are suggested to iteratively update low-and high-resolution feature residues. Inspired by recent studies on attention models, we propose a Spatial Attention Block (SAB) to learn the cross-correlation across features at different layers. Based on the assumption that a good SR image should be close to the original LR image after down-sampling. We propose a Refined Back Projection Block (RBPB) for final reconstruction. Extensive experiments on some public and AIM2019 Image Super-Resolution Challenge datasets show that the proposed ABPN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.