nils-herrmann/clean_scirepeval huggingface dataset:
Analogy Search Engine: Finding Analogies in Cross-Domain Research Papers

In recent years, with the rapid proliferation of research publications in the field of Artificial Intelligence, it is becoming increasingly difficult for researchers to effectively keep up with all the latest research in one's own domains. However, history has shown that scientific breakthroughs often come from collaborations of researchers from different domains. Traditional search algorithms like Lexical search, which look for literal matches or synonyms and variants of the query words, are not effective for discovering cross-domain research papers and meeting the needs of researchers in this age of information overflow. In this paper, we developed and tested an innovative semantic search engine, Analogy Search Engine (ASE), for 2000 AI research paper abstracts across domains like Language Technologies, Robotics, Machine Learning, Computational Biology, Human Computer Interactions, etc. ASE combines recent theories and methods from Computational Analogy and Natural Language Processing to go beyond keyword-based lexical search and discover the deeper analogical relationships among research paper abstracts. We experimentally show that ASE is capable of finding more interesting and useful research papers than baseline elasticsearch. Furthermore, we believe that the methods used in ASE go beyond academic paper and will benefit many other document search tasks.
The Risk of Contagion Spreading and its Optimal Control in the Economy

The global crisis of 2008 provoked a heightened interest among scientists to study the phenomenon, its propagation and negative consequences. The process of modelling the spread of a virus is commonly used in epidemiology. Conceptually, the spread of a disease among a population is similar to the contagion process in economy. This similarity allows considering the contagion in the world financial system using the same mathematical model of infection spread that is often used in epidemiology. Our research focuses on the dynamic behaviour of contagion spreading in the global financial network. The effect of infection by a systemic spread of risks in the network of national banking systems of countries is tested. An optimal control problem is then formulated to simulate a control that may avoid significant financial losses. The results show that the proposed approach describes well the reality of the world economy, and emphasizes the importance of international relations between countries on the financial stability.
Evidence against the decaying dark matter interpretation of the 3.5 keV line from blank sky observations

X-ray observations of nearby clusters and galaxies have reported an unexpected X-ray line around 3.5 keV. This line has received significant attention due to its possible explanation through decaying dark matter; in particular, decaying sterile neutrino models, with a sterile neutrino mass around 7 keV, provide a good fit to the available data. We use over 30 Ms of XMM-Newton blank-sky observations to search for evidence of the 3.5 keV line consistent with arising from decaying dark matter within the ambient halo of the Milky Way. We find the strongest limits to-date on the lifetime of dark matter in this mass range, strongly disfavoring the possibility that the 3.5 keV line originates from dark matter decay.
Observable tests of self-interacting dark matter in galaxy clusters: BCG wobbles in a constant density core

Models of cold dark matter (CDM) predict that the distribution of dark matter in galaxy clusters should be cuspy, centrally concentrated. Constant density cores would be strong evidence for beyond CDM physics, such as self-interacting dark matter (SIDM). An observable consequence would be oscillations of the brightest cluster galaxy (BCG) in otherwise relaxed galaxy clusters. Offset BCGs have indeed been observed – but only interpreted via a simplified, analytic model of oscillations. We compare these observations to the BAryons and HAloes of MAssive Sysmtes (BAHAMAS)–SIDM suite of cosmological simulations, which include SIDM and a fully hydrodynamical treatment of star formation and feedback. We predict that the median offset of BCGs increases with the SIDM cross-section, cluster mass, and the amount of stellar mass within 10 kpc, while CDM exhibits no trend in mass. Interpolating between the simulated cross-sections, we find that the observations (of 10 clusters) are consistent with CDM at the ∼1.5σ level, and prefer cross-section σ/m < 0.12(0.39) cm2 g−1 at 68 per cent (95 per cent) confidence level. This is on the verge of ruling out velocity-independent dark matter self-interactions as the solution to discrepancies between the predicted and observed behaviour of dwarf galaxies, and will be improved by larger surveys by Euclid or Super-pressure Balloon-borne Imaging Telescope (SuperBIT).
A global analysis of dark matter signals from 27 dwarf spheroidal galaxies using 11 years of Fermi-LAT observations

We search for a dark matter signal in 11 years of Fermi-LAT gamma-ray data from 27 Milky Way dwarf spheroidal galaxies with spectroscopically measured J-factors. Our analysis includes uncertainties in J-factors and background normalisations and compares results from a Bayesian and a frequentist perspective. We revisit the dwarf spheroidal galaxy Reticulum II, confirming that the purported gamma-ray excess seen in Pass 7 data is much weaker in Pass 8, independently of the statistical approach adopted. We introduce for the first time posterior predictive distributions to quantify the probability of a dark matter detection from another dwarf galaxy given a tentative excess. A global analysis including all 27 dwarfs shows no indication for a signal in nine annihilation channels. We present stringent new Bayesian and frequentist upper limits on the dark matter cross section as a function of dark matter mass. The best-fit dark matter parameters associated with the Galactic Centre excess are excluded by at least 95% confidence level/posterior probability in the frequentist/Bayesian framework in all cases. However, from a Bayesian model comparison perspective, dark matter annihilation within the dwarfs is not strongly disfavoured compared to a background-only model. These results constitute the highest exposure analysis on the most complete sample of dwarfs to date. Posterior samples and likelihood maps from this study are publicly available.
Outer entropy and quasilocal energy

Author(s): Bousso, R; Nomura, Y; Remmen, GN |  We define the coarse-grained entropy of a "normal" surface σ, i.e., a surface that is neither trapped nor antitrapped. Following Engelhardt and Wall, the entropy is defined in terms of the area of an auxiliary extremal surface. This area is maximized over all auxiliary geometries that can be constructed in the interior of σ, while holding fixed the spatial exterior (the outer wedge). We argue that the area is maximized when the stress tensor in the auxiliary geometry vanishes, and we develop a formalism for computing it under this assumption. The coarse-grained entropy can be interpreted as a quasilocal energy of σ. This energy possesses desirable properties such as positivity and monotonicity, which derive directly from its information-theoretic definition.
Gravitational waveforms from spectral Einstein code simulations: Neutron star-neutron star and low-mass black hole-neutron star binaries

Gravitational waveforms from numerical simulations are a critical tool to test and analytically calibrate the waveform models used to study the properties of merging compact objects. In this paper, we present a series of high-accuracy waveforms produced with the spectral Einstein code (SpEC) for systems involving at least one neutron star. We provide for the first time waveforms with subradian accuracy over more than twenty cycles for low-mass black hole-neutron star binaries, including binaries with nonspinning objects, and binaries with rapidly spinning neutron stars that maximize the impact on the gravitational wave signal of the near-resonant growth of the fundamental excitation mode of the neutron star (f-mode). We also provide for the first time with SpEC a high-accuracy neutron star-neutron star waveform. These waveforms are made publicly available as part of the SxS catalogue. We compare our results to analytical waveform models currently implemented in data analysis pipelines. For most simulations, the models lie outside of the predicted numerical errors in the last few orbits before merger, but do not show systematic deviations from the numerical results: comparing different models appears to provide reasonable estimates of the modeling errors. The sole exception is the equal-mass simulation using a rapidly counterrotating neutron star to maximize the impact of the excitation of the f-mode, for which all models perform poorly. This is however expected, as even the single model that takes f-mode excitation into account ignores the significant impact of the neutron star spin on the f-mode excitation frequency.
Structure formation and exotic compact objects in a dissipative dark sector

We present the complete history of structure formation in a simple dissipative dark-sector model. The model has only two particles: a dark electron, which is a subdominant component of dark matter, and a dark photon. Dark-electron perturbations grow from primordial overdensities, become non-linear, and form dense dark galaxies. Bremsstrahlung cooling leads to fragmentation of the dark-electron halos into clumps that vary in size from a few to millions of solar masses, depending on the particle model parameters. In particular, we show that asymmetric dark stars and black holes form within the Milky Way from the collapse of dark electrons. These exotic compact objects may be detected and their properties measured at new high-precision astronomical observatories, giving insight into the particle nature of the dark sector without the requirement of non-gravitational interactions with the visible sector. Our analysis demonstrates that a full study of the structure-formation history is needed to assess correctly the viability of exotic compact objects in dissipative dark-sector models.
3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans

We introduce 3D-SIS, a novel neural network architecture for 3D semantic instance segmentation in commodity RGB-D scans. The core idea of our method to jointly learn from both geometric and color signal, thus enabling accurate instance predictions. Rather than operate solely on 2D frames, we observe that most computer vision applications have multi-view RGB-D input available, which we leverage to construct an approach for 3D instance segmentation that effectively fuses together these multi-modal inputs. Our network leverages high-resolution RGB input by associating 2D images with the volumetric grid based on the pose alignment of the 3D reconstruction. For each image, we first extract 2D features for each pixel with a series of 2D convolutions; we then backproject the resulting feature vector to the associated voxel in the 3D grid. This combination of 2D and 3D feature learning allows significantly higher accuracy object detection and instance segmentation than state-of-the-art alternatives. We show results on both synthetic and real-world public benchmarks, achieving an improvement in mAP of over 13 on real-world data.
Multi Instance Learning For Unbalanced Data

In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective. We show that when the data is unbalanced and the family of classifiers is sufficiently rich, the SI method is a useful learning algorithm. In particular, we show that larger data imbalance, a quality that is typically perceived as negative, in fact implies a better resilience of the algorithm to the statistical dependencies of the objects in bags. In addition, our results shed new light on some known issues with the SI method in the setting of linear classifiers, and we show that these issues are significantly less likely to occur in the setting of neural networks. We demonstrate our results on a synthetic dataset, and on the COCO dataset for the problem of patch classification with weak image level labels derived from captions.
Tiling an Equilateral Triangle

Let $ABC$ be an equilateral triangle. For certain triangles $T$ (the "tile") and certain $N$, it is possible to cut $ABC$ into $N$ copies of $T$. It is known that only certain shapes of $T$ are possible, but until now very little was known about the possible values of $N$. Here we prove that for $N>3$, $N$ cannot be prime.
Malthusian Reinforcement Learning

Here we explore a new algorithmic framework for multi-agent reinforcement learning, called Malthusian reinforcement learning, which extends self-play to include fitness-linked population size dynamics that drive ongoing innovation. In Malthusian RL, increases in a subpopulation's average return drive subsequent increases in its size, just as Thomas Malthus argued in 1798 was the relationship between preindustrial income levels and population growth. Malthusian reinforcement learning harnesses the competitive pressures arising from growing and shrinking population size to drive agents to explore regions of state and policy spaces that they could not otherwise reach. Furthermore, in environments where there are potential gains from specialization and division of labor, we show that Malthusian reinforcement learning is better positioned to take advantage of such synergies than algorithms based on self-play.
Data Lake Organization

We consider the problem of creating a navigation structure that allows a user to most effectively navigate a data lake. We define an organization as a graph that contains nodes representing sets of attributes within a data lake and edges indicating subset relationships among nodes. We present a new probabilistic model of how users interact with an organization and define the likelihood of a user finding a table using the organization. We propose the data lake organization problem as the problem of finding an organization that maximizes the expected probability of discovering tables by navigating an organization. We propose an approximate algorithm for the data lake organization problem. We show the effectiveness of the algorithm on both real data lakes containing data from open data portals and on benchmarks that emulate the observed characteristics of real data lakes. Through a formal user study, we show that navigation can help users discover relevant tables that cannot be found by keyword search. In addition, in our study, 42% of users preferred the use of navigation and 58% preferred keyword search, suggesting these are complementary and both useful modalities for data discovery in data lakes. Our experiments show that data lake organizations take into account the data lake distribution and outperform an existing hand-curated taxonomy and a common baseline organization.
Fuzzy Controller of Reward of Reinforcement Learning For Handwritten Digit Recognition

Recognition of human environment with computer systems always was a big deal in artificial intelligence. In this area handwriting recognition and conceptualization of it to computer is an important area in it. In the past years with growth of machine learning in artificial intelligence, efforts to using this technique increased. In this paper is tried to using fuzzy controller, to optimizing amount of reward of reinforcement learning for recognition of handwritten digits. For this aim first a sample of every digit with 10 standard computer fonts, given to actor and then actor is trained. In the next level is tried to test the actor with dataset and then results show improvement of recognition when using fuzzy controller of reinforcement learning.
Statistical mechanics of Twitter

We build models for the distribution of social states in Twitter communities. States can be defined by the participation vs silence of individuals in conversations that surround key words, and we approximate the joint distribution of these binary variables using the maximum entropy principle, finding the least structured models that match the mean probability of individuals tweeting and their pairwise correlations. These models provide very accurate, quantitative descriptions of higher order structure in these social networks. The parameters of these models seem poised close to critical surfaces in the space of possible models, and we observe scaling behavior of the data under coarse– graining. These results suggest that simple models, grounded in statistical physics, may provide a useful point of view on the larger data sets now emerging from complex social systems.
From Satellite Imagery to Disaster Insights

The use of satellite imagery has become increasingly popular for disaster monitoring and response. After a disaster, it is important to prioritize rescue operations, disaster response and coordinate relief efforts. These have to be carried out in a fast and efficient manner since resources are often limited in disaster-affected areas and it's extremely important to identify the areas of maximum damage. However, most of the existing disaster mapping efforts are manual which is time-consuming and often leads to erroneous results. In order to address these issues, we propose a framework for change detection using Convolutional Neural Networks (CNN) on satellite images which can then be thresholded and clustered together into grids to find areas which have been most severely affected by a disaster. We also present a novel metric called Disaster Impact Index (DII) and use it to quantify the impact of two natural disasters - the Hurricane Harvey flood and the Santa Rosa fire. Our framework achieves a top F1 score of 81.2% on the gridded flood dataset and 83.5% on the gridded fire dataset.
Surface curvature guides early construction activity in mound-building termites

Termite colonies construct towering, complex mounds, in a classic example of distributed agents coordinating their activity via interaction with a shared environment. The traditional explanation for how this coordination occurs focuses on the idea of a ‘cement pheromone’, a chemical signal left with deposited soil that triggers further deposition. Recent research has called this idea into question, pointing to a more complicated behavioural response to cues perceived with multiple senses. In this work, we explored the role of topological cues in affecting early construction activity in Macrotermes. We created artificial surfaces with a known range of curvatures, coated them with nest soil, placed groups of major workers on them and evaluated soil displacement as a function of location at the end of 1 h. Each point on the surface has a given curvature, inclination and absolute height; to disambiguate these factors, we conducted experiments with the surface in different orientations. Soil displacement activity is consistently correlated with surface curvature, and not with inclination nor height. Early exploration activity is also correlated with curvature, to a lesser degree. Topographical cues provide a long-term physical memory of building activity in a manner that ephemeral pheromone labelling cannot. Elucidating the roles of these and other cues for group coordination may help provide organizing principles for swarm robotics and other artificial systems. This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.
Double Majority and Generalized Brexit: Explaining Counterintuitive Results

A mathematical analysis of the distribution of voting power in the Council of the European Union operating according to the Treaty of Lisbon is presented. We study the effects of Brexit on the voting power of the remaining members, measured by the Penrose--Banzhaf Index. We note that the effects in question are non-monotonic with respect to voting weights, and that some member states will lose power after Brexit. We use the normal approximation of the Penrose--Banzhaf Index in double-majority games to show that such non-monotonicity is in most cases inherent in the double-majority system, but is strongly exacerbated by the peculiarities of the EU population vector. Furthermore, we investigate consequences of a hypothetical "generalized Brexit", i.e., NN-exit of another member state (from a 28-member Union), noting that the effects on voting power are non-monotonic in most cases, but strongly depend on the size of the country leaving the Union.
DSNet for Real-Time Driving Scene Semantic Segmentation

We focus on the very challenging task of semantic segmentation for autonomous driving system. It must deliver decent semantic segmentation result for traffic critical objects real-time. In this paper, we propose a very efficient yet powerful deep neural network for driving scene semantic segmentation termed as Driving Segmentation Network (DSNet). DSNet achieves state-of-the-art balance between accuracy and inference speed through efficient units and architecture design inspired by ShuffleNet V2 and ENet. More importantly, DSNet highlights classes most critical with driving decision making through our novel Driving Importance-weighted Loss. We evaluate DSNet on Cityscapes dataset, our DSNet achieves 71.8% mean Intersection-over-Union (IoU) on validation set and 69.3% on test set. Class-wise IoU scores show that Driving Importance-weighted Loss could improve most driving critical classes by a large margin. Compared with ENet, DSNet is 18.9% more accurate and 1.1+ times faster which implies great potential for autonomous driving application.
Unsupervised Single Image Dehazing Using Dark Channel Prior Loss

Single image dehazing is a critical stage in many modern-day autonomous vision applications. Early prior-based methods often involved a time-consuming minimization of a hand-crafted energy function. Recent learning-based approaches utilize the representational power of deep neural networks (DNNs) to learn the underlying transformation between hazy and clear images. Due to inherent limitations in collecting matching clear and hazy images, these methods resort to training on synthetic data, constructed from indoor images and corresponding depth information. This may result in a possible domain shift when treating outdoor scenes. We propose a completely unsupervised method of training via minimization of the well-known, Dark Channel Prior (DCP) energy function. Instead of feeding the network with synthetic data, we solely use real-world outdoor images and tune the network’s parameters by directly minimizing the DCP. Although our “Deep DCP” technique can be regarded as a fast approximator of DCP, it actually improves its results significantly. This suggests an additional regularization obtained via the network and learning process. Experiments show that our method performs on par with large-scale supervised methods.