nils-herrmann/clean_scirepeval huggingface dataset:
Time evolution of the hierarchical networks between PubMed MeSH terms

Hierarchical organisation is a prevalent feature of many complex networks appearing in nature and society. A relating interesting, yet less studied question is how does a hierarchical network evolve over time? Here we take a data driven approach and examine the time evolution of the network between the Medical Subject Headings (MeSH) provided by the National Center for Biotechnology Information (NCBI, part of the U. S. National Library of Medicine). The network between the MeSH terms is organised into 16 different, yearly updated hierarchies such as “Anatomy”, “Diseases”, “Chemicals and Drugs”, etc. The natural representation of these hierarchies is given by directed acyclic graphs, composed of links pointing from nodes higher in the hierarchy towards nodes in lower levels. Due to the yearly updates, the structure of these networks is subject to constant evolution: new MeSH terms can appear, terms becoming obsolete can be deleted or be merged with other terms, and also already existing parts of the network may be rewired. We examine various statistical properties of the time evolution, with a special focus on the attachment and detachment mechanisms of the links, and find a few general features that are characteristic for all MeSH hierarchies. According to the results, the hierarchies investigated display an interesting interplay between non-uniform preference with respect to multiple different topological and hierarchical properties.
Task-assisted Motion Planning in Partially Observable Domains

We present an integrated Task-Motion Planning framework for robot navigation in belief space. Autonomous robots operating in real world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. To this end, we propose a framework for integrating belief space reasoning within a hybrid task planner. The expressive power of PDDL+ combined with heuristic-driven semantic attachments performs the propagated and posterior belief estimates while planning. The underlying methodology for the development of the combined hybrid planner is discussed, providing suggestions for improvements and future work. Furthermore we validate key aspects of our approach using a realistic scenario in simulation.
CNN-based PET sinogram repair to mitigate defective block detectors

Positron emission tomography (PET) scanners continue to increase sensitivity and axial coverage by adding an ever expanding array of block detectors. As they age, one or more block detectors may lose sensitivity due to a malfunction or component failure. The sinogram data missing as a result thereof can lead to artifacts and other image degradations. We propose to mitigate the effects of malfunctioning block detectors by carrying out sinogram repair using a deep convolutional neural network. Experiments using whole-body patient studies with varying amounts of raw data removed are used to show that the neural network significantly outperforms previously published methods with respect to normalized mean squared error for raw sinograms, a multi-scale structural similarity measure for reconstructed images and with regard to quantitative accuracy.
Continuous Value Iteration (CVI) Reinforcement Learning and Imaginary Experience Replay (IER) For Learning Multi-Goal, Continuous Action and State Space Controllers

This paper presents a novel model-free Reinforcement Learning algorithm for learning behavior in continuous action, state, and goal spaces. The algorithm approximates optimal value functions using non-parametric estimators. It is able to efficiently learn to reach multiple arbitrary goals in deterministic and nondeterministic environments. To improve generalization in the goal space, we propose a novel sample augmentation technique. Using these methods, robots learn faster and overall better controllers. We benchmark the proposed algorithms using simulation and a real-world voltage controlled robot that learns to maneuver in a non-observable Cartesian task space.
Neural Harmonic-plus-Noise Waveform Model with Trainable Maximum Voice Frequency for Text-to-Speech Synthesis

Neural source-filter (NSF) models are deep neural networks that produce waveforms given input acoustic features. They use dilated-convolution-based neural filter modules to filter sine-based excitation for waveform generation, which is different from WaveNet and flow-based models. One of the NSF models, called harmonic-plus-noise NSF (h-NSF) model, uses separate pairs of source and neural filters to generate harmonic and noise waveform components. It is close to WaveNet in terms of speech quality while being superior in generation speed. 
The h-NSF model can be improved even further. While h-NSF merges the harmonic and noise components using pre-defined digital low- and high-pass filters, it is well known that the maximum voice frequency (MVF) that separates the periodic and aperiodic spectral bands are time-variant. Therefore, we propose a new h-NSF model with time-variant and trainable MVF. We parameterize the digital low- and high-pass filters as windowed-sinc filters and predict their cut-off frequency (i.e., MVF) from the input acoustic features. Our experiments demonstrated that the new model can predict a good trajectory of the MVF and produce high-quality speech for a text-to-speech synthesis system.
Infochain: A Decentralized System for Truthful Information Elicitation

Incentive mechanisms play a pivotal role in collecting correct and reliable information from self-interested agents. Peer-prediction mechanisms are game-theoretic mechanisms that incentivize agents for reporting the information truthfully, even when the information is unverifiable in nature. Traditionally, a trusted third party implements these mechanisms. We built Infochain, a decentralized system for information elicitation. Infochain ensures transparent, trustless and cost-efficient collection of information from self-interested agents without compromising the game-theoretical guarantees of the peer-prediction mechanisms. In this paper, we address various non-trivial challenges in implementing these mechanisms in Ethereum and provide experimental analysis.
Analysis on MathSciNet database: some preliminary results

In this paper we initiate some investigations on MathSciNet database. For many mathematicians this website is used on a regular basis, but surprisingly except for the information provided by MathSciNet itself, there exist almost no independent investigations or independent statistics on this database. This current research has been triggered by a rumor: do international collaborations increase the number of citations of an academic work in mathematics ? We use MathSciNet for providing some information about this rumor, and more generally pave the way for further investigations on or with MathSciNet.
Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts

This work aims at modeling how the meaning of gradable adjectives of size (‘big’, ‘small’) can be learned from visually-grounded contexts. Inspired by cognitive and linguistic evidence showing that the use of these expressions relies on setting a threshold that is dependent on a specific context, we investigate the ability of multi-modal models in assessing whether an object is ‘big’ or ‘small’ in a given visual scene. In contrast with the standard computational approach that simplistically treats gradable adjectives as ‘fixed’ attributes, we pose the problem as relational: to be successful, a model has to consider the full visual context. By means of four main tasks, we show that state-of-the-art models (but not a relatively strong baseline) can learn the function subtending the meaning of size adjectives, though their performance is found to decrease while moving from simple to more complex tasks. Crucially, models fail in developing abstract representations of gradable adjectives that can be used compositionally.
Explainable AI: A Neurally-Inspired Decision Stack Framework

European Law now requires AI to be explainable in the context of adverse decisions affecting European Union (EU) citizens. At the same time, it is expected that there will be increasing instances of AI failure as it operates on imperfect data. This paper puts forward a neurally-inspired framework called decision stacks that can provide for a way forward in research aimed at developing explainable AI. Leveraging findings from memory systems in biological brains, the decision stack framework operationalizes the definition of explainability and then proposes a test that can potentially reveal how a given AI decision came to its conclusion.
A search for cosmological anisotropy using the Lyman alpha forest from SDSS quasar spectra

The cosmological principle, the combined assumptions of cosmological isotropy and homogeneity, underpins the standard model of big bang cosmology with which we interpret astronomical observations. A new test of isotropy over the redshift range 2 < z < 4 and across large angular scales on the sky is presented. We use the cosmological distribution of neutral hydrogen, as probed by the Ly α forest seen towards distant quasars. The Sloan Digital Sky Survey provides the largest data set of quasar spectra available to date. We use combined information from Data Releases 12 and 14 to select a sample of 142 661 quasars most suitable for this purpose. The scales covered by the data extend beyond post-inflation causality scales, thus probing initial conditions in the early Universe. We identify significant spatially correlated systematic effects that can emulate cosmological anisotropy. Once these systematics have been accounted for, the data are found to be consistent with isotropy, providing an important independent check on the standard model, consistent with results from cosmic microwave background data.
Physiological and Affective Computing through Thermal Imaging: A Survey

Thermal imaging-based physiological and affective computing is an emerging research area enabling technologies to monitor our bodily functions and understand psychological and affective needs in a contactless manner. However, up to recently, research has been mainly carried out in very controlled lab settings. As small size and even low-cost versions of thermal video cameras have started to appear on the market, mobile thermal imaging is opening its door to ubiquitous and real-world applications. Here we review the literature on the use of thermal imaging to track changes in physiological cues relevant to affective computing and the technological requirements set so far. In doing so, we aim to establish computational and methodological pipelines from thermal images of the human skin to affective states and outline the research opportunities and challenges to be tackled to make ubiquitous real-life thermal imaging-based affect monitoring a possibility.
Temporal and Spatial Evolutions of a Large Sunspot Group and Great Auroral Storms Around the Carrington Event in 1859

The Carrington event is considered to be one of the most extreme space weather events in observational history within a series of magnetic storms caused by extreme interplanetary coronal mass ejections from a large and complex active region that emerged on the solar disk. In this article, we study the temporal and spatial evolutions of the source sunspot active region and visual aurorae and compare this storm with other extreme space weather events on the basis of their auroral spatial evolution. Sunspot drawings by Schwabe, Secchi, and Carrington describe the position and morphology of the source active region at that time. Visual auroral reports from the Russian Empire, Iberia, Ireland, Oceania, and Japan fill the spatial gap of auroral visibility and revise the time series of auroral visibility in middle to low magnetic latitudes. The reconstructed time series is compared with magnetic measurements and shows the correspondence between low‐latitude to mid‐latitude aurorae and the phase of magnetic storms. The spatial evolution of the auroral oval is compared with those of other extreme space weather events in 1872, 1909, 1921, and 1989 as well as their storm intensity and contextualizes the Carrington event, as one of the most extreme space weather events, but likely not unique.
Physics-Based Rendering for Improving Robustness to Rain

To improve the robustness to rain, we present a physically-based rain rendering pipeline for realistically inserting rain into clear weather images. Our rendering relies on a physical particle simulator, an estimation of the scene lighting and an accurate rain photometric modeling to augment images with arbitrary amount of realistic rain or fog. We validate our rendering with a user study, proving our rain is judged 40% more realistic that state-of-the-art. Using our generated weather augmented Kitti and Cityscapes dataset, we conduct a thorough evaluation of deep object detection and semantic segmentation algorithms and show that their performance decreases in degraded weather, on the order of 15% for object detection and 60% for semantic segmentation. Furthermore, we show refining existing networks with our augmented images improves the robustness of both object detection and semantic segmentation algorithms. We experiment on nuScenes and measure an improvement of 15% for object detection and 35% for semantic segmentation compared to original rainy performance. Augmented databases and code are available on the project page.
Artificial Intelligence Approaches

Artificial Intelligence (AI) has received tremendous attention from academia, industry, and the general public in recent years. The integration of geography and AI, or GeoAI, provides novel approaches for addressing a variety of problems in the natural environment and our human society. This entry briefly reviews the recent development of AI with a focus on machine learning and deep learning approaches. We discuss the integration of AI with geography and particularly geographic information science, and present a number of GeoAI applications and possible future directions.
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation

Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present HigherHRNet: a novel bottom-up human pose estimation method for learning scale-aware representations using high-resolution feature pyramids. Equipped with multi-resolution supervision for training and multi-resolution aggregation for inference, the proposed approach is able to solve the scale variation challenge in bottom-up multi-person pose estimation and localize keypoints more precisely, especially for small person. The feature pyramid in HigherHRNet consists of feature map outputs from HRNet and upsampled higher-resolution outputs through a transposed convolution. HigherHRNet outperforms the previous best bottom-up method by 2.5% AP for medium person on COCO test-dev, showing its effectiveness in handling scale variation. Furthermore, HigherHRNet achieves new state-of-the-art result on COCO test-dev (70.5% AP) without using refinement or other post-processing techniques, surpassing all existing bottom-up methods. HigherHRNet even surpasses all top-down methods on CrowdPose test (67.6% AP), suggesting its robustness in crowded scene.
GALACTICNUCLEUS: A high angular-resolution JHKs imaging survey of the Galactic centre

The characterisation of the extinction curve in the near-infrared (NIR) is fundamental to analysing the structure and stellar population of the Galactic centre (GC), whose analysis is hampered by the extreme interstellar extinction (AV ~ 30 mag) that varies on arc-second scales. Recent studies indicate that the behaviour of the extinction curve might be more complex than previously assumed, pointing towards a variation of the extinction curve as a function of wavelength.
Aims. We aim to analyse the variations of the extinction index, α, with wavelength, line-of-sight, and absolute extinction, extending previous analyses to a larger area of the innermost regions of the Galaxy.
Methods. We analysed the whole GALACTICNUCLEUS survey, a high-angular resolution (~0.2″) JHKs NIR survey specially designed to observe the GC in unprecedented detail. It covers a region of ~6000 pc2, comprising fields in the nuclear stellar disc, the inner bulge, and the transition region between them. We applied two independent methods based on red clump (RC) stars to constrain the extinction curve and analysed its variation superseding previous studies.
Results. We used more than 165 000 RC stars and increased the size of the regions analysed significantly to confirm that the extinction curve varies with the wavelength. We estimated a difference Δα = 0.21 ± 0.07 between the obtained extinction indices, αJH = 2.44 ± 0.05 and αHKs = 2.23 ± 0.05. We also concluded that there is no significant variation of the extinction curve with wavelength, with the line-of-sight or the absolute extinction. Finally, we computed the ratios between extinctions, AJ∕AH = 1.87 ± 0.03 and AH/AKs = 1.84 ± 0.03, consistent with all the regions of the GALACTICNUCLEUS catalogue.
Thermal instability of halo gas heated by streaming cosmic rays

Heating of virialized gas by streaming cosmic rays (CRs) may be energetically important in galaxy haloes, groups, and clusters. We present a linear thermal stability analysis of plasmas heated by streaming CRs. We separately treat equilibria with and without background gradients, and with and without gravity. We include both CR streaming and diffusion along the magnetic-field direction. Thermal stability depends strongly on the ratio of CR pressure to gas pressure, which determines whether modes are isobaric or isochoric. Modes with $\boldsymbol {k \cdot B }\ne 0$ are strongly affected by CR diffusion. When the streaming time is shorter than the CR diffusion time, thermally unstable modes (with $\boldsymbol {k \cdot B }\ne 0$) are waves propagating at a speed ∝ the Alfvén speed. Halo gas in photoionization equilibrium is thermally stable independent of CR pressure, while gas in collisional ionization equilibrium is unstable for physically realistic parameters. In gravitationally stratified plasmas, the oscillation frequency of thermally overstable modes can be higher in the presence of CR streaming than the buoyancy/free-fall frequency. This may modify the critical tcool/tff at which multiphase gas is present. The criterion for convective instability of a stratified, CR-heated medium can be written in the familiar Schwarzschild form dseff/dz < 0, where seff is an effective entropy involving the gas and CR pressures. We discuss the implications of our results for the thermal evolution and multiphase structure of galaxy haloes, groups, and clusters.
Relations between phenomenological and physical parameters in the hot coronae of AGNs computed with the MoCA code

The primary X-ray emission in active galactic nuclei (AGNs) is widely believed to be due to Comptonisation of the thermal radiation from the accretion disc in a corona of hot electrons. The resulting spectra can, in first approximation, be modelled with a cut-off power law, the photon index and the high-energy roll-over encoding information on the physical properties of the X ray emitting region. The photon index and the high-energy curvature of AGNs (Γ, Ec) have been largely studied since the launch of X-ray satellites operating above 10 keV. However, high-precision measurements of these two observables have only been obtained in recent years thanks to the unprecedented sensitivity of NuSTAR up to 79 keV.
Aims. We aim at deriving relations between Γ, Ec phenomenological parameters and the intrinsic properties of the X-ray-emitting region (the hot corona), namely the optical depth and temperature.
Methods. We use MoCA (Monte Carlo code for Comptonisation in Astrophysics) to produce synthetic spectra for the case of an AGN with MBH = 1.5 × 108 M⊙ and ṁ = 0.1 and then compared them with the widely used power-law model with an exponential high-energy cutoff.
Results. We provide phenomenological relations relating Γ and Ec with the opacity and temperature of the coronal electrons for the case of spherical and slab-like coronae. These relations give origin to a well defined parameter space which fully contains the observed values. Exploiting the increasing number of high-energy cut-offs quoted in the literature, we report on the comparison of physical quantities obtained using MoCA with those estimated using commonly adopted spectral Comptonisation models. Finally, we discuss the negligible impact of different black hole masses and accretion rates on the inferred relations.
Orientation of the crescent image of M 87*

The first image of the black hole (BH) M87* obtained by the Event Horizon Telescope (EHT) has the shape of a crescent extending from the E to SWW position angles, while the observed direction of the large-scale jet is NWW. Images based on numerical simulations of BH accretion flows suggest that on average the projected BH spin axis would be oriented SSW. We explore highly simplified toy models for geometric distribution and kinematics of emitting regions in the Kerr metric, perform ray tracing to calculate the corresponding images, and simulate their observation by the EHT to calculate the corresponding visibilities and closure phases. We strictly assume that (1) the BH spin vector is fixed to the jet axis, (2) the emitting regions are stationary and symmetric with respect to the BH spin, (3) the emissivities are isotropic in the local rest frames. Emission from the crescent sector SSE - SWW can be readily explained in terms of an equatorial ring with either circular or plunging geodesic flows, regardless of the value of BH spin. In the case of plane-symmetric polar caps with plunging geodesic flows, the dominant image of the cap located behind the BH is sensitive to the angular momentum of the emitter. Within the constraints of our model, we have not found a viable explanation for the observed brightness of the SEE sector. Most likely, the SEE 'hotspot' has been produced by a non-stationary localised perturbation in the inner accretion flow. Alternatively, it could result from locally anisotropic synchrotron emissivities. Multi-epoch and polarimetric results from the EHT will be essential to verify the theoretically expected alignment of the BH spin with the large-scale jet.
qnm: A Python package for calculating Kerr quasinormal modes, separation constants, and spherical-spheroidal mixing coefficients

$\mathtt{qnm}$ is an open-source Python package for computing the Kerr quasinormal mode frequencies, angular separation constants, and spherical-spheroidal mixing coefficients. The $\mathtt{qnm}$ package includes a Leaver solver with the Cook-Zalutskiy spectral approach to the angular sector, and a caching mechanism to avoid repeating calculations. We provide a large cache of low $\ell, m, n$ modes, which can be downloaded and installed with a single function call, and interpolated to provide good initial guess for root-polishing at new values of spin.
Feature Gradients: Scalable Feature Selection via Discrete Relaxation

In this paper we introduce Feature Gradients, a gradient-based search algorithm for feature selection. Our approach extends a recent result on the estimation of learnability in the sublinear data regime by showing that the calculation can be performed iteratively (i.e., in mini-batches) and in linear time and space with respect to both the number of features D and the sample size N . This, along with a discrete-to-continuous relaxation of the search domain, allows for an efficient, gradient-based search algorithm among feature subsets for very large datasets. Crucially, our algorithm is capable of finding higher-order correlations between features and targets for both the N > D and N < D regimes, as opposed to approaches that do not consider such interactions and/or only consider one regime. We provide experimental demonstration of the algorithm in small and large sample-and feature-size settings.