nils-herrmann/clean_scirepeval huggingface dataset:
The Leavitt law of Milky Way Cepheids from Gaia DR2 static companion parallaxes

Classical Cepheids (CCs) are at the heart of the empirical extragalactic distance ladder. Milky Way CCs are the only stars of this class accessible to trigonometric parallax measurements. Until recently, the most accurate trigonometric parallaxes of Milky Way CCs were the HST/FGS measurements collected by Benedict et al. (2002, 2007). Unfortunately, the second Gaia data release (GDR2) has not yet delivered reliable parallaxes for Galactic CCs, failing to replace the HST/FGS sample as the foundation of all Galactic calibrations of the Leavitt law. We aim at calibrating independently the Leavitt law of Milky Way CCs based on the GDR2 catalog of trigonometric parallaxes. As a proxy for the parallaxes of a sample of 23 Galactic CCs, we adopt the GDR2 parallaxes of their spatially resolved companions. As the latter are unsaturated, photometrically stable stars, this novel approach allows us to bypass the GDR2 bias on the parallax of the CCs that is induced by saturation and variability. We present new Galactic calibrations of the Leavitt law in the J, H, K, V, Wesenheit WH and Wesenheit WVK bands based on the GDR2 parallaxes of the CC companions. We show that the adopted value of the zero point of the GDR2 parallaxes, within a reasonable range, has a limited impact on our Leavitt law calibration. However, we find a significant difference with respect to the calibration based on the HST/FGS parallaxes, that corresponds to an FGS parallax zero point offset of approx. +0.2 mas. The discrepancy that we observe between the GDR2 and HST/FGS parallaxes has important consequences on the existing Galactic calibrations of the Leavitt law. We note that our results translate into a Hubble constant of 68.43 +/- 2.08 km/s/Mpc and 69.30 +/- 2.08 km/s/Mpc for a GDR2 parallax offset of 0.029 mas and 0.046 mas, respectively.
GLADAS: Gesture Learning for Advanced Driver Assistance Systems

Human-computer interaction (HCI) is crucial for the safety of lives as autonomous vehicles (AVs) become commonplace. Yet, little effort has been put toward ensuring that AVs understand humans on the road. In this paper, we present GLADAS, a simulator-based research platform designed to teach AVs to understand pedestrian hand gestures. GLADAS supports the training, testing, and validation of deep learning-based self-driving car gesture recognition systems. We focus on gestures as they are a primordial (i.e, natural and common) way to interact with cars. To the best of our knowledge, GLADAS is the first system of its kind designed to provide an infrastructure for further research into human-AV interaction. We also develop a hand gesture recognition algorithm for self-driving cars, using GLADAS to evaluate its performance. Our results show that an AV understands human gestures 85.91% of the time, reinforcing the need for further research into human-AV interaction.
IDEAIS: Smart Voice Assistants to Improve Interaction with SDIs

A critical goal, is that organizations and citizens can easily access the geographic information required for good governance. However, despite the costly efforts of governments to create and implement Spatial Data Infrastructures (SDIs), this goal is far from being achieved. This is partly due to the lack of usability of the geoportals through which the geographic information is accessed. In this position paper, we present IDEAIS, a research network composed of multiple Ibero-American partners to address this usability issue through the use of Intelligent Systems, in particular Smart Voice Assistants, to efficiently recover and access geographic information.
Immersive virtual worlds: Multi-sensory virtual environments for health and safety training

Virtual environments (VEs) offer potential benefits to health and safety training: exposure to dangerous (virtual) environments; the opportunity for experiential learning; and a high level of control over the training, in that aspects can be repeated or reviewed based on the trainee's performance. However, VEs are typically presented as audiovisual (AV) systems, whereas engagement of other senses could increase the immersion in the virtual experience. Moreover, other senses play a key role in certain health and safety contexts, for example the feel of heat and smell in a fire or smell in a fuel leak. A multisensory (MS) VE was developed, which provided simulated heat and smell in accordance with events in a virtual world. As users approached a virtual fire, they felt heat from three 2 kW heaters and smelled smoke from a scent diffuser. Behaviours in the MS VE demonstrated higher validity than those in a comparable AV VE, which ratings and verbatim responses indicated was down to a greater belief that participants were in a real fire. However, a study of the effectiveness of the MS VE as a training tool demonstrated that it did not offer benefits over AV as measured by a written knowledge test and subjective ratings of engagement, attitude towards health and safety and desire to repeat. However, the study found further evidence for the use of AV VEs in health and safety training, particularly as the subjective ratings were generally better than for PowerPoint based training. Despite the lack of evidence for MS simulation on traditional measures of training, the different attitudes and experiences of users suggest that it may have value as a system for changing trainees' attitudes towards their personal safety and awareness. This view was supported by feedback from industrial partners.
Interactive Light Field Tilt-Shift Refocus with Generalized Shift-and-Sum

Since their introduction more than two decades ago, light fields have gained considerable interest in graphics and vision communities due to their ability to provide the user with interactive visual content. One of the earliest and most common light field operations is digital refocus, enabling the user to choose the focus and depth-of-field for the image after capture. A common interactive method for such an operation utilizes disparity estimations, readily available from the light field, to allow the user to point-and-click on the image to chose the location of the refocus plane. 
In this paper, we address the interactivity of a lesser-known light field operation: refocus to a non-frontoparallel plane, simulating the result of traditional tilt-shift photography. For this purpose we introduce a generalized shift-and-sum framework. Further, we show that the inclusion of depth information allows for intuitive interactive methods for placement of the refocus plane. In addition to refocusing, light fields also enable the user to interact with the viewpoint, which can be easily included in the proposed generalized shift-and-sum framework.
Assistive Gym: A Physics Simulation Framework for Assistive Robotics

Autonomous robots have the potential to serve as versatile caregivers that improve quality of life for millions of people worldwide. Yet, conducting research in this area presents numerous challenges, including the risks of physical interaction between people and robots. Physics simulations have been used to optimize and train robots for physical assistance, but have typically focused on a single task. In this paper, we present Assistive Gym, an open source physics simulation framework for assistive robots that models multiple tasks. It includes six simulated environments in which a robotic manipulator can attempt to assist a person with activities of daily living (ADLs): itch scratching, drinking, feeding, body manipulation, dressing, and bathing. Assistive Gym models a person’s physical capabilities and preferences for assistance, which are used to provide a reward function. We present baseline policies trained using reinforcement learning for four different commercial robots in the six environments. We demonstrate that modeling human motion results in better assistance and we compare the performance of different robots. Overall, we show that Assistive Gym is a promising tool for assistive robotics research.
Size and density sorting of dust grains in SPH simulations of protoplanetary discs – II. Fragmentation

Grain growth and fragmentation are important processes in building up large dust aggregates in protoplanetary discs. Using a 3D two-phase (gas–dust) sph code, we investigate the combined effects of growth and fragmentation of a multiphase dust with different fragmentation thresholds in a time-evolving disc. We find that our fiducial disc, initially in a fragmentation regime, moves towards a pure-growth regime in a few thousands years. Time-scales change as a function of the disc and dust properties. When fragmentation is efficient, it produces, in different zones of the disc, Fe/Si and rock/ice ratios different from those predicted when only pure growth is considered. Chemical fractionation and the depletion/enrichment in iron observed in some chondrites can be linked to the size–density sorting and fragmentation properties of precursor dusty grains. We suggest that aggregation of chondritic components could have occurred where/when fragmentation was not efficient if their aerodynamical sorting has to be preserved. Chondritic components would allow aerodynamical sorting in a fragmentation regime only if they have similar fragmentation properties. We find that, in the inner disc, and for the same interval of time, fragmenting dust can grow larger when compared to the size of grains predicted by pure growth. This counter-intuitive behaviour is due to the large amount of dust that piles up in a fragmenting zone followed by the rapid growth that occurs when this zone transitions to a pure growth regime. As an important consequence, dust can overcome the radial-drift barrier within a few thousands years.
NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification

Deep learning is attracting significant interest in the neuroimaging community as a means to diagnose psychiatric and neurological disorders from structural magnetic resonance images. However, there is a tendency amongst researchers to adopt architectures optimized for traditional computer vision tasks, rather than design networks customized for neuroimaging data. We address this by introducing NEURO-DRAM, a 3D recurrent visual attention model tailored for neuroimaging classification. The model comprises an agent which, trained by reinforcement learning, learns to navigate through volumetric images, selectively attending to the most informative regions for a given task. When applied to Alzheimer's disease prediction, NEURODRAM achieves state-of-the-art classification accuracy on an out-of-sample dataset, significantly outperforming a baseline convolutional neural network. When further applied to the task of predicting which patients with mild cognitive impairment will be diagnosed with Alzheimer's disease within two years, the model achieves state-of-the-art accuracy with no additional training. Encouragingly, the agent learns, without explicit instruction, a search policy in agreement with standardized radiological hallmarks of Alzheimer's disease, suggesting a route to automated biomarker discovery for more poorly understood disorders.
Non-contact Infant Sleep Apnea Detection

Sleep apnea is a breathing disorder where a person repeatedly stops breathing in sleep. Early detection is crucial for infants because it might bring long term adversities. The existing accurate detection mechanism (pulse oximetry) is a skin contact measurement. The existing non-contact mechanisms (acoustics, video processing) are not accurate enough. This paper presents a novel algorithm for detecting sleep apnea with video processing. The solution is non-contact, accurate and lightweight enough to run on a single board computer. The paper discusses the accuracy of the algorithm on real data, advantages of the new algorithm, its limitations and suggests future improvements.
LISA: Towards Learned DNA Sequence Search

Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics. Genomics data analytics at this scale requires overcoming performance bottlenecks, such as searching for short DNA sequences over long reference sequences. In this paper, we introduce LISA (Learned Indexes for Sequence Analysis), a novel learning-based approach to DNA sequence search. As a first proof of concept, we focus on accelerating one of the most essential flavors of the problem, called exact search. LISA builds on and extends FM-index, which is the state-of-the-art technique widely deployed in genomics tool-chains. Initial experiments with human genome datasets indicate that LISA achieves up to a factor of 4X performance speedup against its traditional counterpart.
Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space

Combining model-based and model-free deep reinforcement learning has shown great promise for improving sample efficiency on complex control tasks while still retaining high performance. Incorporating imagination is a recent effort in this direction inspired by human mental simulation of motor behavior. We propose a learning-adaptive imagination approach which, unlike previous approaches, takes into account the reliability of the learned dynamics model used for imagining the future. Our approach learns an ensemble of disjoint local dynamics models in latent space and derives an intrinsic reward based on learning progress, motivating the controller to take actions leading to data that improves the models. The learned models are used to generate imagined experiences, augmenting the training set of real experiences. We evaluate our approach on learning vision-based robotic grasping and show that it significantly improves sample efficiency and achieves near-optimal performance in a sparse reward environment.
Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)

We present a recurrent neural network based system for automatic quality estimation of natural language generation (NLG) outputs, which jointly learns to assign numerical ratings to individual outputs and to provide pairwise rankings of two different outputs. The latter is trained using pairwise hinge loss over scores from two copies of the rating network. We use learning to rank and synthetic data to improve the quality of ratings assigned by our system: We synthesise training pairs of distorted system outputs and train the system to rank the less distorted one higher. This leads to a 12% increase in correlation with human ratings over the previous benchmark. We also establish the state of the art on the dataset of relative rankings from the E2E NLG Challenge (Dusek et al., 2019), where synthetic data lead to a 4% accuracy increase over the base model.
Structured Pruning of Large Language Models

Large language models have recently achieved state of the art performance across a wide variety of natural language tasks. Meanwhile, the size of these models and their latency have significantly increased, which makes their usage costly, and raises an interesting question: do language models need to be large? We study this question through the lens of model compression. We present a novel, structured pruning approach based on low rank factorization and augmented Lagrangian L0 norm regularization. Our structured approach achieves significant inference speedups while matching or outperforming our unstructured pruning baseline at various sparsity levels. We apply our method to state of the art models on the enwiki8 dataset and obtain a 1.19 perplexity score with just 5M parameters, vastly outperforming a model of the same size trained from scratch. We also demonstrate that our method can be applied to language model fine-tuning by pruning the BERT model on several downstream classification benchmarks.
MetaPix: Few-Shot Video Retargeting

We address the task of unsupervised retargeting of human actions from one video to another. We consider the challenging setting where only a few frames of the target is available. The core of our approach is a conditional generative model that can transcode input skeletal poses (automatically extracted with an off-the-shelf pose estimator) to output target frames. However, it is challenging to build a universal transcoder because humans can appear wildly different due to clothing and background scene geometry. Instead, we learn to adapt - or personalize - a universal generator to the particular human and background in the target. To do so, we make use of meta-learning to discover effective strategies for on-the-fly personalization. One significant benefit of meta-learning is that the personalized transcoder naturally enforces temporal coherence across its generated frames; all frames contain consistent clothing and background geometry of the target. We experiment on in-the-wild internet videos and images and show our approach improves over widely-used baselines for the task.
CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning

Computer vision has undergone a dramatic revolution in performance, driven in large part through deep features trained on large-scale supervised datasets. However, much of these improvements have focused on static image analysis; video understanding has seen rather modest improvements. Even though new datasets and spatiotemporal models have been proposed, simple frame-by-frame classification methods often still remain competitive. We posit that current video datasets are plagued with implicit biases over scene and object structure that can dwarf variations in temporal structure. In this work, we build a video dataset with fully observable and controllable object and scene bias, and which truly requires spatiotemporal understanding in order to be solved. Our dataset, named CATER, is rendered synthetically using a library of standard 3D objects, and tests the ability to recognize compositions of object movements that require long-term reasoning. In addition to being a challenging dataset, CATER also provides a plethora of diagnostic tools to analyze modern spatiotemporal video architectures by being completely observable and controllable. Using CATER, we provide insights into some of the most recent state of the art deep video architectures.
On the Toral Rank Conjecture and Variants of Equivariant Formality

An action of a compact Lie group is called equivariantly formal, if the Leray--Serre spectral sequence of its Borel fibration degenerates at the E_2-term. This term is as prominent as it is restrictive. 
In this article, also motivated by the lack of junction between the notion of equivariant formality and the concept of formality of spaces (surging from rational homotopy theory) we suggest two new variations of equivariant formality: "MOD-formal actions" and "actions of formal core". 
We investigate and characterize these new terms in many different ways involving various tools from rational homotopy theory, Hirsch--Brown models, $A_\infty$-algebras, etc., and, in particular, we provide different applications ranging from actions on symplectic manifolds and rationally elliptic spaces to manifolds of non-negative sectional curvature. 
A major motivation for the new definitions was that an almost free action of a torus $T^n\curvearrowright X$ possessing any of the two new properties satisfies the toral rank conjecture, i.e. $dim H^*(X;Q)\geq 2^n$. This generalizes and proves the toral rank conjecture for actions with formal orbit spaces.
Referring Expression Object Segmentation with Caption-Aware Consistency

Referring expressions are natural language descriptions that identify a particular object within a scene and are widely used in our daily conversations. In this work, we focus on segmenting the object in an image specified by a referring expression. To this end, we propose an end-to-end trainable comprehension network that consists of the language and visual encoders to extract feature representations from both domains. We introduce the spatial-aware dynamic filters to transfer knowledge from text to image, and effectively capture the spatial information of the specified object. To better communicate between the language and visual modules, we employ a caption generation network that takes features shared across both domains as input, and improves both representations via a consistency that enforces the generated sentence to be similar to the given referring expression. We evaluate the proposed framework on two referring expression datasets and show that our method performs favorably against the state-of-the-art algorithms.
Panoptic-DeepLab

We present Panoptic-DeepLab, a bottom-up and single-shot approach for panoptic segmentation. Our Panoptic-DeepLab is conceptually simple and delivers state-of-the-art results. In particular, we adopt the dual-ASPP and dual-decoder structures specific to semantic, and instance segmentation, respectively. The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e.g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression. Our single Panoptic-DeepLab sets the new state-of-art at all three Cityscapes benchmarks, reaching 84.2% mIoU, 39.0% AP, and 65.5% PQ on test set, and advances results on the other challenging Mapillary Vistas.
On the duration of the embedded phase of star formation

Feedback from massive stars plays a key role in molecular cloud evolution. After the onset of star formation, the young stellar population is exposed by photoionization, winds, supernovae, and radiation pressure from massive stars. Recent observations of nearby galaxies have provided the evolutionary timeline between molecular clouds and exposed young stars, but the duration of the embedded phase of massive star formation is still ill-constrained. We measure how long massive stellar populations remain embedded within their natal cloud, by applying a statistical method to six nearby galaxies at $20{-}100~\mbox{${\rm ~pc}$}$ resolution, using CO, Spitzer 24$\rm \, \mu m$, and H α emission as tracers of molecular clouds, embedded star formation, and exposed star formation, respectively. We find that the embedded phase (with CO and 24$\rm \, \mu m$ emission) lasts for 2−7 Myr and constitutes $17{-}47{{\ \rm per\ cent}}$ of the cloud lifetime. During approximately the first half of this phase, the region is invisible in H α, making it heavily obscured. For the second half of this phase, the region also emits in H α and is partially exposed. Once the cloud has been dispersed by feedback, 24$\rm \, \mu m$ emission no longer traces ongoing star formation, but remains detectable for another 2−9 Myr through the emission from ambient CO-dark gas, tracing star formation that recently ended. The short duration of massive star formation suggests that pre-supernova feedback (photoionization and winds) is important in disrupting molecular clouds. The measured time-scales do not show significant correlations with environmental properties (e.g. metallicity). Future JWST observations will enable these measurements routinely across the nearby galaxy population.