nils-herrmann/clean_scirepeval huggingface dataset:
Experiential AI

Experiential AI is proposed as a new research agenda in which artists and scientists come together to dispel the mystery of algorithms and make their mechanisms vividly apparent. It addresses the challenge of finding novel ways of opening up the field of artificial intelligence to greater transparency and collaboration between human and machine. The hypothesis is that art can mediate between computer code and human comprehension to overcome the limitations of explanations in and for AI systems. Artists can make the boundaries of systems visible and offer novel ways to make the reasoning of AI transparent and decipherable. Beyond this, artistic practice can explore new configurations of humans and algorithms, mapping the terrain of inter-agencies between people and machines. This helps to viscerally understand the complex causal chains in environments with AI components, including questions about what data to collect or who to collect it about, how the algorithms are chosen, commissioned and configured or how humans are conditioned by their participation in algorithmic processes.
Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks

To address the limitations of existing magnitude-based pruning algorithms in cases where model weights or activations are of large and similar magnitude, we propose a novel perspective to discover parameter redundancy among channels and accelerate deep CNNs via channel pruning. Precisely, we argue that channels revealing similar feature information have functional overlap and that most channels within each such similarity group can be removed without compromising model's representational power. After deriving an effective metric for evaluating channel similarity through probabilistic modeling, we introduce a pruning algorithm via hierarchical clustering of channels. In particular, the proposed algorithm does not rely on sparsity training techniques or complex data-driven optimization and can be directly applied to pre-trained models. Extensive experiments on benchmark datasets strongly demonstrate the superior acceleration performance of our approach over prior arts. On ImageNet, our pruned ResNet-50 with 30% FLOPs reduced outperforms the baseline model.
A 20-Year Community Roadmap for Artificial Intelligence Research in the US

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. 
Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.
Scene-based Factored Attention for Image Captioning

Image captioning has attracted ever-increasing research attention in the multimedia community. To this end, most cutting-edge works rely on an encoder-decoder framework with attention mechanisms, which have achieved remarkable progress. However, such a framework does not consider scene concepts to attend visual information, which leads to sentence bias in caption generation and defects the performance correspondingly. We argue that such scene concepts capture higher-level visual semantics and serve as an important cue in describing images. In this paper, we propose a novel scene-based factored attention module for image captioning. Specifically, the proposed module first embeds the scene concepts into factored weights explicitly and attends the visual information extracted from the input image. Then, an adaptive LSTM is used to generate captions for specific scene types. Experimental results on Microsoft COCO benchmark show that the proposed scene-based attention module improves model performance a lot, which outperforms the state-of-the-art approaches under various evaluation metrics.
Regression Constraint for an Explainable Cervical Cancer Classifier

This article adresses the problem of automatic squamous cells classification for cervical cancer screening using Deep Learning methods. We study different architectures on a public dataset called Herlev dataset, which consists in classifying cells, obtained by cervical pap smear, regarding the severity of the abnormalities they represent. Furthermore, we use an attribution method to understand which cytomorphological features are actually learned as discriminative to classify severity of the abnormalities. Through this paper, we show how we trained a performant classifier: 74.5\% accuracy on severity classification and 94\% accuracy on normal/abnormal classification.
The Northumberland Dolphin Dataset: A Multimedia Individual Cetacean Dataset for Fine-Grained Categorisation

Methods for cetacean research include photo-identification (photo-id) and passive acoustic monitoring (PAM) which generate thousands of images per expedition that are currently hand categorised by researchers into the individual dolphins sighted. With the vast amount of data obtained it is crucially important to develop a system that is able to categorise this quickly. The Northumberland Dolphin Dataset (NDD) is an on-going novel dataset project made up of above and below water images of, and spectrograms of whistles from, white-beaked dolphins. These are produced by photo-id and PAM data collection methods applied off the coast of Northumberland, UK. This dataset will aid in building cetacean identification models, reducing the number of human-hours required to categorise images. Example use cases and areas identified for speed up are examined.
Large-scale traffic signal control using machine learning: some traffic flow considerations

This paper uses supervised learning, random search and deep reinforcement learning (DRL) methods to control large signalized intersection networks. The traffic model is Cellular Automaton rule 184, which has been shown to be a parameter-free representation of traffic flow, and is the most efficient implementation of the Kinematic Wave model with triangular fundamental diagram. We are interested in the steady-state performance of the system, both spatially and temporally: we consider a homogeneous grid network inscribed on a torus, which makes the network boundary-free, and drivers choose random routes. As a benchmark we use the longest-queue-first (LQF) greedy algorithm. We find that: (i) a policy trained with supervised learning with only two examples outperforms LQF, (ii) random search is able to generate near-optimal policies, (iii) the prevailing average network occupancy during training is the major determinant of the effectiveness of DRL policies. When trained under free-flow conditions one obtains DRL policies that are optimal for all traffic conditions, but this performance deteriorates as the occupancy during training increases. For occupancies > 75% during training, DRL policies perform very poorly for all traffic conditions, which means that DRL methods cannot learn under highly congested conditions. We conjecture that DRL's inability to learn under congestion might be explained by a property of urban networks found here, whereby even a very bad policy produces an intersection throughput higher than downstream capacity. This means that the actual throughput tends to be independent of the policy. Our findings imply that it is advisable for current DRL methods in the literature to discard any congested data when training, and that doing this will improve their performance under all traffic conditions.
Rendering Point Clouds with Compute Shaders

We propose a compute shader based point cloud rasterizer with up to 10 times higher performance than classic point-based rendering with the GL_POINT primitive. In addition to that, our rasterizer offers 5 byte depth-buffer precision with uniform or customizable distribution, and we show that it is possible to implement a high-quality splatting method that blends together overlapping fragments while still maintaining higher frame-rates than the traditional approach.
Nine axes of merit for technosignature searches

It can be difficult to develop an effective and balanced search strategy in SETI, especially from a funding perspective, given the diverse methodologies and myriad orthogonal proposals for the best technosignatures. Here I propose a framework to compare the relative advantages and disadvantages of various proposed technosignatures based on nine ‘axes of merit’. This framework was first developed at the NASA Technosignatures Workshop in Houston in 2018 and published in that report. I give the definition and rationale behind the nine axes as well as the history of each axis in the SETI and technosignature literature. These axes are then applied to three classes of technosignature searches as an illustration of their use. An open-source software tool is available to allow technosignature researchers to make their own version of the figure.
Relighting humans

Relighting of human images has various applications in image synthesis. For relighting, we must infer albedo, shape, and illumination from a human portrait. Previous techniques rely on human faces for this inference, based on spherical harmonics (SH) lighting. However, because they often ignore light occlusion, inferred shapes are biased and relit images are unnaturally bright particularly at hollowed regions such as armpits, crotches, or garment wrinkles. This paper introduces the first attempt to infer light occlusion in the SH formulation directly. Based on supervised learning using convolutional neural networks (CNNs), we infer not only an albedo map, illumination but also a light transport map that encodes occlusion as nine SH coefficients per pixel. The main difficulty in this inference is the lack of training datasets compared to unlimited variations of human portraits. Surprisingly, geometric information including occlusion can be inferred plausibly even with a small dataset of synthesized human figures, by carefully preparing the dataset so that the CNNs can exploit the data coherency. Our method accomplishes more realistic relighting than the occlusion-ignored formulation.
A Characterization of Mean Squared Error for Estimator with Bagging

Bagging can significantly improve the generalization performance of unstable machine learning algorithms such as trees or neural networks. Though bagging is now widely used in practice and many empirical studies have explored its behavior, we still know little about the theoretical properties of bagged predictions. In this paper, we theoretically investigate how the bagging method can reduce the Mean Squared Error (MSE) when applied on a statistical estimator. First, we prove that for any estimator, increasing the number of bagged estimators $N$ in the average can only reduce the MSE. This intuitive result, observed empirically and discussed in the literature, has not yet been rigorously proved. Second, we focus on the standard estimator of variance called unbiased sample variance and we develop an exact analytical expression of the MSE for this estimator with bagging. 
This allows us to rigorously discuss the number of iterations $N$ and the batch size $m$ of the bagging method. From this expression, we state that only if the kurtosis of the distribution is greater than $\frac{3}{2}$, the MSE of the variance estimator can be reduced with bagging. This result is important because it demonstrates that for distribution with low kurtosis, bagging can only deteriorate the performance of a statistical prediction. Finally, we propose a novel general-purpose algorithm to estimate with high precision the variance of a sample.
Robust Learning with Jacobian Regularization

Design of reliable systems must guarantee stability against input perturbations. In machine learning, such guarantee entails preventing overfitting and ensuring robustness of models against corruption of input data. In order to maximize stability, we analyze and develop a computationally efficient implementation of Jacobian regularization that increases classification margins of neural networks. The stabilizing effect of the Jacobian regularizer leads to significant improvements in robustness, as measured against both random and adversarial input perturbations, without severely degrading generalization properties on clean data.
Learning Conditional Deformable Templates with Convolutional Networks

We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks. Conventional methods for template creation and image alignment to the template have undergone decades of rich technical development. In these frameworks, templates are constructed using an iterative process of template estimation and alignment, which is often computationally very expensive. Due in part to this shortcoming, most methods compute a single template for the entire population of images, or a few templates for specific sub-groups of the data. In this work, we present a probabilistic model and efficient learning strategy that yields either universal or conditional templates, jointly with a neural network that provides efficient alignment of the images to these templates. We demonstrate the usefulness of this method on a variety of domains, with a special focus on neuroimaging. This is particularly useful for clinical applications where a pre-existing template does not exist, or creating a new one with traditional methods can be prohibitively expensive. Our code and atlases are available online as part of the VoxelMorph library at this http URL.
The total stellar halo mass of the Milky Way

We measure the total stellar halo luminosity using red giant branch (RGB) stars selected from Gaia data release 2. Using slices in magnitude, colour, and location on the sky, we decompose RGB stars belonging to the disc and halo by fitting two-dimensional Gaussians to the Galactic proper motion distributions. The number counts of RGB stars are converted to total stellar halo luminosity using a suite of isochrones weighted by age and metallicity, and by applying a volume correction based on the stellar halo density profile. Our method is tested and calibrated using Galaxia and N-body models. We find a total luminosity (out to 100 kpc) of $L_{\rm halo} = 7.9 \pm 2.0 \times 10^8\, \mathrm{L}_\odot$ excluding Sgr, and $L_{\rm halo} = 9.4 \pm 2.4 \times 10^8\, \mathrm{L}_\odot$ including Sgr. These values are appropriate for our adopted stellar halo density profile and metallicity distribution, but additional systematics related to these assumptions are quantified and discussed. Assuming a stellar mass-to-light ratio appropriate for a Kroupa initial mass function (M⋆/L = 1.5), we estimate a stellar halo mass of $M^\star _{\rm halo} = 1.4 \pm 0.4\times 10^9 \, \mathrm{M}_\odot$. This mass is larger than previous estimates in the literature, but is in good agreement with the emerging picture that the (inner) stellar halo is dominated by one massive dwarf progenitor. Finally, we argue that the combination of a ${\sim}10^9\, \mathrm{M}_\odot$ mass and an average metallicity of 〈[Fe/H]〉 ∼ −1.5 for the Galactic halo points to an ancient (∼10 Gyr) merger event.
High-velocity outflows in massive post-starburst galaxies at z > 1

We investigate the prevalence of galactic-scale outflows in post-starburst (PSB) galaxies at high redshift (1 < z < 1.4), using the deep optical spectra available in the UKIDSS Ultra Deep Survey (UDS). We use a sample of ∼40 spectroscopically confirmed PSBs, recently identified in the UDS field, and perform a stacking analysis in order to analyse the structure of strong interstellar absorption features such as Mg ii (λ2800 Å). We find that for massive ($M_* \gt 10^{10}\rm \, M_{\odot }$) PSBs at z > 1, there is clear evidence for a strong blue-shifted component to the Mg ii absorption feature, indicative of high-velocity outflows ($v_{\rm out}\sim 1150\pm 160\rm \, km\, s^{-1}$) in the interstellar medium. We conclude that such outflows are typical in massive PSBs at this epoch, and potentially represent the residual signature of a feedback process that quenched these galaxies. Using full spectral fitting, we also obtain a typical stellar velocity dispersion σ* for these PSBs of $\sim 200\rm \, km\, s^{-1}$, which confirms they are intrinsically massive in nature (dynamical mass $M_{\rm d}\sim 10^{11}\rm \, M_{\odot }$). Given that these high-z PSBs are also exceptionally compact (re ∼ 1–$2\rm \, kpc$) and spheroidal (Sérsic index n ∼ 3), we propose that the outflowing winds may have been launched during a recent compaction event (e.g. major merger or disc collapse) that triggered either a centralized starburst or active galactic nuclei (AGN) activity. Finally, we find no evidence for AGN signatures in the optical spectra of these PSBs, suggesting they were either quenched by stellar feedback from the starburst itself, or that if AGN feedback is responsible, the AGN episode that triggered quenching does not linger into the post-starburst phase.
Sulfate Aerosol Hazes and SO2 Gas as Constraints on Rocky Exoplanets’ Surface Liquid Water

Despite surface liquid water’s importance to habitability, observationally diagnosing its presence or absence on exoplanets is still an open problem. Inspired within the solar system by the differing sulfur cycles on Venus and Earth, we investigate thick sulfate (H2SO4–H2O) aerosol haze and high trace mixing ratios of SO2 gas as observable atmospheric features whose sustained existence is linked to the near absence of surface liquid water. We examine the fundamentals of the sulfur cycle on a rocky planet with an ocean and an atmosphere in which the dominant forms of sulfur are SO2 gas and H2SO4–H2O aerosols (as on Earth and Venus). We build a simple but robust model of the wet, oxidized sulfur cycle to determine the critical amounts of sulfur in the atmosphere–ocean system required for detectable levels of SO2 and a detectable haze layer. We demonstrate that for physically realistic ocean pH values (pH ≳ 6) and conservative assumptions on volcanic outgassing, chemistry, and aerosol microphysics, surface liquid water reservoirs with greater than 10−3 Earth oceans are incompatible with a sustained observable H2SO4–H2O haze layer and sustained observable levels of SO2. Thus, we propose the observational detection of an H2SO4–H2O haze layer and of SO2 gas as two new remote indicators that a planet does not host significant surface liquid water.
Spatial variations in the Milky Way disc metallicity–age relation

Stellar ages are a crucial component to studying the evolution of the Milky Way. Using Gaia DR2 distance estimates, it is now possible to estimate stellar ages for a larger volume of evolved stars through isochrone matching. This work presents [M/H]–age and [α/M]–age relations derived for different spatial locations in the Milky Way disc. These relations are derived by hierarchically modelling the star formation history of stars within a given chemical abundance bin. For the first time, we directly observe that significant variation is apparent in the [M/H]–age relation as a function of both Galactocentric radius and distance from the disc mid-plane. The [M/H]–age relations support claims that radial migration has a significant effect in the plane of the disc. Using the [M/H] bin with the youngest mean age at each radial zone in the plane of the disc, the present-day metallicity gradient is measured to be −0.059 ± 0.010 dex kpc−1, in agreement with Cepheids and young field stars. We find a vertically flared distribution of young stars in the outer disc, confirming predictions of models and previous observations. The mean age of the [M/H]–[α/M] distribution of the solar neighbourhood suggests that the high-[M/H] stars are not an evolutionary extension of the low-α sequence. Our observational results are important constraints to Galactic simulations and models of chemical evolution.
Galactic Globular Clusters: A new catalog of masses, structural parameters, velocity dispersion profiles, proper motions and space orbits

We collected radial velocities of more than 50.000 individual stars in 156 Galactic globular clusters (GGC) and matched them with HST photometry and Gaia DR2 proper motions. This allowed us to derive the GGC’s mean proper motions and space velocities. By fitting a large set of N-body simulations to their velocity dispersion and surface density profiles, combined with new measurements of their internal radially dependent mass functions, we have determined their present-day masses and structural parameters, and for 144 GGCs their internal kinematics. We also derive the initial cluster masses by calculating the cluster orbits backwards in time applying suitable recipes to account for mass-loss and dynamical friction. The new fundamental parameters of GGCs are publicly available via an online database, which will regularly be updated.
Zee-Burst: A New Probe of Neutrino Nonstandard Interactions at IceCube.

We propose a new way to probe nonstandard interactions (NSI) of neutrinos with matter using the ultrahigh energy (UHE) neutrino data at current and future neutrino telescopes. We consider the Zee model of radiative neutrino mass generation as a prototype, which allows two charged scalars-one SU(2)_{L} doublet and one singlet, both being leptophilic, to be as light as 100 GeV, thereby inducing potentially observable NSI with electrons. We show that these light charged Zee scalars could give rise to a Glashow-like resonance feature in the UHE neutrino event spectrum at the IceCube neutrino observatory and its high-energy upgrade IceCube-Gen2, which can probe a sizable fraction of the allowed NSI parameter space.