nils-herrmann/clean_scirepeval huggingface dataset:
A Projectional Ansatz to Reconstruction

Recently the field of inverse problems has seen a growing usage of mathematically only partially understood learned and non-learned priors. Based on first principles, we develop a projectional approach to inverse problems that addresses the incorporation of these priors, while still guaranteeing data consistency. We implement this projectional method (PM) on the one hand via very general Plug-and-Play priors and on the other hand, via an end-to-end training approach. To this end, we introduce a novel alternating neural architecture, allowing for the incorporation of highly customized priors from data in a principled manner. We also show how the recent success of Regularization by Denoising (RED) can, at least to some extent, be explained as an approximation of the PM. Furthermore, we demonstrate how the idea can be applied to stop the degradation of Deep Image Prior (DIP) reconstructions over time.
Quintessential inflation with a trap and axionic dark matter

We study a new model of quintessential inflation which is inspired by supergravity and string theory. The model features a kinetic pole, which gives rise to the inflationary plateau, and a runaway quintessential tail. We envisage a coupling between the inflaton and the Peccei-Quinn (PQ) field which terminates the roll of the runaway inflaton and traps the latter at an enhanced symmetry point (ESP), thereby breaking the PQ symmetry. The kinetic density of the inflaton is transferred to the newly created thermal bath of the hot big bang due to the decay of PQ particles. The model successfully accounts for the observations of inflation and dark energy with natural values of the model parameters, while also resolving the strong CP problem of QCD and generating axionic dark matter, without isocurvature perturbations. Trapping the inflaton at the ESP ensures that the model does not suffer from the infamous 5th force problem, which typically plagues quintessence.
Label Aware Graph Convolutional Network - Not All Edges Deserve Your Attention

Graph classification is practically important in many domains. To solve this problem, one usually calculates a low-dimensional representation for each node in the graph with supervised or unsupervised approaches. Most existing approaches consider all the edges between nodes while overlooking whether the edge will brings positive or negative influence to the node representation learning. In many real-world applications, however, some connections among the nodes can be noisy for graph convolution, and not all the edges deserve your attention. In this work, we distinguish the positive and negative impacts of the neighbors to the node in graph node classification, and propose to enhance the graph convolutional network by considering the labels between the neighbor edges. We present a novel GCN framework, called Label-aware Graph Convolutional Network (LAGCN), which incorporates the supervised and unsupervised learning by introducing the edge label predictor. As a general model, LAGCN can be easily adapted in various previous GCN and enhance their performance with some theoretical guarantees. Experimental results on multiple real-world datasets show that LAGCN is competitive against various state-of-the-art methods in graph classification.
Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments

Vision-based autonomous driving through imitation learning mimics the behaviors of human drivers by training on pairs of data of raw driver-view images and actions. However, there are other cues, e.g. gaze behavior, available from human drivers that have yet to be exploited. Previous research has shown that novice human learners can benefit from observing experts' gaze patterns. We show here that deep neural networks can also benefit from this. We demonstrate different approaches to integrating gaze information into imitation networks. Our results show that the integration of gaze information improves the generalization performance of networks to unseen environments.
Eppur Si Riscalda - and yet, It (Just) Heats Up: Further Comments on “Quantifying Hot Carrier and Thermal Contributions in Plasmonic Photocatalysis”

<div>Our Comment  on recent attempts to distinguish thermal and non-thermal (``hot carrier'') contributions to plasmon-assisted photocatalysis [2] initiated a re-evaluation process of previous literature on the topic within the nano-plasmonics and chemistry communities. The Response of Zhou et al  attempts to defend the claims of the original paper [2].</div><div><br></div><div>In this manuscript, we show that the Response  presents additional data that further validates our central criticism: inaccurately measured temperatures (that are lower than the actual temperature of the catalyst) led Zhou \etal to incorrectly claim conclusive evidence of non-thermal effects. We identify flaws in the experimental setup (e.g. the use of the default settings for the thermal camera and incorrect positioning of the thermometer) that may have led Zhou et al to make such claims. We further show that the Response contains several factual errors and does not address the technical problems we identified with the data acquisition in . We demonstrate that both the Response  and the original paper  contain additional faults, for example, in the power determination and in the normalization of the rate to the catalyst volume, and exhibit misconceptions regarding the thermo-optic response of metal nanostructures. The burden of proof required by the proposal of a novel physical mechanism has simply not been met, especially when the existing data can be modeled exquisitely by conventional theory.</div><div>[1] Y. Sivan, J. Baraban, I. W. Un & Y. Dubi, . <a href="https://science.sciencemag.org/content/364/6439/eaaw9367.abstract">https://science.sciencemag.org/content/364/6439/eaaw9367.abstract</a> </div><div><br></div><div>[2] <a href="https://science.sciencemag.org/content/362/6410/69">https://science.sciencemag.org/content/362/6410/69</a></div><div><br></div><div>[3] <a href="https://science.sciencemag.org/content/364/6439/eaaw9545">https://science.sciencemag.org/content/364/6439/eaaw9545</a></div>
RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators From RL Policies

This letter addresses two challenges facing sampling-based kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationally intractable steering between these candidate states. Through the combination of sampling-based planning, a Rapidly Exploring Randomized Tree (RRT) and an efficient kinodynamic motion planner through machine learning, we propose an efficient solution to long-range planning for kinodynamic motion planning. First, we use deep reinforcement learning to learn an obstacle-avoiding policy that maps a robot's sensor observations to actions, which is used as a local planner during planning and as a controller during execution. Second, we train a reachability estimator in a supervised manner, which predicts the RL policy's time to reach a state in the presence of obstacles. Lastly, we introduce RL-RRT that uses the RL policy as a local planner, and the reachability estimator as the distance function to bias tree-growth towards promising regions. We evaluate our method on three kinodynamic systems, including physical robot experiments. Results across all three robots tested indicate that RL-RRT outperforms state of the art kinodynamic planners in efficiency, and also provides a shorter path finish time than a steering function free method. The learned local planner policy and accompanying reachability estimator demonstrate transferability to the previously unseen experimental environments, making RL-RRT fast because the expensive computations are replaced with simple neural network inference.
Measure for characterizing heavy-tailed networks

Heavy-tailed networks are often characterized in the literature by their degree distribution's similarity to a power law. However, many heavy-tailed networks in real life do not have power-law degree distributions, and in many applications the scale-free nature of the network is irrelevant so long as the network possesses hubs. Here we present the Cooke-Nieboer index (CNI), a non-asymptotic measure of the heavy-tailedness of a network's degree distribution which does not presume a power-law form. The CNI is easy to calculate, and clearly distinguishes between networks with power-law, exponential, and symmetric degree distributions.
Variational Autoencoders and Nonlinear ICA: A Unifying Framework

The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often, we're interested in going a step further, and want to approximate the true joint distribution over observed and latent variables, including the true prior and posterior distributions over latent variables. This is known to be generally impossible due to unidentifiability of the model. We address this issue by showing that for a broad family of deep latent-variable models, identification of the true joint distribution over observed and latent variables is actually possible up to very simple transformations, thus achieving a principled and powerful form of disentanglement. Our result requires a factorized prior distribution over the latent variables that is conditioned on an additionally observed variable, such as a class label or almost any other observation. We build on recent developments in nonlinear ICA, which we extend to the case with noisy, undercomplete or discrete observations, integrated in a maximum likelihood framework. The result also trivially contains identifiable flow-based generative models as a special case.
Mobile phone data's potential for informing infrastructure planning in developing countries

High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a go to proxy to evaluate population density, activity and social characteristics. They offer additional advantages for infrastructure planning such as being updated in real-time, including mobility information and recording temporary visitors' activity. We combine various data sets from Senegal to evaluate mobile phone data's potential to replace insufficient census data for infrastructure planning in developing countries. As an applied case, we test their ability at predicting accurately domestic electricity consumption. We show that, contrary to common belief, average mobile phone activity is not well correlated with population density. However, it can provide better electricity consumption estimates than basic census data. More importantly, we successfully use curve and network clustering techniques to enhance the accuracy of the predictions, to recover good population mapping potential and to reduce the collection of informative data for planning to substantially smaller samples.
Sparse Networks from Scratch: Faster Training without Losing Performance

We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels. We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to identify layers and weights which reduce the error efficiently. Sparse momentum redistributes pruned weights across layers according to the mean momentum magnitude of each layer. Within a layer, sparse momentum grows weights according to the momentum magnitude of zero-valued weights. We demonstrate state-of-the-art sparse performance on MNIST, CIFAR-10, and ImageNet, decreasing the mean error by a relative 8%, 15%, and 6% compared to other sparse algorithms. Furthermore, we show that sparse momentum reliably reproduces dense performance levels while providing up to 5.61x faster training. In our analysis, ablations show that the benefits of momentum redistribution and growth increase with the depth and size of the network. Additionally, we find that sparse momentum is insensitive to the choice of its hyperparameters suggesting that sparse momentum is robust and easy to use.
Impact of galaxy mergers on the colours of cluster galaxies

We examine the ultraviolet and optical colours of 906 cluster galaxies from the KASI-Yonsei Deep Imaging Survey of Clusters (KYDISC). The sample has been divided into two categories, morphologically disturbed and undisturbed galaxies, based on the visual signatures related to recent mergers. We find that galaxies with signatures of recent mergers show significantly bluer colours than undisturbed galaxies. Disturbed galaxies populate more on the cluster outskirts, suggesting recent accretion into the cluster environment, which implies that disturbed galaxies can be less influenced by the environmental quenching process and remain blue. However, we still detect bluer colours of disturbed galaxies in all locations (cluster core and outskirts) for the fixed morphology, which is difficult to understand just considering the difference in time since infall into a cluster. Moreover, blue disturbed galaxies show features seemingly related to recent star formation. Therefore, we suspect that mergers make disturbed galaxies keep their blue colour longer than undisturbed galaxies under the effect of the environmental quenching through either merger-induced star formation or central gas concentration which is less vulnerable for gas stripping.
The Tidal Disruption of Sun-like Stars by Massive Black Holes

We present the first simulations of the tidal disruption of stars with realistic structures and compositions by massive black holes (BHs). We build stars in the stellar evolution code MESA and simulate their disruption in the 3D adaptive-mesh hydrodynamics code FLASH, using an extended Helmholtz equation of state and tracking 49 elements. We study the disruption of a 1M⊙ star and 3M⊙ star at zero-age main sequence (ZAMS), middle-age, and terminal-age main sequence (TAMS). The maximum BH mass for tidal disruption increases by a factor of ∼2 from stellar radius changes due to main-sequence (MS) evolution; this is equivalent to varying BH spin from 0 to 0.75. The shape of the mass fallback rate curves is different from the results for polytropes of Guillochon & Ramirez-Ruiz. The peak timescale tpeak increases with stellar age, while the peak fallback rate decreases with age, and these effects diminish with increasing impact parameter β. For a β = 1 disruption of a 1M⊙ star by a 106 M⊙ BH, from ZAMS to TAMS, tpeak increases from 30 to 54 days, while decreases from 0.66 to 0.14 M⊙ yr−1. Compositional anomalies in nitrogen, helium, and carbon can occur before the peak timescale for disruptions of MS stars, which is in contrast to predictions from the “frozen-in” model. More massive stars can show stronger anomalies at earlier times, meaning that compositional constraints can be key in determining the mass of the disrupted star. The abundance anomalies predicted by these simulations provide a natural explanation for the spectral features and varying line strengths observed in tidal disruption events.
Possible Early Linear Acceleration of Proto-neutron Stars via Asymmetric Neutrino Emission in Core-collapse Supernovae

In this Letter we present the result of an axisymmetric core-collapse supernovae simulation conducted with appropriate treatments of neutrino transport and proper motions of proto-neutron stars (PNSs), in which a remarkable PNS acceleration is observed in association with asymmetric neutrino emissions 300 ms after bounce. We find that these asymmetric neutrino emissions play important roles in the acceleration of PNSs in this phase. The correlation between the PNS proper motion and the asymmetric ejecta is similar to that in a neutron star (NS) kick of hydrodynamic origin. Both electron-type neutrinos (νe) and their anti-particles ( ) have a ∼10% level of asymmetry between the northern and southern hemispheres, while other heavy-leptonic neutrinos (νx) have much a smaller asymmetry of ∼1%. The emissions of and νx are higher in the hemisphere of stronger shock expansion, whereas the νe emission is enhanced in the opposite hemisphere: in total, the neutrinos carry some linear momentum to the hemisphere of the stronger shock expansion. This asymmetry is attributed to the non-spherical distribution of electron-fraction (Ye) in the envelope of PNS. Although it is similar to lepton-emission self-sustained asymmetry, the Ye asymmetry seems to be associated with the PNS motion: the latter triggers lateral circular motions in the envelope of PNS by breaking the symmetry of the matter distribution there, which is then sustained by a combination of convection, lateral neutrino diffusion, and matter-pressure gradient. Our findings may have an influence on the current theories on the NS kick mechanism, although long-term simulations are required to assess their impact on later evolution.
LakhNES: Improving Multi-instrumental Music Generation with Cross-domain Pre-training

We are interested in the task of generating multi-instrumental music scores. The Transformer architecture has recently shown great promise for the task of piano score generation; here we adapt it to the multi-instrumental setting. Transformers are complex, high-dimensional language models which are capable of capturing long-term structure in sequence data, but require large amounts of data to fit. Their success on piano score generation is partially explained by the large volumes of symbolic data readily available for that domain. We leverage the recently-introduced NES-MDB dataset of four-instrument scores from an early video game sound synthesis chip (the NES), which we find to be well-suited to training with the Transformer architecture. To further improve the performance of our model, we propose a pre-training technique to leverage the information in a large collection of heterogeneous music, namely the Lakh MIDI dataset. Despite differences between the two corpora, we find that this transfer learning procedure improves both quantitative and qualitative performance for our primary task.
H0LiCOW – XIII. A 2.4 per cent measurement of H0 from lensed quasars: 5.3σ tension between early- and late-Universe probes

We present a measurement of the Hubble constant (H0) and other cosmological parameters from a joint analysis of six gravitationally lensed quasars with measured time delays. All lenses except the first are analysed blindly with respect to the cosmological parameters. In a flat Λ cold dark matter (ΛCDM) cosmology, we find $H_{0} = 73.3_{-1.8}^{+1.7}~\mathrm{km~s^{-1}~Mpc^{-1}}$, a $2.4{{\ \rm per\ cent}}$ precision measurement, in agreement with local measurements of H0 from type Ia supernovae calibrated by the distance ladder, but in 3.1σ tension with Planck observations of the cosmic microwave background (CMB). This method is completely independent of both the supernovae and CMB analyses. A combination of time-delay cosmography and the distance ladder results is in 5.3σ tension with Planck CMB determinations of H0 in flat ΛCDM. We compute Bayes factors to verify that all lenses give statistically consistent results, showing that we are not underestimating our uncertainties and are able to control our systematics. We explore extensions to flat ΛCDM using constraints from time-delay cosmography alone, as well as combinations with other cosmological probes, including CMB observations from Planck, baryon acoustic oscillations, and type Ia supernovae. Time-delay cosmography improves the precision of the other probes, demonstrating the strong complementarity. Allowing for spatial curvature does not resolve the tension with Planck. Using the distance constraints from time-delay cosmography to anchor the type Ia supernova distance scale, we reduce the sensitivity of our H0 inference to cosmological model assumptions. For six different cosmological models, our combined inference on H0 ranges from ∼73 to 78 km s−1 Mpc−1, which is consistent with the local distance ladder constraints.
Structure and phase behavior of polymer-linked colloidal gels.

Low-density "equilibrium" gels that consist of a percolated, kinetically arrested network of colloidal particles and are resilient to aging can be fabricated by restricting the number of effective bonds that form between the colloids. Valence-restricted patchy particles have long served as one archetypal example of such materials, but equilibrium gels can also be realized through a synthetically simpler and scalable strategy that introduces a secondary linker, such as a small ditopic molecule, to mediate the bonds between the colloids. Here, we consider the case where the ditopic linker molecules are low-molecular-weight polymers and demonstrate using a model colloid-polymer mixture how macroscopic properties such as the phase behavior as well as the microstructure of the gel can be designed through the polymer molecular weight and concentration. The low-density window for equilibrium gel formation is favorably expanded using longer linkers while necessarily increasing the spacing between all colloids. However, we show that blends of linkers with different sizes enable wider variation in microstructure for a given target phase behavior. Our computational study suggests a robust and tunable strategy for the experimental realization of equilibrium colloidal gels.
Increasing Gender Diversity and Inclusion in Scientific Committees and Related Activities at STScI

We present a new initiative by the Women in Astronomy Forum at Space Telescope Science Institute (STScI) to increase gender diversity and inclusion in STScI's scientific committees and the activities they generate. This initiative offers new and uniform guidelines on binary gender representation goals for each committee and recommendations on how to achieve them in a homogeneous way, as well as metrics and tools to track progress towards defined goals. While the new guidelines presented in the paper focus on binary gender representation, they can be adapted and implemented to support all minority groups. By creating diverse committees and making them aware of, and trained on implicit bias, we expect to create a diverse outcome in the activities they generate, which, in turn, will advance science further and faster.
Fully Convolutional Networks for Handwriting Recognition

Handwritten text recognition is challenging because of the virtually infinite ways a human can write the same message. Our fully convolutional handwriting model takes in a handwriting sample of unknown length and outputs an arbitrary stream of symbols. Our dual stream architecture uses both local and global context and mitigates the need for heavy preprocessing steps such as symbol alignment correction as well as complex post processing steps such as connectionist temporal classification, dictionary matching or language models. Using over 100 unique symbols, our model is agnostic to Latin-based languages, and is shown to be quite competitive with state of the art dictionary based methods on the popular IAM and RIMES datasets. When a dictionary is known, we further allow a probabilistic character error rate to correct errant word blocks. Finally, we introduce an attention based mechanism which can automatically target variants of handwriting, such as slant, stroke width, or noise.