nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Differentiation for Adjoint Stencil Loops

Stencil loops are a common motif in computations including convolutional neural networks, structured-mesh solvers for partial differential equations, and image processing. Stencil loops are easy to parallelise, and their fast execution is aided by compilers, libraries, and domain-specific languages. Reverse-mode automatic differentiation, also known as algorithmic differentiation, autodiff, adjoint differentiation, or back-propagation, is sometimes used to obtain gradients of programs that contain stencil loops. Unfortunately, conventional automatic differentiation results in a memory access pattern that is not stencil-like and not easily parallelisable. In this paper we present a novel combination of automatic differentiation and loop transformations that preserves the structure and memory access pattern of stencil loops, while computing fully consistent derivatives. The generated loops can be parallelised and optimised for performance in the same way and using the same tools as the original computation. We have implemented this new technique in the Python tool PerforAD, which we release with this paper along with test cases derived from seismic imaging and computational fluid dynamics applications.
Visual Appearance Analysis of Forest Scenes for Monocular SLAM

Monocular simultaneous localisation and mapping (SLAM) is a cheap and energy efficient way to enable Unmanned Aerial Vehicles (UAVs) to safely navigate managed forests and gather data crucial for monitoring tree health. SLAM research, however, has mostly been conducted in structured human environments, and as such is poorly adapted to unstructured forests. In this paper, we compare the performance of state of the art monocular SLAM systems on forest data and use visual appearance statistics to characterise the differences between forests and other environments, including a photorealistic simulated forest. We find that SLAM systems struggle with all but the most straightforward forest terrain and identify key attributes (lighting changes and in-scene motion) which distinguish forest scenes from “classic” urban datasets. These differences offer an insight into what makes forests harder to map and open the way for targeted improvements. We also demonstrate that even simulations that look impressive to the human eye can fail to properly reflect the difficult attributes of the environment they simulate, and provide suggestions for more closely mimicking natural scenes.
Geodesic Learning via Unsupervised Decision Forests

Geodesic distance is the shortest path between two points in a Riemannian manifold. Manifold learning algorithms, such as Isomap, seek to learn a manifold that preserves geodesic distances. However, such methods operate on the ambient dimensionality, and are therefore fragile to noise dimensions. We developed an unsupervised random forest method (URerF) to approximately learn geodesic distances in linear and nonlinear manifolds with noise. URerF operates on low-dimensional sparse linear combinations of features, rather than the full observed dimensionality. To choose the optimal split in a computationally efficient fashion, we developed a fast Bayesian Information Criterion statistic for Gaussian mixture models. We introduce geodesic precision-recall curves which quantify performance relative to the true latent manifold. Empirical results on simulated and real data demonstrate that URerF is robust to high-dimensional noise, where as other methods, such as Isomap, UMAP, and FLANN, quickly deteriorate in such settings. In particular, URerF is able to estimate geodesic distances on a real connectome dataset better than other approaches.
Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model

Intent Detection and Slot Filling are two pillar tasks in Spoken Natural Language Understanding. Common approaches adopt joint Deep Learning architectures in attention-based recurrent frameworks. In this work, we aim at exploiting the success of "recurrence-less" models for these tasks. We introduce Bert-Joint, i.e., a multi-lingual joint text classification and sequence labeling framework. The experimental evaluation over two well-known English benchmarks demonstrates the strong performances that can be obtained with this model, even when few annotated data is available. Moreover, we annotated a new dataset for the Italian language, and we observed similar performances without the need for changing the model.
Visus: An Interactive System for Automatic Machine Learning Model Building and Curation

While the demand for machine learning (ML) applications is booming, there is a scarcity of data scientists capable of building such models. Automatic machine learning (AutoML) approaches have been proposed that help with this problem by synthesizing end-to-end ML data processing pipelines. However, these follow a best-effort approach and a user in the loop is necessary to curate and refine the derived pipelines. Since domain experts often have little or no expertise in machine learning, easy-to-use interactive interfaces that guide them throughout the model building process are necessary. In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems. We describe the framework used to ground our design choices and a usage scenario enabled by Visus. Finally, we discuss the feedback received in user testing sessions with domain experts.
An introduction to higher categorical algebra

This article is a survey of algebra in the $\infty$-categorical context, as developed by Lurie in "Higher Algebra", and is a chapter in the "Handbook of Homotopy Theory". We begin by introducing symmetric monoidal stable $\infty$-categories, such as the derived $\infty$-category of a commutative ring, before turning to our main example, the $\infty$-category of spectra. We then go on to consider ring spectra and their $\infty$-categories of modules, as well as basic constructions such as localization, completion, and dualizability. We conclude with a brief account of the cotangent complex and deformation theory.
A Comparison of Flare Forecasting Methods. II. Benchmarks, Metrics, and Performance Results for Operational Solar Flare Forecasting Systems

Solar flares are extremely energetic phenomena in our solar system. Their impulsive and often drastic radiative increases, particularly at short wavelengths, bring immediate impacts that motivate solar physics and space weather research to understand solar flares to the point of being able to forecast them. As data and algorithms improve dramatically, questions must be asked concerning how well the forecasting performs; crucially, we must ask how to rigorously measure performance in order to critically gauge any improvements. Building upon earlier-developed methodology of Paper I (Barnes et al. 2016), international representatives of regional warning centers and research facilities assembled in 2017 at the Institute for Space-Earth Environmental Research, Nagoya University, Japan to, for the first time, directly compare the performance of operational solar flare forecasting methods. Multiple quantitative evaluation metrics are employed, with the focus and discussion on evaluation methodologies given the restrictions of operational forecasting. Numerous methods performed consistently above the “no-skill” level, although which method scored top marks is decisively a function of flare event definition and the metric used; there was no single winner. Following in this paper series, we ask why the performances differ by examining implementation details (Leka et al. 2019), and then we present a novel analysis method to evaluate temporal patterns of forecasting errors in Paper IV (Park et al. 2019). With these works, this team presents a well-defined and robust methodology for evaluating solar flare forecasting methods in both research and operational frameworks and today’s performance benchmarks against which improvements and new methods may be compared.
Physics webpages create barriers to participation for people with disabilities: Five steps to increase digital accessibility

While there have been numerous calls to increase the participation of people with disabilities in STEM, many postsecondary institutions are not equipped to support students with disabilities. We examined the digital accessibility of 139 webpages from 73 postsecondary institutions that contained information about the undergraduate physics curriculum and graduate research programs. We selected these webpages as they are common entry points for students interested in pursuing a physics degree. We used Tenon and Mac OS X's Voiceover software to assess the accessibility of these webpages as measured by alignment with the Web Content Accessibility Guidelines (WCAG) 2.0. We found only one webpage was accessible for students with disabilities. We present five common accessibility errors we identified in the webpages in our sample, suggested solutions for these errors, and implications for students with disabilities, instructors and staff, institutional administration, and the broader physics community.
Hybridized Threshold Clustering for Massive Data

As the size $n$ of datasets become massive, many commonly-used clustering algorithms (for example, $k$-means or hierarchical agglomerative clustering (HAC) require prohibitive computational cost and memory. In this paper, we propose a solution to these clustering problems by extending threshold clustering (TC) to problems of instance selection. TC is a recently developed clustering algorithm designed to partition data into many small clusters in linearithmic time (on average). Our proposed clustering method is as follows. First, TC is performed and clusters are reduced into single "prototype" points. Then, TC is applied repeatedly on these prototype points until sufficient data reduction has been obtained. Finally, a more sophisticated clustering algorithm is applied to the reduced prototype points, thereby obtaining a clustering on all $n$ data points. This entire procedure for clustering is called iterative hybridized threshold clustering (IHTC). Through simulation results and by applying our methodology on several real datasets, we show that IHTC combined with $k$-means or HAC substantially reduces the run time and memory usage of the original clustering algorithms while still preserving their performance. Additionally, IHTC helps prevent singular data points from being overfit by clustering algorithms.
On Inductive Biases in Deep Reinforcement Learning

Many deep reinforcement learning algorithms contain inductive biases that sculpt the agent's objective and its interface to the environment. These inductive biases can take many forms, including domain knowledge and pretuned hyper-parameters. In general, there is a trade-off between generality and performance when algorithms use such biases. Stronger biases can lead to faster learning, but weaker biases can potentially lead to more general algorithms. This trade-off is important because inductive biases are not free; substantial effort may be required to obtain relevant domain knowledge or to tune hyper-parameters effectively. In this paper, we re-examine several domain-specific components that bias the objective and the environmental interface of common deep reinforcement learning agents. We investigated whether the performance deteriorates when these components are replaced with adaptive solutions from the literature. In our experiments, performance sometimes decreased with the adaptive components, as one might expect when comparing to components crafted for the domain, but sometimes the adaptive components performed better. We investigated the main benefit of having fewer domain-specific components, by comparing the learning performance of the two systems on a different set of continuous control problems, without additional tuning of either system. As hypothesized, the system with adaptive components performed better on many of the new tasks.
Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape

The permutation symmetry of neurons in each layer of a deep neural network gives rise not only to multiple equivalent global minima of the loss function, but also to first-order saddle points located on the path between the global minima. In a network of $d-1$ hidden layers with $n_k$ neurons in layers $k = 1, \ldots, d$, we construct smooth paths between equivalent global minima that lead through a `permutation point' where the input and output weight vectors of two neurons in the same hidden layer $k$ collide and interchange. We show that such permutation points are critical points with at least $n_{k+1}$ vanishing eigenvalues of the Hessian matrix of second derivatives indicating a local plateau of the loss function. We find that a permutation point for the exchange of neurons $i$ and $j$ transits into a flat valley (or generally, an extended plateau of $n_{k+1}$ flat dimensions) that enables all $n_k!$ permutations of neurons in a given layer $k$ at the same loss value. Moreover, we introduce high-order permutation points by exploiting the recursive structure in neural network functions, and find that the number of $K^{\text{th}}$-order permutation points is at least by a factor $\sum_{k=1}^{d-1}\frac{1}{2!^K}{n_k-K \choose K}$ larger than the (already huge) number of equivalent global minima. In two tasks, we illustrate numerically that some of the permutation points correspond to first-order saddles (`permutation saddles'): first, in a toy network with a single hidden layer on a function approximation task and, second, in a multilayer network on the MNIST task. Our geometric approach yields a lower bound on the number of critical points generated by weight-space symmetries and provides a simple intuitive link between previous mathematical results and numerical observations.
Visualizing Uncertainty and Saliency Maps of Deep Convolutional Neural Networks for Medical Imaging Applications

Deep learning models are now used in many different industries, while in certain domains safety is not a critical issue in the medical field it is a huge concern. Not only, we want the models to generalize well but we also want to know the models confidence respect to its decision and which features matter the most. Our team aims to develop a full pipeline in which not only displays the uncertainty of the models decision but also, the saliency map to show which sets of pixels of the input image contribute most to the predictions.
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions

Adversarial examples raise questions about whether neural network models are sensitive to the same visual features as humans. In this paper, we first detect adversarial examples or otherwise corrupted images based on a class-conditional reconstruction of the input. To specifically attack our detection mechanism, we propose the Reconstructive Attack which seeks both to cause a misclassification and a low reconstruction error. This reconstructive attack produces undetected adversarial examples but with much smaller success rate. Among all these attacks, we find that CapsNets always perform better than convolutional networks. Then, we diagnose the adversarial examples for CapsNets and find that the success of the reconstructive attack is highly related to the visual similarity between the source and target class. Additionally, the resulting perturbations can cause the input image to appear visually more like the target class and hence become non-adversarial. This suggests that CapsNets use features that are more aligned with human perception and have the potential to address the central issue raised by adversarial examples.
UV regulated star formation in high-redshift galaxies

The first galaxies forming a few hundred million years after the big bang are the key drivers of cosmic evolution and ideal laboratories to study theories of galaxy formation. We here study the role of UV radiation in suppressing star formation in primordial galaxies by destroying molecular hydrogen, the main coolant in primordial gas, and provide estimates of cold dense gas at the onset of star formation. To accomplish this goal, we perform three-dimensional cosmological simulations of minihaloes in different environments forming at z ∼ 25 by varying strength of background UV flux below the Lyman limit between 0.01–1000 in units of $\rm J_{21}=10^{-21}\, erg \,cm^{-2} \,s^{-1} \,Hz^{-1} \,sr^{-1}$. Particularly, we include photodetachment of $\rm H^-$, the self-shielding of $\rm H_2$, which both were neglected in previous studies and use updated reaction rates. Our results show that depending on the background level $\rm H_2$ formation is suppressed, delaying gravitational collapse until haloes reach the atomic cooling limit. We find that the formation of cold dense molecular gas and subsequently star formation gets delayed by 100–230 Myr depending on the level of the background radiation and the growth history of the dark matter haloes. The fraction of dense self-shielded gas is a strong function of the background flux and exponentially declines with the strength of incident UV flux above $\rm J_{21} \ge  We find that taking into account $\rm H_2$ self-shielding is crucial for accurately estimating the amount of cold dense gas available for star formation.
Analysis of superfast encoding performance for electronic structure simulations

In our recent work, we have examined various fermion to qubit mappings in the context of quantum simulation including the original Bravyi-Kitaev Superfast encoding (OSE) as well as a generalized version (GSE). We return to OSE and compare it against the Jordan-Wigner (JW) transform for quantum chemistry considering the number of qubits required, the Pauli weight of terms in the transformed Hamiltonians, and the $L_1$ norm of the Hamiltonian. We considered a test set of molecular systems known as the Atomization Energy 6 (AE6) as well as Hydrogen lattices. Our results showed that the resource efficiency of OSE is strongly affected by the spatial locality of the underlying single-particle basis. We find that OSE is outperformed by JW when the orbitals in the underlying single-particle basis are highly overlapping, which limits its applicability to near-term quantum chemistry simulations utilizing standard basis sets. In contrast, when orbitals are overlapping with only few others, as is the case of Hydrogen lattices with very tight orbitals, OSE fares comparatively better. Our results illustrate the importance of choosing the right combination of basis sets and fermion to qubit mapping to get the most out of a quantum device when simulating physical systems.
Dark and luminous satellites of LMC-mass galaxies in the FIRE simulations

Within lambda cold dark matter ($\Lambda$CDM), dwarf galaxies like the Large Magellanic Cloud (LMC) are expected to host numerous dark matter subhaloes, several of which should host faint dwarf companions. Recent Gaia proper motions confirm new members of the LMC system in addition to the previously known SMC, including two classical dwarf galaxies ($M_\ast$$\gt 10^5$ M$_{\odot }$; Carina and Fornax) as well as several ultrafaint dwarfs (Car2, Car3, Hor1, and Hyd1). We use the Feedback In Realistic Environments (FIRE) simulations to study the dark and luminous (down to ultrafaint masses, $M_\ast$$\sim$6$\times 10^ {3}$ M$_{\odot }$) substructure population of isolated LMC-mass hosts ($M_{\text{200m}}$ = 1–3$\times 10^ {11}$ M$_{\odot }$) and place the Gaia  + DES results in a cosmological context. By comparing number counts of subhaloes in simulations with and without baryons, we find that, within 0.2 $r_{\text{200m}}$, LMC-mass hosts deplete $\sim$30 per cent of their substructure, significantly lower than the $\sim$70 per cent of substructure depleted by Milky Way (MW) mass hosts. For our highest resolution runs ($m_\text{bary}$  = 880 M$_{\odot }$), $\sim 5\!-\!10$ subhaloes form galaxies with $M_\ast$$\ge 10^{4}$ M$_{\odot }$ , in agreement with the seven observationally inferred pre-infall LMC companions. However, we find steeper simulated luminosity functions than observed, hinting at observation incompleteness at the faint end. The predicted DM content for classical satellites in FIRE agrees with observed estimates for Carina and Fornax, supporting the case for an LMC association. We predict that tidal stripping within the LMC potential lowers the inner dark matter density of ultrafaint companions of the LMC. Thus, in addition to their orbital consistency, the low densities of dwarfs Car2, Hyd1, and Hyd2 reinforce their likelihood of Magellanic association.
Simulating galaxy formation in f(R) modified gravity: matter, halo, and galaxy statistics

We present an analysis of the matter, halo, and galaxy clustering in f(R)-gravity employing the SHYBONE full-physics hydrodynamical simulation suite. Our analysis focuses on the interplay between baryonic feedback and f(R)-gravity in the matter power spectrum, the matter and halo correlation functions, the halo and galaxy–host–halo mass function, the subhalo and satellite–galaxy count, and the correlation function of the stars in our simulations. Our studies of the matter power spectrum in full-physics simulations in f(R)-gravity show that it will be very difficult to derive accurate fitting formulae for the power spectrum enhancement in f(R)-gravity which include baryonic effects. We find that the enhancement of the halo mass function due to f(R)-gravity and its suppression due to feedback effects do not show significant back-reaction effects and can thus be estimated from independent general relativity-hydro and f(R) dark matter only simulations. Our simulations furthermore show that the number of subhaloes and satellite-galaxies per halo is not significantly affected by f(R)-gravity. Low-mass haloes are nevertheless more likely to be populated by galaxies in f(R)-gravity. This suppresses the clustering of stars and the galaxy correlation function in the theory compared to standard cosmology.
High-redshift test of gravity using enhanced growth of small structures probed by the neutral hydrogen distribution

Future 21-cm intensity mapping surveys such as SKA can provide precise information on the spatial 
distribution of the neutral hydrogen (HI) in the postreionization epoch. This information will allow us to 
test the standard Λ cold dark matter paradigm and with that the nature of gravity. In this work, we employ 
the SHYBONE simulations, which model galaxy formation in fðRÞ modified gravity using the IllustrisTNG 
model, to study the effects of modified gravity on HI abundance and power spectra. We find that the 
enhanced growth low-mass dark matter halos experience in fðRÞ gravity at high redshifts alters the HI 
power spectrum and can be observable through 21-cm intensity mapping. Our results suggest that the HI 
power spectrum is suppressed by ∼13% on scales k ≲ 2h Mpc−1 at z ¼ 2 for F6, a fðRÞ model which 
passes most observational constraints. We show that this suppression can be detectable by SKA1-MID with 
1000 hours of exposure time, making HI clustering a novel test of gravity at high redshift.