nils-herrmann/clean_scirepeval huggingface dataset:
How sound are our ultralight axion approximations?

Ultra-light axions (ULAs) are a promising dark-matter candidate. ULAs may have implications for small-scale challenges to the $\Lambda$CDM model, and arise in string scenarios. ULAs are already constrained by cosmic microwave background (CMB) experiments and large-scale structure surveys, and will be probed with much greater sensitivity by future efforts. It is challenging to compute observables in ULA scenarios with sufficient speed and accuracy for cosmological data analysis because the ULA field oscillates rapidly. In past work, an effective fluid approximation has been used to make these computations feasible. Here this approximation is tested against an exact solution of the ULA equations, comparing the induced error of CMB observables with the sensitivity of current and future experiments. In the most constrained mass range for a ULA dark matter component ($10^{-27}~{\rm eV}\leq m_{\rm ax}\leq 10^{-25}~{\rm eV}$), the induced bias on the allowed ULA fraction of dark matter from Planck data is less than $1\sigma$. In the cosmic-variance limit (including temperature and polarization data), the bias is $\lesssim 2\sigma$ for primary CMB anisotropies, with more severe biases (as high as $\sim 4\sigma$) resulting for less reliable versions of the effective fluid approximation. If all of the standard cosmological parameters are fixed by other measurements, the expected bias rises to $4-20\sigma$ (well beyond the validity of the Fisher approximation), though the required level of degeneracy breaking will not be achieved by any planned surveys.
Introduction to Renormalisation

These are introductory lecture notes aimed at beginning graduate students covering fundamental concepts and ideas behind the renormalisation group. Our main goal is to motivate it and then explore its consequences, in the context of quantum field theory. We shall reproduce the standard results without needing any mention of cancellation of infinities. To keep the story simple and compact, we'll ignore the role of symmetry in this game. We'll also restrict to scalar fields, and not mention neither fermions nor gauge fields. Moreover, we'll only briefly mention the connection with statistical field theory. 
Knowledge of quantum mechanics and special relativity is essential. Prior knowledge of quantum field theory is not required, however, this is not designed to be an introduction to QFT, so it may not be the most pedagogical way to introduce the topic. Previous familiarity with path integrals is also not assumed.
Three-dimensional structure of the Sagittarius dwarf spheroidal core from RR Lyrae

We obtain distances to a sample of RR Lyrae in the central core of the Sagittarius dwarf spheroidal galaxy from OGLE data. We use these distances, along with RR Lyrae from \emph{Gaia} DR2, to measure the shape of the stellar distribution within the central $\sim$ 2 kpc. The best-fit stellar distribution is triaxial, with axis ratios 1 : 0.76 : 0.43. A prolate spheroid model is ruled out at high statistical significance relative to the triaxial model. The major axis is aligned nearly parallel to the sky plane as seen by an Earth-based observer and is nearly perpendicular to the direction of the Galactic center. This result may be compared to cosmological simulations which generally predict that the major axis of the dark matter distribution of subhalos is aligned with the Galactic center. The triaxial structure that we obtain can provide important constraints on the Sagittarius progenitor, as well as the central dark matter distribution under the assumption of dynamical equilibrium.
Cross-correlating 21 cm and galaxy surveys: implications for cosmology and astrophysics

We forecast astrophysical and cosmological parameter constraints from synergies between 21 cm intensity mapping and wide field optical galaxy surveys (both spectroscopic and photometric) over $z \sim 0-3$. We focus on the following survey combinations in this work: (i) a CHIME-like and DESI-like survey in the northern hemisphere, (ii) an LSST-like and SKA I MID-like survey and (ii) a MeerKAT-like and DES-like survey in the southern hemisphere. We work with the $\Lambda$CDM cosmological model having parameters $\{h, \Omega_m, n_s, \Omega_b, \sigma_8\}$, parameters $v_{c,0}$ and $\beta$ representing the cutoff and slope of the HI-halo mass relation in the previously developed HI halo model framework, and a parameter $Q$ that represents the scale dependence of the optical galaxy bias. Using a Fisher forecasting framework, we explore (i) the effects of the HI and galaxy astrophysical uncertainties on the cosmological parameter constraints, assuming priors from the present knowledge of the astrophysics, (ii) the improvements on astrophysical constraints over their current priors in the three configurations considered, (ii) the tightening of the constraints on the parameters relative to the corresponding HI auto-correlation surveys alone.
Trans-Planckian censorship and inflationary cosmology

We study the implications of the recently proposed Trans-Planckian Censorship Conjecture (TCC) for early universe cosmology and in particular inflationary cosmology. The TCC leads to the conclusion that if we want inflationary cosmology to provide a successful scenario for cosmological structure formation, the energy scale of inflation has to be lower than $10^9$ GeV. Demanding the correct amplitude of the cosmological perturbations then forces the generalized slow-roll parameter $\epsilon$ of the model to be very small ($<10^{-31}$). This leads to the prediction of a negligible amplitude of primordial gravitational waves. For slow-roll inflation models, it also leads to severe fine tuning of initial conditions.
Churn Prediction with Sequential Data and Deep Neural Networks. A Comparative Analysis

Off-the-shelf machine learning algorithms for prediction such as regularized logistic regression cannot exploit the information of time-varying features without previously using an aggregation procedure of such sequential data. However, recurrent neural networks provide an alternative approach by which time-varying features can be readily used for modeling. This paper assesses the performance of neural networks for churn modeling using recency, frequency, and monetary value data from a financial services provider. Results show that RFM variables in combination with LSTM neural networks have larger top-decile lift and expected maximum profit metrics than regularized logistic regression models with commonly-used demographic variables. Moreover, we show that using the fitted probabilities from the LSTM as feature in the logistic regression increases the out-of-sample performance of the latter by 25 percent compared to a model with only static features.
(Nearly) Sample-Optimal Sparse Fourier Transform in Any Dimension; RIPless and Filterless

In this paper, we consider the extensively studied problem of computing a k-sparse approximation to the d-dimensional Fourier transform of a length n signal. Our algorithm uses O(k log k log n) samples, is dimension-free, operates for any universe size, and achieves the strongest ℓ_∞/ℓ_2 guarantee, while running in a time comparable to the Fast Fourier Transform. In contrast to previous algorithms which proceed either via the Restricted Isometry Property or via filter functions, our approach offers a fresh perspective to the sparse Fourier Transform problem.
Supervised Vector Quantized Variational Autoencoder for Learning Interpretable Global Representations

Learning interpretable representations of data remains a central challenge in deep learning. When training a deep generative model, the observed data are often associated with certain categorical labels, and, in parallel with learning to regenerate data and simulate new data, learning an interpretable representation of each class of data is also a process of acquiring knowledge. Here, we present a novel generative model, referred to as the Supervised Vector Quantized Variational AutoEncoder (S-VQ-VAE), which combines the power of supervised and unsupervised learning to obtain a unique, interpretable global representation for each class of data. Compared with conventional generative models, our model has three key advantages: first, it is an integrative model that can simultaneously learn a feature representation for individual data point and a global representation for each class of data; second, the learning of global representations with embedding codes is guided by supervised information, which clearly defines the interpretation of each code; and third, the global representations capture crucial characteristics of different classes, which reveal similarity and differences of statistical structures underlying different groups of data. We evaluated the utility of S-VQ-VAE on a machine learning benchmark dataset, the MNIST dataset, and on gene expression data from the Library of Integrated Network-Based Cellular Signatures (LINCS). We proved that S-VQ-VAE was able to learn the global genetic characteristics of samples perturbed by the same class of perturbagen (PCL), and further revealed the mechanism correlations between PCLs. Such knowledge is crucial for promoting new drug development for complex diseases like cancer.
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in Clutter

Recent research demonstrated that it is feasible to end-to-end train multi-task deep visuomotor policies for robotic manipulation using variations of learning from demonstration (LfD) and reinforcement learning (RL). In this paper, we extend the capabilities of end-to-end LfD architectures to object manipulation in clutter. We start by introducing a data augmentation procedure called Accept Synthetic Objects as Real (ASOR). Using ASOR we develop two network architectures: implicit attention ASOR-IA and explicit attention ASOR-EA. Both architectures use the same training data (demonstrations in uncluttered environments) as previous approaches. Experimental results show that ASOR-IA and ASOR-EA succeed in a significant fraction of trials in cluttered environments where previous approaches never succeed. In addition, we find that both ASOR-IA and ASOR-EA outperform previous approaches even in uncluttered environments, with ASOR-EA performing better even in clutter compared to the previous best baseline in an uncluttered environment.
Computational Chemistry on Quantum Computers

The purpose of this experiment was to use the known analytical techniques to study the creation, simulation, and measurements of molecular Hamiltonians. The techniques used consisted of the Linear Combination of Atomic Orbitals (LCAO), the Linear Combination of Unitaries (LCU), and the Phase Estimation Algorithm (PEA). The molecules studied were $H_2$ with and without spin, as well as $He_2$ without spin. Hamiltonians were created under the LCAO basis, and reconstructed using the Jordan-Winger transform in order to create a linear combination of Pauli spin operators. The lengths of each molecular Hamiltonian greatly increased from the $H_2$ without spin, to $He_2$. This resulted in a reduced ability to simulate the Hamiltonians under ideal conditions. Thus, only low orders of l = 1 and l = 2 were used when expanding the Hamiltonian in accordance to the LCU method of simulation. The resulting Hamiltonians were measured using PEA, and plotted against function of $\frac{2\pi(K)}{N}$ and the probability distribution of each register. The resolution of the graph was dependent on the amount of registers, N, being used. However, the reduction of order hardly changed the image of the $H_2$ graphs. Qualitative comparisons between the three molecules were drawn.
Exascale Deep Learning for Scientific Inverse Problems

We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grouping of gradient tensors. These new techniques produce an optimal overlap between computation and communication and result in near-linear scaling (0.93) of distributed training up to 27,600 NVIDIA V100 GPUs on the Summit Supercomputer. We demonstrate our gradient reduction techniques in the context of training a Fully Convolutional Neural Network to approximate the solution of a longstanding scientific inverse problem in materials imaging. The efficient distributed training on a dataset size of 0.5 PB, produces a model capable of an atomically-accurate reconstruction of materials, and in the process reaching a peak performance of 2.15(4) EFLOPS$_{16}$.
Social contagion models on hypergraphs

Our understanding of the dynamics of complex networked systems has increased significantly in the last two decades. However, most of our knowledge is built upon assuming pairwise relations among the system's components. This is often an oversimplification, for instance, in social interactions that occur frequently within groups. To overcome this limitation, here we study the dynamics of social contagion on hypergraphs. We develop an analytical framework and provide numerical results for arbitrary hypergraphs, which we also support with Monte Carlo simulations. Our analyses show that the model has a vast parameter space, with first- and second-order transitions, bistability, and hysteresis. Phenomenologically, we also extend the concept of latent heat to social contexts, which might help understanding oscillatory social behaviors. Our work unfolds the research line of higher-order models and the analytical treatment of hypergraphs, posing new questions and paving the way for modeling dynamical processes on higher-order structures.
Anchor Loss: Modulating Loss Scale Based on Prediction Difficulty

We propose a novel loss function that dynamically re-scales the cross entropy based on prediction difficulty regarding a sample. Deep neural network architectures in image classification tasks struggle to disambiguate visually similar objects. Likewise, in human pose estimation symmetric body parts often confuse the network with assigning indiscriminative scores to them. This is due to the output prediction, in which only the highest confidence label is selected without taking into consideration a measure of uncertainty. In this work, we define the prediction difficulty as a relative property coming from the confidence score gap between positive and negative labels. More precisely, the proposed loss function penalizes the network to avoid the score of a false prediction being significant. To demonstrate the efficacy of our loss function, we evaluate it on two different domains: image classification and human pose estimation. We find improvements in both applications by achieving higher accuracy compared to the baseline methods.
Selecting a scale for spatial confounding adjustment

Unmeasured, spatially structured factors can confound associations between spatial environmental exposures and health outcomes. Adding flexible splines to a regression model is a simple approach for spatial confounding adjustment, but the spline degrees of freedom do not provide an easily interpretable spatial scale. We describe a method for quantifying the extent of spatial confounding adjustment in terms of the Euclidean distance at which variation is removed. We develop this approach for confounding adjustment with splines and using Fourier and wavelet filtering. We demonstrate differences in the spatial scales that these bases can represent and provide a comparison of methods for selecting the amount of confounding adjustment. We find the best performance for selecting the amount of adjustment by using an information criterion evaluated on an outcome model without exposure. We apply this method to spatial adjustment in an analysis of fine particulate matter and blood pressure in a cohort of US women.
ProvMark: A Provenance Expressiveness Benchmarking System

System level provenance is of widespread interest for applications such as security enforcement and information protection. However, testing the correctness or completeness of provenance capture tools is challenging and currently done manually. In some cases there is not even a clear consensus about what behavior is correct. We present an automated tool, ProvMark, that uses an existing provenance system as a black box and reliably identifies the provenance graph structure recorded for a given activity, by a reduction to subgraph isomorphism problems handled by an external solver. ProvMark is a beginning step in the much needed area of testing and comparing the expressiveness of provenance systems. We demonstrate ProvMark's usefuless in comparing three capture systems with different architectures and distinct design philosophies.
Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters

Encoding the scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many vision tasks especially when dealing with multiscale inputs. We study, in this paper, a scaling-translationequivariant (ST -equivariant) CNN with joint convolutions across the space and the scaling group, which is shown to be both sufficient and necessary to achieve ST equivariant representations. To reduce the model complexity and computational burden, we decompose the convolutional filters under two pre-fixed separable bases and truncate the expansion to low-frequency components. A further benefit of the truncated filter expansion is the improved deformation robustness of the equivariant representation. Numerical experiments demonstrate that the proposed scaling-translation-equivariant networks with decomposed convolutional filters (ScDCFNet) achieves significantly improved performance in multiscale image classification and better interpretability than regular CNNs at a reduced model size.
Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting

Traffic forecasting approaches are critical to developing adaptive strategies for mobility. Traffic patterns have complex spatial and temporal dependencies that make accurate forecasting on large highway networks a challenging task. Recently, diffusion convolutional recurrent neural networks (DCRNNs) have achieved state-of-the-art results in traffic forecasting by capturing the spatiotemporal dynamics of the traffic. Despite the promising results, however, applying DCRNNs for large highway networks still remains elusive because of computational and memory bottlenecks. This paper presents an approach for implementing a DCRNN for a large highway network that overcomes these limitations. This approach uses a graph-partitioning method to decompose a large highway network into smaller networks and trains them independently. The efficacy of the graph-partitioning-based DCRNN approach to model the traffic on a large California highway network with 11,160 sensor locations is demonstrated. An overlapping-nodes approach for the graph-partitioning-based DCRNN is developed to include sensor locations from partitions that are geographically close to a given partition. Furthermore, it is demonstrated that the DCRNN model can be used to forecast the speed and flow simultaneously and that the forecasted values preserve fundamental traffic flow dynamics. This approach to developing DCRNN models that represent large highway networks can be a potential core capability in advanced highway traffic monitoring systems, where a trained DCRNN model forecasting traffic at all sensor locations can be used to adjust traffic management strategies proactively based on anticipated future conditions.
Improving Robustness In Speaker Identification Using A Two-Stage Attention Model

In this paper a novel framework to tackle speaker recognition using a two-stage attention model is proposed. In recent years, the use of deep neural networks, such as time delay neural network (TDNN), and attention model have boosted speaker recognition performance. However, it is still a challenging task to tackle speaker recognition in severe acoustic environments. To build a robust speaker recognition system against noise, we employ a two-stage attention model and combine it with a TDNN model. In this framework, the attention mechanism is used in two aspects: embedding space and temporal space. The embedding attention model built in embedding space is to highlight the importance of each embedding element by weighting them using self attention. The frame attention model built in temporal space aims to find which frames are significant for speaker recognition. To evaluate the effectiveness and robustness of our approach, we use the TIMIT dataset and test our approach in the condition of five kinds of noise and different signal-noise-ratios (SNRs). In comparison with three strong baselines, CNN, TDNN and TDNN+attention, the experimental results show that the use of our approach outperforms them in different conditions. The correct recognition rate obtained using our approach can still reach 49.1%, better than any baselines, even if the noise is Gaussian white Noise and the SNR is 0dB.
A Visual Analytics Framework for Adversarial Text Generation

This paper presents a framework which enables a user to more easily make corrections to adversarial texts. While attack algorithms have been demonstrated to automatically build adversaries, changes made by the algorithms can often have poor semantics or syntax. Our framework is designed to facilitate human intervention by aiding users in making corrections. The framework extends existing attack algorithms to work within an evolutionary attack process paired with a visual analytics loop. Using an interactive dashboard a user is able to review the generation process in real time and receive suggestions from the system for edits to be made. The adversaries can be used to both diagnose robustness issues within a single classifier or to compare various classifier options. With the weaknesses identified, the framework can also be used as a first step in mitigating adversarial threats. The framework can be used as part of further research into defense methods in which the adversarial examples are used to evaluate new countermeasures. We demonstrate the framework with a word swapping attack for the task of sentiment classification.
Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping

Random feature mapping (RFM) is a popular method for speeding up kernel methods at the cost of losing a little accuracy. We study kernel ridge regression with random feature mapping (RFM-KRR) and establish novel out-of-sample error upper and lower bounds. While out-of-sample bounds for RFM-KRR have been established by prior work, this paper's theories are highly interesting for two reasons. On the one hand, our theories are based on weak and valid assumptions. In contrast, the existing theories are based on various uncheckable assumptions, which makes it unclear whether their bounds are the nature of RFM-KRR or simply the consequence of strong assumptions. On the other hand, our analysis is completely based on elementary linear algebra and thereby easy to read and verify. Finally, our experiments lend empirical supports to the theories.
Gene ologs: a categorical framework for Gene Ontology

Gene Ontology (GO) is the most important resource for gene function annotation. It provides a way to unify biological knowledge across different species via a dynamic and controlled vocabulary. GO is now widely represented in the Semantic Web standard Web Ontology Language (OWL). OWL renders a rich logic constructs to GO but also has its limitations. On the other hand, olog is a different language for ontology and it is based on category theory. Due to its solid mathematical background, olog can be rigorously formulated yet considerably expressive. These advantages make ologs complementary, if not better than, OWL. We therefore think it worthwhile to adopt ologs for GO and took an initial step in this work.