nils-herrmann/clean_scirepeval huggingface dataset:
A Dynamical Study of Extraplanar Diffuse Ionized Gas in NGC 5775

The structure and kinematics of gaseous, disk–halo interfaces are imprinted with the processes that transfer mass, metals, and energy between galactic disks and their environments. We study the extraplanar diffuse ionized gas (eDIG) layer in the interacting, star-forming galaxy NGC 5775 to better understand the consequences of star formation feedback on the dynamical state of the thick-disk interstellar medium. Combining emission-line spectroscopy from the Robert Stobie Spectrograph on the Southern African Large Telescope with radio continuum observations from Continuum Halos in Nearby Galaxies—an EVLA Survey, we ask whether thermal, turbulent, magnetic field, and cosmic-ray pressure gradients can stably support the eDIG layer in dynamical equilibrium. This model fails to reproduce the observed exponential electron scale heights of the eDIG thick disk and halo on the northeast ( kpc) and southwest ( kpc) sides of the galaxy at R < 11 kpc. We report the first definitive detection of an increasing eDIG velocity dispersion as a function of height above the disk. Blueshifted gas along the minor axis at large distances from the midplane hints at a disk–halo circulation and/or ram pressure effects caused by the ongoing interaction with NGC 5774. This work motivates further integral field unit and/or Fabry–Perot spectroscopy of galaxies with a range of star formation rates to develop a spatially resolved understanding of the role of star formation feedback in shaping the kinematics of the disk–halo interface.
The low density and magnetization of a massive galaxy halo exposed by a fast radio burst

Probing a galaxy halo with a radio burst Fast radio bursts (FRBs) are millisecond flashes of radio emission from distant galaxies. It has only recently become possible to locate single bursts precisely enough to determine the host galaxy. Prochaska et al. have observed and localized a FRB using a radio interferometer. The line of sight to the host galaxy coincidentally passes through the outskirts of a closer foreground galaxy. By analyzing the propagation of the FRB, the authors put constraints on the density and magnetization of gas in the outskirts of the foreground galaxy. The technique provides complementary information to existing methods using background quasars. Science, this issue p. 231 A fast radio burst passing through the outskirts of a foreground galaxy constrains the gas density and magnetization. Present-day galaxies are surrounded by cool and enriched halo gas extending for hundreds of kiloparsecs. This halo gas is thought to be the dominant reservoir of material available to fuel future star formation, but direct constraints on its mass and physical properties have been difficult to obtain. We report the detection of a fast radio burst (FRB 181112), localized with arcsecond precision, that passes through the halo of a foreground galaxy. Analysis of the burst shows that the halo gas has low net magnetization and turbulence. Our results imply predominantly diffuse gas in massive galactic halos, even those hosting active supermassive black holes, contrary to some previous results.
Terrestrial and martian heat flow limits on dark matter

If dark matter is efficiently captured by a planet, energy released in its annihilation can exceed that planet's total heat output. Building on prior work, we treat Earth's composition and dark matter capture in detail and present improved limits on dark matter-nucleon scattering cross sections for dark matter masses ranging from 0.1 to $10^{10}$ GeV. We also extend Earth limits by applying the same treatment to Mars. The scope of dark matter models considered is expanded to include spin-dependent nuclear interactions including isospin-independent, proton only, and neutron only interactions. We find that Earth and Mars heating bounds are alleviated for dark matter s-wave self-annihilation cross sections $\lesssim 10^{-44}~{\rm cm^2}$.
Revisiting (s)neutrino dark matter in natural SUSY

We study natural supersymmetric scenarios with light right-handed neutrino superfields, and consider the possibility of having either a neutrino or a sneutrino as a dark matter candidate. For the former, we evaluate the possibility of having SUSY corrections on the $\nu_4\to\nu_\ell\gamma$ decay rate, such that the NuStar bounds are relaxed. We find that corrections are too small. For sneutrino dark matter, we consider thermal and non-thermal production, taking into account freeze-out, freeze-in and super-WIMP mechanisms. For the non-thermal case, we find that the $\tilde\nu_R$ can reproduce the observed relic density by adjusting the R-sneutrino mass and Yukawa couplings. For the thermal case, we find the need to extend the model in order to enhance sneutrino annihilations, which we exemplify in a model with an extended gauge symmetry.
Extreme Language Model Compression with Optimal Subwords and Shared Projections

Pre-trained deep neural network language models such as ELMo, GPT, BERT and XLNet have recently achieved state-of-the-art performance on a variety of language understanding tasks. However, their size makes them impractical for a number of scenarios, especially on mobile and edge devices. In particular, the input word embedding matrix accounts for a significant proportion of the model's memory footprint, due to the large input vocabulary and embedding dimensions. Knowledge distillation techniques have had success at compressing large neural network models, but they are ineffective at yielding student models with vocabularies different from the original teacher models. We introduce a novel knowledge distillation technique for training a student model with a significantly smaller vocabulary as well as lower embedding and hidden state dimensions. Specifically, we employ a dual-training mechanism that trains the teacher and student models simultaneously to obtain optimal word embeddings for the student vocabulary. We combine this approach with learning shared projection matrices that transfer layer-wise knowledge from the teacher model to the student model. Our method is able to compress the BERT_BASE model by more than 60x, with only a minor drop in downstream task metrics, resulting in a language model with a footprint of under 7MB. Experimental results also demonstrate higher compression efficiency and accuracy when compared with other state-of-the-art compression techniques.
On the Expressiveness of LARA: A Unified Language for Linear and Relational Algebra

We study the expressive power of the LARA language -- a recently proposed unified model for expressing relational and linear algebra operations -- both in terms of traditional database query languages and some analytic tasks often performed in machine learning pipelines. We start by showing LARA to be expressive complete with respect to first-order logic with aggregation. Since LARA is parameterized by a set of user-defined functions which allow to transform values in tables, the exact expressive power of the language depends on how these functions are defined. We distinguish two main cases depending on the level of genericity queries are enforced to satisfy. Under strong genericity assumptions the language cannot express matrix convolution, a very important operation in current machine learning operations. This language is also local, and thus cannot express operations such as matrix inverse that exhibit a recursive behavior. For expressing convolution, one can relax the genericity requirement by adding an underlying linear order on the domain. This, however, destroys locality and turns the expressive power of the language much more difficult to understand. In particular, although under complexity assumptions the resulting language can still not express matrix inverse, a proof of this fact without such assumptions seems challenging to obtain.
Stochastic Prototype Embeddings

Supervised deep-embedding methods project inputs of a domain to a representational space in which same-class instances lie near one another and different-class instances lie far apart. We propose a probabilistic method that treats embeddings as random variables. Extending a state-of-the-art deterministic method, Prototypical Networks (Snell et al., 2017), our approach supposes the existence of a class prototype around which class instances are Gaussian distributed. The prototype posterior is a product distribution over labeled instances, and query instances are classified by marginalizing relative prototype proximity over embedding uncertainty. We describe an efficient sampler for approximate inference that allows us to train the model at roughly the same space and time cost as its deterministic sibling. Incorporating uncertainty improves performance on few-shot learning and gracefully handles label noise and out-of-distribution inputs. Compared to the state-of-the-art stochastic method, Hedged Instance Embeddings (Oh et al., 2019), we achieve superior large- and open-set classification accuracy. Our method also aligns class-discriminating features with the axes of the embedding space, yielding an interpretable, disentangled representation.
Three-body closed chain of interactive (an)harmonic oscillators and the algebra sl(4)

In this work we study 2- and 3-body oscillators with quadratic and sextic pairwise potentials which depend on relative distances, , between particles. The two-body harmonic oscillator is two-parametric and can be reduced to a one-dimensional radial Jacobi oscillator with hidden algebra , while in the 3-body case such a reduction is not possible in general. Our study is restricted to solutions in the space of relative motion which are functions of mutual (relative) distances only (S-states). We pay special attention to the cases where all masses of the particles and spring constants are unequal as well as to the atomic, where one mass is infinite, and molecular, where two masses are infinite, limits; it is a non-integrable system. In general, a three-body harmonic oscillator is 7-parametric depending on three masses and three spring constants, and frequency; it is an exactly-solvable problem with spectra linear in three quantum numbers and with hidden algebra . In particular, the first and second order integrals of the 3-body oscillator for unequal masses are searched: it is shown that for certain relations involving masses and spring constants the system becomes maximally (minimally) superintegrable in the case of two (one) relations.
Strategic reciprocity improves academic performance in public elementary school children

Social networks are pivotal for learning. Yet, we still lack a full understanding of the mechanisms connecting networks with learning outcomes. Here, we present the results of a large scale study (946 elementary school children from 45 different classrooms) designed to understand the social strategies used by elementary school children. We mapped the social networks of students using both, a non-anonymous version of a prisoner's dilemma and a survey of nominated friendships, and compared the strategies played by students with their GPAs. We found that higher GPA students invest more strategically in their relationships, cooperating more generously with friends and less generously with non-friends than lower GPA students. Our findings suggest that the higher selectivity of social capital investments by high performing students may be one of the mechanisms helping them reap the learning benefits of their social networks.
GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning

We present GraphMix, a regularization technique for Graph Neural Network based semi-supervised object classification, leveraging the recent advances in the regularization of classical deep neural networks. Specifically, we propose a unified approach in which we train a fully-connected network jointly with the graph neural network via parameter sharing, interpolation-based regularization, and self-predicted-targets. Our proposed method is architecture agnostic in the sense that it can be applied to any variant of graph neural networks which applies a parametric transformation to the features of the graph nodes. Despite its simplicity, with GraphMix we can consistently improve results and achieve or closely match state-of-the-art performance using even simpler architectures such as Graph Convolutional Networks, across three established graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as three newly proposed datasets : Cora-Full, Co-author-CS and Co-author-Physics.
Benefit of Interpolation in Nearest Neighbor Algorithms

The over-parameterized models attract much attention in the era of data science and deep learning. It is empirically observed that although these models, e.g. deep neural networks, over-fit the training data, they can still achieve small testing error, and sometimes even {\em outperform} traditional algorithms which are designed to avoid over-fitting. The major goal of this work is to sharply quantify the benefit of data interpolation in the context of nearest neighbors (NN) algorithm. Specifically, we consider a class of interpolated weighting schemes and then carefully characterize their asymptotic performances. Our analysis reveals a U-shaped performance curve with respect to the level of data interpolation, and proves that a mild degree of data interpolation {\em strictly} improves the prediction accuracy and statistical stability over those of the (un-interpolated) optimal $k$NN algorithm. This theoretically justifies (predicts) the existence of the second U-shaped curve in the recently discovered double descent phenomenon. Note that our goal in this study is not to promote the use of interpolated-NN method, but to obtain theoretical insights on data interpolation inspired by the aforementioned phenomenon.
Deep-learning-based Breast CT for Radiation Dose Reduction

Cone-beam breast computed tomography (CT) provides true 3D breast images with isotropic resolution and high-contrast information, detecting calcifications as small as a few hundred microns and revealing subtle tissue differences. However, breast is highly sensitive to x-ray radiation. It is critically important for healthcare to reduce radiation dose. Few-view cone-beam CT only uses a fraction of x-ray projection data acquired by standard cone-beam breast CT, enabling significant reduction of the radiation dose. However, insufficient sampling data would cause severe streak artifacts in CT images reconstructed using conventional methods. In this study, we propose a deep-learning-based method to establish a residual neural network model for the image reconstruction, which is applied for few-view breast CT to produce high quality breast CT images. We respectively evaluate the deep-learning-based image reconstruction using one third and one quarter of x-ray projection views of the standard cone-beam breast CT. Based on clinical breast imaging dataset, we perform a supervised learning to train the neural network from few-view CT images to corresponding full-view CT images. Experimental results show that the deep learning-based image reconstruction method allows few-view breast CT to achieve a radiation dose <6 mGy per cone-beam CT scan, which is a threshold set by FDA for mammographic screening.
A Theoretical Analysis of the Number of Shots in Few-Shot Learning

Few-shot classification is the task of predicting the category of an example from a set of few labeled examples. The number of labeled examples per category is called the number of shots (or shot number). Recent works tackle this task through meta-learning, where a meta-learner extracts information from observed tasks during meta-training to quickly adapt to new tasks during meta-testing. In this formulation, the number of shots exploited during meta-training has an impact on the recognition performance at meta-test time. Generally, the shot number used in meta-training should match the one used in meta-testing to obtain the best performance. We introduce a theoretical analysis of the impact of the shot number on Prototypical Networks, a state-of-the-art few-shot classification method. From our analysis, we propose a simple method that is robust to the choice of shot number used during meta-training, which is a crucial hyperparameter. The performance of our model trained for an arbitrary meta-training shot number shows great performance for different values of meta-testing shot numbers. We experimentally demonstrate our approach on different few-shot classification benchmarks.
Revisit Knowledge Distillation: a Teacher-free Framework

Knowledge Distillation (KD) aims to distill the knowledge of a cumbersome teacher model into a lightweight student model. Its success is generally attributed to the privileged information on similarities among categories provided by the teacher model, and in this sense, only strong teacher models are deployed to teach weaker students in practice. In this work, we challenge this common belief by following experimental observations: 1) beyond the acknowledgment that the teacher can improve the student, the student can also enhance the teacher significantly by reversing the KD procedure; 2) a poorly-trained teacher with much lower accuracy than the student can still improve the latter significantly. To explain these observations, we provide a theoretical analysis of the relationships between KD and label smoothing regularization. We prove that 1) KD is a type of learned label smoothing regularization and 2) label smoothing regularization provides a virtual teacher model for KD. From these results, we argue that the success of KD is not fully due to the similarity information between categories, but also to the regularization of soft targets, which is equally or even more important. 
Based on these analyses, we further propose a novel Teacher-free Knowledge Distillation (Tf-KD) framework, where a student model learns from itself or manually-designed regularization distribution. The Tf-KD achieves comparable performance with normal KD from a superior teacher, which is well applied when teacher model is unavailable. Meanwhile, Tf-KD is generic and can be directly deployed for training deep neural networks. Without any extra computation cost, Tf-KD achieves up to 0.65\% improvement on ImageNet over well-established baseline models, which is superior to label smoothing regularization. The codes are in: \url{this https URL}
Disentangling Speech and Non-Speech Components for Building Robust Acoustic Models from Found Data

In order to build language technologies for majority of the languages, it is important to leverage the resources available in public domain on the internet - commonly referred to as `Found Data'. However, such data is characterized by the presence of non-standard, non-trivial variations. For instance, speech resources found on the internet have non-speech content, such as music. Therefore, speech recognition and speech synthesis models need to be robust to such variations. In this work, we present an analysis to show that it is important to disentangle the latent causal factors of variation in the original data to accomplish these tasks. Based on this, we present approaches to disentangle such variations from the data using Latent Stochastic Models. Specifically, we present a method to split the latent prior space into continuous representations of dominant speech modes present in the magnitude spectra of audio signals. We propose a completely unsupervised approach using multinode latent space variational autoencoders (VAE). We show that the constraints on the latent space of a VAE can be in-fact used to separate speech and music, independent of the language of the speech. This paper also analytically presents the requirement on the number of latent variables for the task based on distribution of the speech data.
Tilings of $(2\times2\times n)$-board with colored cubes and bricks

Several articles deal with tilings with squares and dominoes on 2-dimensional boards, but only a few on boards in 3-dimensional space. We examine a tiling problem with colored cubes and bricks of $(2\times2\times n)$-board in three dimensions. After a short introduction and the definition of breakability we show a way to get the number of the tilings of an $n$-long board considering the $(n-1)$-long board. It describes recursively the number of possible breakable and unbreakable tilings. Finally, we give some identities for the recursions using breakability. The method of determining the recursions in space can be useful in mathematical education as well.
"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks via Reward Shaping

In order to learn effectively, robots must be able to extract the intangible context by which task progress and mistakes are defined. In the domain of reinforcement learning, much of this information is provided by the reward function. Hence, reward shaping is a necessary part of how we can achieve state-of-the-art results on complex, multi-step tasks. However, comparatively little work has examined how reward shaping should be done so that it captures task context, particularly in scenarios where the task is long-horizon and failure is highly consequential. Our Schedule for Positive Task (SPOT) reward trains our Efficient Visual Task (EVT) model to solve problems that require an understanding of both task context and workspace constraints of multi-step block arrangement tasks. In simulation EVT can completely clear adversarial arrangements of objects by pushing and grasping in 99% of cases vs an 82% baseline in prior work. For random arrangements EVT clears 100% of test cases at 86% action efficiency vs 61% efficiency in prior work. EVT + SPOT is also able to demonstrate context understanding and complete stacks in 74% of trials compared to a baseline of 5% with EVT alone. To our knowledge, this is the first instance of a Reinforcement Learning based algorithm successfully completing such a challenge. Code is available at https://github.com/jhu-lcsr/good robot.
Globular Cluster Intrinsic Iron Abundance Spreads. I. Catalog

We present an up-to-date catalog of intrinsic iron abundance spreads in the 55 Milky Way globular clusters (GCs) for which sufficiently precise spectroscopic measurements are available. Our method combines multiple data sets when possible to improve the statistics, taking into account the fact that different methods and instruments can lead to systematically offset metallicities. Only high spectral resolution (R > 14,000) studies that measure the equivalent widths of individual iron lines are found to have uncertainties on the metallicities of the individual stars that can be calibrated sufficiently well for the intrinsic dispersion to be separated cleanly from a random measurement error. The median intrinsic iron spread is found to be 0.045 dex, which is small but unambiguously measured to be nonzero in most cases. There is large variation between clusters, but more luminous GCs, above 105 L⊙, have increasingly large iron spreads on average; no trend between the iron spread and metallicity is found.
Learning Pixel Representations for Generic Segmentation

Deep learning approaches to generic (non-semantic) segmentation have so far been indirect and relied on edge detection. This is in contrast to semantic segmentation, where DNNs are applied directly. We propose an alternative approach called Deep Generic Segmentation (DGS) and try to follow the path used for semantic segmentation. Our main contribution is a new method for learning a pixel-wise representation that reflects segment relatedness. This representation is combined with a CRF to yield the segmentation algorithm. We show that we are able to learn meaningful representations that improve segmentation quality and that the representations themselves achieve state-of-the-art segment similarity scores. The segmentation results are competitive and promising.