nils-herrmann/clean_scirepeval huggingface dataset:
Characterizing the nature of the unresolved point sources in the Galactic Center: An assessment of systematic uncertainties

The Galactic Center Excess (GCE) of GeV gamma rays can be explained as a signal of annihilating dark matter or of emission from unresolved astrophysical sources, such as millisecond pulsars. Evidence for the latter is provided by a statistical procedure---referred to as Non-Poissonian Template Fitting (NPTF)---that distinguishes the smooth distribution of photons expected for dark matter annihilation from a "clumpy" photon distribution expected for point sources. In this paper, we perform an extensive study of the NPTF on simulated data, exploring its ability to recover the flux and luminosity function of unresolved sources at the Galactic Center. When astrophysical background emission is perfectly modeled, we find that the NPTF successfully distinguishes between the dark matter and point source hypotheses when either component makes up the entirety of the GCE. When the GCE is a mixture of dark matter and point sources, the NPTF may fail to reconstruct the correct contribution of each component. We further study the impact of mismodeling the Galactic diffuse backgrounds, finding that while a dark matter signal could be attributed to point sources in some outlying cases for the scenarios we consider, the significance of a true point source signal remains robust. Our work enables us to comment on a recent study by Leane and Slatyer (2019) that questions prior NPTF conclusions because the method does not recover an artificial dark matter signal injected on actual Fermi data. We demonstrate that the failure of the NPTF to extract an artificial dark matter signal can be natural when point sources are present in the data---with the effect further exacerbated by the presence of diffuse mismodeling---and does not on its own invalidate the conclusions of the NPTF analysis in the Inner Galaxy.
Axions as a probe of solar metals

If axions or axion-like particles exist and are detected, they will not only extend the standard model of particle physics but will also open a new way to probe their sources. Axion helioscopes aim to detect axions which are produced in the core of the sun. Their spectrum contains information about the solar interior and could in principle help to solve the conflict between high and low metallicity solar models. Using the planned International Axion Observatory (IAXO) as an example, we show that helioscopes could measure the strength of characteristic emission peaks caused by the presence of heavier elements with good precision. In order to determine unambiguously the elemental abundances from this information, an improved modelling of the states of atoms inside the solar plasma is required.
Relation between the Migdal Effect and Dark Matter-Electron Scattering in Isolated Atoms and Semiconductors.

A key strategy for sub-GeV dark matter direct detection is searches for small ionization signals that arise from dark matter-electron scattering or from the "Migdal" effect in dark matter-nucleus scattering. We show that the theoretical description of both processes is closely related, allowing for a principal mapping between them. We explore this for noble-liquid targets and, for the first time, estimate the Migdal effect in semiconductors using a crystal form factor. We present new constraints using XENON10, XENON100, and SENSEI data, and give projections for proposed experiments.
Neutrino Emissions from Tidal Disruption Remnants

We study high-energy neutrino emissions from tidal disruption remnants (TDRs) around supermassive black holes. The neutrinos are produced by the decay of charged pions originating in ultrarelativistic protons that are accelerated there. In the standard theory of tidal disruption events (TDEs), there are four distinct phases from the debris circularization of stellar debris to super- and sub-Eddington to radiatively inefficient accretion flows (RIAFs). In addition, we consider the magnetically arrested disk (MAD) state in both the super-Eddington accretion and RIAF phases. We find that there are three promising cases to produce neutrino emissions: the super-Eddington accretion phase of the MAD state and the RIAF phases of both the non-MAD and MAD states. In the super-Eddington MAD state, the enhanced magnetic field makes it possible to accelerate the protons to with the other given appropriate parameters. The neutrino energy is then at the peak of the energy spectrum. For Mbh ≳ 107.7 M⊙, the neutrino light curve is proportional to , while it follows the standard decay rate for . In both cases, the large luminosity and characteristic light curves diagnose the super-Eddington MAD state in TDEs. In the RIAF phase of the non-MAD state, we find and , and its light curve is proportional to . This indicates that one can identify whether the existing RIAFs are the TDE origin or not. TDRs are potentially a population of hidden neutrino sources invisible in gamma-rays.
Time-varying Polarized Gamma-Rays from GRB 160821A: Evidence for Ordered Magnetic Fields

GRB 160821A is the third most energetic gamma-ray burst observed by the Fermi gamma-ray space telescope. Based on the observations made by the Cadmium Zinc Telluride Imager on board AstroSat, here we report the most conclusive evidence to date of (i) high linear polarization ( detection), and (ii) variation of polarization angle with time, occurring twice during the rise and decay phase of the burst at 3.5σ and 3.1σ detections, respectively. All confidence levels are reported for two parameters of interest. These observations strongly suggest synchrotron radiation produced in magnetic field lines that are highly ordered on angular scales of 1/Γ, where Γ is the Lorentz factor of the outflow.
An application of CNNs to time sequenced one dimensional data in radiation detection

A Convolutional Neural Network architecture was used to classify various isotopes of time-sequenced gamma-ray spectra, a typical output of a radiation detection system of a type commonly fielded for security or environmental measurement purposes. A two-dimensional surface (waterfall plot) in time-energy space is interpreted as a monochromatic image and standard image-based CNN techniques are applied. This allows for the time-sequenced aspects of features in the data to be discovered by the network, as opposed to standard algorithms which arbitrarily time bin the data to satisfy the intuition of a human spectroscopist. The CNN architecture and results are presented along with a comparison to conventional techniques. The results of this novel application of image processing techniques to radiation data will be presented along with a comparison to more conventional adaptive
The First Astrophysical Result of Hisaki: A Search for the EUV He Lines in a Massive Cool Core Cluster at z = 0.7

Molecular cold gas and star formation have been observed at centers of cool core clusters, albeit at a level much smaller than expected from the classic cooling model. Feedback from the supermassive black hole is likely to have prevented hot gas from cooling. However, the exact cooling and heating processes are poorly understood. The missing key piece is the link between the hot gas (107 K) and cold gas (103 K). Using the extreme ultraviolet spectrometer on board Hisaki, we explore a distant galaxy cluster, RCS2 J232727.6-020437, one of the most massive cool core clusters with a cooling rate of 400 M⊙ yr−1. We aim to detect gas at intermediate temperatures (3×104 K) emitting He iα and He iβ at rest wavelengths of 58.4 nm and 53.7 nm, respectively. Our target resides at z = 0.6986, for which these He i lines shift away from the absorption of the Galaxy. Our findings show that the amount of 104–5 K gas at the center of this cluster is smaller than expected if cooling there was uninhibited, which demonstrates that feedback both operates and is efficient for massive clusters at these epochs.
Analyzing Customer Feedback for Product Fit Prediction

One of the biggest hurdles for customers when purchasing fashion online, is the difficulty of finding products with the right fit. In order to provide a better online shopping experience, platforms need to find ways to recommend the right product sizes and the best fitting products to their customers. These recommendation systems, however, require customer feedback in order to estimate the most suitable sizing options. Such feedback is rare and often only available as natural text. In this paper, we examine the extraction of product fit feedback from customer reviews using natural language processing techniques. In particular, we compare traditional methods with more recent transfer learning techniques for text classification, and analyze their results. Our evaluation shows, that the transfer learning approach ULMFit is not only comparatively fast to train, but also achieves highest accuracy on this task. The integration of the extracted information with actual size recommendation systems is left for future work.
Linear Noise Approximation of Intensity-Driven Signal Transduction Channels

Biochemical signal transduction, a form of molecular communication, can be modeled using graphical Markov channels with input-modulated transition rates. Such channel models are strongly non-Gaussian. In this paper we use a linear noise approximation to construct a novel class of Gaussian additive white noise channels that capture essential features of fully- and partially-observed intensity- driven signal transduction. When channel state transitions that are sensitive to the input signal are directly observable, high-frequency information is transduced more efficiently than low-frequency information, and the mutual information rate per bandwidth (spectral efficiency) is significantly greater than when sensitive transitions and observable transitions are disjoint. When both observable and hidden transitions are input-sensitive, we observe a superadditive increase in spectral efficiency.
Higgs physics beyond the Standard Model

We give a brief review of beyond-the-Standard Model (BSM) extensions of the Standard Model (SM) Higgs sector. Going from very simple to more complicated models, our survey includes models with additional scalar singlet fields, the Two Higgs Doublet Model (2HDM) and the Higgs sector of the Minimal Supersymmetric Standard Model (MSSM). We discuss the impact of current experimental results from LHC Higgs searches and measurements and the prospective reach of the LHC in the high-luminosity (HL) phase. We furthermore highlight possible new collider signatures within these models that have not been experimentally probed to date.
DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides

The following lists two main reasons for withdrawal for the public. 1. There are some problems in the method and results, and there is a lot of room for improvement. In terms of method, "Pre-trained Datasets (PD)" represents selecting a small amount from the online test set, which easily causes the model to overfit the online test set and could not obtain robust performance. More importantly, the proposed DFPENet has a high redundancy by combining the Attention Gate Mechanism and Gate Convolution Networks, and we need to revisit the section of geological feature fusion, in terms of results, we need to further improve and refine. 2.  I then would use their comments to significantly improve this manuscript. Therefore, we finally decided to withdraw this manuscript in arXiv, and we will update to arXiv with the final accepted manuscript to facilitate more researchers to use our proposed comprehensive and general scheme to recognize and segment seismic landslides more efficiently.
Interactive Language Learning by Question Answering

Humans observe and interact with the world to acquire knowledge. However, most existing machine reading comprehension (MRC) tasks miss the interactive, information-seeking component of comprehension. Such tasks present models with static documents that contain all necessary information, usually concentrated in a single short substring. Thus, models can achieve strong performance through simple word- and phrase-based pattern matching. We address this problem by formulating a novel text-based question answering task: Question Answering with Interactive Text (QAit). In QAit, an agent must interact with a partially observable text-based environment to gather information required to answer questions. QAit poses questions about the existence, location, and attributes of objects found in the environment. The data is built using a text-based game generator that defines the underlying dynamics of interaction with the environment. We propose and evaluate a set of baseline models for the QAit task that includes deep reinforcement learning agents. Experiments show that the task presents a major challenge for machine reading systems, while humans solve it with relative ease.
SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension

A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS). However, an NLI that is trained in the general domain is hard to apply in the spatial domain due to the idiosyncrasy and expressiveness of the spatial questions. Inspired by the machine comprehension model, we propose a spatial comprehension model that is able to recognize the meaning of spatial entities based on the semantics of the context. The spatial semantics learned from the spatial comprehension model is then injected to the natural language question to ease the burden of capturing the spatial-specific semantics. With our spatial comprehension model and information injection, our NLI for the spatial domain, named SpatialNLI, is able to capture the semantic structure of the question and translate it to the corresponding syntax of an executable query accurately. We also experimentally ascertain that SpatialNLI outperforms state-of-the-art methods.
Combinatorics of the geometry of Wilson loop diagrams I: equivalence classes via matroids and polytopes

Wilson loop diagrams are an important tool in studying scattering amplitudes of SYM 
 
 
 
$N=4$

 
 theory and are known by previous work to be associated to positroids. We characterize the conditions under which two Wilson loop diagrams give the same positroid, prove that an important subclass of subdiagrams (exact subdiagrams) corresponds to uniform matroids, and enumerate the number of different Wilson loop diagrams that correspond to each positroid cell. We also give a correspondence between those positroids which can arise from Wilson loop diagrams and directions in associahedra.
Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective

Attempts from different disciplines to provide a fundamental understanding of deep learning have advanced rapidly in recent years, yet a unified framework remains relatively limited. In this article, we provide one possible way to align existing branches of deep learning theory through the lens of dynamical system and optimal control. By viewing deep neural networks as discrete-time nonlinear dynamical systems, we can analyze how information propagates through layers using mean field theory. When optimization algorithms are further recast as controllers, the ultimate goal of training processes can be formulated as an optimal control problem. In addition, we can reveal convergence and generalization properties by studying the stochastic dynamics of optimization algorithms. This viewpoint features a wide range of theoretical study from information bottleneck to statistical physics. It also provides a principled way for hyper-parameter tuning when optimal control theory is introduced. Our framework fits nicely with supervised learning and can be extended to other learning problems, such as Bayesian learning, adversarial training, and specific forms of meta learning, without efforts. The review aims to shed lights on the importance of dynamics and optimal control when developing deep learning theory.
Comparing perturbation models for evaluating stability of neuroimaging pipelines

With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across domains, tools, data sets, and computational infrastructures, but numerical instabilities are thought to be a core contributor. In neuroimaging, unexpected deviations have been observed when varying operating systems, software implementations, or adding negligible quantities of noise. In the field of numerical analysis, these issues have recently been explored through Monte Carlo Arithmetic, a method involving the instrumentation of floating-point operations with probabilistic noise injections at a target precision. Exploring multiple simulations in this context allows the characterization of the result space for a given tool or operation. In this article, we compare various perturbation models to introduce instabilities within a typical neuroimaging pipeline, including (i) targeted noise, (ii) Monte Carlo Arithmetic, and (iii) operating system variation, to identify the significance and quality of their impact on the resulting derivatives. We demonstrate that even low-order models in neuroimaging such as the structural connectome estimation pipeline evaluated here are sensitive to numerical instabilities, suggesting that stability is a relevant axis upon which tools are compared, alongside more traditional criteria such as biological feasibility, computational efficiency, or, when possible, accuracy. Heterogeneity was observed across participants which clearly illustrates a strong interaction between the tool and data set being processed, requiring that the stability of a given tool be evaluated with respect to a given cohort. We identify use cases for each perturbation method tested, including quality assurance, pipeline error detection, and local sensitivity analysis, and make recommendations for the evaluation of stability in a practical and analytically focused setting. Identifying how these relationships and recommendations scale to higher order computational tools, distinct data sets, and their implication on biological feasibility remain exciting avenues for future work.
Solving Math Word Problems with Double-Decoder Transformer

This paper proposes a Transformer-based model to generate equations for math word problems. It achieves much better results than RNN models when copy and align mechanisms are not used, and can outperform complex copy and align RNN models. We also show that training a Transformer jointly in a generation task with two decoders, left-to-right and right-to-left, is beneficial. Such a Transformer performs better than the one with just one decoder not only because of the ensemble effect, but also because it improves the encoder training procedure. We also experiment with adding reinforcement learning to our model, showing improved performance compared to MLE training.
Randomly initialized EM algorithm for two-component Gaussian mixture achieves near optimality in O(√n) iterations

We analyze the classical EM algorithm for parameter estimation in the symmetric two-component Gaussian mixtures in $d$ dimensions. We show that, even in the absence of any separation between components, provided that the sample size satisfies $n=\Omega(d \log^3 d)$, the randomly initialized EM algorithm converges to an estimate in at most $O(\sqrt{n})$ iterations with high probability, which is at most $O((\frac{d \log^3 n}{n})^{1/4})$ in Euclidean distance from the true parameter and within logarithmic factors of the minimax rate of $(\frac{d}{n})^{1/4}$. Both the nonparametric statistical rate and the sublinear convergence rate are direct consequences of the zero Fisher information in the worst case. Refined pointwise guarantees beyond worst-case analysis and convergence to the MLE are also shown under mild conditions. 
This improves the previous result of Balakrishnan et al \cite{BWY17} which requires strong conditions on both the separation of the components and the quality of the initialization, and that of Daskalakis et al \cite{DTZ17} which requires sample splitting and restarting the EM iteration.
Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting

Crowd counting presents enormous challenges in the form of large variation in scales within images and across the dataset. These issues are further exacerbated in highly congested scenes. Approaches based on straightforward fusion of multi-scale features from a deep network seem to be obvious solutions to this problem. However, these fusion approaches do not yield significant improvements in the case of crowd counting in congested scenes. This is usually due to their limited abilities in effectively combining the multi-scale features for problems like crowd counting. To overcome this, we focus on how to efficiently leverage information present in different layers of the network. Specifically, we present a network that involves: (i) a multi-level bottom-top and top-bottom fusion (MBTTBF) method to combine information from shallower to deeper layers and vice versa at multiple levels, (ii) scale complementary feature extraction blocks (SCFB) involving cross-scale residual functions to explicitly enable flow of complementary features from adjacent conv layers along the fusion paths. Furthermore, in order to increase the effectiveness of the multi-scale fusion, we employ a principled way of generating scale-aware ground-truth density maps for training. Experiments conducted on three datasets that contain highly congested scenes (ShanghaiTech, UCF_CC_50, and UCF-QNRF) demonstrate that the proposed method is able to outperform several recent methods in all the datasets
Learning a Multi-Domain Curriculum for Neural Machine Translation

Most data selection research in machine translation focuses on improving a single domain. We perform data selection for multiple domains at once. This is achieved by carefully introducing instance-level domain-relevance features and automatically constructing a training curriculum to gradually concentrate on multi-domain relevant and noise-reduced data batches. Both the choice of features and the use of curriculum are crucial for balancing and improving all domains, including out-of-domain. In large-scale experiments, the multi-domain curriculum simultaneously reaches or outperforms the individual performance and brings solid gains over no-curriculum training.
The Single-Face Ideal Orientation Problem in Planar Graphs

We consider the ideal orientation problem in planar graphs. In this problem, we are given an undirected graph $G$ with positive edge lengths and $k$ pairs of distinct vertices $(s_1, t_1), \dots, (s_k, t_k)$ called terminals, and we want to assign an orientation to each edge such that for all $i$ the distance from $s_i$ to $t_i$ is preserved or report that no such orientation exists. We show that the problem is NP-hard in planar graphs. On the other hand, we show that the problem is polynomial-time solvable in planar graphs when $k$ is fixed, the vertices $s_1, t_1, \dots, s_k, t_k$ are all on the same face, and no two of terminal pairs cross (a pair $(s_i, t_i)$ crosses $(s_j, t_j)$ if the cyclic order of the vertices is $s_i,s_j,t_i,t_j$). For serial instances, we give a simpler and faster algorithm running in $O(n \log n)$ time, even if $k$ is part of the input. (An instance is serial if the terminals appear in cyclic order $u_1, v_1, \dots, u_k, v_k$, where for each $i$ we have either $(u_i, v_i) = (s_i, t_i)$ or $(u_i, v_i) = (t_i, s_i)$.) Finally, we consider a generalization of the problem in which the sum of the distances from $s_i$ to $t_i$ is to be minimized; in this case we give an algorithm for serial instances running in $O(kn^5)$ time.