nils-herrmann/clean_scirepeval huggingface dataset:
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data. This paper studies CNN on text categorization to exploit the 1D structure (namely, word order) of text data for accurate prediction. Instead of using low-dimensional word vectors as input as is often done, we directly apply CNN to high-dimensional text data, which leads to directly learning embedding of small text regions for use in classification. In addition to a straightforward adaptation of CNN from image to text, a simple but new variation which employs bag-of-word conversion in the convolution layer is proposed. An extension to combine multiple convolution layers is also explored for higher accuracy. The experiments demonstrate the effectiveness of our approach in comparison with state-of-the-art methods.
Approximate Lifted Inference with Probabilistic Databases

This paper proposes a new approach for approximate evaluation of #P-hard queries with probabilistic databases. In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking their minimum. We provide an algorithm that takes into account important schema information to enumerate only the minimal necessary plans among all possible plans. Importantly, this algorithm is a strict generalization of all known results of PTIME self-join-free conjunctive queries: A query is safe if and only if our algorithm returns one single plan. We also apply three relational query optimization techniques to evaluate all minimal safe plans very fast. We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of probabilistic methods over non-probabilistic methods for ranking query answers.
Inflationary universe from higher-derivative quantum gravity

We consider higher-derivative quantum gravity where renormalization group improved effective action beyond one-loop approximation is derived. Using this effective action, the quantum-corrected FRW equations are analyzed. De Sitter universe solution is found. It is demonstrated that such de Sitter inflationary universe is instable. The slow-roll inflationary parameters are calculated. The contribution of renormalization group improved Gauss-Bonnet term to quantum-corrected FRW equations as well as to instability of de Sitter universe is estimated. It is demonstrated that in this case the spectral index and tensor-to-scalar ratio are consistent with Planck data.
Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine

The increased affordability of whole genome sequencing has motivated its use for phenotypic studies. We address the problem of learning interpretable models for discrete phenotypes from whole genomes. We propose a general approach that relies on the Set Covering Machine and ak-mer representation of the genomes. We show results for the problem of predicting the resistance of Pseudomonas aeruginosa, an important human pathogen, against 4 antibiotics. Our results demonstrate that extremely sparse models which are biologically relevant can be learnt using this approach.
A Globally Unevolving Universe

A scalar-tensor theory of gravity is formulated in which $G$ and particle masses are allowed to vary. The theory yields a globally static cosmological model with no evolutionary timescales, no cosmological coincidences, and no flatness and horizon `problems'. It can be shown that the energy densities of dark energy ($\rho_{DE}$) and non-relativistic baryons and dark matter ($\rho_{M}$) are related by $\rho_{DE}=2\rho_{M}$, in agreement with current observations, if DE is associated with the canonical kinetic and potential energy densities of the scalar fields. Under general assumptions, the model favors light fermionic dark matter candidates (e.g., sterile neutrinos). The main observed features of the CMB are naturally explained in this model, including the spectral flatness of its perturbations on the largest angular scales, and the observed adiabatic and gaussian nature of density perturbations. More generally, we show that many of the cosmological observables, normally attributed to the dynamics of expanding space, could be of kinematic origin. In gravitationally bound systems, the values of G and particle masses spontaneously freeze out by a symmetry breaking of the underlying conformal symmetry, and the theory reduces to standard general relativity (with, e.g., all solar system tests satisfied).
Simulating momentum exchange in the dark sector

Low energy interactions between particles are often characterised by elastic scattering. Just as electrons undergo Thomson scattering with photons, dark matter particles may experience an analogous form of momentum exchange with dark energy. We investigate the influence such an interaction has on the formation of linear and nonlinear cosmic structure, by running for the first time a suite of N-body simulations with different dark energy equations of state and scattering cross sections. In models where the linear matter power spectrum is suppressed by the scattering, we find that on nonlinear scales the power spectrum is strongly enhanced. This is due to the friction term increasing the efficiency of gravitational collapse, which also leads to a scale-independent amplification of the concentration and mass functions of halos. The opposite trend is found for models characterised by an increase of the linear matter power spectrum normalisation. More quantitatively, we find that power spectrum deviations at nonlinear scales ($k \approx 10\, h/$Mpc) are roughly ten times larger than their linear counterparts, exceeding $100%$ for the largest value of the scattering cross section considered in the present work. Similarly, the concentration-mass relation and the halo mass function show deviations up to $100%$ and $20%$, respectively, over a wide range of masses. Therefore, we conclude that nonlinear probes of structure formation might provide much tighter constraints on the scattering cross section between dark energy and dark matter as compared to the present bounds based on linear observables.
The eightfold way to dissipation

We provide a complete characterization of hydrodynamic transport consistent with the second law of thermodynamics at arbitrary orders in the gradient expansion. A key ingredient in facilitating this analysis is the notion of adiabatic hydrodynamics, which enables isolation of the genuinely dissipative parts of transport. We demonstrate that most transport is adiabatic. Furthermore, of the dissipative part, only terms at the leading order in gradient expansion are constrained to be sign-deﬁnite by the second law (as has been derived before).
Highly comparative fetal heart rate analysis

A database of fetal heart rate (FHR) time series measured from 7 221 patients during labor is analyzed with the aim of learning the types of features of these recordings that are informative of low cord pH. Our `highly comparative' analysis involves extracting over 9 000 time-series analysis features from each FHR time series, including measures of autocorrelation, entropy, distribution, and various model fits. This diverse collection of features was developed in previous work . We describe five features that most accurately classify a balanced training set of 59 `low pH' and 59 `normal pH' FHR recordings. We then describe five of the features with the strongest linear correlation to cord pH across the full dataset of FHR time series. The features identified in this work may be used as part of a system for guiding intervention during labor in future. This work successfully demonstrates the utility of comparing across a large, interdisciplinary literature on time-series analysis to automatically contribute new scientific results for specific biomedical signal processing challenges.
REGULATORY CAPITAL MODELING FOR CREDIT RISK

The Basel II internal ratings-based (IRB) approach to capital adequacy for credit risk plays an important role in protecting the banking sector against insolvency. We outline the mathematical foundations of regulatory capital for credit risk, and extend the model specification of the IRB approach to a more general setting than the usual Gaussian case. It rests on the proposition that quantiles of the distribution of conditional expectation of portfolio percentage loss may be substituted for quantiles of the portfolio loss distribution. We present a more compact proof of this proposition under weaker assumptions. Then, constructing a portfolio that is representative of credit exposures of the Australian banking sector, we measure the rate of convergence, in terms of number of obligors, of empirical loss distributions to the asymptotic (infinitely fine-grained) portfolio loss distribution. Moreover, we evaluate the sensitivity of credit risk capital to dependence structure as modeled by asset correlations and elliptical copulas. Access to internal bank data collected by the prudential regulator distinguishes our research from other empirical studies on the IRB approach.
The Entropy of Attention and Popularity in YouTube Videos

The vast majority of YouTube videos never become popular, languishing in obscurity with few views, no likes, and no comments. We use information theoretical measures based on entropy to examine how time series distributions of common measures of popularity in videos from YouTube's "Trending videos" and "Most recent" video feeds relate to the theoretical concept of attention. While most of the videos in the "Most recent" feed are never popular, some 20% of them have distributions of attention metrics and measures of entropy that are similar to distributions for "Trending videos". We analyze how the 20% of "Most recent" videos that become somewhat popular differ from the 80% that do not, then compare these popular "Most recent" videos to different subsets of "Trending videos" to try to characterize and compare the attention each receives.
New Insights and Perspectives on the Natural Gradient Method

Natural gradient descent is an optimization method traditionally motivated from the perspective of information geometry, and works well for many applications as an alternative to stochastic gradient descent. In this paper we critically analyze this method and its properties, and show how it can be viewed as a type of approximate 2nd-order optimization method, where the Fisher information matrix can be viewed as an approximation of the Hessian. This perspective turns out to have significant implications for how to design a practical and robust version of the method. Additionally, we make the following contributions to the understanding of natural gradient and 2nd-order methods: a thorough analysis of the convergence speed of stochastic natural gradient descent (and more general stochastic 2nd-order methods) as applied to convex quadratics, a critical examination of the oft-used "empirical" approximation of the Fisher matrix, and an analysis of the (approximate) parameterization invariance property possessed by natural gradient methods, which we show still holds for certain choices of the curvature matrix other than the Fisher, but notably not the Hessian.
A Lorentz gauge theory of gravity

We present a Lorentz gauge theory of gravity in which the metric is not dynamical. Spherically symmetric weak field solutions are studied. We show that this solution contains the Schwarzschild spacetime at least to the first order of perturbation. Next, we present a special case of the theory where the Schwarzschild metric is an exact solution. It is also shown that the de Sitter space is an exact vacuum solution of this special case and as a result the theory is able to explain the expansion of the universe with no need for dark energy. Within this special case, quantization of the theory is also studied, the basic Feynman diagrams are derived and the renormalizability of the theory is studied using the power-counting method. We show that under a certain condition the theory is power-counting renormalizable.
Deep Distributed Random Samplings for Supervised Learning: An Alternative to Random Forests?

In (\cite{zhang2014nonlinear,zhang2014nonlinear2}), we have viewed machine learning as a coding and dimensionality reduction problem, and further proposed a simple unsupervised dimensionality reduction method, entitled deep distributed random samplings (DDRS). In this paper, we further extend it to supervised learning incrementally. The key idea here is to incorporate label information into the coding process by reformulating that each center in DDRS has multiple output units indicating which class the center belongs to. The supervised learning method seems somewhat similar with random forests (\cite{breiman2001random}), here we emphasize their differences as follows. (i) Each layer of our method considers the relationship between part of the data points in training data with all training data points, while random forests focus on building each decision tree on only part of training data points independently. (ii) Our method builds gradually-narrowed network by sampling less and less data points, while random forests builds gradually-narrowed network by merging subclasses. (iii) Our method is trained more straightforward from bottom layer to top layer, while random forests build each tree from top layer to bottom layer by splitting. (iv) Our method encodes output targets implicitly in sparse codes, while random forests encode output targets by remembering the class attributes of the activated nodes. Therefore, our method is a simpler, more straightforward, and maybe a better alternative choice, though both methods use two very basic elements---randomization and nearest neighbor optimization---as the core. This preprint is used to protect the incremental idea from (\cite{zhang2014nonlinear,zhang2014nonlinear2}). Full empirical evaluation will be announced carefully later.
The inductive theory of natural selection

The theory of natural selection has two forms. Deductive theory describes how populations change over time. One starts with an initial population and some rules for change. From those assumptions, one calculates the future state of the population. Deductive theory predicts how populations adapt to environmental challenge. Inductive theory describes the causes of change in populations. One starts with a given amount of change. One then assigns different parts of the total change to particular causes. Inductive theory analyzes alternative causal models for how populations have adapted to environmental challenge. This chapter emphasizes the inductive analysis of cause.
Curriculum learning of multiple tasks

Sharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data. However, in a realistic scenario of multi-task learning not all tasks are equally related to each other, hence it could be advantageous to transfer information only between the most related tasks. In this work we propose an approach that processes multiple tasks in a sequence with sharing between subsequent tasks instead of solving all tasks jointly. Subsequently, we address the question of curriculum learning of tasks, i.e. finding the best order of tasks to be learned. Our approach is based on a generalization bound criterion for choosing the task order that optimizes the average expected classification performance over all tasks. Our experimental results show that learning multiple related tasks sequentially can be more effective than learning them jointly, the order in which tasks are being solved affects the overall performance, and that our model is able to automatically discover a favourable order of tasks.
Nested Variational Compression in Deep Gaussian Processes

Deep Gaussian processes provide a flexible approach to probabilistic modelling of data using either supervised or unsupervised learning. For tractable inference approximations to the marginal likelihood of the model must be made. The original approach to approximate inference in these models used variational compression to allow for approximate variational marginalization of the hidden variables leading to a lower bound on the marginal likelihood of the model [Damianou and Lawrence, 2013]. In this paper we extend this idea with a nested variational compression. The resulting lower bound on the likelihood can be easily parallelized or adapted for stochastic variational inference.
Model-Independent Pricing of Asian Options via Optimal Martingale Transport

In this article we discuss the problem of calculating optimal model-independent (robust) bounds for the price of Asian options with discrete and continuous averaging. We will give geometric characterisations of the maximising and the minimising pricing model for certain types of Asian options in discrete and continuous time. In discrete time the problem is reduced to finding the optimal martingale transport for the cost function $|x+y|$. In the continuous time case we consider the cases with one and two given marginals. We describe the maximising models in both of these cases as well as the minimising model in the one-marginal case and relate the two-marginals case to the discrete time problem with two marginals.
Structure learning of antiferromagnetic Ising models

In this paper we investigate the computational complexity of learning the graph structure underlying a discrete undirected graphical model from i.i.d. samples. Our first result is an unconditional computational lower bound of Ω(pd/2) for learning general graphical models on p nodes of maximum degree d, for the class of so-called statistical algorithms recently introduced by Feldman et al. . The construction is related to the notoriously difficult learning parities with noise problem in computational learning theory. Our lower bound suggests that the O(pd+2) runtime required by Bresler, Mossel, and Sly's [2] exhaustive-search algorithm cannot be significantly improved without restricting the class of models. 
 
Aside from structural assumptions on the graph such as it being a tree, hypertree, tree-like, etc., many recent papers on structure learning assume that the model has the correlation decay property. Indeed, focusing on ferromagnetic Ising models, Bento and Montanari  showed that all known low-complexity algorithms fail to learn simple graphs when the interaction strength exceeds a number related to the correlation decay threshold. Our second set of results gives a class of repelling (antiferromagnetic) models that have the opposite behavior: very strong interaction allows efficient learning in time O(p2). We provide an algorithm whose performance interpolates between O(p2) and O(pd+2) depending on the strength of the repulsion.
New redshift z ≃ 9 galaxies in the Hubble Frontier Fields: implications for early evolution of the UV luminosity density

We present the results of a new search for galaxies at redshift z ≃ 9 in the first two Hubble Frontier Fields with completed HST WFC3/IR and ACS imaging. To ensure robust photometric redshift solutions, and to minimize incompleteness, we confine our search to objects with H_160 30 mag (within a 0.5-arcsec diameter aperture), and insist on detections in both H_160 and J_140. The result is a survey covering an effective area (after accounting for magnification) of 10.9 arcmin2, which yields 12 galaxies at 8.4 8.4 sources reported by Zheng et al. In the MACSJ0416.1−240 cluster field, we report five objects, and explain why each of these eluded detection or classification as z ≃ 9 galaxies in the published searches of the shallower CLASH data. Finally, we uncover four z ≃ 9 galaxies from the MACSJ0416.1−240 parallel field. Based on the published magnification maps, we find that only one of these 12 galaxies is likely boosted by more than a factor of 2 by gravitational lensing. Consequently, we are able to perform a fairly straightforward reanalysis of the normalization of the z ≃ 9 UV galaxy luminosity function as explored previously in the HUDF12 programme. We conclude that the new data strengthen the evidence for a continued smooth decline in UV luminosity density (and hence star formation rate density) from z ≃ 8 to 9, contrary to recent reports of a marked drop-off at these redshifts. This provides further support for the scenario in which early galaxy evolution is sufficiently extended to explain cosmic reionization.
Revised Filter Profiles and Zero Points for Broadband Photometry

Estimating accurate bolometric fluxes for stars requires reliable photometry to absolutely flux calibrate the spectra. This is a significant problem for studies of very bright stars, which are generally saturated in modern photometric surveys. Instead we must rely on photometry with less precise calibration. We utilize precisely flux-calibrated spectra to derive improved filter bandpasses and zero points for the most common sources of photometry for bright stars. In total, we test 39 different filters in the General Catalog of Photometric Data as well as those from Tycho-2 and Hipparcos. We show that utilizing inaccurate filter profiles from the literature can create significant color terms resulting in fluxes that deviate by ≳10% from actual values. To remedy this we employ an empirical approach; we iteratively adjust the literature filter profile and zero point, convolve it with catalog spectra, and compare to the corresponding flux from the photometry. We adopt the passband values that produce the best agreement between photometry and spectroscopy and are independent of stellar color. We find that while most zero points change by < 5%, a few systems change by 10–15%. Our final profiles and zero points are similar to recent estimates from the literature. Based on determinations of systematic errors in our selected spectroscopic libraries, we estimate that most of our improved zero points are accurate to 0.5–1%.
PROBING THE FERMI BUBBLES IN ULTRAVIOLET ABSORPTION: A SPECTROSCOPIC SIGNATURE OF THE MILKY WAY'S BICONICAL NUCLEAR OUTFLOW

Giant lobes of plasma extend ≈55° above and below the Galactic center, glowing in emission from gamma rays (the Fermi Bubbles) to microwaves and polarized radio waves. We use ultraviolet absorption-line spectra from the Hubble Space Telescope to constrain the velocity of the outflowing gas within these regions, targeting the quasar PDS 456 (ℓ, b = 10.°4, +11.°2). This sightline passes through a clear biconical structure seen in hard X-ray and gamma-ray emission near the base of the northern Fermi Bubble. We report two high-velocity metal absorption components, at vLSR = −235 and +250 km s−1, which cannot be explained by co-rotating gas in the Galactic disk or halo. Their velocities are suggestive of an origin on the front and back side of an expanding biconical outflow emanating from the Galactic center. We develop simple kinematic biconical outflow models that can explain the observed profiles with an outflow velocity of ≳900 km s−1 and a full opening angle of ≈110° (matching the X-ray bicone). This indicates Galactic center activity over the last ≈2.5–4.0 Myr, in line with age estimates of the Fermi Bubbles. The observations illustrate the use of UV spectroscopy to probe the properties of swept-up gas venting into the Fermi Bubbles.