nils-herrmann/clean_scirepeval huggingface dataset:
Comparing the Sensitivity of Social Networks, Web Graphs, and Random Graphs with Respect to Vertex Removal

The sensitivity of networks to the removal of vertices has been studied extensively over the last 15 years. A common approach to measuring this sensitivity is (i) successively removing vertices following a specific removal strategy and (ii) comparing the original and the modified network using a specific comparison method. In this paper we apply a wide range of removal strategies and comparison methods in order to study the sensitivity of medium-sized networks from the real world and randomly generated networks. In the first part of our study we observe that social networks and web graphs differ in sensitivity. When removing vertices, social networks are robust, web graphs are not. This effect is consistent with the work of Boldi et al. who analyzed very large social networks and web graphs. For randomly generated networks we find that their sensitivity depends significantly on the comparison method. The choice of removal strategy has surprisingly marginal impact on the sensitivity for removal strategies derived from common centrality measures. However, the removal strategy has a strong impact when removing the vertices in random order.
Reflections on organization, emergence, and control in sociotechnical systems

Human and artificial organizations may be described as networks of interacting parts. Those parts exchange data and control information and, as a result of these interactions, organizations produce emergent behaviors and purposes -- traits the characterize "the whole" as "greater than the sum of its parts". In this chapter it is argued that, rather than a static and immutable property, emergence should be interpreted as the result of dynamic interactions between forces of opposite sign: centripetal (positive) forces strengthening emergence by consolidating the whole and centrifugal (negative) forces that weaken the social persona and as such are detrimental to emergence. The result of this interaction is called in this chapter as "quality of emergence". This problem is discussed in the context of a particular class of organizations: conventional hierarchies. We highlight how traditional designs produce behaviors that may severely impact the quality of emergence. Finally we discuss a particular class of organizations that do not suffer from the limitations typical of strict hierarchies and result in greater quality of emergence. In some case, however, these enhancements are counterweighted by a reduced degree of controllability and verifiability.
Estimation of priors in natural images

We investigate prior distributions in natural images by using Boltzmann machine, to find some possible universal properties and individual characteristics of natural images. For simplicity, we specifically focus on binary pictures. We find that in most cases there emerges a structure with two sublattices, and the nearest-neighbor and next-nearest-neighbor interactions correspondingly take two discriminative values, which reflects individual characteristics of each set of pictures. On the other hand, in a longer spacial scale, a longer-range (though still rapidly-decaying) ferromagnetic interaction commonly appear in all the cases. The characteristic length scale of this longer-range interaction is universally about up to four lattice spacing ξ ≈ 4. These results are derived by using the mean-field method which effectively reduces the computational time required in Boltzmann machine. An improved mean-field method called the Bethe approximation also gives the same result, which reinforces the validity of our analysis and findings.
Clustering multi-way data: a novel algebraic approach

In this paper, we develop a method for unsupervised clustering of two- way (matrix) data by combining two recent innovations from dierent elds: the Sparse Subspace Clustering (SSC) algorithm (10), which groups points coming from a union of subspaces into their respective subspaces, and the t-product (18), which was introduced to provide a matrix-like multiplication for third order tensors. Our algorithm is analogous to SSC in that an \anity" between dierent data points is built using a sparse self-representation of the data. Unlike SSC, we employ the t-product in the self-representation. This allows us more exibility in modeling; in fact, SSC is a special case of our method. When using the t-product, three-way arrays are treated as matrices whose ele- ments (scalars) are n-tuples or tubes. Convolutions take the place of scalar multi- plication. This framework allows us to embed the 2-D data into a vector-space-like structure called a free module over a commutative ring. These free modules retain many properties of complex inner-product spaces, and we leverage that to provide theoretical guarantees on our algorithm. We show that compared to vector-space counterparts, SSmC achieves higher accuracy and better able to cluster data with less preprocessing in some image clustering problems. In particular we show the performance of the proposed method on Weizmann face database, the Extended Yale B Face database and the MNIST handwritten digits database.
The NIFTY way of Bayesian signal inference

We introduce NIFTY, "Numerical Information Field Theory", a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTY can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTY as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D3PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy.
Frozen quantum coherence.

We analyze under which dynamical conditions the coherence of an open quantum system is totally unaffected by noise. For a single qubit, specific measures of coherence are found to freeze under different conditions, with no general agreement between them. Conversely, for an N-qubit system with even N, we identify universal conditions in terms of initial states and local incoherent channels such that all bona fide distance-based coherence monotones are left invariant during the entire evolution. This finding also provides an insightful physical interpretation for the freezing phenomenon of quantum correlations beyond entanglement. We further obtain analytical results for distance-based measures of coherence in two-qubit states with maximally mixed marginals.
On the Spectroscopic Boundaries Between Normal and Peculiar Type~I Supernovae

The spectrum of a supernova is a summation of numerous overlapping atomic line signatures. Consequently, empirical measurements are limited in application when compound features are assumed to be due to one or two spectral lines. Here I address matters of spectroscopic boundaries between normal and peculiar type I supernovae using multi-component empirical metrics. I discuss some obstacles faced when using supernova spectra to pair model with data and I demonstrate how spectrum synthesis can benefit from fairly complete observational coverage in wavelength and time.
An $\{l_1,l_2,l_{\infty}\}$-Regularization Approach to High-Dimensional Errors-in-variables Models

Several new estimation methods have been recently proposed for the linear regression model with observation error in the design. Different assumptions on the data generating process have motivated different estimators and analysis. In particular, the literature considered (1) observation errors in the design uniformly bounded by some $\bar \delta$, and (2) zero mean independent observation errors. Under the first assumption, the rates of convergence of the proposed estimators depend explicitly on $\bar \delta$, while the second assumption has been applied when an estimator for the second moment of the observational error is available. This work proposes and studies two new estimators which, compared to other procedures for regression models with errors in the design, exploit an additional $l_{\infty}$-norm regularization. The first estimator is applicable when both (1) and (2) hold but does not require an estimator for the second moment of the observational error. The second estimator is applicable under (2) and requires an estimator for the second moment of the observation error. Importantly, we impose no assumption on the accuracy of this pilot estimator, in contrast to the previously known procedures. As the recent proposals, we allow the number of covariates to be much larger than the sample size. We establish the rates of convergence of the estimators and compare them with the bounds obtained for related estimators in the literature. These comparisons show interesting insights on the interplay of the assumptions and the achievable rates of convergence.
Unified Description of Quantum Mechanics on a Curved Space

Starting with the first-order singular Lagrangian, the problem of the quantization of a dynamical system constrained to a submanifold embedded in the higher-dimensional Euclidean space is investigated within the framework of operatorial quantization formalism. Through the projection operator method (POM) with the constraint star-products, it is shown that both of the constraint quantum system with the usual constraint and that with the derivative-type constraint are naturally constructed from one Lagarangian. It is proved that the system with the usual constraint is the sub-system of that with the derivative-type one. Furthermore, the quantization of the dynamical system subject to both of the usual constraint and the derivative-type one is investigated by the POM, and the quantum corrections in the resultant Hamiltonians are discussed.
Surface Tension Adjustment in a Pseudo-Potential Lattice Boltzmann Model

Pseudo-potential lattice Boltzmann models have been widely applied in many multiphase simulations. However, most of these models still suffer from some drawbacks such as spurious velocities and untunable surface tension. In this paper, we aim to discuss the surface tension of a popular pseudo-potential model proposed by Kupershtokh et al., which has attracted much attention due to its simplicity and stability. The influence of a parameter on the surface tension in the model is analyzed. Based on the analysis, we proposed a method to adjust surface tension by changing the parameter in the model. However, the density distribution and the stability of the model also depend on the parameter. To adjust the surface tension independently, the pressure tensor modifying method is introduced and numerically tested. The simulation results show that, by applying the pressure tensor modifying method, the surface tension can be adjusted with little influence on the stability and density distributions.
Compacton matter waves in binary Bose gases under strong nonlinear management

The existence of compacton matter waves in binary mixtures of quasi-one-dimensional Bose-Einstein 
condensates in deep optical lattices, and in the presence of nonlinearity management, is demonstrated. For 
this, we derive an averaged vector discrete nonlinear Schr¨odinger equation (DNLSE) and show that compacton 
solutions of different types can exist as stable excitations. Stability properties are studied by linear analysis 
and by direct numerical integrations of the DNLSE system and their dependence on the inter- and intraspecies 
scattering lengths investigated. We show that under proper management conditions, compactons can be very 
robust excitations that can emerge spontaneously from generic initial conditions. A possible experimental setting 
for compacton observation is also discussed.
Approximate Subspace-Sparse Recovery with Corrupted Data via Constrained $\ell_1$-Minimization

High-dimensional data often lie in low-dimensional subspaces corresponding to different classes they belong to. Finding sparse representations of data points in a dictionary built using the collection of data helps to uncover low-dimensional subspaces and address problems such as clustering, classification, subset selection and more. In this paper, we address the problem of recovering sparse representations for noisy data points in a dictionary whose columns correspond to corrupted data lying close to a union of subspaces. We consider a constrained $\ell_1$-minimization and study conditions under which the solution of the proposed optimization satisfies the approximate subspace-sparse recovery condition. More specifically, we show that each noisy data point, perturbed from a subspace by a noise of the magnitude of $\varepsilon$, will be reconstructed using data points from the same subspace with a small error of the order of $O(\varepsilon)$ and that the coefficients corresponding to data points in other subspaces will be sufficiently small, \ie, of the order of $O(\varepsilon)$. We do not impose any randomness assumption on the arrangement of subspaces or distribution of data points in each subspace. Our framework is based on a novel generalization of the null-space property to the setting where data lie in multiple subspaces, the number of data points in each subspace exceeds the dimension of the subspace, and all data points are corrupted by noise. Moreover, assuming a random distribution for data points, we further show that coefficients from the desired support not only reconstruct a given point with high accuracy, but also have sufficiently large values, \ie, of the order of $O(1)$.
Scale-invariant spectrum of Lee-Wick model in de Sitter spacetime

We obtain a scale-invariant spectrum from the Lee-Wick model in de Sitter spacetime. This model is a fourth-order scalar theory whose mass parameter is determined by M2=2H2. The Harrison-Zel'dovich scale-invariant spectrum is obtained by Fourier transforming the propagator in position space as well as by computing the power spectrum directly. It shows clearly that the LW scalar theory provides a truly scale-invariant spectrum in whole de Sitter, while the massless scalar propagation in de Sitter shows a scale-invariant spectrum in the superhorizon region only.
Particle Metropolis-adjusted Langevin algorithms

This paper proposes a new sampling scheme based on Langevin dynamics that is applicable within pseudo-marginal and particle Markov chain Monte Carlo algorithms. We investigate this algorithm's theoretical properties under standard asymptotics, which correspond to an increasing dimension of the parameters, $n$. Our results show that the behaviour of the algorithm depends crucially on how accurately one can estimate the gradient of the log target density. If the error in the estimate of the gradient is not sufficiently controlled as dimension increases, then asymptotically there will be no advantage over the simpler random-walk algorithm. However, if the error is sufficiently well-behaved, then the optimal scaling of this algorithm will be $O(n^{-1/6})$ compared to $O(n^{-1/2})$ for the random walk. Our theory also gives guidelines on how to tune the number of Monte Carlo samples in the likelihood estimate and the proposal step-size.
Diffraction physics with ALICE at the LHC

The ALICE experiment is equipped with a wide range of detectors providing excellent tracking and particle identification in the central region, as well as forward detectors with extended pseudorapidity coverage, which are well suited for studying diffractive processes. Cross section measurements of single and double diffractive processes performed by ALICE in pp collisions at $\sqrt{s}=0.9,~2.76,~7$~TeV will be reported. Currently, ALICE is studying double-gap events in pp collisions at $\sqrt{s}=7$~TeV, which give an insight into the central diffraction processes: current status and future perspectives will be discussed. The upgrade plans for diffraction studies, further extending the pseudorapidity acceptance of the ALICE setup for the forthcoming Run 2 of the LHC, will be outlined.
Distribution of dark energy in the vicinity of compact objects

The distribution of dark energy density in the vicinity of compact static objects is analyzed. Dark energy is assumed to be in the form of a scalar field with three parameters: the background density, the equation of state parameter and the effective sound speed. Compact object is assumed to be a homogeneous spherical object of constant radius. We use the solutions of the hydrodynamical equations for dark energy in the gravitational fields of such objects for cases of static distribution of dark energy in the vicinity of star and stationary accretion onto black hole in order to analyze the possibility of constraining of the parameters of dark energy from astrophysical data. We show that dependence of density of dark energy in the vicinity of such object on the effective sound speed, background density and equation of state parameter of dark energy makes it possible to try such tests. Here we exploit the accuracy of determination of masses of Sun and black hole in the center of Milky Way to obtain the lower limit on the effective sound speed of dark energy.
ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient

Stochastic gradient algorithms have been the main focus of large-scale learning problems and they led to important successes in machine learning. The convergence of SGD depends on the careful choice of learning rate and the amount of the noise in stochastic estimates of the gradients. In this paper, we propose a new adaptive learning rate algorithm, which utilizes curvature information for automatically tuning the learning rates. The information about the element-wise curvature of the loss function is estimated from the local statistics of the stochastic first order gradients. We further propose a new variance reduction technique to speed up the convergence. In our preliminary experiments with deep neural networks, we obtained better performance compared to the popular stochastic gradient algorithms.
Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models

The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validation. In this article, we consider Gaussian latent variable models where the integration over the latent values is approximated using the Laplace method or expectation propagation (EP). We study the properties of several Bayesian leave-one-out (LOO) cross-validation approximations that in most cases can be computed with a small additional cost after forming the posterior approximation given the full data. Our main objective is to assess the accuracy of the approximative LOO cross-validation estimators. That is, for each method (Laplace and EP) we compare the approximate fast computation with the exact brute force LOO computation. Secondarily, we evaluate the accuracy of the Laplace and EP approximations themselves against a ground truth established through extensive Markov chain Monte Carlo simulation. Our empirical results show that the approach based upon a Gaussian approximation to the LOO marginal distribution (the so-called cavity distribution) gives the most accurate and reliable results among the fast methods.
Model Selection in High-Dimensional Misspecified Models

Model selection is indispensable to high-dimensional sparse modeling in selecting the best set of covariates among a sequence of candidate models. Most existing work assumes implicitly that the model is correctly specified or of fixed dimensions. Yet model misspecification and high dimensionality are common in real applications. In this paper, we investigate two classical Kullback-Leibler divergence and Bayesian principles of model selection in the setting of high-dimensional misspecified models. Asymptotic expansions of these principles reveal that the effect of model misspecification is crucial and should be taken into account, leading to the generalized AIC and generalized BIC in high dimensions. With a natural choice of prior probabilities, we suggest the generalized BIC with prior probability which involves a logarithmic factor of the dimensionality in penalizing model complexity. We further establish the consistency of the covariance contrast matrix estimator in a general setting. Our results and new method are supported by numerical studies.
Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory

The pathwise coordinate optimization is one of the most important computational frameworks for high dimensional convex and nonconvex sparse learning problems. It differs from the classical coordinate optimization algorithms in three salient features: {\it warm start initialization}, {\it active set updating}, and {\it strong rule for coordinate preselection}. Such a complex algorithmic structure grants superior empirical performance, but also poses significant challenge to theoretical analysis. To tackle this long lasting problem, we develop a new theory showing that these three features play pivotal roles in guaranteeing the outstanding statistical and computational performance of the pathwise coordinate optimization framework. Particularly, we analyze the existing pathwise coordinate optimization algorithms and provide new theoretical insights into them. The obtained insights further motivate the development of several modifications to improve the pathwise coordinate optimization framework, which guarantees linear convergence to a unique sparse local optimum with optimal statistical properties in parameter estimation and support recovery. This is the first result on the computational and statistical guarantees of the pathwise coordinate optimization framework in high dimensions. Thorough numerical experiments are provided to support our theory.
A MILLISECOND INTERFEROMETRIC SEARCH FOR FAST RADIO BURSTS WITH THE VERY LARGE ARRAY

We report on the first millisecond timescale radio interferometric search for the new class of transient known as fast radio bursts (FRBs). We used the Very Large Array (VLA) for a 166 hr, millisecond imaging campaign to detect and precisely localize an FRB. We observed at 1.4 GHz and produced visibilities with 5 ms time resolution over 256 MHz of bandwidth. Dedispersed images were searched for transients with dispersion measures from 0 to 3000 pc cm−3. No transients were detected in observations of high Galactic latitude fields taken from 2013 September though 2014 October. Observations of a known pulsar show that images typically had a thermal-noise limited sensitivity of 120 mJy beam−1 ( 8 &sgr; ?> ; Stokes I) in 5 ms and could detect and localize transients over a wide field of view. Our nondetection limits the FRB rate to less than 7 × 10 4 ?> sky−1 day−1 (95% confidence) above a fluence limit of 1.5 Jy ms. The VLA rate limit is consistent with past estimates when published flux limits are recalculated with a homogeneous definition that includes effects of primary beam attenuation, dispersion, pulse width, and sky brightness. This calculation revises the FRB rate downward by a factor of 2, giving the VLA observations a roughly 50% chance of detecting a typical FRB, assuming a pulse width of 3 ms. A 95% confidence constraint would require 600 hr of similar VLA observing. Our survey also limits the repetition rate of an FRB to 2 times less than any known repeating millisecond radio transient.