nils-herrmann/clean_scirepeval huggingface dataset:
Big Learning with Bayesian Methods

Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistic methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including nonparametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications.
Mutual Information-Based Unsupervised Feature Transformation for Heterogeneous Feature Subset Selection

Conventional mutual information (MI) based feature selection (FS) methods are unable to handle heterogeneous feature subset selection properly because of data format differences or estimation methods of MI between feature subset and class label. A way to solve this problem is feature transformation (FT). In this study, a novel unsupervised feature transformation (UFT) which can transform non-numerical features into numerical features is developed and tested. The UFT process is MI-based and independent of class label. MI-based FS algorithms, such as Parzen window feature selector (PWFS), minimum redundancy maximum relevance feature selection (mRMR), and normalized MI feature selection (NMIFS), can all adopt UFT for pre-processing of non-numerical features. Unlike traditional FT methods, the proposed UFT is unbiased while PWFS is utilized to its full advantage. Simulations and analyses of large-scale datasets showed that feature subset selected by the integrated method, UFT-PWFS, outperformed other FT-FS integrated methods in classification accuracy.
Consistency of Cheeger and Ratio Graph Cuts

This paper establishes the consistency of a family of graph-cut-based algorithms for clustering of data clouds. We consider point clouds obtained as samples of a ground-truth measure. We investigate approaches to clustering based on minimizing objective functionals defined on proximity graphs of the given sample. Our focus is on functionals based on graph cuts like the Cheeger and ratio cuts. We show that minimizers of the these cuts converge as the sample size increases to a minimizer of a corresponding continuum cut (which partitions the ground truth measure). Moreover, we obtain sharp conditions on how the connectivity radius can be scaled with respect to the number of sample points for the consistency to hold. We provide results for two-way and for multiway cuts. Furthermore we provide numerical experiments that illustrate the results and explore the optimality of scaling in dimension two.
A Latent Source Model for Online Collaborative Filtering

Despite the prevalence of collaborative filtering in recommendation systems, there has been little theoretical development on why and how well it works, especially in the "online" setting, where items are recommended to users over time. We address this theoretical gap by introducing a model for online recommendation systems, cast item recommendation under the model as a learning problem, and analyze the performance of a cosine-similarity collaborative filtering method. In our model, each of $n$ users either likes or dislikes each of $m$ items. We assume there to be $k$ types of users, and all the users of a given type share a common string of probabilities determining the chance of liking each item. At each time step, we recommend an item to each user, where a key distinction from related bandit literature is that once a user consumes an item (e.g., watches a movie), then that item cannot be recommended to the same user again. The goal is to maximize the number of likable items recommended to users over time. Our main result establishes that after nearly $\log(km)$ initial learning time steps, a simple collaborative filtering algorithm achieves essentially optimal performance without knowing $k$. The algorithm has an exploitation step that uses cosine similarity and two types of exploration steps, one to explore the space of items (standard in the literature) and the other to explore similarity between users (novel to this work).
Noise Benefits in Expectation-Maximization Algorithms

This dissertation shows that careful injection of noise into sample data can substantially speed up Expectation-Maximization algorithms. Maximization algorithms are a class of iterative algorithms for extracting maximum likelihood estimates from corrupted or incomplete data. The convergence speed-up is an example of a noise benefit or "stochastic resonance" in statistical signal processing. The dissertation presents derivations of sufficient conditions for such noise-benefits and demonstrates the speed-up in some ubiquitous signal-processing algorithms. These algorithms include parameter estimation for mixture models, the $k$-means clustering algorithm, the Baum-Welch algorithm for training hidden Markov models, and backpropagation for training feedforward artificial neural networks. This dissertation also analyses the effects of data and model corruption on the more general Bayesian inference estimation framework. The main finding is a theorem guaranteeing that uniform approximators for Bayesian model functions produce uniform approximators for the posterior pdf via Bayes theorem. This result also applies to hierarchical and multidimensional Bayesian models.
Possible indication of momentum-dependent asymmetric dark matter in the sun.

Broad disagreement persists between helioseismological observables and predictions of solar models computed with the latest surface abundances. Here we show that most of these problems can be solved by the presence of asymmetric dark matter coupling to nucleons as the square of the momentum q exchanged in the collision. We compute neutrino fluxes, small frequency separations, surface helium abundances, sound speed profiles, and convective zone depths for a number of models, showing more than a 6σ preference for q^{2} models over others, and over the standard solar model. The preferred mass (3 GeV) and reference dark matter-nucleon cross section (10^{-37}  cm^{2} at q_{0}=40  MeV) are within the region of parameter space allowed by both direct detection and collider searches.
Zeldovich pancakes at redshift zero: the equilibration state and phase-space properties

One of the components of the cosmic web are sheets, which are commonly referred to as Zeldovich pancakes. These are structures which have only collapsed along one dimension, as opposed to filaments or galaxies and cluster, which have collapsed along two or three dimensions. These pancakes have recently received renewed interest, since they have been shown to be useful tools for an independent method to determine galaxy cluster masses. We consider sheet-like structures resulting from cosmological simulations, which were previously used to establish the cluster mass determination method, and we show through their level of equilibration, that these structures have indeed only collapsed along the one dimension. We also extract the density profiles of these pancake, which agrees acceptably well with theoretical expectations. We derive the observable velocity distribution function (VDF) analytically by generalizing the Eddington method to one dimension, and we compare with the distribution function from the numerical simulation.
A Greedy, Flexible Algorithm to Learn an Optimal Bayesian Network Structure

In this report paper we first present a report of the Advanced Machine Learning Course Project on the provided data set and then present a novel heuristic algorithm for exact Bayesian network (BN) structure discovery that uses decomposable scoring functions. Our algorithm follows a different approach to solve the problem of BN structure discovery than the previously used methods such as Dynamic Programming (DP) and Branch and Bound to reduce the search space and find the global optima space for the problem. The algorithm we propose has some degree of flexibility that can make it more or less greedy. The more the algorithm is set to be greedy, the more the speed of the algorithm will be, and the less optimal the final structure. Our algorithm runs in a much less time than the previously known methods and guarantees to have an optimality of close to 99%. Therefore, it sacrifices less than one percent of score of an optimal structure in order to gain a much lower running time and make the algorithm feasible for large data sets (we may note that we never used any toolbox except for result validation)
Disorder-induced Purcell enhancement in nanoparticle chains

In this paper we report on numerical study of plasmonic nanoparticle chains with long-range dipole-dipole interaction. We have shown that introduction of positional disorder gives a peak in the density of resonant states (DOS) at the frequency of individual nanoparticle resonance. This peak is referred to Dyson singularity in one-dimensional disordered structures [Dyson F., 1953] and, according to our calculations, governs the spectral properties of local DOS. This provides disorder-induced Purcell enhancement that can found its applications in random lasers and for SERS spectroscopy. We stress that this effect relates not only to plasmonic nanoparticles but to an arbitrary chain of nanoparticles or atoms with resonant polarizabilities.
HCRS: A hybrid clothes recommender system based on user ratings and product features

Nowadays, online clothes-selling business has become popular and extremely attractive because of its convenience and cheap-and-fine price. Good examples of these successful Web sites include Yintai.com, Vancl.com and Shop.vipshop.com which provide thousands of clothes for online shoppers. The challenge for online shoppers lies on how to find a good product from lots of options. In this article, we propose a collaborative clothes recommender for easy shopping. One of the unique features of this system is the ability to recommend clothes in terms of both user ratings and clothing attributes. Experiments in our simulation environment show that the proposed recommender can better satisfy the needs of users.
Efficient Fuzzy Search Engine with B -Tree Search Mechanism

Search engines play a vital role in day to day life on internet. People use search engines to find content on internet. Cloud computing is the computing concept in which data is stored and accessed with the help of a third party server called as cloud. Data is not stored locally on our machines and the software's and information are provided to user if user demands for it. Search queries are the most important part in searching data on internet. A search query consists of one or more than one keywords. A search query is searched from the database for exact match, and the traditional searchable schemes do not tolerate minor typos and format inconsistencies, which happen quite frequently. This drawback makes the existing techniques unsuitable and they offer very low efficiency. In this paper, i will for the first time formulate the problem of effective fuzzy search by introducing tree search methodologies. I will explore the benefits of B trees in search mechanism and use them to have an efficient keyword search. I have taken into consideration the security analysis strictly so as to get a secure and privacy-preserving system.
Swimming in a crystal.

We study catalytic Janus particles and Escherichia coli bacteria swimming in a two-dimensional colloidal crystal. The Janus particles orbit individual colloids and hop between colloids stochastically, with a hopping rate that varies inversely with fuel (hydrogen peroxide) concentration. At high fuel concentration, these orbits are stable for 100s of revolutions, and the orbital speed oscillates periodically as a result of hydrodynamic, and possibly also phoretic, interactions between the swimmer and the six neighbouring colloids. Motile E. coli bacteria behave very differently in the same colloidal crystal: their circular orbits on plain glass are rectified into long, straight runs, because the bacteria are unable to turn corners inside the crystal.
Electrical properties of polar membranes

Biological membranes are capacitors that can be charged by applying a field across the membrane. The charges on the capacitor exert a force on the membrane that leads to electrostriction, i.e. a thinning of the membrane. Since the force is quadratic in voltage, negative and positive voltage have an identical influence on the physics of symmetric membranes. However, this is not the case for a membrane with an asymmetry leading to a permanent electric polarization. Positive and negative voltages of identical magnitude lead to different properties. Such an asymmetry can originate from a lipid composition that is different on the two monolayers of the membrane, or from membrane curvature. The latter effect is called 'flexoelectricity'. As a consequence of permanent polarization, the membrane capacitor is discharged at a voltage different from zero. This leads to interesting electrical phenomena such as outward or inward rectification of membrane permeability. 
Here, we introduce a generalized theoretical framework, that treats capacitance, polarization, flexoelectricity and piezoelectricity in the same language.
On Trading American Put Options with Interactive Volatility

We introduce a simple stochastic volatility model, whose novelty consists in taking into account hitting times of the asset price, and study the optimal stopping problem corresponding to a put option whose time horizon (after the asset price hits a certain level) is exponentially distributed. We obtain explicit optimal stopping rules in various cases one of which is interestingly complex because of an unexpected disconnected continuation region. Finally, we discuss in detail how these stopping rules could be used for trading an American put when the trader expects a market drop in the near future.
PLUTO: Penalized Unbiased Logistic Regression Trees

We propose a new algorithm called PLUTO for building logistic regression trees to binary response data. PLUTO can capture the nonlinear and interaction patterns in messy data by recursively partitioning the sample space. It fits a simple or a multiple linear logistic regression model in each partition. PLUTO employs the cyclical coordinate descent method for estimation of multiple linear logistic regression models with elastic net penalties, which allows it to deal with high-dimensional data efficiently. The tree structure comprises a graphical description of the data. Together with the logistic regression models, it provides an accurate classifier as well as a piecewise smooth estimate of the probability of "success". PLUTO controls selection bias by: (1) separating split variable selection from split point selection; (2) applying an adjusted chi-squared test to find the split variable instead of exhaustive search. A bootstrap calibration technique is employed to further correct selection bias. Comparison on real datasets shows that on average, the multiple linear PLUTO models predict more accurately than other algorithms.
Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

We propose a family of efficient algorithms for learning the parameters of a Bayesian network from incomplete data. Our approach is based on recent theoretical analyses of missing data problems, which utilize a graphical representation, called the missingness graph. In the case of MCAR and MAR data, this graph need not be explicit, and yet we can still obtain closed-form, asymptotically consistent parameter estimates, without the need for inference. When this missingness graph is explicated (based on background knowledge), even partially, we can obtain even more accurate estimates with less data. Empirically, we illustrate how we can learn the parameters of large networks from large datasets, which are beyond the scope of algorithms like EM (which require inference).
THE POSSIBLE MOON OF KEPLER-90g IS A FALSE POSITIVE

The discovery of an exomoon would provide deep insights into planet formation and the habitability of planetary systems, with transiting examples being particularly sought after. Of the hundreds of Kepler planets now discovered, the seven-planet system Kepler-90 is unusual for exhibiting an unidentified transit-like signal in close proximity to one of the transits of the long-period gas-giant Kepler-90g, as noted by Cabrera et al. As part of the “Hunt for Exomoons with Kepler” project, we investigate this possible exomoon signal and find it passes all conventional photometric, dynamical, and centroid diagnostic tests. However, pixel-level light curves indicate that the moon-like signal occurs on nearly all of the target's pixels, which we confirm using a novel way of examining pixel-level data which we dub the “transit centroid.” This test reveals that the possible exomoon to Kepler-90g is likely a false positive, perhaps due to a cosmic ray induced sudden pixel sensitivity dropout. This work highlights the extreme care required for seeking non-periodic low-amplitude transit signals, such as exomoons.
On the Recovery of Galaxy Properties from SED Fitting Solutions

We explore the ability of four different inverse population synthesis codes to recover the physical properties of galaxies from their spectra by SED fitting. Three codes, DynBaS, TGASPEX, and GASPEX, have been implemented by the authors and are described in detail in the paper. STARLIGHT, the fourth code, is publicly available. DynBaS selects dynamically a different spectral basis to expand the spectrum of each target galaxy, and TGASPEX uses an unconstrained age basis, whereas GASPEX and STARLIGHT use for all fits a fixed spectral basis selected a priori by the code developers. Variable and unconstrained basis reflect the peculiarities of the fitted spectrum and allow for simple and robust solutions to the problem of extracting galaxy parameters from spectral fits. We assemble a Synthetic Spectral Atlas of Galaxies (SSAG), comprising 100,000 galaxy spectra corresponding to an equal number of star formation histories based on the recipe of Chen et al. We select a subset of 120 galaxies from SSAG with a color distribution similar to that of local galaxies in the seventh data release (DR7) of the Sloan Digital Sky Survey (SDSS), and produce 30 random noise realizations for each of these spectra. For each spectrum, we recover the mass, mean age, metallicity, internal dust extinction, and velocity dispersion characterizing the dominant stellar population in the problem galaxy. All methods produce almost-perfect fits to the target spectrum, but the recovered physical parameters can differ significantly. Our tests provide a quantitative measure of the accuracy and precision with which these parameters are recovered by each method. From a statistical point of view, all methods yield similar precisions, whereas DynBaS produces solutions with minimal systematic biases in the distributions of residuals for all of these parameters. We caution the reader that the results obtained in our consistency tests represent lower limits to the uncertainties in parameter determination. Our tests compare theoretical galaxy spectra built from the same synthesis models used in the fits. Using different synthesis models and the lack of particular stellar types in the synthesis models but present in real galaxies will increase these errors considerably. Additional sources of error expected to be present in real galaxy spectra are not easy to emulate, and again will result in larger errors.
Characterizing extragalactic anomalous microwave emission in NGC 6946 with CARMA

Using 1 cm and 3 mm observations from the Combined Array for Research in Millimeter-wave Astronomy and 2 mm observations from the Goddard IRAM Superconducting 2 Millimeter Observer observations, we follow up the first extragalactic detection of anomalous microwave emission (AME) reported by Murphy et al. in an extranuclear region (Enuc. 4) of the nearby face-on spiral galaxy NGC 6946. We find the spectral shape and peak frequency of AME in this region to be consistent with models of spinning dust emission. However, the strength of the emission far exceeds the Galactic AME emissivity given the abundance of polycyclic aromatic hydrocarbons (PAHs) in that region. Using our galaxy-wide 1 cm map (21 arcsec resolution), we identify a total of eight 21 arcsec × 21 arcsec regions in NGC 6946 that harbour AME at >95 per cent significance at levels comparable to that observed in Enuc. 4. The remainder of the galaxy has 1 cm emission consistent with or below the observed Galactic AME emissivity per PAH surface density. We probe relationships between the detected AME and dust surface density, PAH emission, and radiation field, though no environmental property emerges to delineate regions with strong versus weak or non-existent AME. On the basis of these data and other AME observations in the literature, we determine that the AME emissivity per unit dust mass is highly variable. We argue that the spinning dust hypothesis, which predicts the AME power to be approximately proportional to the PAH mass, is therefore incomplete.
CONSISTENCY PROOF OF A FRAGMENT OF PV WITH SUBSTITUTION IN BOUNDED ARITHMETIC

This article presents a proof that Buss’s $S_2^2$ can prove the consistency of a fragment of Cook and Urquhart’s PV from which induction has been removed but substitution has been retained. This result improves Beckmann’s result, which proves the consistency of such a system without substitution in bounded arithmetic $S_2^1$. Our proof relies on the notion of “computation” of the terms of PV. In our work, we first prove that, in the system under consideration, if an equation is proved and either its left- or right-hand side is computed, then there is a corresponding computation for its right- or left-hand side, respectively. By carefully computing the bound of the size of the computation, the proof of this theorem inside a bounded arithmetic is obtained, from which the consistency of the system is readily proven. This result apparently implies the separation of bounded arithmetic because Buss and Ignjatović stated that it is not possible to prove the consistency of a fragment of PV without induction but with substitution in Buss’s $S_2^1$. However, their proof actually shows that it is not possible to prove the consistency of the system, which is obtained by the addition of propositional logic and other axioms to a system such as ours. On the other hand, the system that we have considered is strictly equational, which is a property on which our proof relies.