nils-herrmann/clean_scirepeval huggingface dataset:
Characterization of Graphs for Protein Structure Modeling and Recognition of Solubility

AbstractThis paper deals with the relations among structural, topological, and chemical properties of theE.Coli proteome from the vantage point of the solubility/aggregation propensity of proteins. Each E.Coliprotein is initially represented according to its known folded 3D shape. This step consists in representingthe available E.Coli proteins in terms of graphs. We rst analyze those graphs by considering puretopological characterizations, i.e., by analyzing the mass fractal dimension and the distribution underlyingboth shortest paths and vertex degrees. Results con rm the general architectural principles of proteins.Successively, we focus on the statistical properties of a representation of such graphs in terms of vectorscomposed of several numerical features, which we extracted from their structural representation. Wefound that protein size is the main discriminator for the solubility, while however there are other factorsthat help explaining the solubility degree. We nally analyze such data through a novel one-class classi er,with the aim of discriminating among very and poorly soluble proteins. Results are encouraging andconsolidate the potential of pattern recognition techniques when employed to describe complex biologicalsystems.Index terms| Protein analysis; Graph representation; Descriptors and complexity measures for graphs;One-class classi cation.
Variational cross-validation of slow dynamical modes in molecular kinetics.

Markov state models are a widely used method for approximating the eigenspectrum of the molecular dynamics propagator, yielding insight into the long-timescale statistical kinetics and slow dynamical modes of biomolecular systems. However, the lack of a unified theoretical framework for choosing between alternative models has hampered progress, especially for non-experts applying these methods to novel biological systems. Here, we consider cross-validation with a new objective function for estimators of these slow dynamical modes, a generalized matrix Rayleigh quotient (GMRQ), which measures the ability of a rank-m projection operator to capture the slow subspace of the system. It is shown that a variational theorem bounds the GMRQ from above by the sum of the first m eigenvalues of the system's propagator, but that this bound can be violated when the requisite matrix elements are estimated subject to statistical uncertainty. This overfitting can be detected and avoided through cross-validation. These result make it possible to construct Markov state models for protein dynamics in a way that appropriately captures the tradeoff between systematic and statistical errors.
Radio observations of GRB host galaxies

We present 5.5 and 9.0GHz observations of a sample of seventeen GRB host galaxies at 0.5 < z < 1.4, using the radio continuum to explore their star formation properties in the context of the small but growing sample of galaxies with similar observations. Four sources are detected, one of those (GRB100418A) likely due to lingering afterglow emission. We suggest that the previously-reported radio afterglow of GRB100621A may instead be due to host galaxy flux. We see no strong evidence for redshift evolution in the typical star formation rate of GRB hosts, but note that the fraction of ‘dark’ bursts with detections is higher than would be expected given constraints on the more typical long GRB population. We also determine the average radio-derived star formation rates of core collapse supernovae at comparable redshift, and show that these are still well below the limits obtained for GRB hosts, and show evidence for a rise in typical star formation rate with redshift in supernova hosts.
Higher derivatives and power spectrum in effective single field inflation

We study next-to-leading corrections to the effective action of the curvature perturbation obtained by integrating out the coupled heavy isocurvature perturbation. These corrections result from including higher order derivative operators, weighted by the mass scale of the heavy physics, in the effective theory expansion. We find that the correction terms are suppressed by the ratio of the Hubble parameter to the heavy mass scale. The corresponding corrections to the power spectrum of the curvature perturbation are presented for a simple illustrative example.
MONEYBaRL: Exploiting pitcher decision-making using Reinforcement Learning

This manuscript uses machine learning techniques to exploit baseball pitchers' decision making, so-called "Baseball IQ," by modeling the at-bat information, pitch selection and counts, as a Markov Decision Process (MDP). Each state of the MDP models the pitcher's current pitch selection in a Markovian fashion, conditional on the information immediately prior to making the current pitch. This includes the count prior to the previous pitch, his ensuing pitch selection, the batter's ensuing action and the result of the pitch.
A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.

It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.
Twitter User Classification using Ambient Metadata

Microblogging websites, especially Twitter have become an important means of communication, in today's time. Often these services have been found to be faster than conventional news services. With millions of users, a need was felt to classify users based on ambient metadata associated with their user accounts. We particularly look at the effectiveness of the profile description field in order to carry out the task of user classification. Our results show that such metadata can be an effective feature for any classification task.
Emergent time axis from statistic/gravity dualities

We discuss a very naive but natural idea that time emerges as the holographic dimension of gauge systems in euclidean space, which take statistic, e.g. Ising model as concrete implementations. By identifying the renormalization group flow of statistic models with the time flow of dual gravities, we get a universe whose evolution history is qualitatively the same as our real world. We comment highlights projected by this idea on the cosmological constant problem and develop preliminary evidences for the validity of this idea.
Einstein and Planck on mass–energy equivalence in 1905–1906: a modern perspective

Einsteinʼs theoretical analysis of mass–energy equivalence, already at the time, experimentally evident in radioactive decays, in two papers published in 1905, as well as Planckʼs introduction, in 1906, of the concepts of relativistic momentum, and, by invoking Hamiltonʼs principle, relativistic energy, are reviewed and discussed. Claims in the literature that Einsteinʼs analysis was flawed, lacked generality, or was not rigorous, are rebutted.
THE BONES OF THE MILKY WAY

The very long and thin infrared dark cloud “Nessie” is even longer than had been previously claimed, and an analysis of its Galactic location suggests that it lies directly in the Milky Way's mid-plane, tracing out a highly elongated bone-like feature within the prominent Scutum–Centaurus spiral arm. Re-analysis of mid-infrared imagery from the Spitzer Space Telescope shows that this infrared dark cloud (IRDC) is at least two and possibly as many as five times longer than had originally been claimed by Nessie's discoverers; its aspect ratio is therefore at least 300:1 and possibly as large as 800:1. A careful accounting for both the Sun's offset from the Galactic plane (∼25 pc) and the Galactic center's offset from the (lII, bII) = (0, 0) position shows that the latitude of the true Galactic mid-plane at the 3.1 kpc distance to the Scutum–Centaurus Arm is not b = 0, but instead closer to b = −0.4, which is the latitude of Nessie to within a few parsecs. An analysis of the radial velocities of low-density (CO) and high-density (NH3) gas associated with the Nessie dust feature suggests that Nessie runs along the Scutum–Centaurus Arm in position–position–velocity space, which means it likely forms a dense “spine” of the arm in real space as well. The Scutum–Centaurus Arm is the closest major spiral arm to the Sun toward the inner Galaxy, and, at the longitude of Nessie, it is almost perpendicular to our line of sight, making Nessie the easiest feature to see as a shadow elongated along the Galactic plane from our location. Future high-resolution dust mapping and molecular line observations of the harder-to-find Galactic “bones” should allow us to exploit the Sun's position above the plane to gain a (very foreshortened) view “from above” the Milky Way's structure.
DWARF GALAXY ANNIHILATION AND DECAY EMISSION PROFILES FOR DARK MATTER EXPERIMENTS

Gamma-ray searches for dark matter annihilation and decay in dwarf galaxies rely on an understanding of the dark matter density profiles of these systems. Conversely, uncertainties in these density profiles propagate into the derived particle physics limits as systematic errors. In this paper we quantify the expected dark matter signal from 20 Milky Way dwarfs using a uniform analysis of the most recent stellar-kinematic data available. Assuming that the observed stellar populations are equilibrium tracers of spherically symmetric gravitational potentials that are dominated by dark matter, we find that current stellar-kinematic data can predict the amplitudes of annihilation signals to within a factor of a few for the ultra-faint dwarfs of greatest interest. On the other hand, the expected signal from several classical dwarfs (with high-quality observations of large numbers of member stars) can be localized to the ∼20% level. These results are important for designing maximally sensitive searches in current and future experiments using space and ground-based instruments.
Learning From Ordered Sets and Applications in Collaborative Ranking

Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed $5$ stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches $(N!/2)6.93145^{N+1}$ as $N$ approaches infinity. We propose a \texttt{split-and-merge} Metropolis-Hastings procedure that can explore the state-space efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods.
Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis

Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and matrix-variate ordinal data. We show that our model is able to capture latent opinion profile of citizens around the world, and is competitive against state-of-art collaborative filtering techniques on large-scale public datasets. The model thus has the potential to extend application of RBMs to diverse domains such as recommendation systems, product reviews and expert assessments.
Conditional Restricted Boltzmann Machines for Cold Start Recommendations

Restricted Boltzman Machines (RBMs) have been successfully used in recommender systems. However, as with most of other collaborative filtering techniques, it cannot solve cold start problems for there is no rating for a new item. In this paper, we first apply conditional RBM (CRBM) which could take extra information into account and show that CRBM could solve cold start problem very well, especially for rating prediction task. CRBM naturally combine the content and collaborative data under a single framework which could be fitted effectively. Experiments show that CRBM can be compared favourably with matrix factorization models, while hidden features learned from the former models are more easy to be interpreted.
New data, new possibilities: Exploring the insides of Altmetric.com

This paper analyzes Altmetric.com , one of the most important altmetric data providers currently used. We have analyzed a set of publications with doi number indexed in the Web of Science during the period 2011-2013 and collected their data with the Altmetric API. 19% of the original set of papers was retrieved from Altmetric.com including some altmetric data. We identified 16 different social media sources from which Altmetric.com retrieves data. However five of them cover 95.5% of the total set. Twitter (87.1%) and Mendeley (64.8%) have the highest coverage. We conclude that Altmetric.com is a transparent, rich and accurate tool for altmetric data. Nevertheless, there are still potential limitations on its exhaustiveness as well as on the selection of social media sources that need further research.
Opinion Dynamics and Price Formation: a Nonlinear Network Model

Opinions and beliefs determine the evolution of social systems. This is of particular interest in finance, as the increasing complexity of financial systems is coupled with information overload. Opinion formation, therefore, is not always the result of optimal information processing. On the contrary, agents are boundedly rational and naturally tend to observe and imitate others in order to gain further insights. Hence, a certain degree of interaction, which can be envisioned as a network, occurs within the system. Opinions, the interaction network and prices in financial markets are then heavily intertwined and influence one another. We build on previous contributions on adaptive systems, where agents have hetereogenous beliefs, and introduce a dynamic confidence network that captures the interaction and shapes the opinion patterns. The analytical framework we adopt for modeling the interaction is rooted in the opinion dynamics problem. This will allow us to introduce a nonlinear model where the confidence network, opinion dynamics and price formation coevolve in time. A key aspect of the model is the classification of agents according to their topological role in the network, therefore showing that topology matters in determining how of opinions and prices will coevolve. We illustrate the dynamics via simulations, discussing the stylized facts in finance that the model is able to capture. Last, we propose an empirical validation and calibration scheme that makes use of social network data.
RFQ Cooler SHIRaC developent

The development of a new RFQ Cooler, named SHIRaC, was carried out to handle and cool typical SPIRAL 2 beams of large emittances (up to 80 {\pi}.mm.mrad) and high currents (up to 1 {\micro}A). SHIRaC is a part of SPIRAL 2 facility at GANIL laboratory in France. Its purposes are to enhance as much as possible the beam quality: transverse geometric emittance of less than 3 {\pi}.mm.mrad and a longitudinal energy spread close to 1 eV, and to transmit more than 60 % of ions. Numerical simulations and experimental studies have shown that the required beam quality can be reached only in the term of the emittance. The energy spread is very far from expected values. It is sensitive to the space charge and buffer gas diffusion and more importantly to the RF field derivative effect. The latter arises at the RFQ exit and increases with the following RF parameters: the frequency and the amplitude of the RF voltage applied to the RFQ electrodes. Studies allowing to enhance the cooled beam quality, mainly the energy spread reduction, are presented and discussed along this paper. They consist in implementing a miniature RFQ at the RFQ exit. Using the development of these studies, it becomes possible to enhance the cooled beam quality and to reach 1 eV of longitudinal energy spread and less than 1.8 {\pi}.mm.mrad of transverse geometric emittance for beam currents going up to 1 {\micro}A. The transport of the cooled beam from the exit of the extraction section towards a HRS has been done with an electrostatic quadrupole triplet. Simulations and first experimental tests showed that more than 95 % of cooled beams can reach the HRS. Finally, developments related to the nuclearization protection methods aimed to avoid the escape of any nuclear matter from the SHIRaC beamline are studied
On anti-de Sitter oscillators and de Sitter anti-oscillators

We revisit the principal arguments for interpreting the free quantum fields on anti-de Sitter or de Sitter spacetimes of any dimensions as oscillators or respectively anti-oscillators. In addition, we point out that there exists a chart on the de Sitter background where the free Dirac field becomes a genuine anti-oscillator in the non-relativistic limit in the sense of special relativity.
Learning Overcomplete Latent Variable Models through Tensor Methods

We provide guarantees for learning latent variable models emphasizing on the overcomplete regime, where the dimensionality of the latent space exceeds the observed dimensionality. In particular, we consider multiview mixtures, ICA, and sparse coding models. Our main tool is a new algorithm for tensor decomposition that works in the overcomplete regime. In the semi-supervised setting, we exploit label information to get a rough estimate of the model parameters, and then refine it using the tensor method on unlabeled samples. We establish learning guarantees when the number of components scales as k = o(d), where d is the observed dimension, and p is the order of the observed moment employed in the tensor method (usually p = 3, 4). In the unsupervised setting, a simple initialization algorithm based on SVD of the tensor slices is proposed, and the guarantees are provided under the stricter condition that k ≤ βd (where constant β can be larger than 1). For the learning applications, we provide tight sample complexity bounds through novel covering arguments.
On the charge transfer between single-walled carbon nanotubes and graphene

It is important to understand the electronic interaction between single-walled carbon nanotubes (SWNTs) and graphene in order to use them efficiently in multifunctional hybrid devices. Here, we deposited SWNT bundles on graphene-covered copper and SiO2 substrates by chemical vapor deposition and investigated the charge transfer between them by Raman spectroscopy. Our results revealed that, on both copper and SiO2 substrates, graphene donates electrons to the SWNTs, resulting in p-type doped graphene and n-type doped SWNTs.
Estimating Maximally Probable Constrained Relations by Mathematical Programming

Estimating a constrained relation is a fundamental problem in machine learning. Special cases are classification (the problem of estimating a map from a set of to-be-classified elements to a set of labels), clustering (the problem of estimating an equivalence relation on a set) and ranking (the problem of estimating a linear order on a set). We contribute a family of probability measures on the set of all relations between two finite, non-empty sets, which offers a joint abstraction of multi-label classification, correlation clustering and ranking by linear ordering. Estimating (learning) a maximally probable measure, given (a training set of) related and unrelated pairs, is a convex optimization problem. Estimating (inferring) a maximally probable relation, given a measure, is a 01-linear program. It is solved in linear time for maps. It is NP-hard for equivalence relations and linear orders. Practical solutions for all three cases are shown in experiments with real data. Finally, estimating a maximally probable measure and relation jointly is posed as a mixed-integer nonlinear program. This formulation suggests a mathematical programming approach to semi-supervised learning.
PHOTOMETRIC STUDY OF THE PULSATING, ECLIPSING BINARY OO DRA

We present a comprehensive photometric study of the pulsating, eclipsing binary OO Dra. Simultaneous B- and V-band photometry of the star was carried out on 14 nights. A revised orbital period and a new ephemeris were derived from the data. The first photometric solution of the binary system and the physical parameters of the component stars are determined. They reveal that OO Dra could be a detached system with a less-massive secondary component nearly filling its Roche lobe. By subtracting the eclipsing light changes from the data, we obtained the intrinsic pulsating light curves of the hotter, massive primary component. A frequency analysis of the residual light yields two confident pulsation modes in both B- and V-band data with the dominant frequency detected at 41.865 c/d. A brief discussion concerning the evolutionary status and the pulsation nature of the binary system is finally given.
Adaptive Learning in Cartesian Product of Reproducing Kernel Hilbert Spaces

We propose a novel adaptive learning algorithm based on iterative orthogonal projections in the Cartesian product of multiple reproducing kernel Hilbert spaces (RKHSs). The objective is to estimate or track nonlinear functions that are supposed to contain multiple components such as i) linear and nonlinear components and ii) high- and low- frequency components. In this case, the use of multiple RKHSs permits a compact representation of multicomponent functions. The proposed algorithm is where two different methods of the author meet: multikernel adaptive filtering and the algorithm of hyperplane projection along affine subspace (HYPASS). In a particular case, the “sum” space of the RKHSs is isomorphic, under a straightforward correspondence, to the product space, and hence the proposed algorithm can also be regarded as an iterative projection method in the sum space. The efficacy of the proposed algorithm is shown by numerical examples.
Volumes of logistic regression models with applications to model selection

Logistic regression models with $n$ observations and $q$ linearly-independent covariates are shown to have Fisher information volumes which are bounded below by $\pi^q$ and above by ${n \choose q} \pi^q$. This is proved with a novel generalization of the classical theorems of Pythagoras and de Gua, which is of independent interest. The finding that the volume is always finite is new, and it implies that the volume can be directly interpreted as a measure of model complexity. The volume is shown to be a continuous function of the design matrix $X$ at generic $X$, but to be discontinuous in general. This means that models with sparse design matrices can be significantly less complex than nearby models, so the resulting model-selection criterion prefers sparse models. This is analogous to the way that $\ell^1$-regularisation tends to prefer sparse model fits, though in our case this behaviour arises spontaneously from general principles. Lastly, an unusual topological duality is shown to exist between the ideal boundaries of the natural and expectation parameter spaces of logistic regression models.