nils-herrmann/clean_scirepeval huggingface dataset:
QCD results from ATLAS and CMS

— The latest QCD measurements performed with the ATLAS and the CMS detectors operating at the Large Hadron Collider (LHC) are summarized. The results reported here are mostly based on 7 TeV data collected by the experiments during the 2011 and 2010 proton-proton data-taking periods corresponding respectively to integrated luminosity of 4.6 fb−1 and 36 pb−1. PACS 13.87.-a – Jets in large-Q scattering. PACS 14.70.-e – Gauge bosons. PACS 14.65.-q – Quarks. PACS 14.60.-z – Leptons.
Planck constraints on Higgs modulated reheating of renormalization group improved inflation

Within the framework of RG improved inflationary cosmology motivated by asymptotically safe gravity, we study the dynamics of a scalar field which can be interpreted as the Higgs field. The background trajectories of this model can provide sufficient inflationary e-folds and a graceful exit to a radiation dominated phase. We study the possibility of generating primordial curvature perturbations through the Standard Model Higgs boson. This can be achieved under finely tuned parameter choices by making use of the modulated reheating mechanism. The primordial non-gaussianity is expected to be sizable in this model. Though tightly constrained by the newly released Planck CMB data, this model provides a potentially interesting connection between collider and early universe physics.
THE FIRST HARD X-RAY POWER SPECTRAL DENSITY FUNCTIONS OF ACTIVE GALACTIC NUCLEUS

We present results of our power spectral density (PSD) analysis of 30 active galactic nuclei (AGNs) using the 58 month light curves from Swift's Burst Alert Telescope (BAT) in the 14–150 keV band. PSDs were fit using a Monte Carlo based algorithm to take into account windowing effects and measurement error. All but one source were found to be fit very well using an unbroken power law with a slope of ∼ − 1, consistent at low frequencies with previous studies in the 2–10 keV band, with no evidence of a break in the PSD. For five of the highest signal-to-noise ratio sources, we tested the energy dependence of the PSD and found no significant difference in the PSD at different energies. Unlike previous studies of X-ray variability in AGNs, we do not find any significant correlations between the hard X-ray variability and different properties of the AGN including luminosity and black hole mass. The lack of break frequencies and correlations seem to indicate that AGNs are similar to the high state of Galactic black holes.
Molecular hydrogen in the damped Lyman α system towards GRB 120815A at z = 2.36

We present the discovery of molecular hydrogen (H2), including the presence of vibrationally-excited H2* in the optical spectrum of the afterglow of GRB 120815A at z = 2.36 obtained with X-shooter at the VLT. Simultaneous photometric broad-band data from GROND and X-ray observations by Swift/XRT place further constraints on the amount and nature of dust along the sightline. The galactic environment of GRB 120815A is characterized by a strong DLA with log(N(H i)/cm-2) = 21.95 ± 0.10, prominent H2 absorption in the Lyman-Werner bands (log (N(H2)/cm-2) = 20.54 ± 0.13) and thus a molecular gas fraction log f(H2) = -1.14 ± 0.15. The distance d between the absorbing neutral gas and GRB 120815A is constrained via photo-excitation modeling of fine-structure and meta-stable transitions of Fe ii and Ni ii to d = 0.5 ± 0.1 kpc. The DLA metallicity ([Zn/H] = -1.15 ± 0.12), visual extinction (AV ≲ 0.15 mag) and dust depletion ([Zn/Fe] = 1.01 ± 0.10) are intermediate between the values of well-studied, H2-deficient GRB-DLAs observed at high spectral resolution, and the approximately solar metallicity, highly-obscured and H2-rich GRB 080607 sightline. With respect to N(H i), metallicity, as well as dust-extinction and depletion, GRB 120815A is fairly representative of the average properties of GRB-DLAs. This demonstrates that molecular hydrogen is present in at least a fraction of the more typical GRB-DLAs, and H2 and H2* are probably more wide-spread among GRB-selected systems than the few examples of previous detections would suggest. Because H2* transitions are located redwards of the Lyman α absorption, H2* opens a second route for positive searches for molecular absorption also in GRB afterglows at lower redshifts and observed at lower spectral resolution. Further detections of molecular gas in GRB-DLAs would allow statistical studies, and, coupled with host follow-up and sub-mm spectroscopy, provide unprecedented insights into the process and conditions of star-formation at high redshift.
Using memory to identify phase transitions on a Cayley Tree

We provide a concrete and systematic connection between the statistical physics of the Ising ferromagnet on a Cayley tree, and the study of memory in exponentially expanding spaces. Memory turns out to be a clear signal of the `Bethe-Peierls' phase transition, and the average of memory divided by its standard deviation provides a clear signal of the `spin-glass' transition temperature. Numerical Monte Carlo simulations are used to make transparent the existence of the two different transition temperatures. The quantities used to spot the phase transitions with Monte Carlo could be useful when studying other systems where analytical methods don't work.
An Algorithm for Training Polynomial Networks

We consider deep neural networks, in which the output of each node is a quadratic function of its inputs. Similar to other deep architectures, these networks can compactly represent any function on a finite training set. The main goal of this paper is the derivation of an efficient layer-by-layer algorithm for training such networks, which we denote as the \emph{Basis Learner}. The algorithm is a universal learner in the sense that the training error is guaranteed to decrease at every iteration, and can eventually reach zero under mild conditions. We present practical implementations of this algorithm, as well as preliminary experimental results. We also compare our deep architecture to other shallow architectures for learning polynomials, in particular kernel learning.
Reconstructing protein binding patterns from ChIP time-series

Gene transcription requires the orchestrated binding of various proteins to the promoter of a gene. The binding times and binding order of proteins allow to draw conclusions about the proteins' exact function in the recruitment process. Time-resolved ChIP experiments are being used to analyze the order of protein binding for these processes. However, these ChIP signals do not represent the exact protein binding patterns.  We show that for promoter complexes that follow sequential recruitment dynamics the ChIP signal can be understood as a convoluted signal and propose the application of deconvolution methods to recover the protein binding patterns from experimental ChIP time-series. We analyze the suitability of four deconvolution methods: two non-blind deconvolution methods, Wiener deconvolution and Lucy-Richardson deconvolution, and two blind deconvolution methods, blind Lucy-Richardson deconvolution and binary blind deconvolution. We apply these methods to infer the protein binding pattern from ChIP time-series for the pS2 gene.
Supervised Heterogeneous Multiview Learning for Joint Association Study and Disease Diagnosis

Given genetic variations and various phenotypical traits, such as Magnetic Resonance Imaging (MRI) features, we consider two important and related tasks in biomedical research: i)to select genetic and phenotypical markers for disease diagnosis and ii) to identify associations between genetic and phenotypical data. These two tasks are tightly coupled because underlying associations between genetic variations and phenotypical features contain the biological basis for a disease. A variety of models have been applied for disease diagnosis or association studies (e.g., eQTL analysis), separately. To unify these two tasks, we present a new sparse Bayesian approach for joint association study and disease diagnosis. In this approach, common latent features are extracted from different data sources based on sparse projection matrices and used to predict multiple disease severity levels based on Gaussian process ordinal regression; in return, the disease status is used to guide the discovery of relationships between the data sources. To learn the model from data, we develop an efficient variational expectation maximization algorithm. We apply our approach to an imaging genetics dataset for the study of Alzheimer’s Disease (AD). Our method identifies biologically meaningful relationships between genetic variations, MRI features, and AD status, and achieves significantly higher accuracy for predicting ordinal AD stages than the competing methods.
TimeML-strict: clarifying temporal annotation

TimeML is an XML-based schema for annotating temporal information over discourse. The standard has been used to annotate a variety of resources and is followed by a number of tools, the creation of which constitute hundreds of thousands of man-hours of research work. However, the current state of resources is such that many are not valid, or do not produce valid output, or contain ambiguous or custom additions and removals. Difficulties arising from these variances were highlighted in the TempEval-3 exercise, which included its own extra stipulations over conventional TimeML as a response. 
To unify the state of current resources, and to make progress toward easy adoption of its current incarnation ISO-TimeML, this paper introduces TimeML-strict: a valid, unambiguous, and easy-to-process subset of TimeML. We also introduce three resources -- a schema for TimeML-strict; a validator tool for TimeML-strict, so that one may ensure documents are in the correct form; and a repair tool that corrects common invalidating errors and adds disambiguating markup in order to convert documents from the laxer TimeML standard to TimeML-strict.
Web graph compression with fast access

In recent years studying the content of the World Wide Web became a very important yet rather difficult task. There is a need for a compression technique that would allow a web graph representation to be put into the memory while maintaining random access time competitive to the time needed to access uncompressed web graph on a hard drive. 
There are already available techniques that accomplish this task, but there is still room for improvements and this thesis attempts to prove it. It includes a comparison of two methods contained in state of art of this field (BV and k2partitioned) to two already implemented algorithms (rewritten, however, in C++ programming language to maximize speed and resource management efficiency), which are LM and 2D, and introduces the new variant of the latter one, called 2D stripes. 
This thesis serves as well as a proof of concept. The final considerations show positive and negative aspects of all presented methods, expose the feasibility of the new variant as well as indicate future direction for development.
Selection and influence in cultural dynamics

Human societies exhibit many forms of cultural diversity --- in the languages that are spoken, in the opinions and values that are held, and in many other dimensions. An active body of research in the mathematical social sciences has developed models for reasoning about the origins of this diversity, and about how it evolves over time.
 One of the fundamental principles driving cultural diversity is the tension between two forces: influence and selection. Influencerefers to the tendency of people to become similar to those with whom they interact, whereas selection is the tendency of people to interact with those who are already more similar to them, and/or to be more receptive to influence from those who are similar. Both of these forces lead toward outcomes in which people end up interacting with others like themselves, but in different ways: influence tends to promote homogeneity, as people shift their behaviors to become alike, while selection tends to promote fragmentation, in which a society can split into multiple groups that have less and less interaction with each other. Research that offers qualitative analyses for issues such as consensus-building, political polarization, or social stratification can often be interpreted through the lens of this influence-selection tradeoff.
 In order to study the joint effects of influence and selection more formally, we analyze a natural model built upon active lines of work in political opinion formation, cultural diversity, and language evolution. Our model posits an arbitrary graph structure describing which "types" of people can influence one another: this captures effects based on the fact that people are only influenced by sufficiently similar interaction partners. A continuum of individuals occupies the nodes of the graph, representing the division of types (such as opinions or languages) among a large population. Individuals, based on their interactions with others, can move between types in the graph. In a generalization of the model, we introduce another graph structure describing which types of people even so much as come in contact with each other. These restrictions on interaction patterns can significantly alter the dynamics of the process at the population level.
 For the basic version of the model, in which all individuals come in contact with all others, we achieve an essentially complete characterization of (stable) equilibrium outcomes and prove convergence from all starting states: the main theorem states that stable equilibria are exactly those states in which the set of types with non-empty populations is an independent set. For the other extreme case, in which individuals only come in contact with others who have the potential to influence them, the underlying process is significantly more complicated; nevertheless, we achieve an analysis for certain graph structures.
Balancing small fixed and proportional transaction cost in trading strategies

Transaction costs appear in financial markets in more than one form. There are several results in the literature on small proportional transaction cost and not that many on fixed transaction cost. In the present work, we heuristically study the effect of both types of transaction cost by focusing on a portfolio optimization. Here we assume the presence of fixed transaction cost and that there is a balance between fixed and proportional transaction cost, such that none of them dominates the other, asymptotically. We find out that the deviation of value function, when the fixed transaction cost is $\varepsilon$, from the Merton value function, without transaction cost, is of order $\varepsilon^1/2$ which is different from the pure proportional cost of $\varepsilon^2/3$. Based on this, we propose an expansion for the value function in terms of powers of $\varepsilon^1/2$.
Pricing TARNs Using a Finite Difference Method

Target accumulation redemption notes (TARNs) are a popular product with Asian foreign exchange investors. They have a number of variations, but their main feature is that option-like payoffs occur on a set of future dates, depending on whether the underlying exchange rate is above or below a specified strike level. The total payout is capped and the contract expires when the cap is reached. The standard valuation technique for this path-dependent payoff is Monte Carlo simulation. In this article, Luo and Shevchenko develop an efficient finite-difference approximation that accommodates path dependence and even non-diffusive jumps in the exchange rate. In a comparison simulation against Monte Carlo, the new technique achieves much better accuracy in less execution time.
Space-Time-Matter

This essay examines our fundamental conceptions of time, spacetime, the asymmetry of time, and the motion of a quantum mechanical particle. The concept of time has multiple meanings and these are often confused in the literature and must be distinguished if any light is to be thrown on this age-old issue. The asymmetry of time also has different meanings that depend on context-although the fundamental time asymmetry is associated with the expansion of the universe. These and related issues are discussed in both classical and quantum mechanical contexts.
CHANDRA IDENTIFICATION OF 26 NEW BLACK HOLE CANDIDATES IN THE CENTRAL REGION OF M31

We have previously identified 10 M31 black hole candidates (BHCs) in M31 from their X-ray properties alone. They exhibit “hard state” emission spectra that are seen at luminosities ≲10% Eddington in X-ray binaries (XBs) containing a neutron star (NS) or black hole, at luminosities that significantly exceed the NS threshold. Nine of these are associated with globular clusters (GCs); hence, these are most likely low mass X-ray binaries; eight are included in this survey. We have recently discovered that analysis of the long term 0.5–4.5 keV variability of XBs via structure functions allows us to separate XBs from active galactic nuclei, even though the emission spectra are often similar; this has enabled us to search for BHCs outside of GCs. We have identified 26 new BHCs (12 strong, 14 plausible) within 20′ of the M31 nucleus (M31*), using 152 Chandra observations spaced over ∼13 yr; some of our classifications were enhanced with XMM-Newton observations. Of these, seven appear within 100″ of M31*; this supports the theory suggesting that this region experiences enhanced XB production via dynamical processes similar to those seen in GCs. We have found a parameter space where our BHCs are separated from Galactic NS binaries: we show that modeling a simulated hard state spectrum with a disk blackbody + blackbody model yields parameters that lie outside the space occupied by NS binaries that are modeled this way. The probability that our BHCs all lie within the NS parameter space is ∼3 × 10−29.
Left Bit Right: For SPARQL Join Queries with OPTIONAL Patterns (Left-outer-joins)

SPARQL basic graph pattern (BGP) (a.k.a. SQL inner-join) query optimization is a well researched area. However, optimization of OPTIONAL pattern queries (a.k.a. SQL left-outer-joins) poses additional challenges, due to the restrictions on the reordering of left-outer-joins. The occurrence of such queries tends to be as high as 50% of the total queries (e.g., DBPedia query logs). In this paper, we present Left Bit Right (LBR), a technique for well-designed nested BGP and OPTIONAL pattern queries. Through LBR, we propose a novel method to represent such queries using a graph of supernodes, which is used to aggressively prune the RDF triples, with the help of compressed indexes. We also propose novel optimization strategies -- first of a kind, to the best of our knowledge -- that combine together the characteristics of acyclicity of queries, minimality, and nullification, best-match operators. In this paper, we focus on OPTIONAL patterns without UNIONs or FILTERs, but we also show how UNIONs and FILTERs can be handled with our technique using a query rewrite. Our evaluation on RDF graphs of up to and over one billion triples, on a commodity laptop with 8 GB memory, shows that LBR can process well-designed low-selectivity complex queries up to 11 times faster compared to the state-of-the-art RDF column-stores as Virtuoso and MonetDB, and for highly selective queries, LBR is at par with them.
STARLIB: A NEXT-GENERATION REACTION-RATE LIBRARY FOR NUCLEAR ASTROPHYSICS

STARLIB is a next-generation, all-purpose nuclear reaction-rate library. For the first time, this library provides the rate probability density at all temperature grid points for convenient implementation in models of stellar phenomena. The recommended rate and its associated uncertainties are also included. Currently, uncertainties are absent from all other rate libraries, and, although estimates have been attempted in previous evaluations and compilations, these are generally not based on rigorous statistical definitions. A common standard for deriving uncertainties is clearly warranted. STARLIB represents a first step in addressing this deficiency by providing a tabular, up-to-date database that supplies not only the rate and its uncertainty but also its distribution. Because a majority of rates are lognormally distributed, this allows the construction of rate probability densities from the columns of STARLIB. This structure is based on a recently suggested Monte Carlo method to calculate reaction rates, where uncertainties are rigorously defined. In STARLIB, experimental rates are supplemented with: (1) theoretical TALYS rates for reactions for which no experimental input is available, and (2) laboratory and theoretical weak rates. STARLIB includes all types of reactions of astrophysical interest to Z = 83, such as (p, γ), (p, α), (α, n), and corresponding reverse rates. Strong rates account for thermal target excitations. Here, we summarize our Monte Carlo formalism, introduce the library, compare methods of correcting rates for stellar environments, and discuss how to implement our library in Monte Carlo nucleosynthesis studies. We also present a method for accessing STARLIB on the Internet and outline updated Monte Carlo-based rates.
Coupled-channel scattering in 1+1 dimensional lattice model

Based on the Lippmann-Schwinger equation approach, a generalized Luscher’s formula in 1+1 dimensions for two particles scattering in both the elastic and coupled-channel cases in moving frames is derived. A two-dimensional coupled-channel scattering lattice model is presented, which represents a two-coupled-channel resonant scattering scalars system. The Monte Carlo simulation is performed on finite lattices and in various moving frames. The two-dimensional generalized Luscher’s formula is used to extract the scattering amplitudes for the coupled-channel system from the discrete finite-volume spectrum.
A Hybrid Rule Based Fuzzy-Neural Expert System For Passive Network Monitoring

An enhanced approach for network monitoring is to create a network monitoring tool that has artificial intelligence characteristics. There are a number of approaches available. One such approach is by the use of a combination of rule based, fuzzy logic and neural networks to create a hybrid ANFIS system. Such system will have a dual knowledge database approach. One containing membership function values to compare to and do deductive reasoning and another database with rules deductively formulated by an expert (a network administrator). The knowledge database will be updated continuously with newly acquired patterns. In short, the system will be composed of 2 parts, learning from data sets and fine-tuning the knowledge-base using neural network and the use of fuzzy logic in making decision based on the rules and membership functions inside the knowledge base. This paper will discuss the idea, steps and issues involved in creating such a system.
On the Complexity of Query Answering under Matching Dependencies for Entity Resolution

Matching Dependencies (MDs) are a relatively recent proposal for declarative entity resolution. They are rules that specify, given the similarities satisfied by values in a database, what values should be considered duplicates, and have to be matched. On the basis of a chase-like procedure for MD enforcement, we can obtain clean (duplicate-free) instances; actually possibly several of them. The resolved answers to queries are those that are invariant under the resulting class of resolved instances. In previous work we identified some tractable cases (i.e. for certain classes of queries and MDs) of resolved query answering. In this paper we further investigate the complexity of this problem, identifying some intractable cases. For a special case we obtain a dichotomy complexity result.
Enhancements to ACL2 in Versions 5.0, 6.0, and 6.1

We report on highlights of the ACL2 enhancements introduced in ACL2 releases since the 2011 ACL2 Workshop. Although many enhancements are critical for soundness or robustness, we focus in this paper on those improvements that could benefit users who are aware of them, but that might not be discovered in everyday practice.