nils-herrmann/clean_scirepeval huggingface dataset:
Charmed hadron spectroscopy on the lattice for Nf = 2 + 1 flavours

We study the spectra of charmonia, charmed mesons, singly and doubly charmed baryons using Lattice QCD, with 2+1 flavours of fermions. In the case of mesons, we include higher spin states, while for baryons, both positive and negative parity channels were investigated. By means of the variational method, we were able to extract a clean signal from the correlation functions and information about the excited states.
Manipulating Google Scholar Citations and Google Scholar Metrics: simple, easy and tempting

The launch of Google Scholar Citations and Google Scholar Metrics may provoke a revolution in the research evaluation field as it places within every researchers reach tools that allow bibliometric measuring. In order to alert the research community over how easily one can manipulate the data and bibliometric indicators offered by Google s products we present an experiment in which we manipulate the Google Citations profiles of a research group through the creation of false documents that cite their documents, and consequently, the journals in which they have published modifying their H index. For this purpose we created six documents authored by a faked author and we uploaded them to a researcher s personal website under the University of Granadas domain. The result of the experiment meant an increase of 774 citations in 129 papers (six citations per paper) increasing the authors and journals H index. We analyse the malicious effect this type of practices can cause to Google Scholar Citations and Google Scholar Metrics. Finally, we conclude with several deliberations over the effects these malpractices may have and the lack of control tools these tools offer
Asymptotics of Forward Implied Volatility

We prove here a general closed-form expansion formula for forward-start options and the forward implied volatility smile in a large class of models, including the Heston stochastic volatility and time-changed exponential Levy models. This expansion applies to both small and large maturities and is based solely on the properties of the forward characteristic function of the underlying process. The method is based on sharp large deviations techniques and allows us to recover (in particular) many results for the spot implied volatility smile. In passing we (i) show that the forward-start date has to be rescaled in order to obtain nontrivial small-maturity asymptotics, (ii) prove that the forward-start date may influence the large-maturity behavior of the forward smile, and (iii) provide some examples of models with finite quadratic variation where the small-maturity forward smile does not explode.
A bimetal and electret-based converter for thermal energy harvesting

This paper presents a new device able to turn thermal gradients into electricity by using a bimetal-based heat engine coupled to an electrostatic converter. A two-steps conversion is performed: (i) a curved bimetallic strip turns the thermal gradient into a mechanical movement (thermal-to-mechanical conversion) that is (ii) then converted into electricity thanks to an electret-based electrostatic converter (mechanical-to-electrical conversion). An output power up to 5.5uW on a hot source at 50{\deg}C has already been reached, validating this new concept.
Origin of probabilities and their application to the multiverse

We argue using simple models that all successful practical uses of probabilities originate in quantum fluctuations in the microscopic physical world around us, often propagated to macroscopic scales. Thus we claim there is no physically verified fully classical theory of probability. We comment on the general implications of this view, and specifically question the application of classical probability theory to cosmology in cases where key questions are known to have no quantum answer. We argue that the ideas developed here may offer a way out of the notorious measure problems of eternal inflation.
Making Early Predictions of the Accuracy of Machine Learning Applications

The accuracy of machine learning systems is a widely studied research topic. Established techniques such as cross-validation predict the accuracy on unseen data of the classifier produced by applying a given learning method to a given training data set. However, they do not predict whether incurring the cost of obtaining more data and undergoing further training will lead to higher accuracy. In this paper we investigate techniques for making such early predictions. We note that when a machine learning algorithm is presented with a training set the classifier produced, and hence its error, will depend on the characteristics of the algorithm, on training set’s size, and also on its specific composition. In particular we hypothesise that if a number of classifiers are produced, and their observed error is decomposed into bias and variance terms, then although these components may behave differently, their behaviour may be predictable. We test our hypothesis by building models that, given a measurement taken from the classifier created from a limited number of samples, predict the values that would be measured from the classifier produced when the full data set is presented. We create separate models for bias, variance and total error. Our models are built from the results of applying ten
Electromagnetic structure of A = 2 and 3 nuclei in chiral effective field theory

The objectives of the present work are twofold. The first is to address and resolve some of the differences present in independent, chiral-effective-field-theory ({chi}EFT) derivations up to one loop, recently appeared in the literature, of the nuclear charge and current operators. The second objective is to provide a complete set of {chi}EFT predictions for the structure functions and tensor polarization of the deuteron, for the charge and magnetic form factors of {sup 3}He and {sup 3}H, and for the charge and magnetic radii of these few-nucleon systems. The calculations use wave functions derived from high-order chiral two- and three-nucleon potentials and Monte Carlo methods to evaluate the relevant matrix elements. Predictions based on conventional potentials in combination with {chi}EFT charge and current operators are also presented. There is excellent agreement between theory and experiment for all these observables for momentum transfers up to q {<=} 2.0--2.5 fm{sup -1}; for a subset of them, this agreement extends to momentum transfers as high as q ~ 5--6 fm{sup -1}. A complete analysis of the results is provided.
Using Wikipedia to Boost SVD Recommender Systems

Singular Value Decomposition (SVD) has been used successfully in recent years in the area of recommender systems. In this paper we present how this model can be extended to consider both user ratings and information from Wikipedia. By mapping items to Wikipedia pages and quantifying their similarity, we are able to use this information in order to improve recommendation accuracy, especially when the sparsity is high. Another advantage of the proposed approach is the fact that it can be easily integrated into any other SVD implementation, regardless of additional parameters that may have been added to it. Preliminary experimental results on the MovieLens dataset are encouraging.
On Some Integrated Approaches to Inference

We present arguments for the formulation of unified approach to different standard continuous inference methods from partial information. It is claimed that an explicit partition of information into a priori (prior knowledge) and a posteriori information (data) is an important way of standardizing inference approaches so that they can be compared on a normative scale, and so that notions of optimal algorithms become farther-reaching. The inference methods considered include neural network approaches, information-based complexity, and Monte Carlo, spline, and regularization methods. The model is an extension of currently used continuous complexity models, with a class of algorithms in the form of optimization methods, in which an optimization functional (involving the data) is minimized. This extends the family of current approaches in continuous complexity theory, which include the use of interpolatory algorithms in worst and average case settings.
The physics of business cycles and inflation

We analyse four consecutive cycles observed in the USA for employment and inflation. They are driven by three oil price shocks and an intended interest rate shock. Non-linear coupling between the rate equations for consumer products as prey and consumers as predators provides the required instability, but its natural damping is too high for spontaneous cycles. Extending the Lotka-Volterra equations with a small term for collective anticipation yields a second analytic solution without damping. It predicts the base period, phase shifts, and the sensitivity to shocks for all six cyclic variables correctly.
Predicting economic growth with classical physics and human biology

We collect and analyze the data for working time, life expectancy, and the pair output and infrastructure of industrializing nations. During S-functional recovery from disaster the pair's time shifts yield 25 years for the infrastructure's physical lifetime. At G7 level the per capita outputs converge and the time shifts identify a heritable quantity with a reaction time of 62 years. It seems to control demand and the spare time required for enjoying G7 affluence. The sum of spare and working time is fixed by the universal flow of time. This yields analytic solutions for equilibrium, recovery, and long-term evolution for all six variables with biologically stabilized parameters.
THE DEBRIS DISK AROUND γ DORADUS RESOLVED WITH HERSCHEL

We present observations of the debris disk around γ Doradus, an F1V star, from the Herschel Key Programme DEBRIS (Disc Emission via Bias-free Reconnaissance in the Infrared/Submillimetre). The disk is well resolved at 70, 100, and 160 μm, resolved along its major axis at 250 μm, detected but not resolved at 350 μm, and confused with a background source at 500 μm. It is one of our best resolved targets and we find it to have a radially broad dust distribution. The modeling of the resolved images cannot distinguish between two configurations: an arrangement of a warm inner ring at several AU (best fit 4 AU) and a cool outer belt extending from ∼55 to 400 AU or an arrangement of two cool, narrow rings at ∼70 AU and ∼190 AU. This suggests that any configuration between these two is also possible. Both models have a total fractional luminosity of ∼10−5 and are consistent with the disk being aligned with the stellar equator. The inner edge of either possible configuration suggests that the most likely region to find planets in this system would be within ∼55 AU of the star. A transient event is not needed to explain the warm dust's fractional luminosity.
Structure and dynamics of information pathways in online media

Diffusion of information, spread of rumors and infectious diseases are all instances of stochastic processes that occur over the edges of an underlying network. Many times networks over which contagions spread are unobserved, and such networks are often dynamic and change over time. In this paper, we investigate the problem of inferring dynamic networks based on information diffusion data. We assume there is an unobserved dynamic network that changes over time, while we observe the results of a dynamic process spreading over the edges of the network. The task then is to infer the edges and the dynamics of the underlying network.
 We develop an on-line algorithm that relies on stochastic convex optimization to efficiently solve the dynamic network inference problem. We apply our algorithm to information diffusion among 3.3 million mainstream media and blog sites and experiment with more than 179 million different pieces of information spreading over the network in a one year period. We study the evolution of information pathways in the online media space and find interesting insights. Information pathways for general recurrent topics are more stable across time than for on-going news events. Clusters of news media sites and blogs often emerge and vanish in matter of days for on-going news events. Major social movements and events involving civil population, such as the Libyan'{}s civil war or Syria'{}s uprise, lead to an increased amount of information pathways among blogs as well as in the overall increase in the network centrality of blogs and social media sites.
The Greek Public Debt Path: From Zero to Infinity

The aim of the present article is to treat the Greek public debt issue strictly as a curve fitting problem. Thus, based on Eurostat data and using the Mathematica technical computing software, an exponential function that best fits the data is determined modelling how the Greek public debt expands with time. Exploring the main features of this best fit model, it is concluded that the Greek public debt cannot possibly be serviced in the long run unless a radical growth is implemented and/or part of the debt is written off.
Assessing the Bias in Communication Networks Sampled from Twitter

We collect and analyse messages exchanged in Twitter using two of the platform's publicly available APIs (the search and stream specifications). We assess the differences between the two samples, and compare the networks of communication reconstructed from them. The empirical context is given by political protests taking place in May 2012: we track online communication around these protests for the period of one month, and reconstruct the network of mentions and re-tweets according to the two samples. We find that the search API over-represents the more central users and does not offer an accurate picture of peripheral activity; we also find that the bias is greater for the network of mentions. We discuss the implications of this bias for the study of diffusion dynamics and collective action in the digital era, and advocate the need for more uniform sampling procedures in the study of online communication.
Bekenstein and the Holographic Principle: Upper bounds for Entropy

Using the Bekenstein upper bound for the ratio of the entropy $S$ of any bounded system, with energy $E = Mc^2$ and effective size $R$, to its energy $E$ i.e. $S/E < 2\pi k R/\hbar c$, we combine it with the holographic principle (HP) bound ('t Hooft and Susskind) which is $S \le \pi k c^3R^2/\hbar G$. We find that, if both bounds are identical, such bounded system is a black hole (BH). For a system that is not a BH the two upper bounds are different. The entropy of the system must obey the lowest bound. If the bounds are proportional, the result is the proportionality between the mass M of the system and its effective size $R$. When the constant of proportionality is $2G/c^2$ the system in question is a BH, and the two bounds are identical. We analyze the case for a universe. Then the universe is a BH in the sense that its mass $M$ and its Hubble size $R \approx ct$, t the age of the universe, follow the Schwarzschild relation $2GM/c^2 = R$. Finally, for a BH, the Hawking and Unruh temperatures are the same. Applying this to a universe they define the quantum of mass $\sim 10^{-66} g$ for our universe.
The Role of Symmetry and Dissipation in Biolocomotion

In this paper we illustrate the potential role which relative limit cycles may play in biolocomotion. We do this by describing, in great detail, an elementary example of reduction of a lightly dissipative system modeling crawling-type locomotion in 3D. The symmetry group SE(2) is the set of rigid transformations of the horizontal (ground) plane. Given a time-periodic perturbation, the system will admit a relative limit cycle whereupon each period is related to the previous by a fixed translation and rotation along the ground. This toy model identifies how symmetry reduction and dissipation can conspire to create robust behavior in crawling, and possibly walking, locomotion.
Online portfolio selection: A survey

Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining. This article aims to provide a comprehensive survey and a structural understanding of online portfolio selection techniques published in the literature. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then we survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, Follow-the-Winner approaches, Follow-the-Loser approaches, Pattern-Matching--based approaches, and Meta-Learning Algorithms. In addition to the problem formulation and related algorithms, we also discuss the relationship of these algorithms with the capital growth theory so as to better understand the similarities and differences of their underlying trading ideas. This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them understand the state of the art and facilitate their research and practical applications. We also discuss some open issues and evaluate some emerging new trends for future research.
The Effect of Probe Dynamics on Galactic Exploration Timescales

The travel time required for one civilisation to explore the Milky Way using probes is a crucial component of Fermi's Paradox. Previous attempts to estimate this travel time have assumed that the probe's motion is simple, moving at a constant maximum velocity, with powered flight producing the necessary change in velocity required at each star to complete its chosen trajectory. This approach ignores lessons learned from interplanetary exploration, where orbital slingshot maneouvres can provide significant velocity boosts at little to no fuel cost. It is plausible that any attempt to explore the Galaxy would utilise such economising techniques, despite there being an upper limit to these velocity boosts, related to the escape velocity of the object being used to provide the slingshot. 
In order to investigate the effects of these techniques, we present multiple realisations of single probes exploring a small patch of the Milky Way. We investigate 3 separate scenarios, studying the slingshot effect on trajectories defined by simple heuristics. These scenarios are: i) standard powered flight to the nearest unvisited star without using slingshot techniques; ii) flight to the nearest unvisited star using slingshot techniques, and iii) flight to the next unvisited star which provides the maximal velocity boost under a slingshot trajectory. 
We find that adding slingshot velocity boosts can decrease the travel time by up to two orders of magnitude over simple powered flight. In the third case, selecting a route which maximises velocity boosts also reduces the travel time relative to powered flight, but by a much reduced factor. From these simulations, we suggest that adding realistic probe trajectories tends to strengthen Fermi's Paradox.
Mining the Web for the "Voice of the Herd" to Track Stock Market Bubbles

We show that power-law analyses of financial commentaries from newspaper web-sites can be used to identify stock market bubbles, supplementing traditional volatility analyses. Using a four-year corpus of 17,713 online, finance-related articles (10M+ words) from the Financial Times, the New York Times, and the BBC, we show that week-to-week changes in power-law distributions reflect market movements of the Dow Jones Industrial Average (DJI), the FTSE-100, and the NIKKEI-225, Notably, the statistical regularities in language track the 2007 stock market bubble, showing emerging structure in the language of commentators, as progressively greater agreement arose in their positive perceptions of the market. Furthermore, during the bubble period, a marked divergence in positive language occurs as revealed by a Kullback-Leibler analysis.
Assembling large, complex environmental metagenomes

Author(s): Howe, Adina Chuang; Jansson, Janet; Malfatti, Stephanie A; Tringe, Susannah G; Tiedje, James M; Brown, C  The large volumes of sequencing data required to sample complex environments deeply pose new challenges to sequence analysis approaches. De novo metagenomic assembly effectively reduces the total amount of data to be analyzed but requires significant computational resources. We apply two pre-assembly filtering approaches, digital normalization and partitioning, to make large metagenome assemblies more comput\ ationaly tractable. Using a human gut mock community dataset, we demonstrate that these methods result in assemblies nearly identical to assemblies from unprocessed data. We then assemble two large soil metagenomes from matched Iowa corn and native prairie soils. The predicted functional content and phylogenetic origin of the assembled contigs indicate significant taxonomic differences despite similar function. The assembly strategies presented are generic and can be extended to any metagenome; full source code is freely available under a BSD license.
The Illusion of the Perpetual Money Machine

We argue that the present crisis and stalling economy continuing since 2007 are rooted in the delusionary belief in policies based on a "perpetual money machine" type of thinking. We document strong evidence that, since the early 1980s, consumption has been increasingly funded by smaller savings, booming financial profits, wealth extracted from house price appreciation and explosive debt. This is in stark contrast with the productivity-fueled growth that was seen in the 1950s and 1960s. This transition, starting in the early 1980s, was further supported by a climate of deregulation and a massive growth in financial derivatives designed to spread and diversify the risks globally. The result has been a succession of bubbles and crashes, including the worldwide stock market bubble and great crash of October 1987, the savings and loans crisis of the 1980s, the burst in 1991 of the enormous Japanese real estate and stock market bubbles, the emerging markets bubbles and crashes in 1994 and 1997, the LTCM crisis of 1998, the dotcom bubble bursting in 2000, the recent house price bubbles, the financialization bubble via special investment vehicles, the stock market bubble, the commodity and oil bubbles and the debt bubbles, all developing jointly and feeding on each other. Rather than still hoping that real wealth will come out of money creation, we need fundamentally new ways of thinking. In uncertain times, it is essential, more than ever, to think in scenarios: what can happen in the future, and, what would be the effect on your wealth and capital? How can you protect against adverse scenarios? We thus end by examining the question "what can we do?" from the macro level, discussing the fundamental issue of incentives and of constructing and predicting scenarios as well as developing investment insights.
Holography as Cutoff: a proposal for measure of inflationary universes

We propose the holographic principle as a dynamical cutoff for any quantum theory of gravity, incorporating ideas of effective field theory. This is done by viewing the holographic bound as a limit on the number of degrees of freedom that can be turned on before the geometrical picture of gravity loses applicability. We illustrate the proposal by revisiting the problem of defining a measure for homogeneous and isotropic space–times coupled to a scalar field and conclude by discussing the implications to the single scalar field inflationary model.