nils-herrmann/clean_scirepeval huggingface dataset:
Online Learning in a Contract Selection Problem

In an online contract selection problem there is a seller which offers a set of contracts to sequentially arriving buyers whose types are drawn from an unknown distribution. If there exists a profitable contract for the buyer in the offered set, i.e., a contract with payoff higher than the payoff of not accepting any contracts, the buyer chooses the contract that maximizes its payoff. In this paper we consider the online contract selection problem to maximize the sellers profit. Assuming that a structural property called ordered preferences holds for the buyer's payoff function, we propose online learning algorithms that have sub-linear regret with respect to the best set of contracts given the distribution over the buyer's type. This problem has many applications including spectrum contracts, wireless service provider data plans and recommendation systems.
Giant dipole resonance width as a probe for nuclear deformation at finite excitation

The first systematic study of the correlation between the experimental giant dipole resonance (GDR) width and the average deformation � β� of the nucleus at finite excitation is presented for the mass region A ∼ 59 to 208. We show that the width of the GDR (� ) and the quadrupole deformation of the nucleus do not follow a linear relation, as predicted earlier, owing to the GDR-induced quadrupole moment, and the correlation also depends on the mass of the nuclei. The different empirical values of � β� extracted from the experimental GDR width match exceptionally well with the thermal shape fluctuation model. As a result, this universal correlation between
A return to strong radio flaring by Circinus X-1 observed with the Karoo Array Telescope test array KAT-7

Circinus X-1 is a bright and highly variable X-ray binary which displays strong and rapid evolution in all wavebands. Radio flaring, associated with the production of a relativistic jet, occurs periodically on a 17-day timescale. A longer-term envelope modulates the peak radio fluxes in flares, ranging from peaks in excess of a Jansky in the 1970s to an historic low of milliJanskys during the years 1994 to 2007. Here we report first observations of this source with the MeerKAT test array, KAT-7, part of the pathfinder development for the African dish component of the Square Kilometre Array (SKA), demonstrating successful scientific operation for variable and transient sources with the test array. The KAT-7 observations at 1.9 GHz during the period 13 December 2011 to 16 January 2012 reveal in temporal detail the return to the Jansky-level events observed in the 1970s. We compare these data to contemporaneous single-dish measurements at 4.8 and 8.5 GHz with the HartRAO 26-m telescope and X-ray monitoring from MAXI. We discuss whether the overall modulation and recent dramatic brightening is likely to be due to an increase in the power of the jet due to changes in accretion rate or changing Doppler boosting associated with a varying angle to the line of sight.
Probabilistic Nearest Neighbor Queries on Uncertain Moving Object Trajectories

Nearest neighbor (NN) queries in trajectory databases have received significant attention in the past, due to their applications in spatio-temporal data analysis. More recent work has considered the realistic case where the trajectories are uncertain; however, only simple uncertainty models have been proposed, which do not allow for accurate probabilistic search. In this paper, we fill this gap by addressing probabilistic nearest neighbor queries in databases with uncertain trajectories modeled by stochastic processes, specifically the Markov chain model. We study three nearest neighbor query semantics that take as input a query state or trajectory q and a time interval, and theoretically evaluate their runtime complexity. Furthermore we propose a sampling approach which uses Bayesian inference to guarantee that sampled trajectories conform to the observation data stored in the database. This sampling approach can be used in Monte-Carlo based approximation solutions. We include an extensive experimental study to support our theoretical results.
Does model-free forecasting really outperform the true model?

Estimating population models from uncertain observations is an important problem in ecology. Perretti et al. observed that standard Bayesian state–space solutions to this problem may provide biased parameter estimates when the underlying dynamics are chaotic (1). Consequently, forecasts based on these estimates showed poor predictive accuracy compared with simple “model-free” methods, which lead Perretti et al. to conclude that “Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data.” However, a simple modification of the statistical methods also suffices to remove the bias and reverse their results.
Modeling Information Propagation with Survival Theory

Networks provide a 'skeleton' for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which the network inference problems can be solved efficiently by exploiting their convexity. Our additive risk model generalizes several existing network inference models. We show all these models are particular cases of our more general model. Our multiplicative model allows for modeling scenarios in which a node can either increase or decrease the risk of activation of another node, in contrast with previous approaches, which consider only positive risk increments. We evaluate the performance of our network inference algorithms on large synthetic and real cascade datasets, and show that our models are able to predict the length and duration of cascades in real data.
Statistical Inference and String Theory

In this note we expose some surprising connections between string theory and statistical inference. We consider a large collective of agents sweeping out a family of nearby statistical models for an M-dimensional manifold of statistical fitting parameters. When the agents making nearby inferences align along a d-dimensional grid, we find that the pooled probability that the collective reaches a correct inference is the partition function of a non-linear sigma model in d dimensions. Stability under perturbations to the original inference scheme requires the agents of the collective to distribute along two dimensions. Conformal invariance of the sigma model corresponds to the condition of a stable inference scheme, directly leading to the Einstein field equations for classical gravity. By summing over all possible arrangements of the agents in the collective, we reach a string theory. We also use this perspective to quantify how much an observer can hope to learn about the internal geometry of a superstring compactification. Finally, we present some brief speculative remarks on applications to the AdS/CFT correspondence and Lorentzian signature spacetimes.
Making the case of GPUs in courses on computational physics

The process of materials fracture is not yet understood across all levels. This thesis contains detailed description on a model of in-plane fracture along with results obtained using this model. The results from the model are in very good agreement with experimental observations, both with respect to the static scaling of the front (morphology) and a dynamic study of the underlying processes. This is quite remarkable, considering our model is quasistatic, meaning that the dynamics are time independent.Using this model, I have found two scaling regimes which corresponds to the two different regimes found experimentally for in-plane fracture. This is the first model to successfully reproduce these two scaling regimes, allowing us to clearly state the important processes in this constrained form of fracture. Only the geometry is constrained, any material obeying the quite general assumptions in the model should contain the same processes and fracture in the same way.The results indicate that a percolation process is controlling the fracture on small scales. At larger scales, the elastic material properties leads to a stress concentration which eventually constrains damage formation to the immediate area near the fracture front. In the large scale regime I have measured a roughness exponent oflarge = 0.39 ± 0.04 .In the small scale regime, I show data consistent with and present evidence based on several different analyses for a roughness exponent of small = 2/3.
Fighting against uncertainty: an essential issue in bioinformatics

Many bioinformatics problems, such as sequence alignment, gene prediction, phylogenetic tree estimation and RNA secondary structure prediction, are often affected by the 'uncertainty' of a solution, that is, the probability of the solution is extremely small. This situation arises for estimation problems on high-dimensional discrete spaces in which the number of possible discrete solutions is immense. In the analysis of biological data or the development of prediction algorithms, this uncertainty should be handled carefully and appropriately. In this review, I will explain several methods to combat this uncertainty, presenting a number of examples in bioinformatics. The methods include (i) avoiding point estimation, (ii) maximum expected accuracy (MEA) estimations and (iii) several strategies to design a pipeline involving several prediction methods. I believe that the basic concepts and ideas described in this review will be generally useful for estimation problems in various areas of bioinformatics.
Magnetic monopoles at the LHC and in the Cosmos

The magnetic monopole was postulated in 1931 by Dirac to explain electric charge quantisation. Searches for pair-produced monopoles are performed at accelerator facilities whenever a new energy regime is made available. In addition, monopoles with masses too high to be accessible at colliders would still have been produced in the early Universe and such relics can be searched for either in flight or trapped in matter. Here we discuss recent results and future prospects for direct monopole detection at the LHC and in bulk matter searches, with emphasis on the complementarity between the various techniques. Significant improvements of the results from the ATLAS experiment are expected with the development of new triggers. Dedicated LHC experiments will allow to probe wider ranges of monopole charges and masses: the MoEDAL experiment using both nuclear-track detectors and absorbing arrays, and searches for trapped monopoles in accelerator material. Finally, it is highlighted how the first search for monopoles trapped in polar volcanic rocks allows to set new constraints on the abundance of monopoles bound to matter in the Earth's interior and by extension in the primary material that formed the Solar System.
Symbolic complexity for nucleotide sequences: a sign of the genome structure

We introduce a method for estimating the complexity function (which counts the number of observable words of a given length) of a finite symbolic sequence, which we use to estimate the complexity function of coding DNA sequences for several species of the Hominidae family. In all cases, the obtained symbolic complexities show the same characteristic behavior: exponential growth for small word lengths, followed by linear growth for larger word lengths. The symbolic complexities of the species we consider exhibit a systematic trend in correspondence with the phylogenetic tree. Using our method, we estimate the complexity function of sequences obtained by some known evolution models, and in some cases we observe the characteristic exponential-linear growth of the Hominidae coding DNA complexity. Analysis of the symbolic complexity of sequences obtained from a specific evolution model points to the following conclusion: linear growth arises from the random duplication of large segments during the evolution of the genome, while the decrease in the overall complexity from one species to another is due to a difference in the speed of accumulation of point mutations.
A Framework for the Calibration of Social Simulation Models

Simulation with agent-based models is increasingly used in the study of complex socio-technical systems and in social simulation in general. This paradigm offers a number of attractive features, namely the possibility of modeling emergent phenomena within large populations. As a consequence, often the quantity in need of calibration may be a distribution over the population whose relation with the parameters of the model is analytically intractable. Nevertheless, we can simulate. In this paper we present a simulation-based framework for the calibration of agent-based models with distributional output based on indirect inference. We illustrate our method step by step on a model of norm emergence in an online community of peer production, using data from three large Wikipedia communities. Model fit and diagnostics are discussed.
The entropy concept for non-equilibrium states

In earlier work, we presented a foundation for the second law of classical thermodynamics in terms of the entropy principle. More precisely, we provided an empirically accessible axiomatic derivation of an entropy function defined on all equilibrium states of all systems that has the appropriate additivity and scaling properties, and whose increase is a necessary and sufficient condition for an adiabatic process between two states to be possible. Here, after a brief review of this approach, we address the question of defining entropy for non-equilibrium states. Our conclusion is that it is generally not possible to find a unique entropy that has all relevant physical properties. We do show, however, that one can define two entropy functions, called S− and S+, which, taken together, delimit the range of adiabatic processes that can occur between non-equilibrium states. The concept of comparability of states with respect to adiabatic changes plays an important role in our reasoning.
Indication of anomalous heat energy production in a reactor device

An experimental investigation of possible anomalous heat production in a special type of reactor tube named E-Cat HT is carried out. The reactor tube is charged with a small amount of hydrogen loaded nickel powder plus some additives. The reaction is primarily initiated by heat from resistor coils inside the reactor tube. Measurement of the produced heat was performed with high-resolution thermal imaging cameras, recording data every second from the hot reactor tube. The measurements of electrical power input were performed with a large bandwidth three-phase power analyzer. Data were collected in two experimental runs lasting 96 and 116 hours, respectively. An anomalous heat production was indicated in both experiments. The 116-hour experiment also included a calibration of the experimental set-up without the active charge present in the E-Cat HT. In this case, no extra heat was generated beyond the expected heat from the electric input. Computed volumetric and gravimetric energy densities were found to be far above those of any known chemical source. Even by the most conservative assumptions as to the errors in the measurements, the result is still one order of magnitude greater than conventional energy sources.
Towards a Holographic Theory of Cosmology — Threads in a Tapestry

In this paper, we address several fundamental issues in cosmology: What is the nature of dark energy and dark matter? Why is the dark sector so different from ordinary matter? Why is the effective cosmological constant nonzero but so incredibly small? What is the reason behind the emergence of a critical acceleration parameter of magnitude 10-8 cm/s2 in galactic dynamics? We suggest that the holographic principle is the linchpin in a unified scheme to understand these various issues.
On the performance of Usain Bolt in the 100 m sprint

Many university texts on mechanics consider the effect of air drag force, using the slowing down of a parachute as an example. Very few discuss what happens when the drag force is proportional to both u and u2. In this paper we deal with a real problem to illustrate the effect of both terms on the speed of a runner: a theoretical model of the world-record 100 m sprint of Usain Bolt during the 2009 World Championships in Berlin is developed, assuming a drag force proportional to u and to u2. The resulting equation of motion is solved and fitted to the experimental data obtained from the International Association of Athletics Federations, which recorded Bolt's position with a laser velocity guard device. It is worth noting that our model works only for short sprints.
Chiral extrapolations for nucleon electric charge radii

Lattice simulations for the electromagnetic form factors of the nucleon yield insights into the internal structure of hadrons. The logarithmic divergence of the charge radius in the chiral limit poses an interesting challenge in achieving reliable predictions from finite-volume lattice simulations. Recent results near the physical pion mass are examined in order to confront the issue of how the chiral regime is approached. The electric charge radius of the nucleon presents a forum for achieving consistent finite-volume corrections. Newly-developed techniques within the framework of chiral effective field theory are used to achieve a robust extrapolation of the electric charge radius to the physical pion mass, and to infinite volume. The chiral extrapolations exhibit considerable finite-volume dependence; lattice box sizes of L > 7 fm are required in order to achieve a direct lattice simulation result within 2% of the infinite-volume value at the physical point. Predictions of the volume-dependence are provided to guide the interpretation of future lattice results.
Data Quality Principles in the Semantic Web

The increasing size and availability of web data make data quality a core challenge in many applications. Principles of data quality are recognized as essential to ensure that data fit for their intended use in operations, decision-making, and planning. However, with the rise of the Semantic Web, new data quality issues appear and require deeper consideration. In this paper, we propose to extend the data quality principles to the context of Semantic Web. Based on our extensive industrial experience in data integration, we identify five main classes suited for data quality in Semantic Web. For each class, we list the principles that are involved at all stages of the data management process. Following these principles will provide a sound basis for better decision-making within organizations and will maximize long-term data integration and interoperability.
Indexing Medical Images based on Collaborative Experts Reports

A patient is often willing to quickly get, from his physician, reliable analysis and concise explanation according to provided linked medical images. The fact of making choices individually by the patient's physician may lead to malpractices and consequently generates unforeseeable damages. The Institute of Medicine of the National Sciences Academy(IMNAS) in USA published a study estimating that up to 98,000 hospital deathseach year can be attributed to medical malpractice . Moreover, physician, in charge of medical image analysis, might be unavailable at the right time, which may complicate the patient's state. The goal of this paper is to provide to physicians and patients, a social network that permits to foster cooperation and to overcome the problem of unavailability of doctors on site any time. Therefore, patients can submit their medical images to be diagnosed and commented by several experts instantly. Consequently, the need to process opinions and to extract information automatically from the proposed social network became a necessity due to the huge number of comments expressing specialist's reviews. For this reason, we propose a kind of comments' summary keywords-based method which extracts the major current terms and relevant words existing on physicians' annotations. The extracted keywords will present a new and robust method for image indexation. In fact, significant extracted terms will be used later to index images in order to facilitate their discovery for any appropriate use. To overcome this challenge, we propose our Terminology Extraction of Annotation (TEA) mixed approach which focuses on algorithms mainly based on statistical methods and on external semantic resources.
Unveiling the corona of the Milky Way via ram-pressure stripping of dwarf satellites

The spatial segregation between dwarf spheroidal (dSph) and dwarf irregular galaxies in the Local Group has long been regarded as evidence of an interaction with their host galaxies. In this paper, we assume that ram-pressure stripping is the dominant mechanism that removed gas from the dSphs and we use this to derive a lower bound on the density of the corona of the Milky Way at large distances (R similar to 50-90 kpc) from the Galactic Centre. At the same time, we derive an upper bound by demanding that the interstellar medium of the dSphs is in pressure equilibrium with the hot corona. We consider two dwarfs (Sextans and Carina) with well-determined orbits and star formation histories. Our approach introduces several novel features: (i) we use the measured star formation histories of the dwarfs to derive the time at which they last lost their gas and (via a modified version of the Kennicutt-Schmidt relation) their internal gas density at that time; (ii) we use a large suite of 2D hydrodynamical simulations to model the gas stripping; and (iii) we include supernova feedback tied to the gas content. Despite having very different orbits and star formation histories, we find results for the two dSphs that are in excellent agreement with one another. We derive an average particle density of the corona of the Milky Way at R = 50-90 kpc in the range n(cor) = 1.3-3.6 x 10(-4) cm(-3). Including additional constraints from X-ray emission limits and pulsar dispersion measurements (that strengthen our upper bound), we derive Galactic coronal density profiles. Extrapolating these to large radii, we estimate the fraction of baryons (missing baryons) that can exist within the virial radius of the Milky Way. For an isothermal corona (T-cor = 1.8 x 10(6) K), this is small - just 10-20 per cent of the expected missing baryon fraction, assuming a virial mass of 1-2 x 10(12) M-circle dot. Only a hot (T-cor = 3 x 10(6) K) and adiabatic corona can contain all of the Galaxy's missing baryons. Models for the Milky Way must explain why its corona is in a hot adiabatic thermal state; or why a large fraction of its baryons lie beyond the virial radius.