nils-herrmann/clean_scirepeval huggingface dataset:
A method for statistical comparison of histograms

The problem of the testing the hypothesis that two histograms are drawn from the same distribution is a very important problem in many scientific researches. There are several approaches to formalize and resolve this problem. Usually, one-dimensional test statistics is used for this purpose. We propose an approach for testing the hypothesis that two realizations of the random variables in the form of histograms are taken from the same statistical population (i.e. two histograms are drawn from the same distribution). The approach is based on the notion “significance of deviation”, which has a distribution close to standard normal distribution if both histograms are drawn from the same distribution. This approach allows to estimate the statistical difference between two histograms using multi-dimensional test statistics. The distinguishability of histograms is estimated with the help of the construction a number of clones (rehistograms) of the observed histograms. The approach considered in the paper allows to perform the comparison of histograms with a test more powerful, in the cases considered, than those that use only one test statistic. Also, the probability of correct decision is used as an estimate of the quality of the decision about the distinguishability of histograms.
Geant4 applications for modeling molecular transport in complex vacuum geometries

We discuss a novel use of the Geant4 simulation toolkit to model molecular transport in a vacuum environment, in the molecular flow regime. The Geant4 toolkit was originally developed by the high energy physics community to simulate the interactions of elementary particles within complex detector systems. Here its capabilities are utilized to model molecular vacuum transport in geometries where other techniques are impractical. The techniques are verified with an application representing a simple vacuum geometry that has been studied previously both analytically and by basic Monte Carlo simulation. We discuss the use of an application with a very complicated geometry, that of the Large Synoptic Survey Telescope camera cryostat, to determine probabilities of transport of contaminant molecules to optical surfaces where control of contamination is crucial.
Gas cloud G2 can illuminate the black hole population near the galactic center.

Galactic nuclei are expected to be densely populated with stellar- and intermediate-mass black holes. Exploring this population will have important consequences for the observation prospects of gravitational waves as well as understanding galactic evolution. The gas cloud G2 currently approaching Sgr A* provides an unprecedented opportunity to probe the black hole and neutron star population of the Galactic nucleus. We examine the possibility of a G2-cloud-black-hole encounter and its detectability with current x-ray satellites, such as Chandra and NuSTAR. We find that multiple encounters are likely to occur close to the pericenter, which may be detectable upon favorable circumstances. This opportunity provides an additional important science case for leading x-ray observatories to closely follow G2 on its way to the nucleus.
DEMOGRAPHICS OF THE GALAXIES HOSTING SHORT-DURATION GAMMA-RAY BURSTS

We present observations of the afterglows and host galaxies of three short-duration gamma-ray bursts (GRBs): 100625A, 101219A, and 110112A. We find that GRB 100625A occurred in a z = 0.452 early-type galaxy with a stellar mass of ≈4.6 × 109 M☉ and a stellar population age of ≈0.7 Gyr, and GRB 101219A originated in a star-forming galaxy at z = 0.718 with a stellar mass of ≈1.4 × 109 M☉, a star formation rate of ≈16 M☉ yr−1, and a stellar population age of ≈50 Myr. We also report the discovery of the optical afterglow of GRB 110112A, which lacks a coincident host galaxy to i ≳ 26 mag, and we cannot conclusively identify any field galaxy as a possible host. From afterglow modeling, the bursts have inferred circumburst densities of ≈10−4–1 cm−3 and isotropic-equivalent gamma-ray and kinetic energies of ≈1050–1051 erg. These three events highlight the diversity of galactic environments that host short GRBs. To quantify this diversity, we use the sample of 36 Swift short GRBs with robust associations to an environment (∼1/2 of 68 short bursts detected by Swift to 2012 May) and classify bursts originating from four types of environments: late-type (≈50%), early-type (≈15%), inconclusive (≈20%), and “host-less” (lacking a coincident host galaxy to limits of ≳ 26 mag; ≈15%). To find likely ranges for the true late- and early-type fractions, we assign each of the host-less bursts to either the late- or early-type category using probabilistic arguments and consider the scenario that all hosts in the inconclusive category are early-type galaxies to set an upper bound on the early-type fraction. We calculate most likely ranges for the late- and early-type fractions of ≈60%–80% and ≈20%–40%, respectively. We find no clear trend between gamma-ray duration and host type. We also find no change to the fractions when excluding events recently claimed as possible contaminants from the long GRB/collapsar population. Our reported demographics are consistent with a short GRB rate driven by both stellar mass and star formation.
Gravitational scattering of photons off cosmic strings

Photons can gravitationally scatter off a cosmic string loop and gain or lose energy. We consider the spectral distortion induced by cosmic string loops placed in an ambient thermal bath of photons. The fractional deviation from a thermal spectrum caused by cosmic strings is estimated to scale as (GNμ)2z2, where GN is Newton’s constant, μ is the string tension, and z is the cosmological redshift after which spectral distortions can survive. This effect is large enough to potentially be of observational interest.
CosMIn: THE SOLUTION TO THE COSMOLOGICAL CONSTANT PROBLEM

The current acceleration of the universe can be modeled in terms of a cosmological constant Λ. We show that the extremely small value of , the holy grail of theoretical physics, can be understood in terms of a new, dimensionless, conserved number Cosmic Mode Index (CosMIn), which counts the number of modes crossing the Hubble radius during the three phases of evolution of the universe. Theoretical considerations suggest that N ≈ 4π. This single postulate leads us to the correct, observed numerical value of the cosmological constant! This approach also provides a unified picture of cosmic evolution relating the early inflationary phase to the late accelerating phase.
An Algorithm for Finding Minimum d-Separating Sets in Belief Networks

The criterion commonly used in directed acyclic graphs (dags) for testing graphical independence is the well-known d-separation criterion. It allows us to build graphical representations of dependency models (usually probabilistic dependency models) in the form of belief networks, which make easy interpretation and management of independence relationships possible, without reference to numerical parameters (conditional probabilities). In this paper, we study the following combinatorial problem: finding the minimum d-separating set for two nodes in a dug. This set would represent the minimum information (in the sense of minimum number of variables) necessary to prevent these two nodes from influencing each other. The solution to this basic problem and some of its extensions can be useful in several ways, as we shall see later. Our solution is based on a two-step process: first, we reduce the original problem to the simpler one of finding a minimum separating set in an undirected graph, and second, we develop an algorithm for solving it.
Inference Using Message Propagation and Topology Transformation in Vector Gaussian Continuous Networks

We extend continuous Gaussian networks - directed acyclic graphs that encode probabilistic relationships between variables - to its vector form. Vector Gaussian continuous networks consist of composite nodes representing multivariables, that take continuous values. These vector or composite nodes can represent correlations between parents, as opposed to conventional univariate nodes. We derive rules for inference in these networks based on two methods: message propagation and topology transformation. These two approaches lead to the development of algorithms, that can be implemented in either a centralized or a decentralized manner. The domain of application of these networks are monitoring and estimation problems. This new representation along with the rules for inference developed here can be used to derive current Bayesian algorithms such as the Kalman filter, and provide a rich foundation to develop new algorithms. We illustrate this process by deriving the decentralized form of the Kalnaan filter. This work unifies concepts from artificial intelligence and modern control theory.
An Alternative Markov Property for Chain Graphs

Graphical Markov models use graphs, either undirected, directed, or mixed, to represent possible dependences among statistical variables. Applications of undirected graphs (UDGs) include models for spatial dependence and image analysis, while acyctic directed graphs (ADGs), which are especially convenient for statistical analysis, arise in such fields as genetics and psychometrics and as models for expert systems and Bayesian belief networks. Lauritzen, Wermuth, and Frydenberg (LWF) introduced a Markov property for chain graphs, which are mixed graphs that can be used to represent simultaneously both causal and associative dependencies and which include both UDGs and ADGs as special cases. In this paper an alternative Markov property (AMP) for chain graphs is introduced, which in some ways is a more direct extension of the ADG Markov property than is the LWF property for chain graph.
Plan Development using Local Probabilistic Models

Approximate models of world state transitions are necessary when building plans for complex systems operating in dynamic environments. External event probabilities can depend on state feature values as well as time spent in that particular state. We assign temporally-dependent probability functions to state transitions. These functions are used to locally compute state probabilities, which are then used to select highly probable goal paths and eliminate improbable states. This probabilistic model has been implemented in the Cooperative Intelligent Real-time Control Architecture (CIRCA), which combines an AI planner with a separate real-time system such that plans are developed, scheduled, and executed with real-time guarantees. We present flight simulation tests that demonstrate how our probabilistic model may improve CIRCA performance.
Entailment in Probability of Thresholded Generalizations

A nonmonotonic logic of thresholded generalizations is presented. Given propositions a and β from a language L and a positive integer k, the thresholded generalization α →k β means that the conditional probability π(β|α) is at least 1 -ψδk. A two-level probability structure is defined. At the lower level, a model is defined to be a probability function on L. At the upper level, there is a probability distribution over models. A definition is given of what it means for a collection of thresholded generalizations to entail another thresholded generalization. This nonmonotonic entailment relation, called entailment in probability, has the feature that its conclusions are probabilistically trustworthy meaning that, given true premises, it is improbable that an entailed conclusion would be false. A procedure is presented for ascertaining whether any given collection of premises entails any given conclusion. It is shown that entailment in probability is closely related to Goldszmidt and Pearl's System-Z+, thereby demonstrating that System-Z+'s conclusions are probabilistically trustworthy.
Object Recognition with Imperfect Perception and Redundant Description

This paper deals with a scene recognition system in a robotics context. The general problem is to match images with a priori descriptions. A typical mission would consist in identifying an object in an installation with a vision system situated at the end of a manipulator and with a human operator provided description, formulated in a pseudonatural language, and possibly redundant. The originality of this work comes from the nature of the description, from the special attention given to the management of imprecision and uncertainty in the interpretation process and from the way to assess the description redundancy so as to reinforce the overall matching likelihood.
Approximations for Decision Making in the Dempster-Shafer Theory of Evidence

The computational complexity of reasoning within the Dempster-Shafer theory of evidence is one of the main points of criticism this formalism has to face. To overcome this difficulty various approximation algorithms have been suggested that aim at reducing the number of focal elements in the belief functions involved, Besides introducing a new algorithm using this method, this paper describes an empirical study that examines the appropriateness of these approximation procedures in decision making situations. It presents the empirical findings and discusses the various tradeoffs that have to be taken into account when actually applying one of these methods.
A sufficiently fast algorithm for finding close to optimal junction trees

Au algorithm is developed for findiug a close to optimal junction tree of a given graph G. The algorithm has a worst case complexity O(ckna) where a and c are constants, n is the nmnber of vertices, and k is the size of the largest clique in a juuction tree of G in which this size is minimized. The algorithm guarantees that the logarithm of the size of the state space of the heaviest clique in the junction tree produced is less than a constant factor off the optional value. When k = O(log n) our algorithm yields a polynomial inference algorithm for Bayesian networks.
Coping with the Limitations of Rational Inference in the Framework of Possibility Theory

Possibility theory offers a framework where both Lehmann's "preferential inference" and the more productive (but less cautious) "rational closure inference" can be represented. However, there are situations where the second inference does not provide expected results either because it cannot produce them, or even provide counterintuitive conclusions. This state of facts is not due to the principle of selecting a unique ordering of interpretations (which can be encoded by one possibility distribution), but rather to the absence of constraints expressing pieces of knowledge we have implicitly in mind. It is advocated in this paper that constraints induced by independence information can help finding the right ordering of interpretations. In particular, independence constraints can be systematically assumed with respect to formulas composed of literals which do not appear in the conditional knowledge base, or for default rules with respect to situations which are "normal" according to the other default rules in the base. The notion of independence which is used can be easily expressed in the qualitative setting of possibility theory. Moreover, when a counterintuitive plausible conclusion of a set of defaults, is in its rational closure, but not in its preferential closure, it is always possible to repair the set of defaults so as to produce the desired conclusion.
Arguing for Decisions: A Qualitative Model of Decision Making

We develop a qualitative model of decision making with two aims: to describe how people make simple decisions and to enable computer programs to do the same. Current approaches based on Planning or Decision Theory either ignore uncertainty and tradeoffs, or provide languages and algorithms that are too complex for this task. The proposed model provides a language based on rules, a semantics based on high probabilities and lexicographical preferences, and a transparent decision procedure where reasons for and against decisions interact. The model is no substitute for Decision Theory, yet for decisions that people find easy to explain it may provide an appealing alternative.
Tail Sensitivity Analysis in Bayesian Networks

The paper presents an efficient method for simulating the tails of a target variable Z = h(X) which depends on a set of basic variables X = (X1,...,Xn). To this aim, variables Xi;i = 1,...., n are sequentially simulated in such a manner that Z = h(x1,..., xi-1, Xi,..., Xn) is guaranteed to be in the tail of Z. When this method is difficult to apply, an alternative method is proposed, which leads to a low rejection proportion of sample values, when compared with the Monte Carlo method. Both methods are shown to be very useful to perform a sensitivity analysis of Bayesian networks, when very large confidence intervals for the marginal/conditional probabilities are required, as in reliability or risk analysis. The methods are shown to behave best when all scores coincide. The required modifications for this to occur are discussed. The methods are illustrated with several examples and one example of application to a real case is used to illustrate the whole process.
Decision-Analytic Approaches to Operational Decision Making: Application and Observation

Decision analysis (DA) and the rich set of tools developed by researchers in decision making under uncertainty show great potential to penetrate the technological content of the products and services delivered by firms in a variety of industries as well as the business processes used to deliver those products and services to market. In this paper I describe work in progress at Sun Microsystems in the application of decision-analytic methods to Operational Decision Making (ODM) in its World-Wide Operations (WWOPS) Business Management group, Working with members of product engineering, marketing, and sales, operations planners from WWOPS have begun to use a decision-analytic framework called SCRAM (Supply Communication/ Risk Assessment and Management) to structure and solve problems in product planning, tracking, and transition. Concepts such as information value provide a powerful method of managing huge information sets and thereby enable managers to focus attention on factors that matter most for their business. Finally, our process-oriented introduction of decision-analytic methods to Sun managers has led to a focused effort to develop decision support software based on methods from decision-making under uncertainty.
Learning Equivalence Classes of Bayesian-Network Structures

Two Bayesian-network structures are said to be equivalent if the set of distributions that can be represented with one of those structures is identical to the set of distributions that can be represented with the other. Many scoring criteria that are used to learn Bayesian-network structures from data are score equivalent; that is, these criteria do not distinguish among networks that are equivalent. In this paper, we consider using a score equivalent criterion in conjunction with a heuristic search algorithm to perform model selection or model averaging. We argue that it is often appropriate to search among equivalence classes of network structures as opposed to the more common approach of searching among individual Bayesian-network structures. We describe a convenient graphical representation for an equivalence class of structures, and introduce a set of operators that can be applied to that representation by a search algorithm to move among equivalence classes. We show that our equivalence-class operators can be scored locally, and thus share the computational efficiency of traditional operators defined for individual structures. We show experimentally that a greedy model-selection algorithm using our representation yields slightly higher-scoring structures than the traditional approach without any additional time overhead, and we argue that more sophisticated search algorithms are likely to benefit much more.
Independence with Lower and Upper Probabilities

It is shown that the ability of the interval probability representation to capture epistemological independence is severely limited. Two events are epistemologically independent if knowledge of the first event does not alter belief (i.e., probability bounds) about the second. However, iudependence in this form can only exist in a 2-monotone probability function in degenerate cases -- i.e., if the prior bounds are either point probabilities or entirely vacuous. Additional limitations are characterized for other classes of lower probabilities as well. It is argued that these phenomena are a matter of interpretation. They appear to be limitations when one interprets probability bounds as a measure of epistemological indeterminacy (i.e., uncertainty arising from a lack of knowledge), but are exactly as one would expect when probability intervals are interpreted as representations of ontological indeterminacy (indeterminacy introduced by structural approximations).
Inside the SU(3) quark-antiquark QCD flux tube: screening versus quantum widening

In lattice QCD, colour confinement manifests in flux tubes. We compute in detail the quark-antiquark flux tube for pure gauge SU(3) dimension $D=3+1$ for quark-antiquark distances R ranging from 0.4 fm to 1.4 fm. To increase the signal over noise ratio, we apply the improved multihit and extended smearing techniques. We detail the gauge invariant squared components of the colour electric and colour magnetic fields both in the mediator plane between the static quark and static antiquark and in the planes of the sources. We fit the field densities with appropriate ansatze and we observe the screening of the colour fields in all studied planes together with the quantum widening of the flux tube in the mediator plane. All components squared of the colour fields are non-vanishing and are consistent with a penetration length lambda ~ 0.22 to 0.24 fm and an effective screening mass mu ~ 0.9 to 0.8 GeV. The quantum widening of the flux tube is well fitted with a logarithmic law in R.
From Nobel Prize to Project Management: Getting Risks Right

A major source of risk in project management is inaccurate forecasts of project costs, demand, and other impacts. The paper presents a promising new approach to mitigating such risk based on theories of decision-making under uncertainty, which won the 2002 Nobel Prize in economics. First, the paper documents inaccuracy and risk in project management. Second, it explains inaccuracy in terms of optimism bias and strategic misrepresentation. Third, the theoretical basis is presented for a promising new method called “reference class forecasting,” which achieves accuracy by basing forecasts on actual performance in a reference class of comparable projects and thereby bypassing both optimism bias and strategic misrepresentation. Fourth, the paper presents the first instance of practical reference class forecasting, which concerns cost forecasts for large transportation infrastructure projects. Finally, potentials for and barriers to reference class forecasting are assessed.