nils-herrmann/clean_scirepeval huggingface dataset:
X-rays, γ-rays and neutrinos from collisionless shocks in supernova wind breakouts

Some of the observed bursts of X-rays/Gamma-rays associated with supernovae (SNe) as well as very luminous SNe may result from the breakout of the SN shock from an optically thick wind surrounding the progenitor. We show that in such scenarios a collisionless shock necessarily forms during the shock breakout. An intense non-thermal flash of ≲1 MeV gamma rays, hard X-rays and multi-TeV neutrinos is produced simultaneously with and following the typical soft X-ray breakout emission, carrying similar or larger energy than the soft emission. The non-thermal flash is detectable by current X-ray telescopes and may be detectable out to 10's of Mpc by km-scale neutrino telescopes.
A note on the Hamiltonian of the real scalar field

We address the question of ambiguity in defining a Hamiltonian for a scalar field. We point out that the Hamiltonian for a real Klein-Gordon scalar field must be consistent with the energy density obtained from the Schrodinger equation in the non-relativistic regime. To achieve this we had to add some surface terms (total divergencies) to the standard Hamiltonian, which in general will not change the equations of motion, but will redefine energy. As an additional requirement, a Hamiltonian must be able to reproduce the equations of motion directly from Hamilton's equations defined by the principle of the least action. We find that the standard Hamiltonian does not always do so and that the proposed Hamiltonian provides a good non-relativistic limit. This is a hint that something as simple as the Hamiltonian of the real Klein-Gordon scalar field has to be treated carefully. We had illustrated our discussion with an explicit example of the kink solution.
WEYL GEOMETRIES AND TIMELIKE GEODESICS

In view of Ehlers–Pirani–Schild formalism, since 1972 Weyl geometries should be considered to be the most appropriate and complete framework to represent (relativistic) gravitational fields. We shall here show that in any given Lorentzian spacetime (M, g) that admits global timelike vector fields any such vector field u determines an essentially unique Weyl geometry ([g], Γ) such that u is Γ-geodesic (i.e. parallel with respect to Γ).
Optimal dividend policies for piecewise-deterministic compound Poisson risk models

This paper considers the optimal dividend payment problem in piecewise-deterministic compound Poisson risk models. The objective is to maximize the expected discounted dividend payout up to the time of ruin. We provide a comparative study in this general framework of both restricted and unrestricted payment schemes, which were only previously treated separately in certain special cases of risk models in the literature. In the case of restricted payment scheme, the value function is shown to be a classical solution of the corresponding HJB equation, which in turn leads to an optimal restricted payment policy known as the threshold strategy. In the case of unrestricted payment scheme, by solving the associated integro-differential quasi-variational inequality, we obtain the value function as well as an optimal unrestricted dividend payment scheme known as the barrier strategy. When claim sizes are exponentially distributed, we provide easily verifiable conditions under which the threshold and barrier strategies are optimal restricted and unrestricted dividend payment policies, respectively. The main results are illustrated with several examples, including a new example concerning regressive growth rates.
Prediction and modularity in dynamical systems

Identifying and understanding modular organizations is centrally important in the study of complex systems. Several approaches to this problem have been advanced, many framed in information-theoretic terms. Our treatment starts from the complementary point of view of statistical modeling and prediction of dynamical systems. It is known that for finite amounts of training data, simpler models can have greater predictive power than more complex ones. We use the trade-off between model simplicity and predictive accuracy to generate optimal multiscale decompositions of dynamical networks into weakly-coupled, simple modules. State-dependent and causal versions of our method are also proposed.
Tight Approximations of Dynamic Risk Measures

This paper compares two different frameworks recently introduced in the literature for measuring risk in a multi-period setting. The first corresponds to applying a single coherent risk measure to the cumulative future costs, while the second involves applying a composition of one-step coherent risk mappings. We summarize the relative strengths of the two methods, characterize several necessary and sufficient conditions under which one of the measurements always dominates the other, and introduce a metric to quantify how close the two risk measures are. Using this notion, we address the question of how tightly a given coherent measure can be approximated by lower or upper-bounding compositional measures. We exhibit an interesting asymmetry between the two cases: the tightest possible upper-bound can be exactly characterized, and corresponds to a popular construction in the literature, while the tightest-possible lower bound is not readily available. We show that testing domination and computing the approximation factors is generally NP-hard, even when the risk measures in question are comonotonic and law-invariant. However, we characterize conditions and discuss several examples where polynomial-time algorithms are possible. One such case is the well-known Conditional Value-at-Risk measure, which is further explored in our companion paper [Huang, Iancu, Petrik and Subramanian, "Static and Dynamic Conditional Value at Risk" (2012)]. Our theoretical and algorithmic constructions exploit interesting connections between the study of risk measures and the theory of submodularity and combinatorial optimization, which may be of independent interest.
Probing student understanding with alternative questioning strategies

Common research tasks ask students to identify a correct answer and justify their answer choice. We propose expanding the array of research tasks to access different knowledge that students might have. By asking students to discuss answers they may not have chosen naturally, we can investigate students' abilities to explain something that is already established or to disprove an incorrect response. The results of these research tasks also provide us with information about how students' responses vary across the different tasks. We discuss three underused question types, their possible benefits, and some preliminary results from an electric circuits pretest utilizing these novel question types. We find that the answer students most commonly choose as correct is the same choice most commonly eliminated as incorrect. Also, given the correct answer, students can provide valuable reasoning to explain it, but they do not spontaneously identify it as the correct answer.
Exploring complex networks via topological embedding on surfaces.

We demonstrate that graphs embedded on surfaces are a powerful and practical tool to generate, to characterize, and to simulate networks with a broad range of properties. Any network can be embedded on a surface with sufficiently high genus and therefore the study of topologically embedded graphs is non-restrictive. We show that the local properties of the network are affected by the surface genus which determines the average degree, which influences the degree distribution, and which controls the clustering coefficient. The global properties of the graph are also strongly affected by the surface genus which is constraining the degree of interwovenness, changing the scaling properties of the network from large-world kind (small genus) to small- and ultrasmall-world kind (large genus). Two elementary moves allow the exploration of all networks embeddable on a given surface and naturally introduce a tool to develop a statistical mechanics description for these networks. Within such a framework, we study the properties of topologically embedded graphs which dynamically tend to lower their energy towards a ground state with a given reference degree distribution. We show that the cooling dynamics between high and low "temperatures" is strongly affected by the surface genus with the manifestation of a glass-like transition occurring when the distance from the reference distribution is low. We prove, with examples, that topologically embedded graphs can be built in a way to contain arbitrary complex networks as subgraphs. This method opens a new avenue to build geometrically embedded networks on hyperbolic manifolds.
Using Variational Inference and MapReduce to Scale Topic Modeling

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for exploring document collections. Because of the increasing prevalence of large datasets, there is a need to improve the scalability of inference of LDA. In this paper, we propose a technique called MapReduce LDA (Mr. LDA) to accommodate very large corpus collections in the MapReduce framework. In contrast to other techniques to scale inference for LDA, which use Gibbs sampling, we use variational inference. Our solution efficiently distributes computation and is relatively simple to implement. More importantly, this variational implementation, unlike highly tuned and specialized implementations, is easily extensible. We demonstrate two extensions of the model possible with this scalable framework: informed priors to guide topic discovery and modeling topics from a multilingual corpus.
Investment/Consumption Problem in Illiquid Markets with Regime-Switching

We consider an illiquid financial market with different regimes modeled by a continuous-time finite-state Markov chain. The investor can trade a stock only at the discrete arrival times of a Cox process with intensity depending on the market regime. Moreover, the risky asset price is subject to liquidity shocks, which change its rate of return and volatility, and induce jumps on its dynamics. In this setting, we study the problem of an economic agent optimizing her expected utility from consumption under a non-bankruptcy constraint. By using the dynamic programming method, we provide the characterization of the value function of this stochastic control problem in terms of the unique viscosity solution to a system of integro-partial differential equations. We next focus on the popular case of CRRA utility functions, for which we can prove smoothness $C^2$ results for the value function. As an important byproduct, this allows us to get the existence of optimal investment/consumption strategies characterized in feedback forms. We analyze a convergent numerical scheme for the resolution to our stochastic control problem, and we illustrate finally with some numerical experiments the effects of liquidity regimes in the investor's optimal decision.
New limit on the lepton-flavour violating decay $\mu^{+} \to e^{+} \gamma$

We present a new result based on an analysis of the data collected by the MEG detector at the Paul Scherrer Institut in 2009 and 2010, in search of the lepton flavour violating decay mu->e gamma. The likelihood analysis of the combined data sample, which corresponds to a total of 1.8 x 10**14 muon decays, gives a 90% C.L. upper limit of 2.4 x 10**-12 on the branching ratio of the mu->e gamma decay, constituting the most stringent limit on the existence of this decay to date.
The Network of Global Corporate Control

The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic “super-entity” that raises new important issues both for researchers and policy makers.
Optimization with Sparsity-Inducing Penalties

Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the related estimation problems can be cast as convex optimization problems by regularizing the empirical risk with appropriate nonsmooth norms. The goal of this monograph is to present from a general perspective optimization tools and techniques dedicated to such sparsity-inducing penalties. We cover proximal methods, block-coordinate descent, reweighted l2-penalized techniques, working-set and homotopy methods, as well as non-convex formulations and extensions, and provide an extensive set of experiments to compare various algorithms from a computational point of view.
Expanding (3+1)-dimensional universe from a lorentzian matrix model for superstring theory in (9+1) dimensions.

We reconsider the matrix model formulation of type IIB superstring theory in (9+1)-dimensional space-time. Unlike the previous works in which the Wick rotation was used to make the model well defined, we regularize the Lorentzian model by introducing infrared cutoffs in both the spatial and temporal directions. Monte Carlo studies reveal that the two cutoffs can be removed in the large-N limit and that the theory thus obtained has no parameters other than one scale parameter. Moreover, we find that three out of nine spatial directions start to expand at some "critical time," after which the space has SO(3) symmetry instead of SO(9).
Why Philosophers Should Care About Computational Complexity

One might think that, once we know something is computable, how efficiently it can be computed is a practical question with little further philosophical importance. In this essay, I offer a detailed case that one would be wrong. In particular, I argue that computational complexity theory---the field that studies the resources (such as time, space, and randomness) needed to solve computational problems---leads to new perspectives on the nature of mathematical knowledge, the strong AI debate, computationalism, the problem of logical omniscience, Hume's problem of induction and Goodman's grue riddle, the foundations of quantum mechanics, economic rationality, closed timelike curves, and several other topics of philosophical interest. I end by discussing aspects of complexity theory itself that could benefit from philosophical analysis.
Estimation of parameters in DNA mixture analysis

In [7], a Bayesian network for analysis of mixed traces of DNA was presented using gamma distributions for modelling peak sizes in the electropherogram. It was demonstrated that the analysis was sensitive to the choice of a variance factor and hence this should be adapted to any new trace analysed. In this paper, we discuss how the variance parameter can be estimated by maximum likelihood to achieve this. The unknown proportions of DNA from each contributor can similarly be estimated by maximum likelihood jointly with the variance parameter. Furthermore, we discuss how to incorporate prior knowledge about the parameters in a Bayesian analysis. The proposed estimation methods are illustrated through a few examples of applications for calculating evidential value in casework and for mixture deconvolution.
Primordial Gravitational Wave Detectability with Deep Small-Sky CMB Experiments

We use Bayesian estimation on direct T-Q-U CMB polarization maps to forecast errors on the tensor-to- scalar power ratio r, and hence on primordial gravitational waves, as a function of sky coverage fsky. This method filters the quadratic pixel-pixel space into the optimal combinations needed for r detection for cut skies, providing enhanced information over a first-step linear separation into a combination of E, B and mixed modes, and ignoring the latter. With current computational power and for typical resolutions appropriate for r detection, the large matrix inversions required are accurate and fast. We explore two classes of experiments. One is motivated by a long duration balloon experiment like Spider, with pixel noise \propto \sqrt{fsky} for a specified observing period, but also applies to ground-based array experiments. We find that, ignoring systematic effects and foregrounds, an experiment with Spider-like noise over fsky ~0.02-0.2 could place a 2sigma_r ~0.014 (~95% CL) bound, rising to 0.02 with an L-dependent foreground residual. We contrast this with a Planck-like fixed instrumental noise as fsky varies, which gives a Galaxy-masked (fsky = 0.75) 2sigma_r ~0.015, rising to ~0.05 with the foreground residuals. Using for a figure of merit the (marginalized) 1D Shannon entropy of r, taken relative to the first 2003 WMAP1 CMB-only constraint, gives -1.7 (-1.9) bits from the 2010 WMAP7+ACT (2011 WMAP7+SPT) data, forecasts of -6 bits from Spider (plus Planck), of up to -11 bits for post-Planck satellites and -13 bits for a perfect cosmic variance limited experiment. We thus confirm the wisdom of the current strategy for r detection of deeply probed patches covering the fsky minimum-error trough with balloon and ground experiments.
A sentiment analysis of Singapore Presidential Election 2011 using Twitter data with census correction

Sentiment analysis is a new area in text analytics where it focuses on the analysis and understanding of the emotions from the text patterns. This new form of analysis has been widely adopted in customer relation management especially in the context of complaint management. With increasing level of interest in this technology, more and more companies are adopting it and using it to champion their marketing efforts. However, sentiment analysis using twitter has remained extremely difficult to manage due to the sampling bias. In this paper, we will discuss about the application of using reweighting techniques in conjunction with online sentiment divisions to predict the vote percentage that individual candidate will receive. There will be in depth discussion about the various aspects using sentiment analysis to predict outcomes as well as the potential pitfalls in the estimation due to the anonymous nature of the internet.
LARGE PORTFOLIO ASYMPTOTICS FOR LOSS FROM DEFAULT

We prove a law of large numbers for the loss from default and use it for approximating the distribution of the loss from default in large, potentially heterogeneous portfolios. The density of the limiting measure is shown to solve a nonlinear stochastic partial differential equation, and certain moments of the limiting measure are shown to satisfy an infinite system of stochastic differential equations. The solution to this system leads to the distribution of the limiting portfolio loss, which we propose as an approximation to the loss distribution for a large portfolio. Numerical tests illustrate the accuracy of the approximation, and highlight its computational advantages over a direct Monte Carlo simulation of the original stochastic system.
TIME‐CONSISTENT AND MARKET‐CONSISTENT EVALUATIONS

We consider evaluation methods for payoffs with an inherent financial risk as encountered for instance for portfolios held by pension funds and insurance companies. Pricing such payoffs in a way consistent to market prices typically involves combining actuarial techniques with methods from mathematical finance. We propose to extend standard actuarial principles by a new market‐consistent evaluation procedure which we call “two‐step market evaluation.” This procedure preserves the structure of standard evaluation techniques and has many other appealing properties. We give a complete axiomatic characterization for two‐step market evaluations. We show further that in a dynamic setting with continuous stock prices every evaluation which is time‐consistent and market‐consistent is a two‐step market evaluation. We also give characterization results and examples in terms of g‐expectations in a Brownian‐Poisson setting.
Algorithms for Constructing Overlay Networks For Live Streaming

In this paper, we present a polynomial time approximation algorithm for constructing an overlay multicast network for streaming live media events over the Internet. The class of overlay networks constructed by our algorithm include networks used by Akamai Technologies to deliver live media events to a global audience with high fidelity. In particular, we construct networks consisting of three stages of nodes. The nodes in the first stage are the entry points that act as sources for the live streams. Each source forwards each of its streams to one or more nodes in the second stage that are called reflectors. A reflector can split an incoming stream into multiple identical outgoing streams, which are then sent on to nodes in the third and final stage that act as sinks and are located in edge networks near end-users. As the packets in a stream travel from one stage to the next, some of them may be lost. The job of a sink is to combine the packets from multiple instances of the same stream (by reordering packets and discarding duplicates) to form a single instance of the stream with minimal loss. We assume that the loss rate between any pair of nodes in the network is known, and that losses between different pairs are independent, but discuss an extension to tolerate failures that happen in a
Inference of natural selection from interspersed genomic elements based on polymorphism and divergence.

Complete genome sequences contain valuable information about natural selection, but this information is difficult to access for short, widely scattered noncoding elements such as transcription factor binding sites or small noncoding RNAs. Here, we introduce a new computational method, called Inference of Natural Selection from Interspersed Genomically coHerent elemenTs (INSIGHT), for measuring the influence of natural selection on such elements. INSIGHT uses a generative probabilistic model to contrast patterns of polymorphism and divergence in the elements of interest with those in flanking neutral sites, pooling weak information from many short elements in a manner that accounts for variation among loci in mutation rates and coalescent times. The method is able to disentangle the contributions of weak negative, strong negative, and positive selection based on their distinct effects on patterns of polymorphism and divergence. It obtains information about divergence from multiple outgroup genomes using a general statistical phylogenetic approach. The INSIGHT model is efficiently fitted to genome-wide data using an approximate expectation maximization algorithm. Using simulations, we show that the method can accurately estimate the parameters of interest even in complex demographic scenarios, and that it significantly improves on methods based on summary statistics describing polymorphism and divergence. To demonstrate the usefulness of INSIGHT, we apply it to several classes of human noncoding RNAs and to GATA2-binding sites in the human genome.
A numerical adaptation of self-avoiding walk identities from the honeycomb to other 2D lattices

Recently, Duminil-Copin and Smirnov proved a long-standing conjecture of Nienhuis that the connective constant of self-avoiding walks (SAWs) on the honeycomb lattice is A key identity used in that proof depends on the existence of a parafermionic observable for SAWs on the honeycomb lattice. Despite the absence of a corresponding observable for SAWs on the square and triangular lattices, we show that in the limit of large lattices, some of the consequences observed on the honeycomb lattice persist on other lattices. This permits the accurate estimation, though not an exact evaluation, of certain critical amplitudes, as well as critical points, for these lattices. For the honeycomb lattice, an exact amplitude for loops is proved.