nils-herrmann/clean_scirepeval huggingface dataset:
The Nature of Scientific Proof in the Age of Simulations

Is numerical mimicry a third way of establishing truth? 
Kevin Heng received his M.S. and Ph.D. in astrophysics from the Joint Institute for Laboratory Astrophysics (JILA) and the University of Colorado at Boulder. He joined the Institute 
for Advanced Study in Princeton from 2007 to 2010, first as a Member and later as the Frank & Peggy Taplin Member. From 2010 to 2012 he was a Zwicky Prize Fellow at ETH Z¨urich 
(the Swiss Federal Institute of Technology). In 2013, he joined the Center for Space and Habitability (CSH) at the University of Bern, Switzerland, as a tenure-track assistant professor, 
where he leads the Exoplanets and Exoclimes Group. He has worked on, and maintains, a broad range of interests in astrophysics: shocks, extrasolar asteroid belts, planet formation, 
fluid dynamics, brown dwarfs and exoplanets. He coordinates the Exoclimes Simulation Platform (ESP), an open-source set of theoretical tools designed for studying the basic physics and 
chemistry of exoplanetary atmospheres and climates (www.exoclime.org). He is involved in the CHEOPS (Characterizing Exoplanet Satellite) space telescope, a mission approved by the 
European Space Agency (ESA) and led by Switzerland. He spends a fair amount of time humbly learning the lessons gleaned from studying the Earth and Solar System planets, as related 
to him by atmospheric, climate and planetary scientists. He received a Sigma Xi Grant-in-Aid of Research in 2006
Overconsumption, outflows and the quenching of satellite galaxies

The baryon cycle of galaxies is a dynamic process involving the intake, consumption and ejection of vast quantities of gas. In contrast, the conventional picture of satellite galaxies has them methodically turning a large gas reservoir into stars until this reservoir is forcibly removed due to external ram pressure. This picture needs revision. Our modern understanding of the baryon cycle suggests that in some regimes the simple interruption of the fresh gas supply may quench satellite galaxies long before stripping events occur, a process we call overconsumption. We compile measurements from the literature of observed satellite quenching times at a range of redshifts to determine if satellites are principally quenched through orbit-based gas stripping events – either direct stripping of the disc (ram pressure stripping) or the extended gas halo (strangulation) – or from internally driven star formation outflows via overconsumption. These time-scales show significant deviations from the evolution expected for gas stripping mechanisms and suggest that either ram pressure stripping is much more efficient at high redshift, or that secular outflows quench satellites before orbit-based stripping occurs. Given the strong redshift evolution of star formation rates, at high redshift even moderate outflow rates will lead to extremely short delay times with the expectation that high-redshift (z > 1.5) satellites will be quenched almost immediately following the cessation of cosmological inflow. Observations of high-redshift satellites give an indirect but sensitive measure of the outflow rate, with current measurements suggesting that outflows are no larger than 2.5 times the star formation rate for galaxies with a stellar mass of 1010.5 M⊙.
Dynamics of starbursting dwarf galaxies. III. A H I study of 18 nearby objects

We investigate the dynamics of starbursting dwarf galaxies, using both new and archival H I observations. We consider 18 nearby galaxies that have been resolved into single stars by HST observations, providing their star formation history and total stellar mass. We find that 9 objects have a regularly rotating H I disk, 7 have a kinematically disturbed H I disk, and 2 show unsettled H I distributions. Two galaxies (NGC 5253 and UGC 6456) show a velocity gradient along the minor axis of the H I disk, which we interpret as strong radial motions. For galaxies with a regularly rotating disk we derive rotation curves, while for galaxies with a kinematically disturbed disk, we estimate the rotation velocities in their outer parts. We derive baryonic fractions within about 3 optical scale lengths and find that, on average, baryons constitute at least 30% of the total mass. Despite the star formation having injected ~1056 ergs in the ISM in the past ~500 Myr, these starbursting dwarfs have both baryonic and gas fractions similar to those of typical dwarf irregulars, suggesting that they did not eject a large amount of gas out of their potential wells. Appendices are available in electronic form at http://www.aanda.orgH I datacubes (FITS files) are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/566/A71
Automated adaptive inference of coarse-grained dynamical models in systems biology

Cellular regulatory dynamics is driven by large and intricate networks of interactions at the molecular scale, whose sheer size obfuscates understanding. In light of limited experimental data, many parameters of such dynamics are unknown, and thus models built on the detailed, mechanistic viewpoint overfit and are not predictive. At the other extreme, simple ad hoc models of complex processes often miss defining features of the underlying systems. Here we propose an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the amount of available data. Such adaptive models lead to accurate predictions even when microscopic details of the studied systems are unknown due to insufficient data. The approach is computationally tractable, even for a relatively large number of dynamical variables, allowing its software realization, named Sir Isaac, to make successful predictions even when important dynamic variables are unobserved. For example, it matches the known phase space structure for simulated planetary motion data, avoids overfitting in a complex biological signaling system, and produces accurate predictions for a yeast glycolysis model with only tens of data points and over half of the interacting species unobserved.
Sigma-terms and axial charges for hyperons and charmed baryons

We present results for the $\sigma$-terms and axial charges for various hyperons and charmed baryons using $N_f=2+1+1$ twisted mass fermions. For the computation of the three-point function we use the fixed current method. For one of the $N_f=2+1+1$ ensembles with pion mass of 373 MeV we compare the results of the fixed current method with those obtained with a stochastic method for computing the all-to-all propagator involved in the evaluation of the three point functions.
Networks maximising the consensus time of voter models

We explore the networks that yield the largest mean consensus time of voter models under different update rules. By analytical and numerical means, we show that the so-called lollipop graph, barbell graph, and double-star graph maximize the mean consensus time under the update rules called the link dynamics, voter model, and invasion process, respectively. For each update rule, the largest mean consensus time scales as O(N^{3}), where N is the number of nodes in the network.
Best Practices for Administering Concept Inventories

There is a plethora of concept inventories available for faculty to use, but it is not always clear exactly why you would use these tests, or how you should administer them and interpret the results. These research-based tests about physics and astronomy concepts are valuable because they allow for standardized comparisons among institutions, instructors, or over time. In order for these comparisons to be meaningful, you should use best practices for administering the tests. In interviews with 24 physics faculty, we have identified common questions that faculty members have about concept inventories. We have written this article to address common questions from these interviews and provide a summary of best practices for administering concept inventories.
Constraints on cosmological parameters in power-law cosmology

In this paper, we examine observational constraints on the power law cosmology; essentially dependent on two parameters H0 (Hubble constant) and q (deceleration parameter). We investigate the constraints on these parameters using the latest 28 points of H(z) data and 580 points of Union2.1 compilation data and, compare the results with the results of ΛCDM . We also forecast constraints using a simulated data set for the future JDEM, supernovae survey. Our studies give better insight into power law cosmology than the earlier done analysis by Kumar [] indicating it tuning well with Union2.1 compilation data but not with H(z) data. However, the constraints obtained on  and  i.e. H0 average and q average using the simulated data set for the future JDEM, supernovae survey are found to be inconsistent with the values obtained from the H(z) and Union2.1 compilation data. We also perform the statefinder analysis and find that the power-law cosmological models approach the standard ΛCDM model as q → −1. Finally, we observe that although the power law cosmology explains several prominent features of evolution of the Universe, it fails in details.
Improved moving puncture gauge conditions for compact binary evolutions

Robust gauge conditions are critically important to the stability and accuracy of numerical relativity (NR) simulations involving compact objects. Most of the NR community use the highly robust---though decade-old---moving-puncture (MP) gauge conditions for such simulations. It has been argued that in binary black hole (BBH) evolutions adopting this gauge, noise generated near adaptive-mesh-refinement (AMR) boundaries does not converge away cleanly with increasing resolution, severely limiting gravitational waveform accuracy at computationally feasible resolutions. We link this noise to a sharp (short-wavelength), initial outgoing gauge wave crossing into progressively lower resolution AMR grids, and present improvements to the standard MP gauge conditions that focus on stretching, smoothing, and more rapidly settling this outgoing wave. Our best gauge choice greatly reduces gravitational waveform noise during inspiral, yielding less fluctuation in convergence order and $\sim 40%$ lower waveform phase and amplitude errors at typical resolutions. Noise in other physical quantities of interest is also reduced, and constraint violations drop by more than an order of magnitude. We expect these improvements will carry over to simulations of all types of compact binary systems, as well as other $N$+1 formulations of gravity for which MP-like gauge conditions can be chosen.
On the Non-Monotonic Description Logic $\mathcal{ALC}$+T$_{\mathsf{min}}$

In the last 20 years many proposals have been made to incorporate non-monotonic reasoning into description logics, ranging from approaches based on default logic and circumscription to those based on preferential semantics. In particular, the non-monotonic description logic $\mathcal{ALC}$+T$_{\mathsf{min}}$ uses a combination of the preferential semantics with minimization of a certain kind of concepts, which represent atypical instances of a class of elements. One of its drawbacks is that it suffers from the problem known as the \emph{property blocking inheritance}, which can be seen as a weakness from an inferential point of view. In this paper we propose an extension of $\mathcal{ALC}$+T$_{\mathsf{min}}$, namely $\mathcal{ALC}$+T$^+_{\mathsf{min}}$, with the purpose to solve the mentioned problem. In addition, we show the close connection that exists between $\mathcal{ALC}$+T$^+_{\mathsf{min}}$ and concept-circumscribed knowledge bases. Finally, we study the complexity of deciding the classical reasoning tasks in $\mathcal{ALC}$+T$^+_{\mathsf{min}}$.
Une approche CSP pour l'aide à la localisation d'erreurs

We introduce in this paper a new CP-based approach to support errors location in a program for which a counter-example is available, i.e. an instantiation of the input variables that violates the post-condition. To provide helpful information for error location, we generate a constraint system for the paths of the CFG (Control Flow Graph) for which at most k conditional statements may be erroneous. Then, we calculate Minimal Correction Sets (MCS) of bounded size for each of these paths. The removal of one of these sets of constraints yields a maximal satisfiable subset, in other words, a maximal subset of constraints satisfying the post condition. We extend the algorithm proposed by Liffiton and Sakallah \cite{LiS08} to handle programs with numerical statements more efficiently. We present preliminary experimental results that are quite encouraging.
LEVERAGED ETF IMPLIED VOLATILITIES FROM ETF DYNAMICS

The growth of the exchange‐traded fund (ETF) industry has given rise to the trading of options written on ETFs and their leveraged counterparts (LETFs). We study the relationship between the ETF and LETF implied volatility surfaces when the underlying ETF is modeled by a general class of local‐stochastic volatility models. A closed‐form approximation for prices is derived for European‐style options whose payoffs depend on the terminal value of the ETF and/or LETF. Rigorous error bounds for this pricing approximation are established. A closed‐form approximation for implied volatilities is also derived. We also discuss a scaling procedure for comparing implied volatilities across leverage ratios. The implied volatility expansions and scalings are tested in three settings: Heston, limited constant elasticity of variance (CEV), and limited SABR; the last two are regularized versions of the well‐known CEV and SABR models.
Metamorphic Domain-Specific Languages: A Journey into the Shapes of a Language

External or internal domain-specific languages (DSLs) or (fluent) APIs' Whoever you are - a developer or a user of a DSL - you usually have to choose side; you should not! What about metamorphic DSLs that change their shape according to your needs? Our 4-years journey of providing the "right" support (in the domain of feature modeling), led us to develop an external DSL, different shapes of an internal API, and maintain all these languages. A key insight is that there is no one-size-fits-all solution or no clear superiority of a solution compared to another. On the contrary, we found that it does make sense to continue the maintenance of an external and internal DSL. Based on our experience and on an analysis of the DSL engineering field, the vision that we foresee for the future of software languages is their ability to be self-adaptable to the most appropriate shape (including the corresponding integrated development environment) according to a particular usage or task. We call metamorphic DSL such a language, able to change from one shape to another shape.
Blue Emission in Proteins

Recent literatures reported blue-green emission from amyloid fibril as exclusive signature of fibril formation. This unusual visible luminescence is regularly used to monitor fibril growth. Blue-green emission has also been observed in crystalline protein and in solution. However, the origin of this emission is not known exactly. Our spectroscopic study of serum proteins reveals that the blue-green emission is a property of protein monomer. Evidences suggest that semiconductor-like band structure of proteins with the optical band-gap in the visible region is possibly the origin of this phenomenon. We show here that the band structure of proteins is primarily the result of electron delocalization through the peptide chain, rather than through the hydrogen bond network in secondary structure.
Data Requirement for Phylogenetic Inference from Multiple Loci: A New Distance Method

We consider the problem of estimating the evolutionary history of a set of species (phylogeny or species tree) from several genes. It is known that the evolutionary history of individual genes (gene trees) might be topologically distinct from each other and from the underlying species tree, possibly confounding phylogenetic analysis. A further complication in practice is that one has to estimate gene trees from molecular sequences of finite length. We provide the first full data-requirement analysis of a species tree reconstruction method that takes into account estimation errors at the gene level. Under that criterion, we also devise a novel reconstruction algorithm that provably improves over all previous methods in a regime of interest.
Temporal stability of network partitions

We present a method to find the best temporal partition at any time scale and rank the relevance of partitions found at different time scales. This method is based on random walkers coevolving with the network and as such constitutes a generalization of partition stability to the case of temporal networks. We show that, when applied to a toy model and real data sets, temporal stability uncovers structures that are persistent over meaningful time scales as well as important isolated events, making it an effective tool to study both abrupt changes and gradual evolution of a network mesoscopic structures.
Drawdown: from practice to theory and back again

Maximum drawdown, the largest cumulative loss from peak to trough, is one of the most widely used indicators of risk in the fund management industry, but one of the least developed in the context of measures of risk. We formalize drawdown risk as Conditional Expected Drawdown (CED), which is the tail mean of maximum drawdown distributions. We show that CED is a degree one positive homogenous risk measure, so that it can be linearly attributed to factors; and convex, so that it can be used in quantitative optimization. We empirically explore the differences in risk attributions based on CED, Expected Shortfall (ES) and volatility. An important feature of CED is its sensitivity to serial correlation. In an empirical study that fits AR(1) models to US Equity and US Bonds, we find substantially higher correlation between the autoregressive parameter and CED than with ES or with volatility.
Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms

Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number of advantages over conventional computing architectures, such as high emulation speed or low power consumption, but this usually comes at the price of reduced configurability and precision. In this article, we investigate the consequences of several such factors that are common to neuromorphic devices, more specifically limited hardware resources, limited parameter configurability and parameter variations due to fixed-pattern noise and trial-to-trial variability. Our final aim is to provide an array of methods for coping with such inevitable distortion mechanisms. As a platform for testing our proposed strategies, we use an executable system specification (ESS) of the BrainScaleS neuromorphic system, which has been designed as a universal emulation back-end for neuroscientific modeling. We address the most essential limitations of this device in detail and study their effects on three prototypical benchmark network models within a well-defined, systematic workflow. For each network model, we start by defining quantifiable functionality measures by which we then assess the effects of typical hardware-specific distortion mechanisms, both in idealized software simulations and on the ESS. For those effects that cause unacceptable deviations from the original network dynamics, we suggest generic compensation mechanisms and demonstrate their effectiveness. Both the suggested workflow and the investigated compensation mechanisms are largely back-end independent and do not require additional hardware configurability beyond the one required to emulate the benchmark networks in the first place. We hereby provide a generic methodological environment for configurable neuromorphic devices that are targeted at emulating large-scale, functional neural networks.
A tale of two paradigms: the mutual incommensurability of ΛCDM and MOND

The concordance model of cosmology, ΛCDM, provides a satisfactory description of the evolution of the universe and the growth of large-scale structure. Despite considerable effort, this model does not at present provide a satisfactory description of small-scale structure and the dynamics of bound objects like individual galaxies. In contrast, MOND provides a unique and predictively successful description of galaxy dynamics, but is mute on the subject of cosmology. Here I briefly review these contradictory world views, emphasizing the wealth of distinct interlocking lines of evidence that went into the development of ΛCDM while highlighting the practical impossibility that it can provide a satisfactory explanation of the observed MOND phenomenology in galaxy dynamics. I also briefly review the baryon budget in groups and clusters of galaxies where neither paradigm provides an entirely satisfactory description of the data. Relatively little effort has been devoted to the formation of structure in MOND; I re...
Design and Analysis of Experiments in Networks: Reducing Bias from Interference

Estimating the effects of interventions in networks is complicated due to interference, such that the outcomes for one experimental unit may depend on the treatment assignments of other units. Familiar statistical formalism, experimental designs, and analysis methods assume the absence of this interference, and result in biased estimates of causal effects when it exists. While some assumptions can lead to unbiased estimates, these assumptions are generally unrealistic in the context of a network and often amount to assuming away the interference. In this work, we evaluate methods for designing and analyzing randomized experiments under minimal, realistic assumptions compatible with broad interference, where the aim is to reduce bias and possibly overall error in estimates of average effects of a global treatment. In design, we consider the ability to perform random assignment to treatments that is correlated in the network, such as through graph cluster randomization. In analysis, we consider incorporating information about the treatment assignment of network neighbors. We prove sufficient conditions for bias reduction through both design and analysis in the presence of potentially global interference; these conditions also give lower bounds on treatment effects. Through simulations of the entire process of experimentation in networks, we measure the performance of these methods under varied network structure and varied social behaviors, finding substantial bias reductions and, despite a bias–variance tradeoff, error reductions. These improvements are largest for networks with more clustering and data generating processes with both stronger direct effects of the treatment and stronger interactions between units.
An Approach to Forgetting in Disjunctive Logic Programs that Preserves Strong Equivalence

In this paper we investigate forgetting in disjunctive logic programs, where forgetting an atom from a program amounts to a reduction in the signature of that program. The goal is to provide an approach that is syntax-independent, in that if two programs are strongly equivalent, then the results of forgetting an atom in each program should also be strongly equivalent. Our central definition of forgetting is impractical but satisfies this goal: Forgetting an atom is characterised by the set of SE consequences of the program that do not mention the atom to be forgotten. We then provide an equivalent, practical definition, wherein forgetting an atom $p$ is given by those rules in the program that don't mention $p$, together with rules obtained by a single inference step from rules that do mention $p$. Forgetting is shown to have appropriate properties; as well, the finite characterisation results in a modest (at worst quadratic) blowup. Finally we have also obtained a prototype implementation of this approach to forgetting.