nils-herrmann/clean_scirepeval huggingface dataset:
NuSTAR OBSERVATIONS OF THE BULLET CLUSTER: CONSTRAINTS ON INVERSE COMPTON EMISSION

The search for diffuse non-thermal inverse Compton (IC) emission from galaxy clusters at hard X-ray energies has been undertaken with many instruments, with most detections being either of low significance or controversial. Because all prior telescopes sensitive at E > 10 keV do not focus light and have degree-scale fields of view, their backgrounds are both high and difficult to characterize. The associated uncertainties result in lower sensitivity to IC emission and a greater chance of false detection. In this work, we present 266 ks NuSTAR observations of the Bullet cluster, which is detected in the energy range 3–30 keV. NuSTAR's unprecedented hard X-ray focusing capability largely eliminates confusion between diffuse IC and point sources; however, at the highest energies, the background still dominates and must be well understood. To this end, we have developed a complete background model constructed of physically inspired components constrained by extragalactic survey field observations, the specific parameters of which are derived locally from data in non-source regions of target observations. Applying the background model to the Bullet cluster data, we find that the spectrum is well—but not perfectly—described as an isothermal plasma with kT = 14.2 ± 0.2 keV. To slightly improve the fit, a second temperature component is added, which appears to account for lower temperature emission from the cool core, pushing the primary component to kT ∼ 15.3 keV. We see no convincing need to invoke an IC component to describe the spectrum of the Bullet cluster, and instead argue that it is dominated at all energies by emission from purely thermal gas. The conservatively derived 90% upper limit on the IC flux of 1.1 × 10−12 erg s−1 cm−2 (50–100 keV), implying a lower limit on B ≳ 0.2 μG, is barely consistent with detected fluxes previously reported. In addition to discussing the possible origin of this discrepancy, we remark on the potential implications of this analysis for the prospects for detecting IC in galaxy clusters in the future.
Ground-based transit observations of the super-Earth GJ 1214 b ⋆

GJ 1214 b is one of the few known transiting super-Earth-sized exoplanets with a measured mass and radius. It orbits an Mdwarf, only 14.55 pc away, making it a favorable candidate for follow-up studies. However, the composition of GJ 1214 b’s mysterious atmosphere has yet to be fully unveiled.  Our goal is to distinguish between the various proposed atmospheric models to explain the properties of GJ 1214 b: hydrogenrich or hydrogen-He mix, or a heavy molecular weight atmosphere with reflecting high clouds, as latest studies have sugge sted.  Wavelength-dependent planetary radii measurements from the transit depths in the optical/NIR are the best tool to investigate the atmosphere of GJ 1214 b. We present here (i) photometric transit observations with a narrow-band filter centered on 2.14µm and a broad-band I-Bessel filter centered on 0.8665 µm, and (ii) transmission spectroscopy in the H and K atmospheric windows that cover three transits. The obtained photometric and spectrophotometric time series were analyzed with MCMC simulations to measure the planetary radii as a function of wavelength. We determined radii ratios of 0.1173 +0.0022 −0.0024 for I-Bessel and 0.11735 +0.00072 −0.00076 at 2.14µm.  Our measurements indicate a flat transmission spectrum, in a greement with last atmospheric models that favor featureless spectra with clouds and high molecular weight compositions.
Quadratic BSDEs with jumps: related non-linear expectations

In this article, we follow the study of quadratic backward SDEs with jumps,that is to say for which the generator has quadratic growth in the variables (z; u), started in our accompanying paper . Relying on the existence and uniqueness result of , we define the corresponding g-expectations and study some of their properties. We obtain in particular a non-linear Doob-Meyer decomposition for g-submartingales and a downcrossing inequality which implies their regularity in time. As a consequence of these results, we also obtain a converse comparison theorem for our class of BSDEs. Finally, we provide a dual representation for the corresponding dynamic risk measures, and study the properties of their inf-convolution, giving several explicit examples
The bursty dynamics of the Twitter information network

In online social media systems users are not only posting, consuming, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the connection between the two. How does user information posting and seeking behavior interact with the evolution of the underlying social network structure? Here, we study ways in which network structure reacts to users posting and sharing content. We examine the complete dynamics of the Twitter information network, where users post and reshare information while they also create and destroy connections. We find that the dynamics of network structure can be characterized by steady rates of change, interrupted by sudden bursts. Information diffusion in the form of cascades of post re-sharing often creates such sudden bursts of new connections, which significantly change users' local network structure. We also explore the effect of the information content on the dynamics of the network and find evidence that the appearance of new topics and real-world events can lead to significant changes in edge creations and deletions. Lastly, we develop a model that quantifies the dynamics of the network and the occurrence of these bursts as a function of the information spreading through the network. The model can successfully predict which information diffusion events will lead to bursts in network dynamics.
Aggregate Estimation Over Dynamic Hidden Web Databases

Many databases on the web are "hidden" behind (i.e., accessible only through) their restrictive, form-like, search interfaces. Recent studies have shown that it is possible to estimate aggregate query answers over such hidden web databases by issuing a small number of carefully designed search queries through the restrictive web interface. A problem with these existing work, however, is that they all assume the underlying database to be static, while most real-world web databases (e.g., Amazon, eBay) are frequently updated. In this paper, we study the novel problem of estimating/tracking aggregates over dynamic hidden web databases while adhering to the stringent query-cost limitation they enforce (e.g., at most 1,000 search queries per day). Theoretical analysis and extensive real-world experiments demonstrate the effectiveness of our proposed algorithms and their superiority over baseline solutions (e.g., the repeated execution of algorithms designed for static web databases).
BKL conjecture in Bianchi VIII and IX with the ultrarelativistic fluid

We rigorously verify that in the spatially homogeneous spacetimes (Bianchi VIII and IX), the presence of matter does not affect the oscillatory behavior of the solutions to the Einstein field equations as first conjectured by Belinski, Khalatnikov, and Lifshitz (BKL) in 1970. This paper is an extension of the paper by Reiterer and Trubowitz ( and uses the same formalism. We use the ultrarelativistic equation of state.
Optical frequency comb technology for ultra‐broadband radio‐frequency photonics

The outstanding phase‐noise performance of optical frequency combs has led to a revolution in optical synthesis and metrology, covering a myriad of applications, from molecular spectroscopy to laser ranging and optical communications. However, the ideal characteristics of an optical frequency comb are application dependent. In this review, the different techniques for the generation and processing of high‐repetition‐rate (>10 GHz) optical frequency combs with technologies compatible with optical communication equipment are covered. Particular emphasis is put on the benefits and prospects of this technology in the general field of radio‐frequency photonics, including applications in high‐performance microwave photonic filtering, ultra‐broadband coherent communications, and radio‐frequency arbitrary waveform generation.
The Evolution of Galaxy Structure Over Cosmic Time

I present a comprehensive review of the evolution of galaxy structure in the Universe from the first galaxies currently observable at z ∼ 6 down to galaxies observable in the local Universe. Observed changes in galaxy structures reveal formation processes that only galaxy structural analyses can provide. This pedagogical review provides a detailed discussion of the major methods used to study galaxies morphologically and structurally, including the well-established visual method for morphology; S´ ersic fitting to measure galaxy sizes and surface brightness profile shapes; and nonparametric structural methods [such as the concentration (C), asymmetry (A), clumpiness (S) (CAS) method and the Gini/M20 parameters, as well as newer structural indices]. These structural indices measure fundamental properties of galaxies, such as their scale, star-formation rate, and ongoing merger activity. Extensive observational results demonstrate how broad galaxy morphologies and structures change with time up to z ∼ 3, from small, compact and peculiar systems in the distant Universe to the formation of the Hubble sequence, dominated by spirals and ellipticals. Structural methods accurately identify galaxies in mergers and allow measurements of the merger history out to z ∼ 3. I depict properties and evolution of internal structures of galaxies, such as bulges, disks, bars, and at z > 1 large star-forming clumps. I describe the structure and morphologies of host galaxies of active galactic nuclei and starbursts/submillimeter galaxies, along with how morphological galaxy quenching occurs. The role of environment in producing structural changes in galaxies over cosmic time is also discussed. Galaxy sizes can also change with time, with measured sizes up to a factor of 2–5 smaller at high redshift at a given stellar mass. I conclude with a discussion of how the evolving trends, in sizes, structures, and morphologies, reveal the formation mechanisms behind galaxies and provides a new and unique way to test theories of galaxy formation.
Principles of scientific research team formation and evolution

Science is an activity with far-reaching implications for modern society. Understanding how the social organization of science and its fundamental unit, the research team, forms and evolves is therefore of critical significance. Previous studies uncovered important properties of the internal structure of teams, but little attention has been paid to their most basic property: size. This study fills this gap by presenting a model that successfully explains how team sizes in various fields have evolved over the past half century. This model is based on two principles: (i) smaller (core) teams form according to a Poisson process, and (ii) larger (extended) teams begin as core teams but consequently accumulate new members through the process of cumulative advantage based on productivity. Research teams are the fundamental social unit of science, and yet there is currently no model that describes their basic property: size. In most fields, teams have grown significantly in recent decades. We show that this is partly due to the change in the character of team size distribution. We explain these changes with a comprehensive yet straightforward model of how teams of different sizes emerge and grow. This model accurately reproduces the evolution of empirical team size distribution over the period of 50 y. The modeling reveals that there are two modes of knowledge production. The first and more fundamental mode employs relatively small, “core” teams. Core teams form by a Poisson process and produce a Poisson distribution of team sizes in which larger teams are exceedingly rare. The second mode employs “extended” teams, which started as core teams, but subsequently accumulated new members proportional to the past productivity of their members. Given time, this mode gives rise to a power-law tail of large teams (10–1,000 members), which features in many fields today. Based on this model, we construct an analytical functional form that allows the contribution of different modes of authorship to be determined directly from the data and is applicable to any field. The model also offers a solid foundation for studying other social aspects of science, such as productivity and collaboration.
Regge trajectory of the f0(500) resonance from a dispersive connection to its pole

We report here our results on how to obtain the Regge trajectory of a resonance from its pole in a scattering process by imposing analytic constraints in the complex angular momentum plane. The method, suited for resonances that dominate an elastic scattering amplitude, has been applied to the ρ (770) and the f0(500) resonances. Whereas for the former we obtain a linear Regge trajectory, characteristic of ordinary quark-antiquark states, for the latter we find a non-linear trajectory with a much smaller slope at the resonance mass. This provides a strong indication of the non-ordinary nature of the sigma meson.
Bandwidth-Aware Scheduling With SDN in Hadoop: A New Trend for Big Data

Software-defined networking (SDN) is a revolutionary network architecture that separates out network control functions from the underlying equipment and is an increasing trend to help enterprises build more manageable data centers where big data processing emerges as an important part of applications. To concurrently process large-scale data, MapReduce with an open-source implementation named Hadoop is proposed. In practical Hadoop systems, one kind of issue that vitally impacts the overall performance is known as the NP-complete minimum make span11Make span means the time between job’s start time and job’s finish time. problem. One main solution is to assign tasks on data local nodes to avoid link occupation since network bandwidth is a scarce resource. Many methodologies for enhancing data locality are proposed such as the Hadoop default scheduler (HDS) and state-of-the-art scheduler balance-reduce scheduler (BAR). However, all of them either ignore allocating tasks in a global view or disregard available bandwidth as the basis for scheduling. In this paper, we propose a heuristic bandwidth-aware task scheduler bandwidth-aware scheduling with SDN in Hadoop (BASS) to combine Hadoop with SDN. It is not only able to guarantee data locality in a global view but also can efficiently assign tasks in an optimized way. Both examples and experiments demonstrate that BASS has the best performance in terms of job completion time. To our knowledge, BASS is the first to exploit talent of SDN for job scheduling of big data processing and we believe that it points out a new trend for large-scale data processing.
The jsonlite Package: A Practical and Consistent Mapping Between JSON Data and R Objects

A naive realization of JSON data in R maps JSON arrays to an unnamed list, and JSON objects to a named list. However, in practice a list is an awkward, inefficient type to store and manipulate data. Most statistical applications work with (homogeneous) vectors, matrices or data frames. Therefore JSON packages in R typically define certain special cases of JSON structures which map to simpler R types. Currently there exist no formal guidelines, or even consensus between implementations on how R data should be represented in JSON. Furthermore, upon closer inspection, even the most basic data structures in R actually do not perfectly map to their JSON counterparts and leave some ambiguity for edge cases. These problems have resulted in different behavior between implementations and can lead to unexpected output. This paper explicitly describes a mapping between R classes and JSON data, highlights potential problems, and proposes conventions that generalize the mapping to cover all common structures. We emphasize the importance of type consistency when using JSON to exchange dynamic data, and illustrate using examples and anecdotes. The jsonlite R package is used throughout the paper as a reference implementation.
Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence

Organizational growth processes have consistently been shown to exhibit a fatter-than-Gaussian growth-rate distribution in a variety of settings. Long periods of relatively small changes are interrupted by sudden changes in all size scales. This kind of extreme events can have important consequences for the development of biological and socio-economic systems. Existing models do not derive this aggregated pattern from agent actions at the micro level. We develop an agent-based simulation model on a social network. We take our departure in a model by a Schwarzkopf et al. on a scale-free network. We reproduce the fat-tailed pattern out of internal dynamics alone, and also find that it is robust with respect to network topology. Thus, the social network and the local interactions are a prerequisite for generating the pattern, but not the network topology itself. We further extend the model with a parameter that weights the relative fraction of an individual's neighbours belonging to a given organization, representing a contextual aspect of social influence. In the lower limit of this parameter, the fraction is irrelevant and choice of organization is random. In the upper limit of the parameter, the largest fraction quickly dominates, leading to a winner-takes-all situation. We recover the real pattern as an intermediate case between these two extremes.
A survey of dimensionality reduction techniques

Experimental life sciences like biology or chemistry have seen in the recent decades an explosion of the data available from experiments. Laboratory instruments become more and more complex and report hundreds or thousands measurements for a single experiment and therefore the statistical methods face challenging tasks when dealing with such high dimensional data. However, much of the data is highly redundant and can be efficiently brought down to a much smaller number of variables without a significant loss of information. The mathematical procedures making possible this reduction are called dimensionality reduction techniques; they have widely been developed by fields like Statistics or Machine Learning, and are currently a hot research topic. In this review we categorize the plethora of dimension reduction techniques available and give the mathematical insight behind them.
A Simple Regression-Based Method to Map Quantitative Trait Loci Underlying Function-Valued Phenotypes

Most statistical methods for quantitative trait loci (QTL) mapping focus on a single phenotype. However, multiple phenotypes are commonly measured, and recent technological advances have greatly simplified the automated acquisition of numerous phenotypes, including function-valued phenotypes, such as growth measured over time. While methods exist for QTL mapping with function-valued phenotypes, they are generally computationally intensive and focus on single-QTL models. We propose two simple, fast methods that maintain high power and precision and are amenable to extensions with multiple-QTL models using a penalized likelihood approach. After identifying multiple QTL by these approaches, we can view the function-valued QTL effects to provide a deeper understanding of the underlying processes. Our methods have been implemented as a package for R, funqtl.
Studying Gender in Conference Talks -- data from the 223rd meeting of the American Astronomical Society

We present a study on the gender balance, in speakers and attendees, at the recent major astronomical conference, the American Astronomical Society meeting 223, in Washington, DC. We conducted an informal survey, yielding over 300 responses by volunteers at the meeting. Each response included gender data about a single talk given at the meeting, recording the gender of the speaker and all question-askers. In total, 225 individual AAS talks were sampled. We analyze basic statistical properties of this sample. We nd that the gender ratio of the speakers closely matched the gender ratio of the conference attendees. The audience asked an average of 2.8 questions per talk. Talks given by women had a slightly higher number of questions asked (3.2 0.2) than talks given by men (2.6 0.1). The most signicant result from this study is that while the gender ratio of speakers very closely mirrors that of conference attendees, women are under-represented in the question-asker category. We interpret this to be an age-eect, as senior scientists may be more likely to ask questions, and are more commonly men. A strong dependence on the gender of session chairs is found, whereby women ask disproportionately fewer questions in sessions chaired by men. While our results point to laudable progress in gender-balanced speaker selection, we believe future surveys of this kind would help ensure that collaboration at such meetings is as inclusive as possible.
Engaging with massive online courses

The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively unexplored and poorly understood. In a MOOC, each student's complete interaction with the course materials takes place on the Web, thus providing a record of learner activity of unprecedented scale and resolution. In this work, we use such trace data to develop a conceptual framework for understanding how users currently engage with MOOCs. We develop a taxonomy of individual behavior, examine the different behavioral patterns of high- and low-achieving students, and investigate how forum participation relates to other parts of the course. We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across sub-populations. We find that making badges more salient produced increases in forum engagement.
Readout Optical System of Sapphire Disks intended for Long-Term Data Storage

The development of long-term data storage technology is one of the urging problems of our time. This paper presents the results of implementation of technical solution for long-term data storage technology proposed a few years ago on the basis of single crystal sapphire. It is shown that the problem of reading data through a substrate of negative single crystal sapphire can be solved by using for reading a special optical system with a plate of positive single crystal quartz. The experimental results confirm the efficiency of the proposed method of compensation.
Distribution of asset price movement and market potential

In this article we discuss the distribution of asset price movements by introducing a market potential function. From the principle of free energy minimization we analyze two different kinds of market potentials. We obtain a U-shaped potential when market reversion (i.e. contrarian investors) is dominant. On the other hand, if there are more trend followers, flat and logarithmic potentials appear. By using the cyclically adjusted price-to-earning ratio, which is a common valuation tool, we empirically investigate the market data. By studying long term data we observe the historical change of the market potential of the US stock market. Recent US data show that the market potential looks more like a trend-following potential. Next, we compare the market potentials for 12 different countries. Though some countries have similar market potentials, there are specific examples like Japan which exhibits a very flat potential.