nils-herrmann/clean_scirepeval huggingface dataset:
Predicting Successful Memes Using Network and Community Structure

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.
Radiometric 81Kr dating identifies 120,000-year-old ice at Taylor Glacier, Antarctica

Past variations in Earth’s climate and atmospheric composition are recorded in accumulating polar meteoric ice and the air trapped within it. Ice outcrops provide accessible archives of old ice but are difficult to date reliably. Here we demonstrate 81Kr radiometric dating of ice, allowing accurate dating of up to 1.5 million-year-old ice. The technique successfully identifies valuable ice from the previous interglacial period at Taylor Glacier, Antarctica. Our method will enhance the scientific value of outcropping sites as archives of old ice needed for paleoclimatic reconstructions and can aid efforts to extend the ice core record further back in time. We present successful 81Kr-Kr radiometric dating of ancient polar ice. Krypton was extracted from the air bubbles in four ∼350-kg polar ice samples from Taylor Glacier in the McMurdo Dry Valleys, Antarctica, and dated using Atom Trap Trace Analysis (ATTA). The 81Kr radiometric ages agree with independent age estimates obtained from stratigraphic dating techniques with a mean absolute age offset of 6 ± 2.5 ka. Our experimental methods and sampling strategy are validated by (i) 85Kr and 39Ar analyses that show the samples to be free of modern air contamination and (ii) air content measurements that show the ice did not experience gas loss. We estimate the error in the 81Kr ages due to past geomagnetic variability to be below 3 ka. We show that ice from the previous interglacial period (Marine Isotope Stage 5e, 130–115 ka before present) can be found in abundance near the surface of Taylor Glacier. Our study paves the way for reliable radiometric dating of ancient ice in blue ice areas and margin sites where large samples are available, greatly enhancing their scientific value as archives of old ice and meteorites. At present, ATTA 81Kr analysis requires a 40–80-kg ice sample; as sample requirements continue to decrease, 81Kr dating of ice cores is a future possibility.
Theoretical analysis of discreteness-induced transition in autocatalytic reaction dynamics.

Transitions in the qualitative behavior of chemical reaction dynamics with a decrease in molecule number have attracted much attention. Here, a method based on a Markov process with a tridiagonal transition matrix is applied to the analysis of this transition in reaction dynamics. The transition to bistability due to the small-number effect and the mean switching time between the bistable states are analytically calculated in agreement with numerical simulations. In addition, a novel transition involving the reversal of the chemical reaction flow is found in the model under an external flow, and also in a three-component model. The generality of this transition and its correspondence to biological phenomena are also discussed.
Production cross section of neutron-rich isotopes with radioactive and stable beams

The production cross section of neutron-rich isotopes of Ca, Zn, Te, Xe, and Pt are predicted in the diffusive multinucleon transfer reactions with stable and radioactive beams. With these isotopes one can treat the neutron shell evolution beyond $N=28$, 50, 82, and 126. Because of the small cross sections, the production of nuclei near the neutron drip line requires the optimal choice of reaction partners and bombarding energies.
Range expansion of heterogeneous populations.

Risk spreading in bacterial populations is generally regarded as a strategy to maximize survival. Here, we study its role during range expansion of a genetically diverse population where growth and motility are two alternative traits. We find that during the initial expansion phase fast-growing cells do have a selective advantage. By contrast, asymptotically, generalists balancing motility and reproduction are evolutionarily most successful. These findings are rationalized by a set of coupled Fisher equations complemented by stochastic simulations.
Steady-state entanglement of spatially separated qubits via quantum bath engineering

We propose a scheme for driving a dimer of spatially separated qubits into a maximally entangled non-equilibrium steady state. A photon-mediated retarded interaction between the qubits is realized by coupling them to two tunnel-coupled leaky cavities where each cavity is driven by a coherent microwave tone. The proposed cooling mechanism relies on striking the right balance between the unitary and driven-dissipative dynamics of the qubit subsystem. We map the dimer to an effective transverse-field XY model coupled to a non-equilibrium bath that can be suitably engineered through the choice of drive frequencies and amplitudes. We show that both singlet and triplet states can be obtained with remarkable fidelities. The proposed protocol can be implemented with a superconducting circuit architecture that was recently experimentally realized and paves the way to achieving large-scale entangled systems that are arbitrarily long lived.
Dissipation enhanced vibrational sensing in an olfactory molecular switch.

Motivated by a proposed olfactory mechanism based on a vibrationally activated molecular switch, we study electron transport within a donor-acceptor pair that is coupled to a vibrational mode and embedded in a surrounding environment. We derive a polaron master equation with which we study the dynamics of both the electronic and vibrational degrees of freedom beyond previously employed semiclassical (Marcus-Jortner) rate analyses. We show (i) that in the absence of explicit dissipation of the vibrational mode, the semiclassical approach is generally unable to capture the dynamics predicted by our master equation due to both its assumption of one-way (exponential) electron transfer from donor to acceptor and its neglect of the spectral details of the environment; (ii) that by additionally allowing strong dissipation to act on the odorant vibrational mode, we can recover exponential electron transfer, though typically at a rate that differs from that given by the Marcus-Jortner expression; (iii) that the ability of the molecular switch to discriminate between the presence and absence of the odorant, and its sensitivity to the odorant vibrational frequency, is enhanced significantly in this strong dissipation regime, when compared to the case without mode dissipation; and (iv) that details of the environment absent from previous Marcus-Jortner analyses can also dramatically alter the sensitivity of the molecular switch, in particular, allowing its frequency resolution to be improved. Our results thus demonstrate the constructive role dissipation can play in facilitating sensitive and selective operation in molecular switch devices, as well as the inadequacy of semiclassical rate equations in analysing such behaviour over a wide range of parameters.
Causality between time series

Given two time series, can one tell, in a rigorous and quantitative way, the cause and effect between them? Based on a recently rigorized physical notion namely information flow, we arrive at a concise formula and give this challenging question, which is of wide concern in different disciplines, a positive answer. Here causality is measured by the time rate of change of information flowing from one series, say, X2, to another, X1. The measure is asymmetric between the two parties and, particularly, if the process underlying X1 does not depend on X2, then the resulting causality from X2 to X1 vanishes. The formula is tight in form, involving only the commonly used statistics, sample covariances. It has been validated with touchstone series purportedly generated with one-way causality. It has also been applied to the investigation of real world problems; an example presented here is the cause-effect relation between two climate modes, El Ni\~no and Indian Ocean Dipole, which have been linked to the hazards in far flung regions of the globe, with important results that would otherwise be difficult, if not impossible, to obtain.
Fermi surfaces and phase stability of Ba(Fe1−xMx)2As2 (M=Co,Ni,Cu,Zn)

BaFe2As2 with transition-metal doping exhibits a variety of rich phenomena from the coupling of structure, magnetism, and superconductivity. Using density functional theory, we systematically compare the Fermi surfaces (FSs), formation energies (ΔEf), and densities of states (DOSs) of electron-doped Ba(Fe1-xMx)2As2 with M={Co,Ni,Cu,Zn} in tetragonal (I4/mmm) and orthorhombic (Fmmm) structures in nonmagnetic, antiferromagnetic, and paramagnetic (disordered local moment) states. We explain changes to the phase stability (ΔEf) and Fermi surfaces (and nesting) due to chemical and magnetic disorder. We compare our results to observed/assessed properties and contrast alloy theory with the results expected from the rigid-band model. Finally, with alloying, the DOS changes from common band (Co,Ni) to split band (Cu,Zn), which dictates ΔEf and can overwhelm FS-nesting instabilities, as for the Cu and Zn cases.
Multiagent Inverse Reinforcement Learning for Two-Person Zero-Sum Games

The focus of this paper is a Bayesian framework for solving a class of problems termed multiagent inverse reinforcement learning (MIRL). Compared to the well-known inverse reinforcement learning (IRL) problem, MIRL is formalized in the context of stochastic games, which generalize Markov decision processes to game theoretic scenarios. We establish a theoretical foundation for competitive two-agent zero-sum MIRL problems and propose a Bayesian solution approach in which the generative model is based on an assumption that the two agents follow a minimax bipolicy. Numerical results are presented comparing the Bayesian MIRL method with two existing methods in the context of an abstract soccer game. Investigation centers on relationships between the extent of prior information and the quality of learned rewards. Results suggest that covariance structure is more important than mean value in reward priors.
Non-characterizability of belief revision: an application of finite model theory

A formal framework is given for the characterizability of a class of belief revision operators, defined using minimization over a class of partial preorders, by postulates. It is shown that for partial orders characterizability implies a definability property of the class of partial orders in monadic second-order logic. Based on a non-definability result for a class of partial orders, an example is given of a non-characterizable class of revision operators. This appears to be the first non-characterizability result in belief revision.
How physics instruction impacts students' beliefs about learning physics

Faculty care about their students learning to think like physicists, but often don't assess this because it is not clear how to do so. Physics education researchers have created several surveys to assess one important aspect of thinking like a physicist: what students believe that learning physics is all about. In this meta-analysis, we synthesize the results of 24 studies using the Colorado Learning Attitudes about Science Survey (CLASS) and the Maryland Physics Expectations Survey (MPEX) to answer several questions: (1) How does physics instruction impact beliefs of students? We find that in typical physics classes, these beliefs deteriorate or at best stay the same. There are a few types of interventions, including an explicit focus on model-building and/or developing expert-like beliefs that appear to lead to significant improvements in beliefs. However, because the available data oversamples certain types of classes, it is unclear whether these improvements are actually due to the interventions, or to the small class size, student population or level of incoming beliefs these classes. (2) When do physics majors develop expert-like beliefs? We find that physics majors enter their undergraduate education with more favorable beliefs than non-majors and these beliefs remain relatively stable throughout their undergraduate careers. Thus, typical physics courses appear to be selecting students that already have strong beliefs, rather than supporting students in developing strong beliefs. (3) How do beliefs of students impact their learning of physics? There is a small correlation between incoming beliefs about physics and gains on conceptual mechanics surveys. This suggests that students with more expert-like incoming beliefs may learn more in their physics courses, but this finding should be further explored and replicated.
Credit acceptance process strategy case studies - the power of Credit Scoring

The paper is aware of the importance of certain figures that are essential to an understanding of Credit Scoring models in credit acceptance process optimization, namely if the power of discrimination measured by Gini value is increased by 5% then the profit of the process can be increased monthly by about 1 500 kPLN (300 kGBP, 500 kUSD, 350 kEUR). Simple business models of credit loans are also presented: acquisition - installment loan (low price) and cross-sell - cash loans (high price). Scoring models are used to optimize process, to become profitable. Various acceptance strategies with different cutoffs are presented, some are profitable and some are not. Moreover, in a time of prosperity some are preferable whilst the inverse is true during a period of high risk or crisis. To optimize the process four models are employed: three risk models, to predict the probability of default and one typical propensity model to predict the probability of response. It is a simple but very important example of the Customer Lifetime Value (CLTV or CLV) model business, where risk and response models are working together to become a profitable process.
Design space exploration tools for the ByoRISC configurable processor family

In this paper, the ByoRISC (Build your own RISC) configurable application-specific instruction-set processor (ASIP) family is presented. ByoRISCs, as vendor-independent cores, provide extensive architectural parameters over a baseline processor, which can be customized by application-specific hardware extensions (ASHEs). Such extensions realize multi-input multi-output (MIMO) custom instructions with local state and load/store accesses to the data memory. ByoRISCs incorporate a true multi-port register file, zero-overhead custom instruction decoding, and scalable data forwarding mechanisms. Given these design decisions, ByoRISCs provide a unique combination of features that allow their use as architectural testbeds and the seamless and rapid development of new high-performance ASIPs. 
The performance characteristics of ByoRISCs, implemented as vendor-independent cores, have been evaluated for both ASIC and FPGA implementations, and it is proved that they provide a viable solution in FPGA-based system-on-a-chip design. A case study of an image processing pipeline is also presented to highlight the process of utilizing a ByoRISC custom processor. A peak performance speedup of up to 8.5$\times$ can be observed, whereas an average performance speedup of 4.4$\times$ on Xilinx Virtex-4 targets is achieved. In addition, ByoRISC outperforms an experimental VLIW architecture named VEX even in its 16-wide configuration for a number of data-intensive application kernels.
The Unified Astronomy Thesaurus

The Unified Astronomy Thesaurus (UAT) is an open, interoperable and community-supported thesaurus which unifies the existing divergent and isolated Astronomy & Astrophysics vocabularies into a single high-quality, freely-available open thesaurus formalizing astronomical concepts and their inter-relationships. The UAT builds upon the existing IAU Thesaurus with major contributions from the astronomy portions of the thesauri developed by the Institute of Physics Publishing, the American Institute of Physics, and SPIE. We describe the effort behind the creation of the UAT and the process through which we plan to maintain the document updated through broad community participation.
Multiple transitions of the susceptible-infected-susceptible epidemic model on complex networks.

The epidemic threshold of the susceptible-infected-susceptible (SIS) dynamics on random networks having a power law degree distribution with exponent γ>3 has been investigated using different mean-field approaches, which predict different outcomes. We performed extensive simulations in the quasistationary state for a comparison with these mean-field theories. We observed concomitant multiple transitions in individual networks presenting large gaps in the degree distribution and the obtained multiple epidemic thresholds are well described by different mean-field theories. We observed that the transitions involving thresholds which vanish at the thermodynamic limit involve localized states, in which a vanishing fraction of the network effectively contributes to epidemic activity, whereas an endemic state, with a finite density of infected vertices, occurs at a finite threshold. The multiple transitions are related to the activations of distinct subdomains of the network, which are not directly connected.
A MID-LIFE CRISIS? SUDDEN CHANGES IN RADIO AND X-RAY EMISSION FROM SUPERNOVA 1970G

Supernovae (SNe) provide a backdrop from which we can probe the end state of stellar evolution in the final years before the progenitor star explodes. As the shock from the SN expands, the timespan of mass-loss history we are able to probe also extends, providing insight to rapid timescale processes that govern the end state of massive stars. While SNe transition into remnants on timescales of decades to centuries, observations of this phase are currently limited. Here, we present observations of SN 1970G, serendipitously observed during the monitoring campaign of SN 2011fe, which shares the same host galaxy. Utilizing the new Jansky Very Large Array (VLA) upgrade and a deep X-ray exposure taken by Chandra, we are able to recover this middle-aged SN and distinctly resolve it from the H ii cloud with which it is associated. We find that the flux density of SN 1970G has changed significantly since it was last observed—the X-ray luminosity has increased by a factor of ∼3, while we observe a significantly lower radio flux of only 27.5 μJy at 6.75 GHz, a level only detectable through the upgrades now in operation at the Jansky VLA. These changes suggest that SN 1970G has entered a new stage of evolution toward an SN remnant, and we may be detecting the turn-on of the pulsar wind nebula. Deep radio observations of additional middle-aged SNe with the improved radio facilities will provide a statistical census of the delicate transition period between SN and remnant.
Quantifying Information Overload in Social Media and Its Impact on Social Contagions

Information overload has become an ubiquitous problem in modern society. Social media users and microbloggers receive an endless flow of information, often at a rate far higher than their cognitive abilities to process the information. In this paper, we conduct a large scale quantitative study of information overload and evaluate its impact on information dissemination in the Twitter social media site. We model social media users as information processing systems that queue incoming information according to some policies, process information from the queue at some unknown rates and decide to forward some of the incoming information to other users. We show how timestamped data about tweets received and forwarded by users can be used to uncover key properties of their queueing policies and estimate their information processing rates and limits. Such an understanding of users' information processing behaviors allows us to infer whether and to what extent users suffer from information overload. Our analysis provides empirical evidence of information processing limits for social media users and the prevalence of information overloading. The most active and popular social media users are often the ones that are overloaded. Moreover, we find that the rate at which users receive information impacts their processing behavior, including how they prioritize information from different sources, how much information they process, and how quickly they process information. Finally, the susceptibility of a social media user to social contagions depends crucially on the rate at which she receives information. An exposure to a piece of information, be it an idea, a convention or a product, is much less effective for users that receive information at higher rates, meaning they need more exposures to adopt a particular contagion.
Fast Localization of Facial Landmark Points

Localization of salient facial landmark points, such as eye corners or the tip of the nose, is still considered a challenging computer vision problem despite recent efforts. This is especially evident in unconstrained environments, i.e., in the presence of background clutter and large head pose variations. Most methods that achieve state-of-the-art accuracy are slow, and, thus, have limited applications. We describe a method that can accurately estimate the positions of relevant facial landmarks in real-time even on hardware with limited processing power, such as mobile devices. This is achieved with a sequence of estimators based on ensembles of regression trees. The trees use simple pixel intensity comparisons in their internal nodes and this makes them able to process image regions very fast. We test the developed system on several publicly available datasets and analyse its processing speed on various devices. Experimental results show that our method has practical value.
SHOCK CORRUGATION BY RAYLEIGH–TAYLOR INSTABILITY IN GAMMA-RAY BURST AFTERGLOW JETS

Afterglow jets are Rayleigh–Taylor unstable and therefore turbulent during the early part of their deceleration. There are also several processes which actively cool the jet. In this Letter, we demonstrate that if cooling significantly increases the compressibility of the flow, the turbulence collides with the forward shock, destabilizing and corrugating it. In this case, the forward shock is turbulent enough to produce the magnetic fields responsible for synchrotron emission via small-scale turbulent dynamo. We calculate light curves assuming the magnetic field is in energy equipartition with the turbulent kinetic energy and discover that dynamic magnetic fields are well approximated by a constant magnetic-to-thermal energy ratio of 1%, though there is a sizeable delay in the time of peak flux as the magnetic field turns on only after the turbulence has activated. The reverse shock is found to be significantly more magnetized than the forward shock, with a magnetic-to-thermal energy ratio of the order of 10%. This work motivates future Rayleigh–Taylor calculations using more physical cooling models.