nils-herrmann/clean_scirepeval huggingface dataset:
Like, Comment, Repin: User Interaction on Pinterest

We present the results of a study of the Pinterest activity graph. Pinterest is a relatively new and extremely popular content-based social network. Building on a body of work showing that the hidden network whose edges are actual interactions between users is more informative about social relationships that the follower-following network, we study the activity graph composed of links formed by liking, commenting, and repinning, and show that it is very different from the follow network. We collect data about 14 million pins, 7 million repins, 1.6 million likes, and several hundred thousand users and report interesting results about social activity on Pinterest. In particular, we discover that only 12.3% of a user's followers interact with their pins, and over 70% of activity on each user's boards is done by non-followers, but on average, followers who are active perform twice as many actions as non-followers.
Power spectrum of post-inflationary primordial magnetic fields

The origin of large scale magnetic fields is one of the most puzzling topics in cosmology and astrophysics. It is assumed that the observed magnetic fields result from the amplification of an initial field produced in the early universe. In this paper we compute the exact power spectrum of magnetic fields created after inflation best known as post inflationary magnetic fields, using the first order cosmological perturbation theory. Our treatment differs from others works because we include an infrared cutoff which encodes only causal modes in the spectrum. The cross-correlation between magnetic energy density with Lorentz force and the anisotropic part of the electromagnetic field are exactly computed. We compare our results with previous works finding agreement in cases where the ratio between lower and upper cutoff is very small. However, we found that spectrum is strongly affected when this ratio is greater than 0.2. Moreover, the effect of a post inflationary magnetic field with a lower cutoff on the angular power spectrum in the temperature distribution of CMB was also exactly calculated. The main feature is a shift of the spectrum's peak as function of the infrared cutoff, therefore analyzing this effect we could infer the value of this cutoff and thus constraining the primordial magnetic fields generation models.
Quantum potential induced emergence of massive scalar fields in the analogue gravity model of a Bose-Einstein condensate

We show here a general approach to include the quantum potential term in the emergent gravity model of Bose-Einstein condensate by using multiple scales. Our main result shows the emergence of a massive scalar modulating field at larger length scales as a result of Lorentz symmetry breaking at the length scales comparable to the healing length. We also propose that, the nonlocal interactions induced tuning of healing length can be exploited experimentally to observe the systematics of small and large scale coupling as emerges in our present analysis.
Distinguishing black holes and wormholes with orbiting hot spots

The supermassive black hole candidates at the center of every normal galaxy might be wormholes created in the early Universe and connecting either two different regions of our Universe or two different universes in a Multiverse model. Indeed, the origin of these supermassive objects is not well understood, topological non-trivial structures like wormholes are allowed both in general relativity and in alternative theories of gravity, and current observations cannot rule out such a possibility. In a few years, the VLTI instrument GRAVITY will have the capability to image blobs of plasma orbiting near the innermost stable circular orbit of SgrA$^*$, the supermassive black hole candidate in the Milky Way. The secondary image of a hot spot orbiting around a wormhole is substantially different from that of a hot spot around a black hole, because the photon capture sphere of the wormhole is much smaller. The radius of the photon capture sphere is independent of the hot spot model, and therefore its possible detection, which is observationally challenging but not out of reach, can unambiguously test if the center of our Galaxy harbors a wormhole rather than a black hole.
Merchant Sharing Towards a Zero Marginal Cost Economy

This paper is the first attempt to formalize a new field of economics; studding the Intangibles Goods available on the Internet. We are taking advantage of the digital world's specific rules, in particular the zero marginal cost, to propose a theory of trading & sharing unified. A function based money is created as a world-wide currency; "cup". We argue that our system discourage speculation activities while it makes easy captured taxes for governments. The implementation removes the today's paywall on the Internet and provides a simple-to-use, open-source, free-of-charge, highly-secure, person-to-person, privacy-respectful, digital payment tool for citizens, using standard smart-phones with a strong authentication. Next step will be the propagation of the network application and we expect many shared benefits for the whole economics development.
Quantifying MCMC Exploration of Phylogenetic Tree Space

In order to gain an understanding of the effectiveness of phylogenetic Markov chain Monte Carlo (MCMC), it is important to understand how quickly the empirical distribution of the MCMC converges to the posterior distribution. In this article, we investigate this problem on phylogenetic tree topologies with a metric that is especially well suited to the task: the subtree prune-and-regraft (SPR) metric. This metric directly corresponds to the minimum number of MCMC rearrangements required to move between trees in common phylogenetic MCMC implementations. We develop a novel graph-based approach to analyze tree posteriors and find that the SPR metric is much more informative than simpler metrics that are unrelated to MCMC moves. In doing so, we show conclusively that topological peaks do occur in Bayesian phylogenetic posteriors from real data sets as sampled with standard MCMC approaches, investigate the efficiency of Metropolis-coupled MCMC (MCMCMC) in traversing the valleys between peaks, and show that conditional clade distribution (CCD) can have systematic problems when there are multiple peaks.
Evaluating the retrieval effectiveness of web search engines using a representative query sample

Search engine retrieval effectiveness studies are usually small scale, using only limited query samples. Furthermore, queries are selected by the researchers. We address these issues by taking a random representative sample of 1,000 informational and 1,000 navigational queries from a major German search engine and comparing Google's and Bing's results based on this sample. Jurors were found through crowdsourcing, and data were collected using specialized software, the Relevance Assessment Tool (RAT). We found that although Google outperforms Bing in both query types, the difference in the performance for informational queries was rather low. However, for navigational queries, Google found the correct answer in 95.3% of cases, whereas Bing only found the correct answer 76.6% of the time. We conclude that search engine performance on navigational queries is of great importance, because users in this case can clearly identify queries that have returned correct results. So, performance on this query type may contribute to explaining user satisfaction with search engines.
Why we need an independent index of the Web

The path to greater diversity, as we have seen, cannot be achieved by merely hoping for a new search engine nor will government support for a single alternative achieve this goal. What is instead required is to create the conditions that will make establishing such a search engine possible in the first place. I describe how building and maintaining a proprietary index is the greatest deterrent to such an undertaking. We must first overcome this obstacle. Doing so will still not solve the problem of the lack of diversity in the search engine marketplace. But it may establish the conditions necessary to achieve that desired end.
Optimal stopping under model uncertainty: randomized stopping times approach

In this work we consider optimal stopping problems with conditional convex risk measures called optimised certainty equivalents. Without assuming any kind of time-consistency for the underlying family of risk measures, we derive a novel representation for the solution of the optimal stopping problem. In particular, we generalise the additive dual representation of Rogers (2002) to the case of optimal stopping under uncertainty. Finally, we develop several Monte Carlo algorithms and illustrate their power for optimal stopping under Average Value at Risk.
Image segmentation using frequency locking of coupled oscillators

Synchronization of coupled oscillators is observed at multiple levels of neural systems, and has been shown to play an important function in visual perception. We propose a computing system based on locally coupled oscillator networks for image segmentation. The system can serve as the preprocessing front-end of an image processing pipeline where the common frequencies of clusters of oscillators reflect the segmentation results. To demonstrate the feasibility of our design, the system is simulated and tested on a human face image dataset and its performance is compared with traditional intensity threshold based algorithms. Our system shows both better performance and higher noise tolerance than traditional methods.
Phase Random Walk Trace in High-order Coherence of Two First-order Incoherent Sources

High-order coherence effects between two first-order incoherent sources with fully independent phases have been well studied in the literature, which shows interference fringes with respect to the position separations among different space points. Here we show that this is not the whole story, and find that the high-order coherence effects depend on the mode of the phase random walk of the first-order incoherent sources, which can be controlled artificially and represented geometrically by vectorial polygons. Interestingly, by scanning the detectors along the same direction with the position separations between them kept constant, a set of high-order coherence fringes, which fingerprint the phase random walk of the first-order incoherent sources, can be observed. Our results show that it is possible to control the high-order coherence of two first-order incoherent sources, which could have important practical applications such as superhigh resolution optical lithography.
A Multi-factor Adaptive Statistical Arbitrage Model

This paper examines the implementation of a statistical arbitrage trading strategy based on co-integration relationships where we discover candidate portfolios using multiple factors rather than just price data. The portfolio selection methodologies include K-means clustering, graphical lasso and a combination of the two. Our results show that clustering appears to yield better candidate portfolios on average than naively using graphical lasso over the entire equity pool. A hybrid approach of using the combination of graphical lasso and clustering yields better results still. We also examine the effects of an adaptive approach during the trading period, by re-computing potential portfolios once to account for change in relationships with passage of time. However, the adaptive approach does not produce better results than the one without re-learning. Our results managed to pass the test for the presence of statistical arbitrage test at a statistically significant level. Additionally we were able to validate our findings over a separate dataset for formation and trading periods.
Predicting Central Topics in a Blog Corpus from a Networks Perspective

In today's content-centric Internet, blogs are becoming increasingly popular and important from a data analysis perspective. According to Wikipedia, there were over 156 million public blogs on the Internet as of February 2011. Blogs are a reflection of our contemporary society. The contents of different blog posts are important from social, psychological, economical and political perspectives. Discovery of important topics in the blogosphere is an area which still needs much exploring. We try to come up with a procedure using probabilistic topic modeling and network centrality measures which identifies the central topics in a blog corpus.
My Struggles with the Block Universe

This document is the second installment of three in the Cerro Grande Fire Series. Like its predecessor  "Notes on a Paulian Idea," it is a collection of letters written to various friends and colleagues, most of whom regularly circuit this archive. The unifying theme of all the letters is that each has something to do with the quantum. Particularly, the collection chronicles the emergence of Quantum Bayesianism as a robust view of quantum theory, eventually evolving into the still-more-radical "QBism" (with the B standing for no particular designation anymore), as it took its most distinctive turn away from various Copenhagen Interpretations. Included are many anecdotes from the history of quantum information theory: for instance, the story of the origin of the terms "qubit" and "quantum information" from their originator's own mouth, a copy of a rejection letter written by E. T. Jaynes for one of Rolf Landauer's original erasure-cost principle papers, and much more. Specialized indices are devoted to historical, technical, and philosophical matters. More roundly, the document is an attempt to provide an essential ingredient, unavailable anywhere else, for turning QBism into a live option within the vast spectrum of quantum foundational thought.
Interest rate models and Whittaker functions

I present the technique which can analyse some interest rate models: Constantinides-Ingersoll, CIR-model, geometric CIR and Geometric Brownian Motion. All these models have the unified structure of Whittaker function. The main focus of this text is closed-form solutions of the zero-coupon bond value in these models. In text I emphasize the specific details of mathematical methods of their determination such as Laplace transform and hypergeometric functions.
Quantifying evolutionary dynamics of the basic genome of E. coli

The ~4-Mbp basic genome shared by 32 independent isolates of E. coli representing considerable population diversity has been approximated by whole-genome multiple-alignment and computational filtering designed to remove mobile elements and highly variable regions. Single nucleotide polymorphisms (SNPs) in the 496 basic-genome pairs are identified and clonally inherited stretches are distinguished from those acquired by horizontal transfer (HT) by sharp discontinuities in SNP density. The six least diverged genome-pairs each have only one or two HT stretches, each occupying 42-115-kbp of basic genome and containing at least one gene cluster known to confer selective advantage. At higher divergences, the typical mosaic pattern of interspersed clonal and HT stretches across the entire basic genome are observed, including likely fragmented integrations across a restriction barrier. A simple model suggests that individual HT events are of the order of 10-kbp and are the chief contributor to genome divergence, bringing in almost 12 times more SNPs than point mutations. As a result of continuing horizontal transfer of such large segments, 400 out of the 496 strain-pairs beyond genomic divergence of share virtually no genomic material with their common ancestor. We conclude that the active and continuing horizontal transfer of moderately large genomic fragments is likely to be mediated primarily by a co evolving population of phages that distribute random genome fragments throughout the population by generalized transduction, allowing efficient adaptation to environmental changes.
Learning Modular Structures from Network Data and Node Variables

A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individuals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, groups are termed modules, and the focus is on estimating the network structure among modules. However, estimation solely from node-specific variables can lead to spurious dependencies, and unverifiable structural assumptions are often used for regularization. Here, we propose an extended model that leverages direct observations about the network in addition to node-specific variables. By integrating complementary data types, we avoid the need for structural assumptions. We illustrate theoretical and practical significance of the model and develop a reversible-jump MCMC learning procedure for learning modules and model parameters. We demonstrate the method accuracy in predicting modular structures from synthetic data and capability to learn influence structures in twitter data and regulatory modules in the Mycobacterium tuberculosis gene regulatory network.
Mapping the UK webspace: fifteen years of british universities on the web

This paper maps the national UK web presence on the basis of an analysis of the .uk domain from 1996 to 2010. It reviews previous attempts to use web archives to understand national web domains and describes the dataset. Next, it presents an analysis of the .uk domain, including the overall number of links in the archive and changes in the link density of different second-level domains over time. We then explore changes over time within a particular second-level domain, the academic subdomain .ac.uk, and compare linking practices with variables, including institutional affiliation, league table ranking, and geographic location. We do not detect institutional affiliation affecting linking practices and find only partial evidence of league table ranking affecting network centrality, but find a clear inverse relationship between the density of links and the geographical distance between universities. This echoes prior findings regarding offline academic activity, which allows us to argue that real-world factors like geography continue to shape academic relationships even in the Internet age. We conclude with directions for future uses of web archive resources in this emerging area of research.
Searching for dilaton dark matter with atomic clocks

We propose an experiment to search for ultralight scalar dark matter (DM) with dilatonic interactions. Such couplings can arise for the dilaton as well as for moduli and axion-like particles in the presence of $CP$ violation. Ultralight dilaton DM acts as a background field that can cause tiny but coherent oscillations in Standard Model parameters such as the fine-structure constant and the proton-electron mass ratio. These minute variations can be detected through precise frequency comparisons of atomic clocks. Our experiment extends current searches for drifts in fundamental constants to the well-motivated high-frequency regime. Our proposed setups can probe scalars lighter than $1{0}^{\ensuremath{-}15}\text{ }\text{ }\mathrm{eV}$ with a discovery potential of dilatonic couplings as weak as $1{0}^{\ensuremath{-}11}$ times the strength of gravity, improving current equivalence principle bounds by up to 8 orders of magnitude. We point out potential $1{0}^{4}$ sensitivity enhancements with future optical and nuclear clocks, as well as possible signatures in gravitational-wave detectors. Finally, we discuss cosmological constraints and astrophysical hints of ultralight scalar DM, and show they are complimentary to and compatible with the parameter range accessible to our proposed laboratory experiments.
Be In The Know: Connecting News Articles to Relevant Twitter Conversations

In the era of data-driven journalism, data analytics can deliver tools to support journalists in connecting to new and developing news stories, e.g., as echoed in micro-blogs such as Twitter, the new citizen-driven media. In this paper, we propose a framework for tracking and automatically connecting news articles to Twitter conversations as captured by Twitter hashtags. For example, such a system could alert journalists about news that get a lot of Twitter reaction, so that they can investigate those conversations for new developments in the story, promote their article to a set of interested consumers, or discover general sentiment towards the story. Mapping articles to appropriate hashtags is nevertheless very challenging, due to different language styles used in articles versus tweets, the streaming aspect of news and tweets, as well as the user behavior when marking certain tweet-terms as hashtags. As a case-study, we continuously track the RSS feeds of Irish Times news articles and a focused Twitter stream over a two months period, and present a system that assigns hashtags to each article, based on its Twitter echo. We propose a machine learning approach for classifying and ranking article-hashtag pairs. Our empirical study shows that our system delivers high precision for this task.
The systematic structure and predictability of urban business diversity

Understanding cities is central to addressing major global challenges from climate and health to economic resilience. Although increasingly perceived as fundamental socio-economic units, the detailed fabric of urban economic activities is only now accessible to comprehensive analyses with the availability of large datasets. Here, we study abundances of business categories across U.S. metropolitan statistical areas to investigate how diversity of economic activities depends on city size. A universal structure common to all cities is revealed, manifesting self-similarity in internal economic structure as well as aggregated metrics (GDP, patents, crime). A derivation is presented that explains universality and the observed empirical distribution. The model incorporates a generalized preferential attachment process with ceaseless introduction of new business types. Combined with scaling analyses for individual categories, the theory quantitatively predicts how individual business types systematically change rank with city size, thereby providing a quantitative means for estimating their expected abundances as a function of city size. These results shed light on processes of economic differentiation with scale, suggesting a general structure for the growth of national economies as integrated urban systems.
Lifted Variable Elimination for Probabilistic Logic Programming

Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP2 for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.