nils-herrmann/clean_scirepeval huggingface dataset:
Dynamics of Media Attention

Studies of human attention dynamics analyses how attention is focused on specific topics, issues or people. In online social media, there are clear signs of exogenous shocks, bursty dynamics, and an exponential or powerlaw lifetime distribution. We here analyse the attention dynamics of traditional media, focussing on co-occurrence of people in newspaper articles. The results are quite different from online social networks and attention. Different regimes seem to be operating at two different time scales. At short time scales we see evidence of bursty dynamics and fast decaying edge lifetimes and attention. This behaviour disappears for longer time scales, and in that regime we find Poissonian dynamics and slower decaying lifetimes. We propose that a cascading Poisson process may take place, with issues arising at a constant rate over a long time scale, and faster dynamics at a shorter time scale.
Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data

In this paper, we present a novel approach to predict crime in a geographic space from multiple data sources, in particular mobile phone and demographic data. The main contribution of the proposed approach lies in using aggregated and anonymized human behavioral data derived from mobile network activity to tackle the crime prediction problem. While previous research efforts have used either background historical knowledge or offenders' profiling, our findings support the hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime. In our experimental results with real crime data from London we obtain an accuracy of almost 70% when predicting whether a specific area in the city will be a crime hotspot or not. Moreover, we provide a discussion of the implications of our findings for data-driven crime analysis.
"Look Ma, No Hands!" A Parameter-Free Topic Model

It has always been a burden to the users of statistical topic models to predetermine the right number of topics, which is a key parameter of most topic models. Conventionally, automatic selection of this parameter is done through either statistical model selection (e.g., cross-validation, AIC, or BIC) or Bayesian nonparametric models (e.g., hierarchical Dirichlet process). These methods either rely on repeated runs of the inference algorithm to search through a large range of parameter values which does not suit the mining of big data, or replace this parameter with alternative parameters that are less intuitive and still hard to be determined. In this paper, we explore to "eliminate" this parameter from a new perspective. We first present a nonparametric treatment of the PLSA model named nonparametric probabilistic latent semantic analysis (nPLSA). The inference procedure of nPLSA allows for the exploration and comparison of different numbers of topics within a single execution, yet remains as simple as that of PLSA. This is achieved by substituting the parameter of the number of topics with an alternative parameter that is the minimal goodness of fit of a document. We show that the new parameter can be further eliminated by two parameter-free treatments: either by monitoring the diversity among the discovered topics or by a weak supervision from users in the form of an exemplar topic. The parameter-free topic model finds the appropriate number of topics when the diversity among the discovered topics is maximized, or when the granularity of the discovered topics matches the exemplar topic. Experiments on both synthetic and real data prove that the parameter-free topic model extracts topics with a comparable quality comparing to classical topic models with "manual transmission". The quality of the topics outperforms those extracted through classical Bayesian nonparametric models.
Semantic web service discovery approaches: overview and limitations

The semantic Web service discovery has been given massive attention within the last few years. With the increasing number of Web services available on the web, looking for a particular service has become very difficult, especially with the evolution of the clients needs. In this context, various approaches to discover semantic Web services have been proposed. In this paper, we compare these approaches in order to assess their maturity and their adaptation to the current domain requirements. The outcome of this comparison will help us to identify the mechanisms that constitute the strengths of the existing approaches, and thereafter will serve as guideline to determine the basis for a discovery approach more adapted to the current context of Web services.
Towards Optimal Algorithms for Prediction with Expert Advice

We study the classical problem of prediction with expert advice in the adversarial setting with a geometric stopping time. In 1965, Cover gave the optimal algorithm for the case of 2 experts. In this paper, we design the optimal algorithm, adversary and regret for the case of 3 experts. Further, we show that the optimal algorithm for $2$ and $3$ experts is a probability matching algorithm (analogous to Thompson sampling) against a particular randomized adversary. Remarkably, our proof shows that the probability matching algorithm is not only optimal against this particular randomized adversary, but also minimax optimal. 
Our analysis develops upper and lower bounds simultaneously, analogous to the primal-dual method. Our analysis of the optimal adversary goes through delicate asymptotics of the random walk of a particle between multiple walls. We use the connection we develop to random walks to derive an improved algorithm and regret bound for the case of $4$ experts, and, provide a general framework for designing the optimal algorithm and adversary for an arbitrary number of experts.
Complex Network Approach to Number Theory

In this short paper, following the most recent advances in complex network theory, a new approach to number theory with potential applications to other fields is proposed. The model by Garcia-Perez, Serrano and Boguna, introduces an algorithm which allows to create a bipartite graph of integers (with primes and composites) statistically very close to the real one. Since the algorithm is defined a priori, we can have a description of the simulated prime number distribution in terms of a known differential equation, which in general can be treated more easily. The so determined properties of the simulated distribution can give useful hints about the behavior of the real prime number distribution. In principle it could be also possible to demonstrate open questions in number theory, proven the total equivalence of the simulated and real distributions.
Designing and deploying online field experiments

Online experiments are widely used to compare specific design alternatives, but they can also be used to produce generalizable knowledge and inform strategic decision making. Doing so often requires sophisticated experimental designs, iterative refinement, and careful logging and analysis. Few tools exist that support these needs. We thus introduce a language for online field experiments called PlanOut. PlanOut separates experimental design from application code, allowing the experimenter to concisely describe experimental designs, whether common "A/B tests" and factorial designs, or more complex designs involving conditional logic or multiple experimental units. These latter designs are often useful for understanding causal mechanisms involved in user behaviors. We demonstrate how experiments from the literature can be implemented in PlanOut, and describe two large field experiments conducted on Facebook with PlanOut. For common scenarios in which experiments are run iteratively and in parallel, we introduce a namespaced management system that encourages sound experimental practice.
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization

We consider a generic convex optimization problem associated with regularized empirical risk minimization of linear predictors. The problem structure allows us to reformulate it as a convex-concave saddle point problem. We propose a stochastic primal-dual coordinate method, which alternates between maximizing over one (or more) randomly chosen dual variable and minimizing over the primal variable. We also develop an extension to non-smooth and nonstrongly convex loss functions, and an extension with better convergence rate on unnormalized data. Both theoretically and empirically, we show that the SPDC method has comparable or better performance than several state-of-the-art optimization methods.
Reconciliation of RDF* and Property Graphs

Both the notion of Property Graphs (PG) and the Resource Description Framework (RDF) are commonly used models for representing graph-shaped data. While there exist some system-specific solutions to convert data from one model to the other, these solutions are not entirely compatible with one another and none of them appears to be based on a formal foundation. In fact, for the PG model, there does not even exist a commonly agreed-upon formal definition. 
The aim of this document is to reconcile both models formally. To this end, the document proposes a formalization of the PG model and introduces well-defined transformations between PGs and RDF. As a result, the document provides a basis for the following two innovations: On one hand, by implementing the RDF-to-PG transformations defined in this document, PG-based systems can enable their users to load RDF data and make it accessible in a compatible, system-independent manner using, e.g., the graph traversal language Gremlin or the declarative graph query language Cypher. On the other hand, the PG-to-RDF transformation in this document enables RDF data management systems to support compatible, system-independent queries over the content of Property Graphs by using the standard RDF query language SPARQL. Additionally, this document represents a foundation for systematic research on relationships between the two models and between their query languages.
Growth Patterns and Scaling Laws Governing AIDS Epidemic in Brazilian Cities

Brazil holds approximately 1/3 of population living infected with AIDS (acquired immunodeficiency syndrome) in Central and South Americas, and it was also the first developing country to implement a large-scale control and intervention program against AIDS epidemic. In this scenario, we investigate the temporal evolution and current status of the AIDS epidemic in Brazil. Specifically, we analyze records of annual absolute frequency of cases for more than 5000 cities for the first 33 years of the infection in Brazil. We found that (i) the annual absolute frequencies exhibit a logistic-type growth with an exponential regime in the first few years of the AIDS spreading; (ii) the actual reproduction number decaying as a power law; (iii) the distribution of the annual absolute frequencies among cities decays with a power law behavior; (iv) the annual absolute frequencies and the number of inhabitants have an allometric relationship; (v) the temporal evolution of the annual absolute frequencies have different profile depending on the average annual absolute frequencies in the cities. These findings yield a general quantitative description of the AIDS infection dynamics in Brazil since the beginning. They also provide clues about the effectiveness of treatment and control programs against the infection, that has had a different impact depending on the number of inhabitants of cities. In this framework, our results give insights into the overall dynamics of AIDS epidemic, which may contribute to select empirically accurate models.
Philosophical Issues of Black Holes

Black holes are extremely relativistic objects. Physical p rocesses around them occur in a regime where the gravitational field is extremely i ntense. Under such conditions, our representations of space, time, gravity, and t hermodynamics are pushed to their limits. In such a situation philosophical issues na turally arise. In this chapter I review some philosophical questions related to black holes. In particular, the relevance of black holes for the metaphysical dispute between presentists and eternalists, the origin of the second law of thermodynamics and its relation to black holes, the problem of information, black holes and hypercomputing, the nature of determinisim, and the breakdown of predictability in black hole space-times. I maintain that black hole physics can be used to illuminate some important problems in the border between science and philosophy, either epistemology and ontology.
Isotopic ratio, isotonic ratio, isobaric ratio and Shannon information uncertainty

The isoscaling and the isobaric yield ratio difference (IBD) probes, which both are constructed by yield ratio of fragment, provide cancelation of parameters. The information entropy theory is introduced to explain the physical meaning of the isoscaling and IBD probes. The similarity between the isoscaling and IBD results is found, i.e., the information uncertainty determined by the IBD method equals to β−α determined by the isoscaling [α (β) is the parameter fitted from the isotopic (isotonic) yield ratio].
Optimal Consumption and Sale Strategies for a Risk Averse Agent

In this article we consider a special case of an optimal consumption/optimal portfolio problem first studied by Constantinides and Magill and by Davis and Norman, in which an agent with constant relative risk aversion seeks to maximise expected discounted utility of consumption over the infinite horizon, in a model comprising a risk-free asset and a risky asset with proportional transaction costs. The special case that we consider is that the cost of purchases of the risky asset is infinite, or equivalently the risky asset can only be sold and not bought. In this special setting new solution techniques are available, and we can make considerable progress towards an analytical solution. This means we are able to consider the comparative statics of the problem. There are some surprising conclusions, such as consumption rates are not monotone increasing in the return of the asset, nor are the certainty equivalent values of the risky positions monotone in the risk aversion.
Topic Modeling of Hierarchical Corpora /

The sizes of modern digital libraries have grown beyond our capacity to comprehend manually. Thus we need new tools to help us in organizing and browsing large corpora of text that do not require manually examining each document. To this end, machine learning researchers have developed topic models, statistical learning algorithms for automatic comprehension of large collections of text. Topic models provide both global and local views of a corpus; they discover topics that run through the corpus and describe each document as a mixture of the discovered topics. In this dissertation, I consider the topic modeling of corpora whose documents are organized in a multi-level hierarchy. My interest in this subject arose from the need to analyze two sprawling, real-world corpora from the field of computer security. The first is a collection of job postings on a crowdsourcing site, where many advertisers seek cheap human labor for different forms of Web service abuse. I view this corpus as a three- layer tree in which an interior node represents a buyer, and children of the interior node represent the buyer's postings. The second corpus is a collection of threads from an underground Internet forum, where blackhat operatives discuss tactics for abusive forms of Internet marketing such as spamming and search engine optimization. The subforums and threads in this data set form a five- level deep hierarchy. Using these two data sets as test beds, I develop topic models that incorporate hierarchies in corpora. The models I consider can be viewed as special (finite-dimensional) instances of hierarchical Dirichlet processes (HDPs). For these models I show that there exists a simple variational approximation for probabilistic inference and demonstrate a parallel inference algorithm that can scale to corpora with deep hierarchies and large numbers of documents. On several hierarchical corpora, I show advantages of my topic models over other topic models that do not consider hierarchies. Also I compare my variational method to existing implementations of HDPs and find that my approach is faster than Gibbs sampling and able to learn more predictive models than existing variational
Concept-oriented model: inference in hierarchical multidimensional space

In spite of its fundamental importance, inference has not been an inherent function of multidimensional models and analytical applications. These models are mainly aimed at numeric (quantitative) analysis where the notions of inference and semantics are not well defined. In this paper we argue that inference can be and should be integral part of multidimensional data models and analytical applications. It is demonstrated how inference can be defined using only multidimensional terms like axes and coordinates as opposed to using logic-based approaches. We propose a novel approach to inference in multidimensional space based on the concept-oriented model of data and introduce elementary operations which are then used to define constraint propagation and inference procedures. We describe a query language with inference operator and demonstrate its usefulness in solving complex analytical tasks.
Manifest duality invariance for the partially massless graviton

For a special value of the mass, a massive graviton on de Sitter space acquires an enhanced scalar gauge symmetry, and is called partially massless. The partially massless graviton possesses a duality invariance akin to electromagnetic duality. We display this duality in its manifestly local and covariant form, in which it acts to interchange the first order field equations and Bianchi identities of a gauge invariant field strength.
THE INITIATION AND PROPAGATION OF HELIUM DETONATIONS IN WHITE DWARF ENVELOPES

Detonations in helium-rich envelopes surrounding white dwarfs have garnered attention as triggers of faint thermonuclear “.Ia” supernovae and double detonation Type Ia supernovae. However, recent studies have found that the minimum size of a hotspot that can lead to a helium detonation is comparable to, or even larger than, the white dwarf's pressure scale height, casting doubt on the successful ignition of helium detonations in these systems. In this paper, we examine the previously neglected effects of C/O pollution and a full nuclear reaction network, and we consider hotspots with spatially constant pressure in addition to constant density hotspots. We find that the inclusion of these effects significantly decreases the minimum hotspot size for helium-rich detonation ignition, making detonations far more plausible during turbulent shell convection or during double white dwarf mergers. The increase in burning rate also decreases the minimum shell mass in which a helium detonation can successfully propagate and alters the composition of the shell's burning products. The ashes of these low-mass shells consist primarily of silicon, calcium, and unburned helium and metals and may explain the high-velocity spectral features observed in most Type Ia supernovae.
Superdimensional Metamaterial Resonators

We propose a fundamentally new method for the design of metamaterial arrays, valid for any waves modeled by the Helmholtz equation, including scalar optics and acoustics. The design and analysis of these devices is based on eigenvalue and eigenfunction asymptotics of solutions to Schr\"odinger wave equations with harmonic and degenerate potentials. These resonators behave superdimensionally, with a higher local density of eigenvalues and greater concentration of waves than expected from the physical dimension, e.g., planar resonators function as 3- or higher-dimensional media, and bulk material as effectively of dimension 4 or higher. Applications include antennas with a high density of resonant frequencies and giant focussing, and are potentially broadband.
10, 000+ Times Accelerated Robust Subset Selection (ARSS)

Subset selection from massive data with noised information is increasingly popular for various applications. This problem is still highly challenging as current methods are generally slow in speed and sensitive to outliers. To address the above two issues, we propose an accelerated robust subset selection (ARSS) method. Specifically in the subset selection area, this is the first attempt to employ the lp (0 < p ≤ 1)-norm based measure for the representation loss, preventing large errors from dominating our objective. As a result, the robustness against outlier elements is greatly enhanced. Actually, data size is generally much larger than feature length, i.e. N ≫ L. Based on this observation, we propose a speedup solver (via ALM and equivalent derivations) to highly reduce the computational cost, theoretically from O (N4) to O (N2L). Extensive experiments on ten benchmark datasets verify that our method not only outperforms state of the art methods, but also runs 10,000+ times faster than the most related method.
Probabilistic Selection in AgentSpeak(L)

Agent programming is mostly a symbolic discipline and, as such, draws little benefits from probabilistic areas as machine learning and graphical models. However, the greatest objective of agent research is the achievement of autonomy in dynamical and complex environments --- a goal that implies embracing uncertainty and therefore the entailed representations, algorithms and techniques. This paper proposes an innovative and conflict free two layer approach to agent programming that uses already established methods and tools from both symbolic and probabilistic artificial intelligence. Moreover, this framework is illustrated by means of a widely used agent programming example, GoldMiners.
The World Trade Web: A Multiple-Network Perspective

International Trade (IT) plays a fundamental role in today's economy: by connecting world countries production and consumption processes, it radically contributes in shaping their economy and development path. Although its evolving structure and determinants have been widely analyzed in the literature, much less has been done to understand its interplay with other complex phenomena. The aim of this work is, precisely in this direction, to study the relations of IT with International Migration (IM) and Foreign Direct Investments (FDI). In both cases the procedure used is to first approach the problem in a multiple-networks perspective and than deepen the analysis by using ad hoc econometrics techniques. With respect to IM, a general positive correlation with IT is highlighted and product categories for which this effect is stronger are identified and cross-checked with previous classifications. Next, employing spatial econometric techniques and proposing a new way to define country neighbors based on the most intense IM flows, direct/indirect network effects are studied and a stronger competitive effect of third country migrants is identified for a specific product class. In the case of FDI, first correlations between the two networks are identified, highlighting how they can be mostly explained by countries economic/demographic size and geographical distance. Then, using the Heckman selection model with a gravity equation, (non-linear) components arising from distance, position in the Global Supply Chain and presence of Regional Trade Agreements are studied. Finally, it is shown how IT and FDI correlation changes with sectors: they are complements in manufacturing, but substitutes in services.