nils-herrmann/clean_scirepeval huggingface dataset:
Network strategies in election campaigns

This study considers a simple variation of the voter model with two competing parties. In particular, we represent the case of political elections, where people can choose to support one of the two candidates or to remain neutral. People operate within a social network and their opinions depend on those of the people with whom they interact. Therefore, they may change their opinions over time, which may mean supporting one particular candidate or none. Candidates attempt to gain people’s support by interacting with them, whether they are in the same social circle (i.e. neighbors) or not. In particular, candidates follow a strategy of interacting for a time with people they do not know (that is, people who are not their neighbors). Our analysis of the proposed model sought to establish which network strategies are the most effective for candidates to gain popular support. We found that the most suitable strategy depends on the topology of the social network. Finally, we investigated the role of charisma in these dynamics. Charisma is relevant in several social contexts, since charismatic people usually exercise a strong influence over others. Our results showed that candidates’ charisma is an important contributory factor to a successful network strategy in election campaigns.
A brief network analysis of Artificial Intelligence publication

In this paper, we present an illustration to the history of Artificial Intelligence(AI) with a statistical analysis of publish since 1940. We collected and mined through the IEEE publish data base to analysis the geological and chronological variance of the activeness of research in AI. The connections between different institutes are showed. The result shows that the leading community of AI research are mainly in the USA, China, the Europe and Japan. The key institutes, authors and the research hotspots are revealed. It is found that the research institutes in the fields like Data Mining, Computer Vision, Pattern Recognition and some other fields of Machine Learning are quite consistent, implying a strong interaction between the community of each field. It is also showed that the research of Electronic Engineering and Industrial or Commercial applications are very active in California. Japan is also publishing a lot of papers in robotics. Due to the limitation of data source, the result might be overly influenced by the number of published articles, which is to our best improved by applying network keynode analysis on the research community instead of merely count the number of publish.
Optimal Strategies for a Long-Term Static Investor

The optimal strategies for a long-term static investor are studied. Given a portfolio of a stock and a bond, we derive the optimal allocation of the capitals to maximize the expected long-term growth rate of a utility function of the wealth. When the bond has a constant interest rate, three models for the underlying stock price processes are studied: Heston model, 3/2 model, and jump diffusion model. We also study the optimal strategies for a portfolio in which the stock price process follows a Black-Scholes model and the bond process has a Vasicek interest rate that is correlated to the stock price.
Bayesian Analysis of Epidemics - Zombies, Influenza, and other Diseases

Mathematical models of epidemic dynamics offer significant insight into predicting and controlling infectious diseases. The dynamics of a disease model generally follow a susceptible, infected, and recovered (SIR) model, with some standard modifications. In this paper, we extend the work of Munz et.al (2009) on the application of disease dynamics to the so-called "zombie apocalypse", and then apply the identical methods to influenza dynamics. Unlike Munz et.al (2009), we include data taken from specific depictions of zombies in popular culture films and apply Markov Chain Monte Carlo (MCMC) methods on improved dynamical representations of the system. To demonstrate the usefulness of this approach, beyond the entertaining example, we apply the identical methodology to Google Trend data on influenza to establish infection and recovery rates. Finally, we discuss the use of the methods to explore hypothetical intervention policies regarding disease outbreaks.
The untold story of the clones: content-agnostic factors that impact YouTube video popularity

Video dissemination through sites such as YouTube can have widespread impacts on opinions, thoughts, and cultures. Not all videos will reach the same popularity and have the same impact. Popularity differences arise not only because of differences in video content, but also because of other "content-agnostic" factors. The latter factors are of considerable interest but it has been difficult to accurately study them. For example, videos uploaded by users with large social networks may tend to be more popular because they tend to have more interesting content, not because social network size has a substantial direct impact on popularity.
 In this paper, we develop and apply a methodology that is able to accurately assess, both qualitatively and quantitatively, the impacts of various content-agnostic factors on video popularity. When controlling for video content, we observe a strong linear "rich-get-richer" behavior, with the total number of previous views as the most important factor except for very young videos. The second most important factor is found to be video age. We analyze a number of phenomena that may contribute to rich-get-richer, including the first-mover advantage, and search bias towards popular videos. For young videos we find that factors other than the total number of previous views, such as uploader characteristics and number of keywords, become relatively more important. Our findings also confirm that inaccurate conclusions can be reached when not controlling for content.
XQuery streaming by Forest Transducers

Streaming of XML transformations is a challenging task and only a few existing systems support streaming. Research approaches generally define custom fragments of XQuery and XPath that are amenable to streaming, and then design custom algorithms for each fragment. These languages have several shortcomings. Here we take a more principled approach to the problem of streaming XQuery-based transformations. We start with an elegant transducer model for which many static analysis problems are well-understood: the Macro Forest Transducer (MFT). We show that a large fragment of XQuery can be translated into MFTs - indeed, a fragment of XQuery, that can express important features that are missing from other XQuery stream engines, such as GCX: our fragment of XQuery supports XPath predicates and let-statements. We then use an existing streaming engine for MFTs and apply a well-founded set of optimizations from functional programming such as strictness analysis and deforestation. Our prototype achieves time and memory efficiency comparable to the fastest known engine for XQuery streaming, GCX. This is surprising because our engine relies on the OCaml built in garbage collector and does not use any specialized buffer management, while GCX's efficiency is due to clever and explicit buffer management.
Reverse Proxy Framework using Sanitization Technique for Intrusion Prevention in Database

With the increasing importance of the internet in our day to day life, data security in web application has become very crucial. Ever increasing on line and real time transaction services have led to manifold rise in the problems associated with the database security. Attacker uses illegal and unauthorized approaches to hijack the confidential information like username, password and other vital details. Hence the real time transaction requires security against web based attacks. SQL injection and cross site scripting attack are the most common application layer attack. The SQL injection attacker pass SQL statement through a web applications input fields, URL or hidden parameters and get access to the database or update it. The attacker take a benefit from user provided data in such a way that the users input is handled as a SQL code. Using this vulnerability an attacker can execute SQL commands directly on the database. SQL injection attacks are most serious threats which take users input and integrate it into SQL query.  Proxy is a technique which is used to sanitize the users inputs that may transform into a database attack. In this technique a data redirector program redirects the users input to the proxy server before it is sent to the application server. At the proxy server, data cleaning algorithm is triggered using a sanitizing application. In this framework we include detection and sanitization of the tainted information being sent to the database and innovate a new prototype.
On the Complexity and Approximation of Binary Evidence in Lifted Inference

Lifted inference algorithms exploit symmetries in probabilistic models to speed up inference. They show impressive performance when calculating unconditional probabilities in relational models, but often resort to non-lifted inference when computing conditional probabilities, because the evidence breaks many of the model's symmetries. Recent theoretical results paint a grim picture, showing that conditioning on binary relations is #P-hard, and in the worst case, no lifting can be expected. In this paper, we identify Boolean rank of the evidence as a key parameter in the complexity of conditioning. We contrast the hardness result by showing that conditioning on binary evidence with bounded Boolean rank is efficient. This opens up the possibility of approximating evidence by a low-rank Boolean matrix factorization that maintains the model's symmetries and admits efficient lifted inference.
Auto-adaptative Laplacian Pyramids for high-dimensional data analysis

Non-linear dimensionality reduction techniques such as manifold learning algorithms have become a common way for processing and analyzing high-dimensional patterns that often have attached a target that corresponds to the value of an unknown function. Their application to new points consists in two steps: first, embedding the new data point into the low dimensional space and then, estimating the function value on the test point from its neighbors in the embedded space. 
However, finding the low dimension representation of a test point, while easy for simple but often not powerful enough procedures such as PCA, can be much more complicated for methods that rely on some kind of eigenanalysis, such as Spectral Clustering (SC) or Diffusion Maps (DM). Similarly, when a target function is to be evaluated, averaging methods like nearest neighbors may give unstable results if the function is noisy. Thus, the smoothing of the target function with respect to the intrinsic, low-dimensional representation that describes the geometric structure of the examined data is a challenging task. 
In this paper we propose Auto-adaptive Laplacian Pyramids (ALP), an extension of the standard Laplacian Pyramids model that incorporates a modified LOOCV procedure that avoids the large cost of the standard one and offers the following advantages: (i) it selects automatically the optimal function resolution (stopping time) adapted to the data and its noise, (ii) it is easy to apply as it does not require parameterization, (iii) it does not overfit the training set and (iv) it adds no extra cost compared to other classical interpolation methods. We illustrate numerically ALP's behavior on a synthetic problem and apply it to the computation of the DM projection of new patterns and to the extension to them of target function values on a radiation forecasting problem over very high dimensional patterns.
Interest communities and flow roles in directed networks: the Twitter network of the UK riots

Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e. groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer and topic. The study of flows also allows us to generate an interest distance, which affords a personalized view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterized by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks.
A physical model for the flickering variability in cataclysmic variables

Aperiodic broad-band variability (also known as flickering) is observed throughout all types of accreting compact objects. Many statistical properties of this variability can be naturally explained with the fluctuating accretion disk model, where variations in the mass-transfer rate through the disk are modulated on the local viscous timescale and propagate towards the central compact object. Here, a recently developed implementation of the model is applied for the first time to the time-averaged, high-frequency variability of a cataclysmic variable star (MV Lyrae) observed with the Kepler satellite. A qualitatively good fit to the data is achieved, suggesting the presence of geometrically thick inner flow with large viscosity parameter, extending from � 0.12R⊙ all the way to the white dwarf surface. A simple spectral model of the system suggests that the geometrically thick component would not contribute much to the observed optical flux originating from the geometrically thin outer disk. Instead, X-ray reprocessing from the geometrically thick disk onto the thin disk is proposed as a mechanism to explain the observed variability. Similar flows are also deduced in accreting neutron stars/black holes (X-ray binaries) and Active Galactic Nuclei. Additionally, eclipse mapping studies of cataclysmic variables also seem to suggest the presence of a geometrically extended flow towards the inner-edges of the accretion disk. The fluctuating accretion disk model applied here is encouraging in understanding the origin of flickering in cataclysmic variables, as well as in X-ray binaries and Active Galactic Nuclei, by providing a unifying scheme by which to explain the observed broad-band variability features observed throughout all compact accreting systems.
Semi-supervised sparse coding

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.
Observables in loop quantum gravity with a cosmological constant

An open issue in loop quantum gravity (LQG) is the introduction of a non-vanishing cosmological constant $\Lambda$. In 3d, Chern-Simons theory provides some guiding lines: $\Lambda$ appears in the quantum deformation of the gauge group. The Turaev-Viro model, which is an example of spin foam model is also defined in terms of a quantum group. By extension, it is believed that in 4d, a quantum group structure could encode the presence of $\Lambda\neq0$. In this article, we introduce by hand the quantum group $\mathcal{U}_{q}(\mathfrak{su}(2))$ into the LQG framework, that is we deal with $\mathcal{U}_{q}(\mathfrak{su}(2))$-spin networks. We explore some of the consequences, focusing in particular on the structure of the observables. Our fundamental tools are tensor operators for $\mathcal{U}_{q}(\mathfrak{su}(2))$. We review their properties and give an explicit realization of the spinorial and vectorial ones. We construct the generalization of the U($n$) formalism in this deformed case, which is given by the quantum group $\mathcal{U}_{q}(\mathfrak{u}(n))$. We are then able to build geometrical observables, such as the length, area or angle operators ... We show that these operators characterize a quantum discrete hyperbolic geometry in the 3d LQG case. Our results confirm that the use of quantum group in LQG can be a tool to introduce a non-zero cosmological constant into the theory.
Want a Good Answer? Ask a Good Question First!

Question Answering (CQA) websites have become valu- able repositories which host a massive volume of human knowl- edge. To maximize the utility of such knowledge, it is essential to evaluate the quality of an existing question or answer, especially soon after it is posted on the CQA website. In this paper, we study the problem of inferring the quality of ques- tions and answers through a case study of a software CQA (Stack Overflow). Our key finding is that the quality of an answer is strongly positively correlated with that of its question. A rmed with this observation, we propose a family of algorithms to jointly pre- dict the quality of questions and answers, for both quantifying nu- merical quality scores and differentiating the high-quali ty ques- tions/answers from those of low quality. We conduct extensive experimental evaluations to demonstrate the effectiveness and ef- ficiency of our methods.
Driving Interconnected Networks to Supercriticality

Networks in the real world do not exist as isolated entities, but they are often part of more complicated structures composed of many interconnected network layers. Recent studies have shown that such mutual dependence makes real networked systems potentially exposed to atypical structural and dynamical behaviors, and thus there is a urgent necessity to better understand the mechanisms at the basis of these anomalies. Previous research has mainly focused on the emergence of atypical properties in relation with the moments of the intra- and inter-layer degree distributions. In this paper, we show that an additional ingredient plays a fundamental role for the possible scenario that an interconnected network can face: the correlation between intra- and inter-layer degrees. For sufficiently high amounts of correlation, an interconnected network can be tuned, by varying the moments of the intra- and inter-layer degree distributions, in distinct topological and dynamical regimes. When instead the correlation between intra- and inter-layer degrees is lower than a critical value, the system enters in a supercricritical regime where dynamical and topological phases are not longer distinguishable.
Optical Counterparts of the X-ray Sources Hercules X-1 and Cygnus X-2: Genuine and Fake

We analyzed his photometry along with other data collected in the literature and Internet. The observations of Sazonov are fake and are not related to real stars. Conclusions contained in his papers do not constitute any scientific value. We describe the properties of these two objects, derived from real observations. The following papers based on the same fake observations and published by him in astro-ph  and  are also inauthentic.
THE BATSE 5B GAMMA-RAY BURST SPECTRAL CATALOG

We present systematic spectral analyses of gamma-ray bursts (GRBs) detected with the Burst and Transient Source Experiment (BATSE) on board the Compton Gamma-Ray Observatory during its entire nine years of operation. This catalog contains two types of spectra extracted from 2145 GRBs, and fitted with five different spectral models resulting in a compendium of over 19,000 spectra. The models were selected based on their empirical importance to the spectral shape of many GRBs, and the analysis performed was devised to be as thorough and objective as possible. We describe in detail our procedures and criteria for the analyses, and present the bulk results in the form of parameter distributions. This catalog should be considered an official product from the BATSE Science Team, and the data files containing the complete results are available from the High-Energy Astrophysics Science Archive Research Center (HEASARC).
Deformations of the vacuum solutions of general relativity subjected to linear constraints

The problem of deforming geometries is particularly important in the context of constructing new exact solutions of Einstein's equation. This issue often appears when extensions of the general relativity are treated, for instance in brane world scenarios. In this paper we investigate spacetimes in which the energy-momentum tensor obeys a linear constraint. Extensions of the usual vacuum and electrovacuum solutions of general relativity are derived and an exact solution is presented. The classes of geometries obtained include a wide variety of compact objects, among them black holes and wormholes. The general metric derived in this work generalizes several solutions already published in the literature. Perturbations around the exact solution are also considered.
Evolution at two levels of gene expression in yeast.

Despite the greater functional importance of protein levels, our knowledge of gene expression evolution is based almost entirely on studies of mRNA levels. In contrast, our understanding of how translational regulation evolves has lagged far behind. Here we have applied ribosome profiling--which measures both global mRNA levels and their translation rates--to two species of Saccharomyces yeast and their interspecific hybrid in order to assess the relative contributions of changes in mRNA abundance and translation to regulatory evolution. We report that both cis- and trans-acting regulatory divergence in translation are abundant, affecting at least 35% of genes. The majority of translational divergence acts to buffer changes in mRNA abundance, suggesting a widespread role for stabilizing selection acting across regulatory levels. Nevertheless, we observe evidence of lineage-specific selection acting on several yeast functional modules, including instances of reinforcing selection acting at both levels of regulation. Finally, we also uncover multiple instances of stop-codon readthrough that are conserved between species. Our analysis reveals the underappreciated complexity of post-transcriptional regulatory divergence and indicates that partitioning the search for the locus of selection into the binary categories of "coding" versus "regulatory" may overlook a significant source of selection, acting at multiple regulatory levels along the path from genotype to phenotype.