nils-herrmann/clean_scirepeval huggingface dataset:
On pricing basket credit default swaps

In this paper we propose a simple and efficient method to compute the ordered default time distributions in both the homogeneous case and the two-group heterogeneous case under the interacting intensity default contagion model. We give the analytical expressions for the ordered default time distributions with recursive formulas for the coefficients, which makes the calculation fast and efficient in finding rates of basket CDSs. In the homogeneous case, we explore the ordered default time in limiting case and further include the exponential decay and the multistate stochastic intensity process. The numerical study indicates that, in the valuation of the swap rates and their sensitivities with respect to underlying parameters, our proposed model outperforms the Monte Carlo method.
The Artificial Regression Market

The Artificial Prediction Market is a recent machine learning technique for multi-class classification, inspired from the financial markets. It involves a number of trained market participants that bet on the possible outcomes and are rewarded if they predict correctly. This paper generalizes the scope of the Artificial Prediction Markets to regression, where there are uncountably many possible outcomes and the error is usually the MSE. For that, we introduce the reward kernel that rewards each participant based on its prediction error and we derive the price equations. Using two reward kernels we obtain two different learning rules, one of which is approximated using Hermite-Gauss quadrature. The market setting makes it easy to aggregate specialized regressors that only predict when an observation falls into their specialization domain. Experiments show that regression markets based on the two learning rules outperform Random Forest Regression on many UCI datasets and are rarely outperformed.
Discrete dynamical genetic programming in XCS

A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using a discrete dynamical system representation within the XCS Learning Classifier System. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such discrete dynamical systems within XCS to solve a number of well-known test problems.
Geometries with the second Poincar\'e symmetry

Chao-Guang Huang, Yu Tian, Xiao-Ning Wu, Zhan Xu, and Bin Zhou 1 Institute of High Energy Physics, and Theoretical Physics Center for Science Facilities, Chinese Academy of Sciences, Beijing 100049, China 2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China 3Institute of Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100190, China, 4 Department of Physics, Tsinghua University, Beijing 100084, China and 5 Department of Physics, Beijing Normal University, Beijing 100875, China.
Spacetime Metrology with LISA Pathfinder

LISA is the proposed ESA-NASA gravitational wave detector in the 0.1 mHz - 0.1 Hz band. LISA Pathfinder is the down-scaled version of a single LISA arm. The arm -- named Doppler link -- can be treated as a differential accelerometer, measuring the relative acceleration between test masses. LISA Pathfinder -- the in-flight test of the LISA instrumentation -- is currently in the final implementation and planned to be launched in 2014. It will set stringent constraints on the ability to put test masses in geodesic motion to within the required differential acceleration of 3\times10^{-14} m s^{-2} Hz^{-1/2} and track their relative motion to within the required differential displacement measurement noise of 9\times10^{-12} m Hz^{-1/2}, around 1 mHz. Given the scientific objectives, it will carry out -- for the first time with such high accuracy required for gravitational wave detection -- the science of spacetime metrology, in which the Doppler link between two free-falling test masses measures the curvature. This thesis contains a novel approach to the calculation of the Doppler response to gravitational waves. It shows that the parallel transport of 4-vectors records the history of gravitational wave signals. In practice, the Doppler link is implemented with 4 bodies in LISA and 3 bodies in LISA Pathfinder. To compensate for noise sources a control logic is implemented during the measurement. The closed-loop dynamics of LISA Pathfinder can be condensed into operators acting on the motion coordinates, handling the couplings, as well as the cross-talks. The scope of system identification is the optimal calibration of the instrument. This thesis describes some data analysis procedures applied to synthetic experiments and shows the relevance of system identification for the success of LISA Pathfinder in demonstrating the principles of spacetime metrology for all future space-based missions.
Your two weeks of fame and your grandmother's

Did celebrity last longer in 1929, 1992 or 2009? We investigate the phenomenon of fame by mining a collection of news articles that spans the twentieth century, and also perform a side study on a collection of blog posts from the last 10 years. By analyzing mentions of personal names, we measure each person's time in the spotlight, and watch the distribution change from a century ago to a year ago. We expected to find a trend of decreasing durations of fame as news cycles accelerated and attention spans became shorter. Instead, we find a remarkable consistency through most of the period we study. Through a century of rapid technological and societal change, through the appearance of Twitter, communication satellites and the Internet, we do not observe a significant change in typical duration of celebrity. We also study the most famous of the famous, and find different results depending on our method for measuring duration of fame. With a method that may be thought of as measuring a spike of attention around a single narrow news story, we see the same result as before: stories last as long now as they did in 1930. A second method, which may be thought of as measuring the duration of public interest in a person, indicates that famous people's presence in the news is becoming longer rather than shorter, an effect most likely driven by the wider distribution and higher volume of media in modern times. Similar studies have been done with much shorter timescales specifically in the context of information spreading on Twitter and similar social networking site. However, to the best of our knowledge, this is the first massive scale study of this nature that spans over a century of archived data, thereby allowing us to track changes across decades.
Jerarca: Efficient Analysis of Complex Networks Using Hierarchical Clustering

How to extract useful information from complex biological networks is a major goal in many fields, especially in genomics and proteomics. We have shown in several works that iterative hierarchical clustering, as implemented in the UVCluster program, is a powerful tool to analyze many of those networks. However, the amount of computation time required to perform UVCluster analyses imposed significant limitations to its use.  We describe the suite Jerarca, designed to efficiently convert networks of interacting units into dendrograms by means of iterative hierarchical clustering. Jerarca is divided into three main sections. First, weighted distances among units are computed using up to three different approaches: a more efficient version of UVCluster and two new, related algorithms called RCluster and SCluster. Second, Jerarca builds dendrograms based on those distances, using well-known phylogenetic algorithms, such as UPGMA or Neighbor-Joining. Finally, Jerarca provides optimal partitions of the trees using statistical criteria based on the distribution of intra- and intercluster connections. Outputs compatible with the phylogenetic software MEGA and the Cytoscape package are generated, allowing the results to be easily visualized.  The four main advantages of Jerarca in respect to UVCluster are: 1) Improved speed of a novel UVCluster algorithm; 2) Additional, alternative strategies to perform iterative hierarchical clustering; 3) Automatic evaluation of the hierarchical trees to obtain optimal partitions; and, 4) Outputs compatible with popular software such as MEGA and Cytoscape.
Value Matters: Predictability of Stock Index Returns

We present a simple dynamical model of stock index returns which is grounded on the ability of the Cyclically Adjusted Price Earning (CAPE) valuation ratio devised by Robert Shiller to predict long-horizon performances of the market. More precisely, we discuss a discrete time dynamics in which the return growth depends on three components: i) a momentum component, naturally justified in terms of agents' belief that expected returns are higher in bullish markets than in bearish ones, ii) a fundamental component proportional to the logarithmic CAPE at time zero. The initial value of the ratio determines the reference growth level, from which the actual stock price may deviate as an effect of random external disturbances, and iii) a driving component which ensures the diffusive behaviour of stock prices. Under these assumptions, we prove that for a sufficiently large horizon the expected rate of return and the expected gross return are linear in the initial logarithmic CAPE, and their variance goes to zero with a rate of convergence consistent with the diffusive behaviour. Eventually this means that the momentum component may generate bubbles and crashes in the short and medium run, nevertheless the valuation ratio remains a good reference point of future long-run returns.
Conocophillips’ Share Price Model Revisited

Three years ago we found a statistically reliable link between ConocoPhillips’ (NYSE: COP) stock price and the difference between the core and headline CPI in the United States. In this article, the original relationship is revisited with new data available since 2009. The agreement between the observed monthly closing price (adjusted for dividends and splits) and that predicted from the CPI difference is confirmed. The original quantitative link is validated. In order to improve the accuracy of the COP price prediction a series of advanced models is developed. The original set of two major CPIs is extended by smaller components of the headline CPIs (e.g. the CPIs of motor fuel and housing energy) and several PPIs (e.g. the PPIs of crude oil and coal) which may be inherently related to ConocoPhillips and other energy companies. These advanced models have demonstrated much lower modeling errors with better statistical properties. The earlier reported quasi-linear trend in the CPI difference is also revisited. This trend allows for an accurate prediction of the COP prices at a five to ten year horizon.
Persistence of social signatures in human communication

We combine cell phone data with survey responses to show that a person’s social signature, as we call the pattern of their interactions with different friends and family members, is remarkably robust. People focus a high proportion of their communication efforts on a small number of individuals, and this behavior persists even when there are changes in the identity of the individuals involved. Although social signatures vary between individuals, a given individual appears to retain a specific social signature over time. Our results are likely to reflect limitations in the ability of humans to maintain many emotionally close relationships, both because of limited time and because the emotional “capital” that individuals can allocate between family members and friends is finite. The social network maintained by a focal individual, or ego, is intrinsically dynamic and typically exhibits some turnover in membership over time as personal circumstances change. However, the consequences of such changes on the distribution of an ego’s network ties are not well understood. Here we use a unique 18-mo dataset that combines mobile phone calls and survey data to track changes in the ego networks and communication patterns of students making the transition from school to university or work. Our analysis reveals that individuals display a distinctive and robust social signature, captured by how interactions are distributed across different alters. Notably, for a given ego, these social signatures tend to persist over time, despite considerable turnover in the identity of alters in the ego network. Thus, as new network members are added, some old network members either are replaced or receive fewer calls, preserving the overall distribution of calls across network members. This is likely to reflect the consequences of finite resources such as the time available for communication, the cognitive and emotional effort required to sustain close relationships, and the ability to make emotional investments.
Social Networks with Competing Products

We introduce a new threshold model of social networks, in which the nodes influenced by their neighbours can adopt one out of several alternatives. We characterize social networks for which adoption of a product by the whole network is possible respectively necessary and the ones for which a unique outcome is guaranteed. These characterizations directly yield polynomial time algorithms that allow us to determine whether a given social network satisfies one of the above properties. We also study algorithmic questions for networks without unique outcomes. We show that the problem of determining whether a final network exists in which all nodes adopted some product is NP-complete. In turn, we also resolve the complexity of the problems of determining whether a given node adopts some respectively, a given product in some respectively, all networks. Further, we show that the problem of computing the minimum possible spread of a product is NP-hard to approximate with an approximation ratio better than Ωn, in contrast to the maximum spread, which is efficiently computable. Finally, we clarify that some of the above problems can be solved in polynomial time when there are only two products.
Transmission of distress in a bank credit network

The European sovereign debt crisis has impaired many European banks. The distress on the European banks may transmit worldwide, and result in a large-scale knock-on default of financial institutions. This study presents a computer simulation model to analyze the risk of insolvency of banks and defaults in a bank credit network. Simulation experiments reproduce the knock-on default, and quantify the impact which is imposed on the number of bank defaults by heterogeneity of the bank credit network, the equity capital ratio of banks, and the capital surcharge on big banks.
Libor model with expiry-wise stochastic volatility and displacement

We develop a multi-factor stochastic volatility Libor model with displacement, where each individual forward Libor is driven by its own square-root stochastic volatility process. The main advantage of this approach is that, maturity-wise, each square-root process can be calibrated to the corresponding cap(let)vola-strike panel at the market. However, since even after freezing the Libors in the drift of this model, the Libor dynamics are not affine, new affine approximations have to be developed in order to obtain Fourier based (approximate) pricing procedures for caps and swaptions. As a result, we end up with a Libor modeling package that allows for efficient calibration to a complete system of cap/swaption market quotes that performs well even in crises times, where structural breaks in vola-strike-maturity panels are typically observed.
More on the Evidence for a Bubble Universe with a Mass ~10^21 M_{\odot}

This is a very informal report that gives further details on the evidence for a bubble universe based on an anomaly in the angular distribution of quasar magnitudes that was presented in a short paper in . This report addresses some concerns of two reviewers. It is meant to be read in conjunction with 1202.4433. There is very little overlap between the two articles. This extended discussion is, by necessity, somewhat more technical in nature. I am grateful for the reviewers' comments that forced me to understand these issues more thoroughly.
Back-reaction as a quantum correction

In this work we will show how the back-reaction can be treated as a quantum correction. The novel semi-classical approach presented here consists in the introduction of adequate quantum corrections into the r-t sector of the black hole metric. Thus, we will obtain corrected values for the temperature, entropy and emission rate, which at leading order coincide with the results in the tunneling frame. We have also applied this technique to the Little String Theory. Interestingly, we have found similar results for the entropy as using string one-loop calculations.
Comment on "First observation of ground state dineutron decay: 16Be".

A recent measurement [Spyrou et al., PRL 108, 102501 (2012)] of the in-flight decay of 16Be into 14Be+n+n has been interpreted as the first case of dineutron emission. Here we point out that the inclusion of the n-n interaction neglected in the description of the direct three-body decay can generate strong enhancements at low n-n relative energy and angle, as observed, without any need to invoke dineutron decay.
Observation of a new Xi(b) baryon

The observation of a new b baryon via its strong decay into Ξ − b π + (plus charge conju-gates) is reported. The measurement uses a data sample of pp collisions at √ s = 7 TeV collected by the CMS experiment at the LHC, corresponding to an integrated luminosity of 5.3 fb − 1 . The known Ξ − b baryon is reconstructed via the decay chain Ξ − b → J/ ψ Ξ − → µ + µ − Λ 0 π − , with Λ 0 → p π − . A peak is observed in the distribution of the difference between the mass of the Ξ − b π + system and the sum of the masses of the Ξ − b and π + , with a signiﬁcance exceeding 5 standard deviations. The mass difference of the peak is 14.84 ± 0.74 (stat.) ± 0.28 (syst.) MeV. The new state most likely corresponds to the J P = 3/2 + companion of the Ξ b .
On a Reliable Peer-Review Process

We propose an enhanced peer-review process where the reviewers are encouraged to truthfully disclose their reviews. We start by modelling that process using a Bayesian model where the uncertainty regarding the quality of the manuscript is taken into account. After that, we introduce a scoring function to evaluate the reported reviews. Under mild assumptions, we show that reviewers strictly maximize their expected scores by telling the truth. We also show how those scores can be used in order to reach consensus.
Keeler's Theorem and Products of Distinct Transpositions

An episode of the television series Futurama features a two-body mind-switching machine, which will not work more than once on the same pair of bodies. After the Futurama community engages in a mind-switching spree, the question is asked, “Can the switching be undone so as to restore all minds to their original bodies?” Ken Keeler found an algorithm that undoes any mind-scrambling permutation with the aid of two “outsiders.” We refine Keeler's result by providing a more efficient algorithm that uses the smallest possible number of switches. We also present best possible algorithms for undoing two natural sequences of switches, each sequence effecting a cyclic mind-scrambling permutation in the symmetric group Sn. Finally, we give necessary and sufficient conditions on m and n for the identity permutation to be expressible as a product of m distinct transpositions in Sn.
"I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper" - A Balanced Survey on Election Prediction using Twitter Data

Predicting X from Twitter is a popular fad within the Twitter research subculture. It seems both appealing and relatively easy. Among such kind of studies, electoral prediction is maybe the most attractive, and at this moment there is a growing body of literature on such a topic. This is not only an interesting research problem but, above all, it is extremely difficult. However, most of the authors seem to be more interested in claiming positive results than in providing sound and reproducible methods. It is also especially worrisome that many recent papers seem to only acknowledge those studies supporting the idea of Twitter predicting elections, instead of conducting a balanced literature review showing both sides of the matter. After reading many of such papers I have decided to write such a survey myself. Hence, in this paper, every study relevant to the matter of electoral prediction using social media is commented. From this review it can be concluded that the predictive power of Twitter regarding elections has been greatly exaggerated, and that hard research problems still lie ahead.
Optimal multifactor trading under proportional transaction costs

Proportional transaction costs present difficult theoretical problems in trading algorithm design, on account of their lack of analytical tractability. The author derives a solution of DT-NT-DT form for an arbitrary model in which the the traded asset has diffusive dynamics described by one or more stochastic risk factors. The width of the NT zone is found to be, as expected, proportional to the cube root of the transaction cost. It is also proportional to the 2/3 power of the volatility of the target position, thereby causing a faster trading strategy to be buffered more than a slower one. The displacement of the middle of the buffer from the costfree position is found to be proportional to the square of the width, and hence to the 2/3 power of the transaction cost; the proportionality constant depends on the expected short-term change in position.
Modelling the Emergence of Spatial Patterns of Economic Activity

This paper describes a simulation model of the spatial development of economic activities over time. The key principle addressed is how spatial patterns of economic activity emerge from decisions of individual firms, which are in turn influenced by the existing spatial configuration. A stylized simulation is presented, in which two types of firms grow at different rates, giving rise to split offs and spatial relocations. The influence of the spatial pattern on individual firms' decisions is implemented in various ways, related to well-known effects such as Jacobs and Marshall externalities described in the economic literature and congestion effects. We demonstrate that different assumptions about the spatial scale of these externalities lead to different spatial configurations. Function concentration (Marshall effects) is more likely to lead to the emergence of subcentres with a specific specialisation. However, the spatial scale of the market and agglomeration effects matters. In particular, if Marshall advantages stretch out over a longer distance, more subcentres emerge. Somewhat surprisingly, congestion seems to have a minor impact on the emerging patterns. The simulation outcomes are intuitively plausible, suggesting that micro-simulation is a promising tool for developing forecasting models to support spatial and economic policies. However, they also articulate the need for validation of the behavioural decision rules, in particular by investigating how growth rates and the spatial scale of externalities differs between different industrial sectors.