nils-herrmann/clean_scirepeval huggingface dataset:
Deanonymisation of Clients in Bitcoin P2P Network

Bitcoin is a digital currency which relies on a distributed set of miners to mint coins and on a peer-to-peer network to broadcast transactions. The identities of Bitcoin users are hidden behind pseudonyms (public keys) which are recommended to be changed frequently in order to increase transaction unlinkability. We present an efficient method to deanonymize Bitcoin users, which allows to link user pseudonyms to the IP addresses where the transactions are generated. Our techniques work for the most common and the most challenging scenario when users are behind NATs or firewalls of their ISPs. They allow to link transactions of a user behind a NAT and to distinguish connections and transactions of different users behind the same NAT. We also show that a natural countermeasure of using Tor or other anonymity services can be cut-off by abusing anti-DoS countermeasures of the Bitcoin network. Our attacks require only a few machines and have been experimentally verified. The estimated success rate is between 11% and 60% depending on how stealthy an attacker wants to be. We propose several countermeasures to mitigate these new attacks.
Analysis and forecasting of trending topics in online media streams

Among the vast information available on the web, social media streams capture what people currently pay attention to and how they feel about certain topics. Awareness of such trending topics plays a crucial role in multimedia systems such as trend aware recommendation and automatic vocabulary selection for video concept detection systems. Correctly utilizing trending topics requires a better understanding of their various characteristics in different social media streams. To this end, we present the first comprehensive study across three major online and social media streams, Twitter, Google, and Wikipedia, covering thousands of trending topics during an observation period of an entire year. Our results indicate that depending on one's requirements one does not necessarily have to turn to Twitter for information about current events and that some media streams strongly emphasize content of specific categories. As our second key contribution, we further present a novel approach for the challenging task of forecasting the life cycle of trending topics in the very moment they emerge. Our fully automated approach is based on a nearest neighbor forecasting technique exploiting our assumption that semantically similar topics exhibit similar behavior. We demonstrate on a large-scale dataset of Wikipedia page view statistics that forecasts by the proposed approach are about 9-48k views closer to the actual viewing statistics compared to baseline methods and achieve a mean average percentage error of 45-19% for time periods of up to 14 days.
Algorithms for the Constrained Perfect Phylogeny with Persistent Characters

The perfect phylogeny is one of the most used models in different areas of computational biology. In this paper we consider the problem of the Persistent Perfect Phylogeny (referred as P-PP) recently introduced to extend the perfect phylogeny model allowing persistent characters, that is characters can be gained and lost at most once. We define a natural generalization of the P-PP problem obtained by requiring that for some pairs (character, species), neither the species nor any of its ancestors can have the character. In other words, some characters cannot be persistent for some species. This new problem is called Constrained P-PP (CP-PP). 
Based on a graph formulation of the CP-PP problem, we are able to provide a polynomial time solution for the CP-PP problem for matrices having an empty conflict-graph. In particular we show that all such matrices admit a persistent perfect phylogeny in the unconstrained case. Using this result, we develop a parameterized algorithm for solving the CP-PP problem where the parameter is the number of characters. A preliminary experimental analysis of the algorithm shows that it performs efficiently and it may analyze real haplotype data not conforming to the classical perfect phylogeny model.
Convergent perturbation theory for lattice models with fermions

The standard perturbation theory in QFT and lattice models leads to the asymptotic expansions. However, an appropriate regularization of the path or lattice integrals allows one to construct convergent series with an infinite radius of the convergence. In the earlier studies, this approach was applied to the purely bosonic systems. Here, using bosonization, we develop the convergent perturbation theory for a toy lattice model with interacting fermionic and bosonic fields.
Three-nucleon forces and spectroscopy of neutron-rich calcium isotopes

We study excited-state properties of neutron-rich calcium isotopes based on chiral two- and three-nucleon interactions. We first discuss the details of our many-body framework, investigate convergence properties, and for two-nucleon interactions benchmark against coupled-cluster calculations. We then focus on the spectroscopy of $^{47\ensuremath{-}56}\mathrm{Ca}$, finding that with both 3N forces and an extended $pf{g}_{9/2}$ valence space, we obtain a good level of agreement with experiment. We also study electromagnetic transitions and find that experimental data are well described by our calculations. In addition, we provide predictions for unexplored properties of neutron-rich calcium isotopes.
Mixed tempered stable distribution

In this paper, we introduce a new parametric distribution, the mixed tempered stable. It has the same structure of the normal variance–mean mixtures but the normality assumption gives way to a semi-heavy tailed distribution. We show that, by choosing appropriately the parameters of the distribution and under the concrete specification of the mixing random variable, it is possible to obtain some well-known distributions as special cases. We employ the mixed tempered stable distribution which has many attractive features for modelling univariate returns. Our results suggest that it is flexible enough to accommodate different density shapes. Furthermore, the analysis applied to statistical time series shows that our approach provides a better fit than competing distributions that are common in the practice of finance.
CONSTRAINING THE LOCATION OF GAMMA-RAY FLARES IN LUMINOUS BLAZARS

Locating the gamma-ray emission sites in blazar jets is a long standing and highly controversial issue. We jointly investigate several constraints on the distance scale r and Lorentz factor Γ of the gamma-ray emitting regions in luminous blazars (primarily flat spectrum radio quasars). Working in the framework of one-zone external radiation Comptonization models, we perform a parameter space study for several representative cases of actual gamma-ray flares in their multiwavelength context. We find a particularly useful combination of three constraints: from an upper limit on the collimation parameter Γθ ≲ 1, from an upper limit on the synchrotron self-Compton (SSC) luminosity LSSC ≲ LX, and from an upper limit on the efficient cooling photon energy Ecool, obs ≲ 100 MeV. These three constraints are particularly strong for sources with low accretion disk luminosity Ld. The commonly used intrinsic pair-production opacity constraint on Γ is usually much weaker than the SSC constraint. The SSC and cooling constraints provide a robust lower limit on the collimation parameter Γθ ≳ 0.1–0.7. Typical values of r corresponding to moderate values of Γ ∼ 20 are in the range 0.1–1 pc, and are determined primarily by the observed variability timescale tvar, obs. Alternative scenarios motivated by the observed gamma-ray/millimeter connection, in which gamma-ray flares of tvar, obs ∼ a few days are located at r ∼ 10 pc, are in conflict with both the SSC and cooling constraints. Moreover, we use a simple light travel time argument to point out that the gamma-ray/millimeter connection does not provide a significant constraint on the location of gamma-ray flares. We argue that spine-sheath models of the jet structure do not offer a plausible alternative to external radiation fields at large distances; however, an extended broad-line region is an idea worth exploring. We propose that the most definite additional constraint could be provided by determination of the synchrotron self-absorption frequency for correlated synchrotron and gamma-ray flares.
Supersymmetric partially interacting dark matter

We present a model of partially interacting dark matter (PIDM) within the framework of supersymmetry with gauge mediated symmetry breaking. Dark sector atoms are produced through Affleck-Dine baryogenesis in the dark sector while avoiding the production of Q-ball relics. We discuss the astrophysical constraints relevant for this model and the possibility of dark galactic disk formation. In addition, jet emission from rotating black holes is discussed in the context of this class of models.
The Computational Impact of Partial Votes on Strategic Voting

In many real world elections, agents are not required to rank all candidates. We study three of the most common methods used to modify voting rules to deal with such partial votes. These methods modify scoring rules (like the Borda count), elimination style rules (like single transferable vote) and rules based on the tournament graph (like Copeland) respectively. We argue that with an elimination style voting rule like single transferable vote, partial voting does not change the situations where strategic voting is possible. However, with scoring rules and rules based on the tournament graph, partial voting can increase the situations where strategic voting is possible. As a consequence, the computational complexity of computing a strategic vote can change. For example, with Borda count, the complexity of computing a strategic vote can decrease or stay the same depending on how we score partial votes.
Analyzing complex networks through correlations in centrality measurements

Many real world systems can be expressed as complex networks of interconnected nodes. It is frequently important to be able to quantify the relative importance of the various nodes in the network, a task accomplished by defining some centrality measures, with different centrality definitions stressing different aspects of the network. It is interesting to know to what extent these different centrality definitions are related for different networks. In this work, we study the correlation between pairs of a set of centrality measures for different real world networks and two network models. We show that the centralities are in general correlated, but with stronger correlations for network models than for real networks. We also show that the strength of the correlation of each pair of centralities varies from network to network. Taking this fact into account, we propose the use of a centrality correlation profile, consisting of the values of the correlation coefficients between all pairs of centralities of interest, as a way to characterize networks. Using the yeast protein interaction network as an example we show also that the centrality correlation profile can be used to assess the adequacy of a network model as a representation of a given real network.
Learning to Act Greedily: Polymatroid Semi-Bandits

Many important optimization problems, such as the minimum spanning tree and minimum-cost flow, can be solved optimally by a greedy method. In this work, we study a learning variant of these problems, where the model of the problem is unknown and has to be learned by interacting repeatedly with the environment in the bandit setting. We formalize our learning problem quite generally, as learning how to maximize an unknown modular function on a known polymatroid. We propose a computationally efficient algorithm for solving our problem and bound its expected cumulative regret. Our gap-dependent upper bound is tight up to a constant and our gap-free upper bound is tight up to polylogarithmic factors. Finally, we evaluate our method on three problems and demonstrate that it is practical.
Gambling in Contests with Random Initial Law

This paper studies a variant of the contest model introduced in Seel and Strack [ (2013) 2033-2048]. In the Seel-Strack contest, each agent or contestant privately observes a Brownian motion, absorbed at zero, and chooses when to stop it. The winner of the contest is the agent who stops at the highest value. The model assumes that all the processes start from a common value $x_0>0$ and the symmetric Nash equilibrium is for each agent to utilise a stopping rule which yields a randomised value for the stopped process. In the two-player contest, this randomised value has a uniform distribution on $[0,2x_0]$. In this paper, we consider a variant of the problem whereby the starting values of the Brownian motions are independent, nonnegative random variables that have a common law $\mu$. We consider a two-player contest and prove the existence and uniqueness of a symmetric Nash equilibrium for the problem. The solution is that each agent should aim for the target law $\nu$, where $\nu$ is greater than or equal to $\mu$ in convex order; $\nu$ has an atom at zero of the same size as any atom of $\mu$ at zero, and otherwise is atom free; on $(0,\infty)$ $\nu$ has a decreasing density; and the density of $\nu$ only decreases at points where the convex order constraint is binding.
Robustness of journal rankings by network flows with different amounts of memory

As the number of scientific journals has multiplied, journal rankings have become increasingly important for scientific decisions. From submissions and subscriptions to grants and hirings, researchers, policy makers, and funding agencies make important decisions influenced by journal rankings such as the ISI journal impact factor. Typically, the rankings are derived from the citation network between a selection of journals and unavoidably depend on this selection. However, little is known about how robust rankings are to the selection of included journals. We compare the robustness of three journal rankings based on network flows induced on citation networks. They model pathways of researchers navigating the scholarly literature, stepping between journals and remembering their previous steps to different degrees: zero‐step memory as impact factor, one‐step memory as Eigenfactor, and two‐step memory, corresponding to zero‐, first‐, and second‐order Markov models of citation flow between journals. We conclude that higher‐order Markov models perform better and are more robust to the selection of journals. Whereas our analysis indicates that higher‐order models perform better, the performance gain for higher‐order Markov models comes at the cost of requiring more citation data over a longer time period.
Discovery of a Thorne–Żytkow object candidate in the Small Magellanic Cloud

Thorne-Zytkow objects (TZOs) are a theoretical class of star in which a compact neutron star is surrounded by a large, diffuse envelope. Supergiant TZOs are predicted to be almost identical in appearance to red supergiants (RSGs). The best features that can be used at present to distinguish TZOs from the general RSG population are the unusually strong heavy-element and Li lines present in their spectra, products of the star's fully convective envelope linking the photosphere with the extraordinarily hot burning region in the vicinity of the neutron star core. Here we present our discovery of a TZO candidate in the Small Magellanic Cloud. It is the first star to display the distinctive chemical profile of anomalous element enhancements thought to be unique to TZOs. The positive detection of a TZO will provide the first direct evidence for a completely new model of stellar interiors, a theoretically predicted fate for massive binary systems, and never-before-seen nucleosynthesis processes that would offer a new channel for Li and heavy-element production in our universe.
Connecting neutrino physics with dark matter

The origin of neutrino masses and the nature of dark matter are two in most pressing open questions in modern astro-particle physics. We consider here the possibility that these two problems are related, and review some theoretical scenarios which offer common solutions. A simple possibility is that the dark matter particle emerges in minimal realizations of the seesaw mechanism, as in the majoron and sterile neutrino scenarios. We present the theoretical motivation for both models and discuss their phenomenology, confronting the predictions of these scenarios with cosmological and astrophysical observations. Finally, we discuss the possibility that the stability of dark matter originates from a flavor symmetry of the leptonic sector. We review a proposal based on an A4 flavor symmetry.
Can turnover go to zero?

Internal crossing of trades between multiple alpha streams results in portfolio turnover reduction. Turnover reduction can be modeled using the correlation structure of the alpha streams. As more and more alphas are added, generally turnover reduces. In this note, we use a factor model approach to address the question of whether the turnover goes to zero or a finite limit as the number of alphas N goes to infinity. We argue that the limiting turnover value is determined by the number of alpha clusters F, not the number of alphas N. This limiting value behaves according to the ‘power law’ ∼F−3/2. So, to achieve zero limiting turnover, the number of alpha clusters must go to infinity along with the number of alphas. We further argue on general grounds that, if the number of underlying tradable instruments is finite, then the turnover cannot go to zero, which implies that the number of alpha clusters also appears to be finite.
On the local extension of the future null infinity

We consider a characteristic problem of the vacuum Einstein equations with part of the initial data given on a future complete null cone with suitable decay, and show that the solution exists uniformly around the null cone for general such initial data. We can then define a segment of the future null infinity. The initial data are not required to be small and the decaying condition inherits from the works of \cite{Ch-K} and \cite{K-N}.
Explicit Investment Rules with Time-to-Build and Uncertainty

We establish explicit socially optimal rules for an irreversible investment decision with time-to-build and uncertainty. Assuming a price sensitive demand function with a random intercept, we provide comparative statics and economic interpretations for three models of demand (arithmetic Brownian, geometric Brownian, and the Cox–Ingersoll–Ross). Committed capacity, that is, the installed capacity plus the investment in the pipeline, must never drop below the best predictor of future demand, minus two biases. The discounting bias takes into account the fact that investment is paid upfront for future use; the precautionary bias multiplies a type of risk aversion index by the local volatility. Relying on the analytical forms, we discuss in detail the economic effects. For example, the impact of volatility on the optimal investment is negligible in some cases. It vanishes in the CIR model for long delays, and in the GBM model for high discount rates.
Towards a Multiagent Decision Support System for Crisis Management

Crisis management is a complex problem raised by the scientific community currently. Decision support systems (DSS) are a suitable solution for such issues, they are indeed able to help emergency managers to prevent and to manage crisis in emergency situations. However, they should be enough flexible and adaptive in order to be efficient to solve complex problems that are plunged in dynamic and unpredictable environments. The approach we propose in this paper addresses this challenge. First, we expose a modelling of information for an emergency environment. Then, we describe an architecture of a multiagent DSS that deals with these information in order to prevent and to manage crisis situations. We focus here on the first level of the system mechanism which intends to perceive and to reflect the evolution of the current situation. Finally, we provide an implementation and an experimentation of the approach using the RoboCupRescue Simulation System.
Optimization via Low-rank Approximation, with Applications to Community Detection in Networks

Community detection is one of the fundamental problems of network analysis. A number of methods for community detection have been proposed, including spectral clustering, modularity, and likelihoodbased methods. Most of these methods have to solve an optimization problem over a discrete set of labels, which is computationally infeasible. Some fast algorithms have been proposed for specific methods or models have been proposed, but only on a case by case basis. Here we propose a general approach for maximizing a function of a network adjacency matrix over discrete labels by projecting the set of labels onto a subspace spanned by leading eigenvectors of the adjacency matrix. The main idea is that projection onto a low-dimensional space makes the feasible set of labels much smaller and the optimization problem much easier. We prove a general result on this method and show how to apply it to several previously proposed community detection criteria, establishing its consistency for label estimation in each case. Simulations and applications to real-world data are included to demonstrate our method performs well for multiple problems over a wide range of parameters.
MODERN GEOMETRY IN NOT-SO-HIGH ECHELONS OF PHYSICS: CASE STUDIES

In this mostly pedagogical tutorial article a brief introduction to modern geometrical treat- ment of fluid dynamics and electrodynamics is provided. The main technical tool is standard theory of differential forms. In fluid dynamics, the approach is based on general theory of integral invariants (due to Poincarand Cartan). Since this stuff is still not considered com- mon knowledge, the first chapter is devoted to an introductory and self-contained exposition of both Poincar´ e version as well as Cartan's extension of the theory. The main emphasis in fluid dynamics part of the text is on explaining basic classical results on vorticity phenomenon (vortex lines, vortex filaments etc.) in ideal fluid. In electrodynamics part, we stress the as- pect of how different (in particular, rotating) observers perceive the same space-time situation. Suitable 3 + 1 decomposition technique of differential forms proves to be useful for that. As a representative (an simple) example we analyze Faraday's law of induction (and explicitly compute the induced voltage) from this point of view.
l1-regularized Outlier Isolation and Regression

This paper proposed a new regression model called $l_1$-regularized outlier isolation and regression (LOIRE) and a fast algorithm based on block coordinate descent to solve this model. Besides, assuming outliers are gross errors following a Bernoulli process, this paper also presented a Bernoulli estimate model which, in theory, should be very accurate and robust due to its complete elimination of affections caused by outliers. Though this Bernoulli estimate is hard to solve, it could be approximately achieved through a process which takes LOIRE as an important intermediate step. As a result, the approximate Bernoulli estimate is a good combination of Bernoulli estimate's accuracy and LOIRE regression's efficiency with several simulations conducted to strongly verify this point. Moreover, LOIRE can be further extended to realize robust rank factorization which is powerful in recovering low-rank component from massive corruptions. Extensive experimental results showed that the proposed method outperforms state-of-the-art methods like RPCA and GoDec in the aspect of computation speed with a competitive performance.