nils-herrmann/clean_scirepeval huggingface dataset:
Motif statistics of artificially evolved and biological networks.

Topological features of gene regulatory networks can be successfully reproduced by a model population evolving under selection for short dynamical attractors. The evolved population of networks exhibit motif statistics, summarized by significance profiles, which closely match those of E. coli, S. cerevsiae, and B. subtilis, in such features as the excess of linear motifs and feedforward loops, and deficiency of feedback loops. The slow relaxation to stasis is a hallmark of a rugged fitness landscape, with independently evolving populations exploring distinct valleys strongly differing in network properties.
Teleportation of entanglement over 143 km

Teleportation of an entangled state, also known as entanglement swapping, plays a vital role in the vision of a global quantum internet, providing unconditionally secure communication, blind cloud computing, and an exponential speedup in distributed quantum computation. In contrast to the teleportation of a single quantum state from one qubit to another, entanglement swapping generates entanglement between two independent qubits that have never interacted in the past. Therefore this protocol represents a key resource for numerous quantum-information applications that has been implemented in many different systems to date. We experimentally demonstrated entanglement swapping over 143 km between the Canary Islands of La Palma and Tenerife, proving the feasibility of this protocol to be implemented in a future global scenario. As a direct consequence of the no-cloning theorem, the deterministic amplification as in classical communication is impossible for unknown quantum states. This calls for more advanced techniques in a future global quantum network, e.g., for cloud quantum computing. A unique solution is the teleportation of an entangled state, i.e., entanglement swapping, representing the central resource to relay entanglement between distant nodes. Together with entanglement purification and a quantum memory it constitutes a so-called quantum repeater. Since the aforementioned building blocks have been individually demonstrated in laboratory setups only, the applicability of the required technology in real-world scenarios remained to be proven. Here we present a free-space entanglement-swapping experiment between the Canary Islands of La Palma and Tenerife, verifying the presence of quantum entanglement between two previously independent photons separated by 143 km. We obtained an expectation value for the entanglement-witness operator, more than 6 SDs beyond the classical limit. By consecutive generation of the two required photon pairs and space-like separation of the relevant measurement events, we also showed the feasibility of the swapping protocol in a long-distance scenario, where the independence of the nodes is highly demanded. Because our results already allow for efficient implementation of entanglement purification, we anticipate our research to lay the ground for a fully fledged quantum repeater over a realistic high-loss and even turbulent quantum channel.
Three unsolved problems in physics of Pc1 magnetospheric waves

In this paper we analyzed the open problems in the physics of ultra-low-frequency electromagnetic waves Pc1, which also known as the "pearls" (frequency range 0.2 -5 Hz). A number of theoretical problems arose because the standard model of excitation and propagation of waves in the magnetosphere, the foundations of which were laid half a century ago, is unable to explain observable properties of Pc1. In this article the problems are formulated and discussed in context of the general physics of geoelectromagnetic waves. In addition, one problem of experimental character is considered. The essence of the problem is that the observations testify in favor of the idea of some human impact on the Pc1 oscillation mode, but difficulties in interpreting of observations indicate the need for further experimental research.
Intensional RDB Manifesto: a Unifying NewSQL Model for Flexible Big Data

In this paper we present a new family of Intensional RDBs (IRDBs) which extends the traditional RDBs with the Big Data and flexible and 'Open schema' features, able to preserve the user-defined relational database schemas and all preexisting user's applications containing the SQL statements for a deployment of such a relational data. The standard RDB data is parsed into an internal vector key/value relation, so that we obtain a column representation of data used in Big Data applications, covering the key/value and column-based Big Data applications as well, into a unifying RDB framework. We define a query rewriting algorithm, based on the GAV Data Integration methods, so that each user-defined SQL query is rewritten into a SQL query over this vector relation, and hence the user-defined standard RDB schema is maintained as an empty global schema for the RDB schema modeling of data and as the SQL interface to stored vector relation. Such an IRDB architecture is adequate for the massive migrations from the existing slow RDBMSs into this new family of fast IRDBMSs by offering a Big Data and new flexible schema features as well.
Tractable Epistemic Reasoning with Functional Fluents, Static Causal Laws and Postdiction

Manfred EppeUniversity of Bremen, Germanymeppe@informatik.uni-bremen.de1. INTRODUCTIONEpistemic reasoning about action and change is a crucial requirement for systems that deal with incom-plete knowledge in the presence of abnormalities, unobservable processes, human-computer interactionand other real-world considerations. A particular type of epistemic inference which is required for di-agnosis tasks in an epistemic context is postiction (Eppe, 2013). Postdiction determines the conditionof an action by observing its effect, and is therefore a fundamental requirement to determine the contextunder which actions occur or to diagnose abnormalities. As an example, consider a smart home 6with arobotic wheelchair that can navigate autonomously within the environment. If the smart home recognizesthat the wheelchair does not arrive at its destination after executing a driving request, it can postdict thatthere must be an abnormality (such as a blocked corridor or a ﬂat tire) that prevents the wheelchair fromdriving. The scenario becomes more complicated if a person is sitting on the wheelchair because hereramiﬁcations (i.e. indirect side-effects of actions) are involved: if the person is sitting on the wheelchair,then it will move as the wheelchair moves.In this work, we are interested in the backward direction of such epistemic ramiﬁcation chains. A smarthome assistance system should be able to postdict that the person is sitting on the wheelchair if the systemobserves that the person’s location changes. This inference is useful to trigger other directly linkedinferences about the conditions of the sit-down action: if the person is not sitting on the wheelchair, thiscould mean that the patient is unconscious and help is required.In addition to incomplete knowledge and ramiﬁcations, the scenario suggests to model knowledge (e.g. aboutthe location of the wheelchair and the person) in a functional manner, which allows for simpler and moreelaboration tolerant modeling of the reasoning domain, as compared to a boolean knowledge model.In order to model and to solve such reasoning problems, we extend the h-approximation (HPX) theory(Eppe, 2013) and present an extended theory called HPX
Dynamic Decision Process Modeling and Relation-line Handling in Distributed Cooperative Modeling System

The Distributed Cooperative Modeling System (DCMS) solves complex decision problems involving a lot of participants with different viewpoints by network based distributed modeling and multi-template aggregation. 
This thesis aims at extending the system with support for dynamic decision making process. First, the thesis presents a discussion of characteristics and optimal policy finding Markov Decision Process as well as a brief introduction to dynamic Bayesian decision network, which is inherently equal to MDP. After that, discussion and implementation of prediction in Markov process for both discrete and continuous random variable are given, as well as several different kinds of correlation analysis among multiple indices which could help decision-makers to realize the interaction of indices and design appropriate policy. 
Appending history data of Macau industry, as the foundation of extending DCMS, is introduced. Additional works include rearrangement of graphical class hierarchy in DCMS, which in turn allows convenient implementation of curve relation-line, which makes template modeling clearer and friendlier.
Individual dynamics induces symmetry in network controllability

Controlling complex networked systems to a desired state is a key research goal in contemporary science. Despite recent advances in studying the impact of network topology on controllability, a comprehensive understanding of the synergistic effect of network topology and individual dynamics on controllability is still lacking. Here we offer a theoretical study with particular interest in the diversity of dynamic units characterized by different types of individual dynamics. Interestingly, we find a global symmetry accounting for the invariance of controllability with respect to exchanging the densities of any two different types of dynamic units, irrespective of the network topology. The highest controllability arises at the global symmetry point, at which different types of dynamic units are of the same density. The lowest controllability occurs when all self-loops are either completely absent or present with identical weights. These findings further improve our understanding of network controllability and have implications for devising the optimal control of complex networked systems in a wide range of fields.
The True (?) Story of Hilbert's Infinite Hotel

What is known as "Hilbert's hotel" is a story of an imaginary hotel with infinitely many rooms that illustrates the bizarre consequences of assuming an actual infinity of objects or events. Since the 1970s it has been used in a variety of arguments, some of them relating to cosmology and others to philosophy and theology. For a long time it has remained unknown whether David Hilbert actually proposed the thought experiment named after him, or whether it was merely a piece of mathematical folklore. It turns out that Hilbert introduced his hotel in a lecture of January 1924, but without publishing it. The counter-intuitive hotel only became better known in 1947, when George Gamow described it in a book, and it took nearly three more decades until it attracted wide interest in scientific, philosophical, and theological contexts. The paper outlines the origin and early history of Hilbert's hotel paradox. At the same time it retracts the author's earlier conclusion that the paradox was originally due to Gamow.
Recurrent events of synchrony in complex networks of pulse-coupled oscillators

We present and analyze deterministic complex networks of pulse-coupled oscillators that exhibit recurrent events comprised of an increase and a decline in synchrony. Events emerging from the networks may form an oscillatory behavior or may be separated by periods of asynchrony with varying duration. The phenomenon is specific to spatial networks with both short- and long-ranged connections and requires delayed interactions and refractoriness of oscillators.
Synchronization in populations of sparsely connected pulse-coupled oscillators

We propose a population model for δ-pulse–coupled oscillators with sparse connectivity. The model is given as an evolution equation for the phase density which takes the form of a partial differential equation with a non-local term. We discuss the existence and stability of stationary solutions and exemplify our approach for integrate-and-fire–like oscillators. While for strong couplings, the firing rate of stationary solutions diverges and solutions disappear, small couplings allow for partially synchronous states which emerge at a supercritical Andronov-Hopf bifurcation.
A survey on tidal analysis and forecasting methods for Tsunami detection

Accurate analysis and forecasting of tidal level are very important tasks for human activities in oceanic and coastal areas. They can be crucial in catastrophic situations like occurrences of Tsunamis in order to provide a rapid alerting to the human population involved and to save lives. Conventional tidal forecasting methods are based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large number of parameters and long-term measured data are required for precise tidal level predictions with harmonic analysis. Furthermore, traditional harmonic methods rely on models based on the analysis of astronomical components and they can be inadequate when the contribution of non-astronomical components, such as the weather, is significant. Other alternative approaches have been developed in the literature in order to deal with these situations and provide predictions with the desired accuracy, with respect also to the length of the available tidal record. These methods include standard high or band pass filtering techniques, although the relatively deterministic character and large amplitude of tidal signals make special techniques, like artificial neural networks and wavelets transform analysis methods, more effective. This paper is intended to provide the communities of both researchers and practitioners with a broadly applicable, up to date coverage of tidal analysis and forecasting methodologies that have proven to be successful in a variety of circumstances, and that hold particular promise for success in the future. Classical and novel methods are reviewed in a systematic and consistent way, outlining their main concepts and components, similarities and differences, advantages and disadvantages.
Investing and Stopping

In this paper we solve the hedge fund manager's optimization problem in a model that allows for investors to enter and leave the fund over time depending on its performance. The manager's payoff at the end of the year will then depend not just on the terminal value of the fund level, but also on the lowest and the highest value reached over that time. We establish equivalence to an optimal stopping problem for Brownian motion; by approximating this problem with the corresponding optimal stopping problem for a random walk we are led to a simple and efficient numerical scheme to find the solution, which we then illustrate with some examples.
Beyond Q-Resolution and Prenex Form: A Proof System for Quantified Constraint Satisfaction

We consider the quantified constraint satisfaction problem (QCSP) which is to decide, given a structure and a first-order sentence (not assumed here to be in prenex form) built from conjunction and quantification, whether or not the sentence is true on the structure. We present a proof system for certifying the falsity of QCSP instances and develop its basic theory; for instance, we provide an algorithmic interpretation of its behavior. Our proof system places the established Q-resolution proof system in a broader context, and also allows us to derive QCSP tractability results.
Cascading randomized weighted majority: A new online ensemble learning algorithm

With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods are online algorithms which take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem in the online ensemble learning literature. The Weighted Majority algorithm and the randomized weighted majority (RWM) are the most well-known solutions to this problem, aiming to converge to the best expert. Since among some expert, the best one does not necessarily have the minimum error in all regions of data space, defining specific regions and converging to the best expert in each of these regions will lead to a better result. In this paper, we aim to resolve this defect of RWM algorithms by proposing a novel online ensemble algorithm to the problem of prediction with expert advice. We propose a cascading version of RWM to achieve not only better experimental results but also a better error bound for sufficiently large datasets.
A Compilation Target for Probabilistic Programming Languages

Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have defined, developed, and tested a probabilistic programming language intermediate representation language we call probabilistic C, which itself can be compiled to machine code by standard compilers and linked to operating system libraries yielding an efficient, scalable, portable probabilistic programming compilation target. This opens up a new hardware and systems research path for optimizing probabilistic programming systems.
Impact of Secondary Acceleration in Gamma-Ray Bursts

It is clear from the observation of charged cosmic rays up to energies of 1020 eV that particle acceleration must occur in astrophysical sources. Acceleration of secondary particles like muons and pions, produced in cosmic ray interactions, are usually neglected, however, when calculating the flux of neutrinos from cosmic ray interactions.  Here, we discuss the acceleration of secondary muons, pions, and kaons in gamma-ray bursts within the internal shock scenario, and their impact on the neutrino fluxes.  We introduce a two-zone model consisting of an acceleration zone (the shocks) and a radiation zone (the plasma downstream the shocks). The acceleration in the shocks, which is an unavoidable consequence of the efficient proton acceleration, requires efficient transport from the radiation back to the acceleration zone. On the other hand, stochastic acceleration in the radiation zone can enhance the secondary spectra of muons and kaons significantly if there is a sufficiently large turbulent region.  Overall, it is plausible that neutrino spectra can be enhanced by up to a factor of two at the peak by stochastic acceleration, that an additional spectral peaks appears from shock acceleration of the secondary muons and pions, and that the neutrino production from kaon decays is enhanced.  Depending on the GRB parameters, the general conclusions concerning the limits to the internal shock scenario obtained by recent IceCube and ANTARES analyses may be affected by up to a factor of two by secondary acceleration. Most of the changes occur at energies above 107 GeV, so the effects for next-generation radio-detection experiments will be more pronounced. In the future, however, if GRBs are detected as high-energy neutrino sources, the detection of one or several pronounced peaks around 106 GeV or higher energies could help to derive the basic properties of the magnetic field strength in the GRB.
Dark matter as a trigger for periodic comet impacts.

Although statistical evidence is not overwhelming, possible support for an approximately 35×106  yr periodicity in the crater record on Earth could indicate a nonrandom underlying enhancement of meteorite impacts at regular intervals. A proposed explanation in terms of tidal effects on Oort cloud comet perturbations as the Solar System passes through the galactic midplane is hampered by lack of an underlying cause for sufficiently enhanced gravitational effects over a sufficiently short time interval and by the time frame between such possible enhancements. We show that a smooth dark disk in the galactic midplane would address both these issues and create a periodic enhancement of the sort that has potentially been observed. Such a disk is motivated by a novel dark matter component with dissipative cooling that we considered in earlier work. We show how to evaluate the statistical evidence for periodicity by input of appropriate measured priors from the galactic model, justifying or ruling out periodic cratering with more confidence than by evaluating the data without an underlying model. We find that, marginalizing over astrophysical uncertainties, the likelihood ratio for such a model relative to one with a constant cratering rate is 3.0, which moderately favors the dark disk model. Our analysis furthermore yields a posterior distribution that, based on current crater data, singles out a dark matter disk surface density of approximately 10M⊙/pc2. The geological record thereby motivates a particular model of dark matter that will be probed in the near future.
Emergent Lorentz signature, fermions, and the standard model

This article investigates the construction of fermions and the formulation of the Standard Model of particle physics in a theory in which the Lorentz signature emerges from an underlying microscopic purely Euclidean $SO(4)$ theory. Couplings to a clock field are responsible for triggering the change of signature of the effective metric in which the standard fields propagate. We demonstrate that Weyl and Majorana fermions can be constructed in this framework. This construction differs from other studies of Euclidean fermions, as the coupling to the clock field allows us to write down an action which flows to the usual action in Minkowski spacetime. We then show how the Standard Model can be obtained in this theory and consider the constraints on non-Standard-Model operators which can appear in the QED sector due to $CPT$ and Lorentz violation.
Modeling Website Popularity Competition in the Attention-Activity Marketplace

How does a new startup drive the popularity of competing websites into oblivion like Facebook famously did to MySpace? This question is of great interest to academics, technologists, and financial investors alike. In this work we exploit the singular way in which Facebook wiped out the popularity of MySpace, Hi5, Friendster, and Multiply to guide the design of a new popularity competition model. Our model provides new insights into what Nobel Laure- ate Herbert A. Simon called the "marketplace of attention," which we recast as the attention-activity marketplace. Our model design is further substantiated by user-level activity of 250,000 MySpace users obtained between 2004 and 2009. The resulting model not only accurately fits the observed Daily Active Users (DAU) of Facebook and its competitors but also predicts their fate four years into the future.
Exchange Rate Predictability in a Changing World

An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world, however, Taylor rule parameters may be subject to structural instabilities, for example in the aftermath of the Global Financial Crisis. This paper forecasts exchange rates using Taylor rules with Time-Varying Parameters (TVP) estimated by Bayesian methods. Focusing on the data from the crisis, we improve upon the random walk for at least half, and for as many as seven out of 10, of the currencies considered. Results are stronger when we allow the TVP of the Taylor rules to differ between countries.