nils-herrmann/clean_scirepeval huggingface dataset:
Interpretation of topologically restricted measurements in lattice sigma-models

We consider models with topological sectors, and difficulties with their Monte Carlo simulation. In particular we are concerned with the situation where a simulation has an extremely long auto-correlation time with respect to the topological charge. Then reliable numerical measurements are possible only within single topological sectors. The challenge is to assemble such restricted measurements to obtain an approximation for the full-fledged result, which corresponds to the correct sampling over the entire set of configurations. Under certain conditions this is possible, and it provides in addition an estimate for the topological susceptibility chi_t. Moreover, the evaluation of chi_t might be feasible even from data in just one topological sector, based on the correlation of the topological charge density. Here we present numerical test results for these techniques in the framework of non-linear sigma-models.
Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data

We propose RoBiRank, a ranking algorithm that is motivated by observing a close connection between evaluation metrics for learning to rank and loss functions for robust classification. The algorithm shows a very competitive performance on standard benchmark datasets against other representative algorithms in the literature. On the other hand, in large scale problems where explicit feature vectors and scores are not given, our algorithm can be efficiently parallelized across a large number of machines; for a task that requires 386,133 x 49,824,519 pairwise interactions between items to be ranked, our algorithm finds solutions that are of dramatically higher quality than that can be found by a state-of-the-art competitor algorithm, given the same amount of wall-clock time for computation.
Visualizing Digital Collections

Data visualizations can greatly enhance searches in digital collections by providing information about the scope and context of a collection and allowing users to more easily browse and explore the contents. This article discusses the benefits of incorporating visualizations into digital collections based on the experiences of the Cold War International History Project (CWIHP) in developing a user-friendly tool for searching and visualizing the project's complex set of historical documents. The article concludes with a tutorial on using the free Library of Congress tool Viewshare to create visualizations based on real data from the CWIHP Digital Archive.
Quantum Gravity for Dummies

I have been asked to write brief, gentle introduction to the basic idea behind the field of “quantum gravity” in 1500 words or less. Doing so appears to be almost as great a challenge as coming up with a consistent theory of quantum gravity. However, I will try. Disclaimer: The views expressed in this article are my own and do not represent the consensus of the quantum gravity community. 1 Semantics To get some idea of quantum gravity it is helpful to look at the meaning of those two words separately. The word “quantum” refers to the theory of quantum mechanics which was developed in the 20th century and which incorporates various seemingly paradoxical properties of light and matter such as wave-particle duality, the uncertainty principle, and the probabilistic nature of measurements into a coherent theoretical framework. Quantum Mechanics undergirds the basic technological framework of the world we live in. Without quantum mechanics we would not understand how semiconductors work and without semiconductors we would not be able to build the integrated circuits that power most modern electronic devices. Without QM we would not have NMR machines, CT scans, electron microscopes, superconductors or superfluids. Many of the theoretical implications of quantum mechanics have been realized outside the laboratory and are embedded in the many layers of technology that surround us. Many more of its implications - quantum computation and teleportation for e.g. - are still in the laboratory, not yet mature enough to venture out into the real world. The word “gravity” refers not to Newton’s theory of gravitation, but to Einstein’s theory of general relativity which superseded the Newtonian conception of gravity. In the Newtonian framework, gravity was thought of as a force which acted between any two massive bodies in a manner given by the well-known formula: F = Gm1m2/r 2 . By 1905 the Newtonian concept of motion, grounded in the postulates of absolute time and absolute space, had dissolved to give way
Interior of Schwarzschild black hole as a relativistic free particle

Using the standard Hamiltonian approach followed by ADM formalism to the geometry of a Schwarzschild black hole, we show that the interior spacetime of Schwarzschild black hole behaves like a relativistic free particle with Planck mass $M_{p}$. We further present the (anti-)BRST quantization of the relativistic free particle representing the interior of Schwarzschild black hole. With help of the (anti-)BRST formalism, we determine the physical states of the system in general Hilbert space. This is a novel observation in the context of gauge theoretic approach to gravity.
R+aR2 loop quantum cosmology

Working in Einstein frame, we introduce, in order to avoid singularities, holonomy corrections to the f(R)=R+aR2 model. We perform a detailed analytical and numerical study when holonomy corrections are taken into account in both Jordan and Einstein frames, obtaining, in Jordan frame, a dynamics which differs qualitatively, at early times, from the one of the original model. More precisely, when holonomy corrections are taken into account, the Universe is not singular, starting at early times in the contracting phase and bouncing to enter the expanding one where, as in the original model, it inflates. This dynamics is completely different from the one obtained in the original R+aR2 model, where the Universe is singular at early times and never bounces. Moreover, we show that these holonomy corrections may lead to better predictions for the inflationary phase as compared with current observations.
More on loops in reheating: non-gaussianities and tensor power spectrum

We consider the single field chaotic m2ϕ2 inflationary model with a period of preheating, where the inflaton decays to another scalar field χ in the parametric resonance regime. In a recent work, one of us has shown that the χ modes circulating in the loops during preheating notably modify the ⟨ζζ⟩ correlation function. We first rederive this result using a different gauge condition hence reconfirm that superhorizon ζ modes are affected by the loops in preheating. Further, we examine how χ loops give rise to non-gaussianity and affect the tensor perturbations. For that, all cubic and some higher order interactions involving two χ fields are determined and their contribution to the non-gaussianity parameter fNL and the tensor power spectrum are calculated at one loop. Our estimates for these corrections show that while a large amount of non-gaussianity can be produced during reheating, the tensor power spectrum receive moderate corrections. We observe that the loop quantum effects increase with more χ fields circulating in the loops indicating that the perturbation theory might be broken down. These findings demonstrate that the loop corrections during reheating are significant and they must be taken into account for precision inflationary cosmology.
Rate of convergence for large coupling limits in Sobolev spaces

We estimate the rate of convergence, in the so-called large coupling limit, for Schrödinger-type operators on bounded domains with “interaction potentials” supported in a compact inclusion. We show that if the boundary of the inclusion is suﬃciently smooth, one essentially recovers the “free Hamiltonian” in the exterior domain with Dirichlet boundary conditions. In addition, we obtain a convergence rate, in L2, that is , where λ is the coupling parameter. Our methods include energy estimates, trace estimates, interpolation, and duality.
Differential Privacy: An Economic Method for Choosing Epsilon

Differential privacy is becoming a gold standard notion of privacy; it offers a guaranteed bound on loss of privacy due to release of query results, even under worst-case assumptions. The theory of differential privacy is an active research area, and there are now differentially private algorithms for a wide range of problems. However, the question of when differential privacy works in practice has received relatively little attention. In particular, there is still no rigorous method for choosing the key parameter ε, which controls the crucial tradeoff between the strength of the privacy guarantee and the accuracy of the published results. In this paper, we examine the role of these parameters in concrete applications, identifying the key considerations that must be addressed when choosing specific values. This choice requires balancing the interests of two parties with conflicting objectives: the data analyst, who wishes to learn something abou the data, and the prospective participant, who must decide whether to allow their data to be included in the analysis. We propose a simple model that expresses this balance as formulas over a handful of parameters, and we use our model to choose ε on a series of simple statistical studies. We also explore a surprising insight: in some circumstances, a differentially private study can be more accurate than a non-private study for the same cost, under our model. Finally, we discuss the simplifying assumptions in our model and outline a research agenda for possible refinements.
Network-Layer Trust in Named-Data Networking

In contrast to today's IP-based host-oriented Internet architecture, Information-Centric Networking (ICN) emphasizes content by making it directly addressable and routable. Named Data Networking (NDN) architecture is an instance of ICN that is being developed as a candidate next-generation Internet architecture. By opportunistically caching content within the network, NDN appears to be well-suited for large-scale content distribution and for meeting the needs of increasingly mobile and bandwidth-hungry applications that dominate today's Internet.
 One key feature of NDN is the requirement for each content object to be digitally signed by its producer. Thus, NDN should be, in principle, immune to distributing fake (aka "poisoned") content. However, in practice, this poses two challenges for detecting fake content in NDN routers: (1) overhead due to signature verification and certificate chain traversal, and (2) lack of trust context, i.e., determining which public keys are trusted to verify which content. Because of these issues, NDN does not force routers to verify content signatures, which makes the architecture susceptible to content poisoning attacks.
 This paper explores root causes of, and some cures for, content poisoning attacks in NDN. In the process, it becomes apparent that meaningful mitigation of content poisoning is contingent upon a network-layer trust management architecture, elements of which we construct, while carefully justifying specific design choices. This work represents the initial effort towards comprehensive trust management for NDN.
Geometry and expressive power of conditional restricted Boltzmann machines

Conditional restricted Boltzmann machines are undirected stochastic neural networks with a layer of input and output units connected bipartitely to a layer of hidden units. These networks define models of conditional probability distributions on the states of the output units given the states of the input units, parametrized by interaction weights and biases. We address the representational power of these models, proving results their ability to represent conditional Markov random fields and conditional distributions with restricted supports, the minimal size of universal approximators, the maximal model approximation errors, and on the dimension of the set of representable conditional distributions. We contribute new tools for investigating conditional probability models, which allow us to improve the results that can be derived from existing work on restricted Boltzmann machine probability models.
A minimax distortion view of differentially private query release

We devise query-set independent mechanisms for the problem of differentially private query release. Specifically, a differentially private mechanism is constructed to publish a synthetic database, and "customized" companion estimators are then derived to provide the best possible answers. Accordingly, the distortion corresponding to the best mechanism at the worst- case query, named the minimax distortion, provides a fundamental characterization. For the general class of statistical queries, by deriving asymptotically sharp upper and lower bounds, we prove that the minimax distortion is O(1/n) as the database size n goes to infinity, with the squared-error distortion measure and fixed dimension of data entries.
Censorship in the Wild: Analyzing Internet Filtering in Syria

Internet censorship is enforced by numerous governments worldwide, however, due to the lack of publicly available information, as well as the inherent risks of performing active measurements, it is often hard for the research community to investigate censorship practices in the wild. Thus, the leak of 600GB worth of logs from 7 Blue Coat SG-9000 proxies, deployed in Syria to filter Internet traffic at a country scale, represents a unique opportunity to provide a detailed snapshot of a real-world censorship ecosystem. This paper presents the methodology and the results of a measurement analysis of the leaked Blue Coat logs, revealing a relatively stealthy, yet quite targeted, censorship. We find that traffic is filtered in several ways: using IP addresses and domain names to block subnets or websites, and keywords or categories to target specific content. We show that keyword-based censorship produces some collateral damage as many requests are blocked even if they do not relate to sensitive content. We also discover that Instant Messaging is heavily censored, while filtering of social media is limited to specific pages. Finally, we show that Syrian users try to evade censorship by using web/socks proxies, Tor, VPNs, and BitTorrent. To the best of our knowledge, our work provides the first analytical look into Internet filtering in Syria.
Progress in Gauge-Higgs Unification on the Lattice

We study a five-dimensional pure SU(2) gauge theory formulated on the orbifold and discretized on the lattice by means of Monte Carlo simulations. The gauge symmetry is explicitly broken to U(1) at the orbifold boundaries. The action is the Wilson plaquette action with a modified weight for the boundary U(1) plaquettes. We study the phase transition and present results for the spectrum and the shape of the static potential on the boundary. The latter is sensitive to the presence of a massive Z-boson, in good agreement with the directly measured Z-boson mass. The results may support an alternative view of the lattice orbifold (stemming from its mean-field study) as a 5d bosonic superconductor.
Nonlinear optomechanics in the stationary regime

We have observed nonlinear transduction of the thermomechanical motion of a nanomechanical resonator when detected as laser transmission through a sideband unresolved optomechanical cavity. Nonlinear detection mechanisms are of considerable interest as special cases allow for quantum nondemolition measurements of the mechanical resonator's energy. We investigate the origin of the nonlinearity in the optomechanical detection apparatus and derive a theoretical framework for the nonlinear signal transduction, and the optical spring effect, from both nonlinearities in the optical transfer function and second order optomechanical coupling. By measuring the dependence of the linear and nonlinear signal transduction -- as well as the mechanical frequency shift -- on laser detuning from optical resonance, we provide estimates of the contributions from the linear and quadratic optomechanical couplings.
Finding coordinated paths for multiple holonomic agents in 2-d polygonal environment

Avoiding collisions is one of the vital tasks for systems of autonomous mobile agents. We focus on the problem of finding continuous coordinated paths for multiple mobile disc agents in a 2-d environment with polygonal obstacles. The problem is PSPACE-hard, with the state space growing exponentially in the number of agents. Therefore, the state of the art methods include mainly reactive techniques and sampling-based iterative algorithms. 
 
We compare the performance of a widely-used reactive method ORCA with three variants of a popular planning algorithm RRT* applied to multi-agent path planning and find that an algorithm combining reactive collision avoidance and RRT* planning, which we call ORCA-RRT* can be used to solve instances that are out of the reach of either of the techniques. We experimentally show that: 1) the reactive part of the algorithm can efficiently solve many multi-agent path finding problems involving large number of agents, for which RRT* algorithm is often unable to find a solution in limited time and 2) the planning component of the algorithm is able to solve many instances containing local minima, where reactive techniques typically fail.
Auto Spell Suggestion for High Quality Speech Synthesis in Hindi

The goal of Text-to-Speech (TTS) synthesis in a particular language is to convert arbitrary input text to intelligible and natural sounding speech. However, for a particular language like Hindi, which is a highly confusing language (due to very close spellings), it is not an easy task to identify errors/mistakes in input text and an incorrect text degrade the quality of output speech hence this paper is a contribution to the development of high quality speech synthesis with the involvement of Spellchecker which generates spell suggestions for misspelled words automatically. Involvement of spellchecker would increase the efficiency of speech synthesis by providing spell suggestions for incorrect input text. Furthermore, we have provided the comparative study for evaluating the resultant effect on to phonetic text by adding spellchecker on to input text.
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method

The word2vec software of Tomas Mikolov and colleagues (this https URL ) has gained a lot of traction lately, and provides state-of-the-art word embeddings. The learning models behind the software are described in two research papers. We found the description of the models in these papers to be somewhat cryptic and hard to follow. While the motivations and presentation may be obvious to the neural-networks language-modeling crowd, we had to struggle quite a bit to figure out the rationale behind the equations. 
This note is an attempt to explain equation (4) (negative sampling) in "Distributed Representations of Words and Phrases and their Compositionality" by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado and Jeffrey Dean.
TEST-PARTICLE ACCELERATION IN A HIERARCHICAL THREE-DIMENSIONAL TURBULENCE MODEL

The acceleration of charged particles is relevant to the solar corona over a broad range of scales and energies. High-energy particles are usually detected in concomitance with large energy release events like solar eruptions and flares. Nevertheless, acceleration can occur at smaller scales, characterized by dynamical activity near current sheets. To gain insight into the complex scenario of coronal charged particle acceleration, we investigate the properties of acceleration with a test-particle approach using three-dimensional magnetohydrodynamic (MHD) models. These are obtained from direct solutions of the reduced MHD equations, well suited for a plasma embedded in a strong axial magnetic field, relevant to the inner heliosphere. A multi-box, multiscale technique is used to solve the equations of motion for protons. This method allows us to resolve an extended range of scales present in the system, namely, from the ion inertial scale of the order of a meter up to macroscopic scales of the order of 10 km (1/100th of the outer scale of the system). This new technique is useful to identify the mechanisms that, acting at different scales, are responsible for acceleration to high energies of a small fraction of the particles in the coronal plasma. We report results that describe acceleration at different stages over a broad range of time, length, and energy scales.
On the relation between identifiability, differential privacy, and mutual-information privacy

This paper investigates the relation between three different notions of privacy: identifiability, differential privacy and mutual-information privacy. Under a privacy-distortion framework, where the distortion is defined to be the expected Hamming distance between the input and output databases, we establish some fundamental connections between these three privacy notions. Given a maximum distortion D, let ε*<sub>i</sub>(D) denote the smallest (best) identifiability level, and ε*<sub>d</sub>(D) the smallest differential privacy level. Then we characterize ε*<sub>i</sub>(D) and ε*<sub>d</sub>(D), and prove that ε*<sub>i</sub>(D) - ε<sub>x</sub> ≤ ε*<sub>d</sub>(D) ≤ ε*<sub>i</sub>(D) for D in some range, where ε<sub>x</sub> is a constant depending on the distribution of the original database X, and diminishes to zero when the distribution of X is uniform. Furthermore, we show that identifiability and mutual-information privacy are consistent in the sense that given a maximum distortion D in some range, there is a mechanism that optimizes the identifiability level and also achieves the best mutual-information privacy.
Comparing open and closed molecular self-assembly

We study theoretically in the present work the self-assembly of molecules in an open system, which is fed by monomers and depleted in partial or complete clusters. Such a scenario is likely to occur for example in the context of viral self-assembly. We provide a general formula for the mean-field size distribution which is valid both at equilibrium in a closed system, and in the stationary state in an open system. This allows us to explore in a simple way out-of-equilibrium features for self-assembly and compare them to equilibrium properties. In particular, we identify a region of parameter space for which the out-of-equilibrium size distribution in the presence of external fluxes is equal to the equilibrium size distribution in the absence of external fluxes, up to a constant renormalization factor. The range of validity of this result and its consequences are discussed.
Perseus I and the NGC 3109 association in the context of the Local Group dwarf galaxy structures

The recently discovered dwarf galaxy Perseus I appears to be associated with the dominant plane of non-satellite galaxies in the Local Group (LG). We predict its velocity dispersion and those of the other isolated dwarf spheroidals Cetus and Tucana to be 6.5, 8.2 and 5.5 km s −1 , respectively. The NGC 3109 association, including the recently discovered dwarf galaxy Leo P, aligns with the dwarf galaxy structures in the LG such that all known nearby non-satellite galaxies in the northern Galactic hemisphere lie in a common thin plane (rms height 53 kpc; diameter 1.2 Mpc). This plane has an orientation similar to the preferred orbital plane of the Milky Way (MW) satellites in the vast polar structure. Five of seven of these northern galaxies were identified as possible backsplash objects, even though only about one is expected from cosmological simulations. This may pose a problem, or instead the search for local backsplash galaxies might be identifying ancient tidal dwarf galaxies expelled in a past major galaxy encounter. The NGC 3109 association supports the notion that material preferentially falls towards the MW from the Galactic south and recedes towards the north, as if the MW were moving through a stream of dwarf galaxies.