nils-herrmann/clean_scirepeval huggingface dataset:
"Explosive regime" should dominate collisions of ultra-high energy cosmic rays

Since the launch of LHC experiments it has been discovered that the high multiplicity trigger in pp, pA collisions finds events behaving differently from the typical (minimally biased) ones. In central pPb case it has been proven that those possess collective phenomena known as the radial, elliptic and triangular flows, similar to what is known in heavy ion (AA) collisions. In this paper we argue that at the ultra-high energies, E_lab ~ 10^{20} eV, of the observed cosmic rays this regime changes from a small-probability fluctuation to a dominant one. We estimate velocity of the transverse collective expansion for the light-light and heavy-light collisions, and find it comparable to what is observed at LHC for the central PbPb case. We argue that significant changes of spectra of various secondaries associated with this phenomenon should be important for the development of the cosmic ray cascades.
Stimulated Raman Spectroscopy with Entangled Light: Enhanced Resolution and Pathway Selection

We propose a novel femtosecond stimulated Raman spectroscopy (FSRS) technique that combines entangled photons with interference detection to select matter pathways and enhance the resolution. Following photoexcitation by an actinic pump, the measurement uses a pair of broad-band entangled photons; one (signal) interacts with the molecule and together with a third narrow-band pulse induces the Raman process. The other (idler) photon provides a reference for the coincidence measurement. This interferometric photon coincidence counting detection allows one to separately measure the Raman gain and loss signals, which is not possible with conventional probe transmission detection. Entangled photons further provide a unique temporal and spectral detection window that can better resolve fast excited-state dynamics compared to classical and correlated disentangled states of light.
Slow Decay of Impact in Equity Markets

Using a proprietary dataset of meta-orders and prediction signals, and assuming a quasi-linear impact model, we deconvolve market impact from past correlated trades and a predictable return component to elicit the temporal dependence of the market impact of a single daily meta-order, over a ten day horizon in various equity markets. We find that the impact of single meta-orders is to a first approximation universal and slowly decays to zero (or to a small value), possibly as a power-law. We show that auto-correlated order-flows and trade information contents fully accounts for the apparent plateau observed in the raw data. We discuss the possible bias introduced by the quasi-linear assumption.
From Social Network to Semantic Social Network in Recommender System

Due the success of emerging Web 2.0, and different social network Web sites such as Amazon and movie lens, recommender systems are creating unprecedented opportunities to help people browsing the web when looking for relevant information, and making choices. Generally, these recommender systems are classified in three categories: content based, collaborative filtering, and hybrid based recommendation systems. Usually, these systems employ standard recommendation methods such as artificial neural networks, nearest neighbor, or Bayesian networks. However, these approaches are limited compared to methods based on web applications, such as social networks or semantic web. In this paper, we propose a novel approach for recommendation systems called semantic social recommendation systems that enhance the analysis of social networks exploiting the power of semantic social network analysis. Experiments on real-world data from Amazon examine the quality of our recommendation method as well as the performance of our recommendation algorithms.
A Spectral Algorithm for Inference in Hidden semi-Markov Models

Hidden semi-Markov models (HSMMs) are latent variable models which allow latent state persistence and can be viewed as a generalization of the popular hidden Markov models (HMMs). In this paper, we introduce a novel spectral algorithm to perform inference in HSMMs. Unlike expectation maximization (EM), our approach correctly estimates the probability of given observation sequence based on a set of training sequences. Our approach is based on estimating moments from the sample, whose number of dimensions depends only logarithmically on the maximum length of the hidden state persistence. Moreover, the algorithm requires only a few matrix inversions and is therefore computationally efficient. Empirical evaluations on synthetic and real data demonstrate the advantage of the algorithm over EM in terms of speed and accuracy, especially for large data sets.
A 52 hours VLT/FORS2 spectrum of a bright z ~ 7 HUDF galaxy: no Ly-α emission

We aim to determine the redshift of GDS-1408, the most solid z~7 galaxy candidate lying in the Hubble Ultra Deep Field. We have used all the VLT spectra of GDS-1408 collected by us and two other groups with FORS2 at VLT in the last five years, for a total integration time of 52hr. The combined spectrum is the deepest ever obtained of a galaxy in the Reionization epoch. We do not detect any emission line or continuum over the whole wavelength range, up to 10100A. Based on an accurate set of simulations, we are able to put a stringent upper limit of f(Lya)<3x10^(-18) erg/s/cm2 at 3-9 sigma in the explored wavelength range, corresponding to a rest-frame equivalent width EW<9A. Combining this limit with the SED modelling we refine the redshift to be z=6.82+/- 0.1 (1-sigma). The same SED fitting indicates that GDS-1408 is relatively extinct (A1600~1) with a dust corrected star formation rate of ~ 20 Msol/yr. The comparison between the un-attenuated equivalent width predicted by the case-B recombination theory and the observed upper limit, provides a limit on the effective Lya escape fraction of f_(esc)^(eff)(Lya)<8%. Even though we cannot rule out a major contribution of the inter/circum galactic medium in damping the line, a plausible interpretation is that GDS-1408 is moderately evolved and contains sufficient gas and dust to attenuate the Lya emission, before it reaches the intergalactic medium. The redshift confirmation of even the best z~7 candidates is very hard to achieve (unless the Lya or unusually strong rest-UV nebular emission lines are present) with the current generation of 8-10m class telescopes. We show that both JWST and E-ELT will be necessary to make decisive progresses. Currently, the increased redshift accuracy obtained with this kind of analysis makes ALMA an interesting option for the redshift confirmation.
Beyond the Born-Oppenheimer approximation with quantum Monte Carlo methods

In this work we develop tools that enable the study of nonadiabatic effects with variational and diffusion Monte Carlo methods. We introduce a highly accurate wave-function ansatz for electron-ion systems that can involve a combination of both clamped ions and quantum nuclei. We explicitly calculate the ground-state energies of ${\mathrm{H}}_{2}$, LiH, ${\mathrm{H}}_{2}\mathrm{O}$, and ${\mathrm{FHF}}^{\ensuremath{-}}$ using fixed-node quantum Monte Carlo with wave-function nodes that explicitly depend on the ion positions. The obtained energies implicitly include the effects arising from quantum nuclei and electron-nucleus coupling. We compare our results to the best theoretical and experimental results available and find excellent agreement.
Non-Monotonic Reasoning and Story Comprehension

This paper develops a Reasoning about Actions and Change framework integrated with Default Reasoning, suitable as a Knowledge Representation and Reasoning framework for Story Comprehension. The proposed framework, which is guided strongly by existing knowhow from the Psychology of Reading and Comprehension, is based on the theory of argumentation from AI. It uses argumentation to capture appropriate solutions to the frame, ramification and qualification problems and generalizations of these problems required for text comprehension. In this first part of the study the work concentrates on the central problem of integration (or elaboration) of the explicit information from the narrative in the text with the implicit (in the readers mind) common sense world knowledge pertaining to the topic(s) of the story given in the text. We also report on our empirical efforts to gather background common sense world knowledge used by humans when reading a story and to evaluate, through a prototype system, the ability of our approach to capture both the majority and the variability of understanding of a story by the human readers in the experiments.
Tensor-polarized structure functions: Tensor structure of deuteron in 2020's

We explain spin structure for a spin-one hadron, in which there are new structure functions, in addition to the ones (F1, F2, g1, g2) which exist for the spin-1/2 nucleon, associated with its tensor structure. The new structure functions are b1, b2, b3, and b4 in deep inelastic scattering of a charged-lepton from a spin-one hadron such as the deuteron. Among them, twist- two functions are related by the Callan-Gross type relation b2 = 2xb1 in the Bjorken scaling limit. First, these new structure functions are introduced, and useful formulae are derived for projection operators of b1-4 from a hadron tensor Wμν. Second, a sum rule is explained for b1, and possible tensor-polarized distributions are discussed by using HERMES data in order to propose future experimental measurements and to compare them with theoretical models. A proposal was approved to measure b1 at the Thomas Jefferson National Accelerator Facility (JLab), so that much progress is expected for b1 in the near future. Third, formalisms of polarized proton-deuteron Drell-Yan processes are explained for probing especially tensor- polarized antiquark distributions, which were suggested by the HERMES data. The studies of the tensor-polarized structure functions will open a new era in 2020's for tensor-structure studies in terms of quark and gluon degrees of freedom, which are very different from ordinary descriptions in terms of nucleons and mesons.
THE CHANDRA PLANETARY NEBULA SURVEY (ChanPlaNS). II. X-RAY EMISSION FROM COMPACT PLANETARY NEBULAE

We present results from the most recent set of observations obtained as part of the Chandra X-ray observatory Planetary Nebula Survey (ChanPlaNS), the first comprehensive X-ray survey of planetary nebulae (PNe) in the solar neighborhood (i.e., within ∼1.5 kpc of the Sun). The survey is designed to place constraints on the frequency of appearance and range of X-ray spectral characteristics of X-ray-emitting PN central stars and the evolutionary timescales of wind-shock-heated bubbles within PNe. ChanPlaNS began with a combined Cycle 12 and archive Chandra survey of 35 PNe. ChanPlaNS continued via a Chandra Cycle 14 Large Program which targeted all (24) remaining known compact (Rneb ≲ 0.4 pc), young PNe that lie within ∼1.5 kpc. Results from these Cycle 14 observations include first-time X-ray detections of hot bubbles within NGC 1501, 3918, 6153, and 6369, and point sources in HbDs 1, NGC 6337, and Sp 1. The addition of the Cycle 14 results brings the overall ChanPlaNS diffuse X-ray detection rate to ∼27% and the point source detection rate to ∼36%. It has become clearer that diffuse X-ray emission is associated with young (≲ 5 × 103 yr), and likewise compact (Rneb ≲ 0.15 pc), PNe with closed structures and high central electron densities (ne ≳ 1000 cm−3), and is rarely associated with PNe that show H2 emission and/or pronounced butterfly structures. Hb 5 is one such exception of a PN with a butterfly structure that hosts diffuse X-ray emission. Additionally, two of the five new diffuse X-ray detections (NGC 1501 and NGC 6369) host [WR]-type central stars, supporting the hypothesis that PNe with central stars of [WR]-type are likely to display diffuse X-ray emission.
A Plausibility Semantics for Abstract Argumentation Frameworks

We propose and investigate a simple plausibility-based extension semantics for abstract argumentation frame- works based on generic instantiations by default knowl- edge bases and the ranking construction paradigm for default reasoning. 1 The goal of the present work is therefore to supple- ment existing instantiation efforts with a simple ranking- based semantic model which interprets arguments and at- tacks by conditional knowledge bases. The well-behaved ranking construction semantics for default reasoning (Wey 96, 98, 03) can then be exploited to specify a new exten- sion semantics for Dung frameworks which allows us to di- rectly evaluate the plausibility of argument collections. Its occasionally unorthodox behaviour may shed a new light on basic argumentation-theoretic assumptions and concepts. We start with an introduction to default reasoning based on the ranking construction paradigm. After a short look at abstract argumentation theory, we show how to interpret abstract argumentation frameworks by instantiating the ar- guments and characterizing the attacks with suitable sets of conditionals describing constraints over ranking mod- els. Based on the concept of generic instantiations, i.e. us - ing minimal assumptions, and plausibility maximization, we then specify a natural ranking-based extension semantics. We conclude with a simple algorithm, some instructive ex- amples, and the discussion of several important properties. 2 Ranking-based default reasoning
Global network structure of dominance hierarchy of ant workers

Dominance hierarchy among animals is widespread in various species and believed to serve to regulate resource allocation within an animal group. Unlike small groups, however, detection and quantification of linear hierarchy in large groups of animals are a difficult task. Here, we analyse aggression-based dominance hierarchies formed by worker ants in Diacamma sp. as large directed networks. We show that the observed dominance networks are perfect or approximate directed acyclic graphs, which are consistent with perfect linear hierarchy. The observed networks are also sparse and random but significantly different from networks generated through thinning of the perfect linear tournament (i.e. all individuals are linearly ranked and dominance relationship exists between every pair of individuals). These results pertain to global structure of the networks, which contrasts with the previous studies inspecting frequencies of different types of triads. In addition, the distribution of the out-degree (i.e. number of workers that the focal worker attacks), not in-degree (i.e. number of workers that attack the focal worker), of each observed network is right-skewed. Those having excessively large out-degrees are located near the top, but not the top, of the hierarchy. We also discuss evolutionary implications of the discovered properties of dominance networks.
Kernel nonnegative matrix factorization without the curse of the pre-image

The nonnegative matrix factorization (NMF) is widely used in signal and image processing, including bio-informatics, blind source separation and hyperspectral image analysis in remote sensing. A great challenge arises when dealing with a nonlinear formulation of the NMF. Within the framework of kernel machines, the models suggested in the literature do not allow the representation of the factorization matrices, which is a fallout of the curse of the pre-image. In this paper, we propose a novel kernel-based model for the NMF that does not suffer from the pre-image problem, by investigating the estimation of the factorization matrices directly in the input space. For different kernel functions, we describe two schemes for iterative algorithms: an additive update rule based on a gradient descent scheme and a multiplicative update rule in the same spirit as in the Lee and Seung algorithm. Within the proposed framework, we develop several extensions to incorporate constraints, including sparseness, smoothness, and spatial regularization with a total-variation-like penalty. The effectiveness of the proposed method is demonstrated with the problem of unmixing hyperspectral images, using well-known real images and results with state-of-the-art techniques.
On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models

The stochastic multi-armed bandit model is a simple abstraction that has proven useful in many different contexts in statistics and machine learning. Whereas the achievable limit in terms of regret minimization is now well known, our aim is to contribute to a better understanding of the performance in terms of identifying the m best arms. We introduce generic notions of complexity for the two dominant frameworks considered in the literature: fixed-budget and fixed-confidence settings. In the fixed-confidence setting, we provide the first known distribution-dependent lower bound on the complexity that involves information-theoretic quantities and holds when m ≥ 1 under general assumptions. In the specific case of two armed-bandits, we derive refined lower bounds in both the fixedcon fidence and fixed-budget settings, along with matching algorithms for Gaussian and Bernoulli bandit models. These results show in particular that the complexity of the fixed-budget setting may be smaller than the complexity of the fixed-confidence setting, contradicting the familiar behavior observed when testing fully specified alternatives. In addition, we also provide improved sequential stopping rules that have guaranteed error probabilities and shorter average running times. The proofs rely on two technical results that are of independent interest: a deviation lemma for self-normalized sums (Lemma 7) and a novel change of measure inequality for bandit models (Lemma 1).
When UV and IR Collide: Inequivalent CFTs From Different Foliations Of AdS

In the AdS/CFT correspondence, CFTs are identified by asymptotic boundary surfaces and the boundary conditions imposed on those surfaces. However, AdS can be foliated in various ways to give different boundaries. We show that the CFTs obtained using certain distinct foliations are different. This difference arises because the asymptotic region of a foliation overlaps with the deep interior region of another. In particular we focus on the CFTs defined on surfaces of large constant radius in global coordinates, RindlerAdS coordinates, and Poincaré coordinates for AdS3. We refer to these as global-CFT, Rindler-CFT and Poincaré-CFT respectively. We demonstrate that the correlators for these CFTs are different and argue that the bulk duals to these should agree up to very close to the respective horizons but then start differing. Since the BTZ black hole is obtained as a quotient of AdS3, we discuss the implications of our results for bulk duals of periodically-identified Poincaré and Rindler-CFTs. Our results are consistent with some recent proposals suggesting a modification of the semi-classical BTZ geometry close to the horizons.
Large Scale Canonical Correlation Analysis with Iterative Least Squares

Canonical Correlation Analysis (CCA) is a widely used statistical tool with both well established theory and favorable performance for a wide range of machine learning problems. However, computing CCA for huge datasets can be very slow since it involves implementing QR decomposition or singular value decomposition of huge matrices. In this paper we introduce L-CCA , a iterative algorithm which can compute CCA fast on huge sparse datasets. Theory on both the asymptotic convergence and finite time accuracy of L-CCA are established. The experiments also show that L-CCA outperform other fast CCA approximation schemes on two real datasets.
Recurrent Novae - A Review

In recent years, recurrent nova eruptions are often observed very intensely in wide range of wavelengths from radio to optical to X-rays. Here I present selected highlights from recent multi-wavelength observations. The enigma of T Pyx is at the heart of this paper. While our current understanding of CV and symbiotic star evolution can explain why certain subset of recurrent novae have high accretion rate, that of T Pyx must be greatly elevated compared to the evolutionary mean. At the same time, we have extensive data to be able to estimate how the nova envelope was ejected in T Pyx, and it turns to be a rather complex tale. One suspects that envelope ejection in recurrent and classical novae in general is more complicated than the textbook descriptions. At the end of the review, I will speculate that these two may be connected.
Riemann hypothesis is not correct

Riemann hypothesis is not correctJINHUA FEIChangLing University Baoji Shannxi P.R.ChinaE-mail: feijinhuayoujian@msn.comAbstract. This paper use Nevanlinna’s Second Main Theorem of thevalue distribution theory, we got an important conclusion by Riemann hy-pothesis. this conclusion contradicts the Theorem 8.12 in Titchmarsh’s book”Theory of the Riemann Zeta-functions”, therefore we prove that Riemannhypothesis is incorrect.Keyword. Nevanlinna’s Second Main Theorems, Riemann zeta functionMR(2000) Subject Classiﬁcation 30D35, 11M06First, we give some notations, deﬁnitions and theorems in the theory ofvalue distribution, its contents see the references  and [2].We writelog
Giant components in directed multiplex networks.

We describe the complex global structure of giant components in directed multiplex networks that generalizes the well-known bow-tie structure, generic for ordinary directed networks. By definition, a directed multiplex network contains vertices of one type and directed edges of m different types. In directed multiplex networks, we distinguish a set of different giant components based on the existence of directed paths of different types between their vertices such that for each type of edges, the paths run entirely through only edges of that type. If, in particular, m=2, we define a strongly viable component as a set of vertices in which for each type of edges each two vertices are interconnected by at least two directed paths in both directions, running through the edges of only this type. We show that in this case, a directed multiplex network contains in total nine different giant components including the strongly viable component. In general, the total number of giant components is 3^{m}. For uncorrelated directed multiplex networks, we obtain exactly the size and the emergence point of the strongly viable component and estimate the sizes of other giant components.
DISCOVERY OF A TRANSITING PLANET NEAR THE SNOW-LINE

In most theories of planet formation, the snow-line represents a boundary between the emergence of the interior rocky planets and the exterior ice giants. The wide separation of the snow-line makes the discovery of transiting worlds challenging, yet transits would allow for detailed subsequent characterization. We present the discovery of Kepler-421b, a Uranus-sized exoplanet transiting a G9/K0 dwarf once every 704.2 days in a near-circular orbit. Using public Kepler photometry, we demonstrate that the two observed transits can be uniquely attributed to the 704.2 day period. Detailed light curve analysis with BLENDER validates the planetary nature of Kepler-421b to >4σ confidence. Kepler-421b receives the same insolation as a body at ∼2 AU in the solar system, as well as a Uranian albedo, which would have an effective temperature of ∼180 K. Using a time-dependent model for the protoplanetary disk, we estimate that Kepler-421b's present semi-major axis was beyond the snow-line after ∼3 Myr, indicating that Kepler-421b may have formed at its observed location.