nils-herrmann/clean_scirepeval huggingface dataset:
Accounting for Earnings Announcements in the Pricing of Equity Options

We study an option pricing framework that accounts for the price impact of an earnings announcement (EA), and analyze the behavior of the implied volatility surface prior to the event. On each known announcement date, we introduce a random jump to the stock price. Applying this idea to extend the Black-Scholes model, we obtain formulas for the pre-EA European option price, its implied volatility and various Greeks. We further incorporate random-time jumps in the stock price by extending the Kou jump-diffusion model to incorporate an EA jump, and provide an analytic option pricing formula. Moreover, we derive analytic bounds and asymptotics for the pre-EA implied volatility under various models. The calibration results demonstrate adequate fit of the entire IV surface prior to an announcement. We also compare the risk-neutral distribution of the EA jump to its historical distribution. Finally, we discuss the valuation and exercise strategy of pre-EA American options, and illustrate an analytical approximation and numerical results.
Spy vs. Spy

Anonymous messaging platforms, such as Secret, Yik Yak and Whisper, have emerged as important social media for sharing one's thoughts without the fear of being judged by friends, family, or the public. Further, such anonymous platforms are crucial in nations with authoritarian governments; the right to free expression and sometimes the personal safety of the author of the message depend on anonymity. Whether for fear of judgment or personal endangerment, it is crucial to keep anonymous the identity of the user who initially posted a sensitive message. In this paper, we consider an adversary who observes a snapshot of the spread of a message at a certain time. Recent advances in rumor source detection shows that the existing messaging protocols are vulnerable against such an adversary. We introduce a novel messaging protocol, which we call adaptive diffusion, and show that it spreads the messages fast and achieves a perfect obfuscation of the source when the underlying contact network is an infinite regular tree: all users with the message are nearly equally likely to have been the origin of the message. Experiments on a sampled Facebook network show that it effectively hides the location of the source even when the graph is finite, irregular and has cycles.
Coupling between Galileon and massive gravity with composite metrics

We investigate the coupling between a Galileon scalar field and massive gravity through composite metrics. We derive the full set of equations of motion for a flat Friedmann-Robertson-Walker background, and study linear perturbations around it. Generally, the nonminimal coupling with the composite metric will excite all 6 degrees of freedom of the spatial metric perturbations, one of which may correspond to the Boulware-Deser ghost.
ER=EPR, GHZ, and the consistency of quantum measurements

This paper illustrates various aspects of the ER=EPR conjecture. It begins with a brief heuristic argument, using the Ryu‐Takayanagi correspondence, for why entanglement between black holes implies the existence of Einstein‐Rosen bridges. The main part of the paper addresses a fundamental question: Is ER=EPR consistent with the standard postulates of quantum mechanics? Naively it seems to lead to an inconsistency between observations made on entangled systems by different observers. The resolution of the paradox lies in the properties of multiple black holes, entangled in the Greenberger‐Horne‐Zeilinger pattern. The last part of the paper is about entanglement as a resource for quantum communication. ER=EPR provides a way to visualize protocols like quantum teleportation. In some sense teleportation takes place through the wormhole, but as usual, classical communication is necessary to complete the protocol.
Pasta nucleosynthesis: Molecular dynamics simulations of nuclear statistical equilibrium

Exotic non-spherical nuclear pasta shapes are expected in nuclear matter at just below saturation density because of competition between short range nuclear attraction and long range Coulomb repulsion.  We explore the impact of nuclear pasta on nucleosynthesis, during neutron star mergers, as cold dense nuclear matter is ejected and decompressed.  We perform classical molecular dynamics simulations with 51200 and 409600 nucleons, that are run on GPUs. We expand our simulation region to decompress systems from an initial density of 0.080 fm^{-3} down to 0.00125 fm^{-3}. We study proton fractions of Y_P=0.05, 0.10, 0.20, 0.30, and 0.40 at T =0.5, 0.75, and 1.0 MeV. We calculate the composition of the resulting systems using a cluster algorithm.  We find final compositions that are in good agreement with nuclear statistical equilibrium models for temperatures of 0.75 and 1 MeV. However, for proton fractions greater than Y_P=0.2 at a temperature of T = 0.5 MeV, the MD simulations produce non-equilibrium results with large rod-like nuclei.  Our MD model is valid at higher densities than simple nuclear statistical equilibrium models and may help determine the initial temperatures and proton fractions of matter ejected in mergers.
Pump-Controlled Modal Interactions in Microdisk Lasers

We demonstrate an effective control of nonlinear interactions of lasing modes in a semiconductor microdisk cavity by shaping the pump profile. A target mode is selected at the expense of its competing modes either by increasing their lasing thresholds or suppressing their power slopes above the lasing threshold. Despite the strong spatial overlap of the lasing modes at the disk boundary, adaptive pumping enables an efficient selection of any lasing mode to be the dominant one, leading to a switch of lasing frequency. The theoretical analysis illustrates both linear and nonlinear effects of selective pumping and quantifies their contributions to lasing-mode selection. This work shows that adaptive pumping not only provides a powerful tool to control the nonlinear process in multimode lasers, but also enables the tuning of lasing characteristic after the lasers have been fabricated.
A Nonparametric Bayesian Analysis of Heterogenous Treatment Effects in Digital Experimentation

Randomized controlled trials play an important role in how Internet companies predict the impact of policy decisions and product changes. In these “digital experiments,” different units (people, devices, products) respond differently to the treatment. This article presents a fast and scalable Bayesian nonparametric analysis of such heterogenous treatment effects and their measurement in relation to observable covariates. New results and algorithms are provided for quantifying the uncertainty associated with treatment effect measurement via both linear projections and nonlinear regression trees (CART and random forests). For linear projections, our inference strategy leads to results that are mostly in agreement with those from the frequentist literature. We find that linear regression adjustment of treatment effect averages (i.e., post-stratification) can provide some variance reduction, but that this reduction will be vanishingly small in the low-signal and large-sample setting of digital experiments. For regression trees, we provide uncertainty quantification for the machine learning algorithms that are commonly applied in tree-fitting. We argue that practitioners should look to ensembles of trees (forests) rather than individual trees in their analysis. The ideas are applied on and illustrated through an example experiment involving 21 million unique users of EBay.com.
Accurate and conservative estimates of MRF log-likelihood using reverse annealing

Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function. Annealed importance sampling (AIS) is widely used to estimate MRF partition functions, and often yields quite accurate results. However, AIS is prone to overestimate the log-likelihood with little indication that anything is wrong. We present the Reverse AIS Estimator (RAISE), a stochastic lower bound on the log-likelihood of an approximation to the original MRF model. RAISE requires only the same MCMC transition operators as standard AIS. Experimental results indicate that RAISE agrees closely with AIS log-probability estimates for RBMs, DBMs, and DBNs, but typically errs on the side of underestimating, rather than overestimating, the log-likelihood.
Active Community Detection in Massive Graphs

A canonical problem in graph mining is the detection of dense communities. This problem is exacerbated for a graph with a large order and size -- the number of vertices and edges -- as many community detection algorithms scale poorly. In this work we propose a novel framework for detecting active communities that consist of the most active vertices in massive graphs. The framework is applicable to graphs having billions of vertices and hundreds of billions of edges. Our framework utilizes a parallelizable trimming algorithm based on a locality statistic to filter out inactive vertices, and then clusters the remaining active vertices via spectral decomposition on their similarity matrix. We demonstrate the validity of our method with synthetic Stochastic Block Model graphs, using Adjusted Rand Index as the performance metric. We further demonstrate its practicality and efficiency on a most recent real-world Hyperlink Web graph consisting of over 3.5 billion vertices and 128 billion edges.
Singularity Resolution + Unitary Evolution + Horizon = Firewall ?

We assume that a quantum gravity theory exists where evolutions are unitary, no information is lost, singularities are resolved, and horizons form. Thus a massive star will collapse to a black hole having a horizon and an interior singularity--resolved region. Based on unitarity and on a postulated relation, we obtain an evolution equation for the size of this region. As the black hole evolves by evaporation and accretions, this region grows, meets the horizon, and becomes accessible to an outside observer. The required time is typically of the order of black hole evaporation time. If the boundary of the singularity--resolved region can be considered as the firewall of Almheiri et al then this marks the appearance of a firewall.
Entropic effects in a nonequilibrium system: Flocks of birds.

When European starlings come together to form a flock, the distribution of their individual velocities narrows around the mean velocity of the flock. We argue that, in a broad class of models for the joint distribution of positions and velocities, this narrowing generates an entropic effect that opposes the cohesion of the flock. The strength of this effect depends strongly on the nature of the interactions among birds: If birds are coupled to a fixed number of neighbors, the entropic forces are weak, while if they couple to all other birds within a fixed distance, the entropic effects are sufficient to tear a flock apart.
A study of the occurrence of supercooling of water

Supercooling of water can be easily studied with a simple apparatus suitable for the student laboratory. We describe such an apparatus and its capabilities. The parameters influencing supercooling include the initial water temperature, as well as the type and temperature of the chilling medium. We correlate the occurrence of supercooling with the ability of the chilling medium to promptly nucleate ice; if it nucleates promptly, the layer of ice crystals formed on the boundary will initiate freezing of the bulk water without supercooling. If the chilling medium is unable to nucleate ice promptly, ice nucleation is delayed and the water supercools. Students can study and compare supercooling of distilled and natural water. Even quite dirty river water may be supercooled by as much as 5 °C.
High Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality

We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models. In particular, we make two contributions: (i) For parameter estimation, we propose a novel high dimensional EM algorithm which naturally incorporates sparsity structure into parameter estimation. With an appropriate initialization, this algorithm converges at a geometric rate and attains an estimator with the (near-)optimal statistical rate of convergence. (ii) Based on the obtained estimator, we propose new inferential procedures for testing hypotheses and constructing condence intervals for low dimensional components of high dimensional parameters. For a broad family of statistical models, our framework establishes the rst computationally feasible approach for optimal estimation and asymptotic inference in high dimensions. Our theory is supported by thorough numerical results.
A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models

We consider the problem of uncertainty assessment for low dimensional components in high dimensional models. Specifically, we propose a decorrelated score function to handle the impact of high dimensional nuisance parameters. We consider both hypothesis tests and confidence regions for generic penalized M-estimators. Unlike most existing inferential methods which are tailored for individual models, our approach provides a general framework for high dimensional inference and is applicable to a wide range of applications. From the testing perspective, we develop general theorems to characterize the limiting distributions of the decorrelated score test statistic under both null hypothesis and local alternatives. These results provide asymptotic guarantees on the type I errors and local powers of the proposed test. Furthermore, we show that the decorrelated score function can be used to construct point and confidence region estimators that are semiparametrically efficient. We also generalize this framework to broaden its applications. First, we extend it to handle high dimensional null hypothesis, where the number of parameters of interest can increase exponentially fast with the sample size. Second, we establish the theory for model misspecification. Third, we go beyond the likelihood framework, by introducing the generalized score test based on general loss functions. Thorough numerical studies are conducted to back up the developed theoretical results.
An OFDM Signal Identification Method for Wireless Communications Systems

Distinction of orthogonal frequency-division multiplexing (OFDM) signals from single-carrier signals is highly important for adaptive receiver algorithms and signal identification applications. OFDM signals exhibit Gaussian characteristics in the time domain, and fourth-order cumulants of Gaussian distributed signals vanish, in contrast to the cumulants of other signals. Thus, fourth-order cumulants can be utilized for OFDM signal identification. In this paper, first, formulations of the estimates of the fourth-order cumulants for OFDM signals are provided. Then, it is shown that these estimates are significantly affected by wireless channel impairments, frequency offset, phase offset, and sampling mismatch. To overcome these problems, a general chi-square constant false-alarm rate Gaussianity test, which employs estimates of cumulants and their covariances, is adapted to the specific case of wireless OFDM signals. Estimation of the covariance matrix of the fourth-order cumulants is greatly simplified, peculiar to the OFDM signals. A measurement setup is developed to analyze the performance of the identification method and for comparison purposes. A parametric measurement analysis is provided, depending on modulation order, signal-to-noise ratio, number of symbols, and degree of freedom of the underlying test. The proposed method outperforms statistical tests that are based on fixed thresholds or empirical values, whereas the a priori information requirement and complexity of the proposed method are lower than the coherent identification techniques.
Understanding the neutron star population with the SKA

Thomas M. Tauris1,2, Victoria M. Kaspi∗3, Rene P. Breton4, Adam T. Deller5, Evan F. Keane6,7, Michael Kramer2, Duncan R. Lorimer8, Maura A. McLaughlin8, Andrea Possenti9, Paul S. Ray10, Ben W. Stappers11, Patrick Weltevrede11 1Argelander-Institute for Astronomy, University of Bonn, Germany; 2MPIfR, Germany; 3McGill University, Canada; 4University of Southampton, UK; 5ASTRON, The Netherlands; 6Swinburne University of Technology, Australia; 7ARC Centre of Excellence for All-Sky Astrophysics (CAASTRO); 8West Virginia University, USA; 9INAF-Osservatorio Astronomico di Cagliari, Italy; 10Naval Research Laboratory, USA; 11University of Manchester, UK E-mail: tauris@astro.uni-bonn.de vkaspi@physics.mcgill.ca
Thermodynamical first laws of black holes in quadratically-extended gravities

Einstein gravities in general dimensions coupled to a cosmological constant and extended with quadratic curvature invariants admit a variety of black holes that may asymptote to Minkowski, anti-de Sitter or Lifshitz spacetimes. We adopt the Wald formalism to derive an explicit formula for calculating the thermodynamical first law for the static black holes with spherical/toric/hyperbolic isometries in these theories. This allows us to derive/rederive the first laws for a wide range of black holes in literature. Furthermore, we construct many new exact solutions and obtain their first laws.
SUPERMASSIVE BLACK HOLES FROM ULTRA-STRONGLY SELF-INTERACTING DARK MATTER

We consider the cosmological consequences if a small fraction ( f ≲ 0.1 ?> ) of the dark matter is ultra-strongly self-interacting, with an elastic self-interaction cross section per unit mass &sgr; ≫ 1 cm 2 g − 1 ?> . This possibility evades all current constraints that assume that the self-interacting component makes up the majority of the dark matter. Nevertheless, even a small fraction of ultra-strongly self-interacting dark matter (uSIDM) can have observable consequences on astrophysical scales. In particular, the uSIDM subcomponent can undergo gravothermal collapse and form seed black holes in the center of a halo. These seed black holes, which form within several hundred halo interaction times, contain a few percent of the total uSIDM mass in the halo. For reasonable values of &sgr; f ?> , these black holes can form at high enough redshifts to grow to ∼ 10 9 M ⊙ ?> quasars by z ≳ 6 ?> , alleviating tension within the standard Λ cold dark matter cosmology. The ubiquitous formation of central black holes in halos could also create cores in dwarf galaxies by ejecting matter during binary black hole mergers, potentially resolving the “too big to fail” problem.
Understand assumptions and know uncertainties: Boscovich and the motion of the Earth

The general prohibition of books advocating heliocentric theory put many progressive Jesuits in a difficult position. One of the most prominent Jesuit scientists of the 18th century, Rogerius Boscovich, was in particularly affected by conflicts between a beautifully simple theory of gravity by Newton, his Jesuit peripatetic education, Church doctrine and the lack of crucial experimental evidence for the motion of the Earth. I present the development of Boscovich's ideas prior to the lifting of the ban, and his retrospective considerations in later writings. These show that Boscovich's acceptance of the motion of the Earth was primarily driven by the existence of a working physical theory that also explained the motion of the Earth, and the lack of a consistent theory that supported any variation of a geocentric system.
Ab initio RNA folding

RNA molecules are essential cellular machines performing a wide variety of functions for which a specific three-dimensional structure is required. Over the last several years, the experimental determination of RNA structures through x-ray crystallography and NMR seems to have reached a plateau in the number of structures resolved each year, but as more and more RNA sequences are being discovered, the need for structure prediction tools to complement experimental data is strong. Theoretical approaches to RNA folding have been developed since the late nineties, when the first algorithms for secondary structure prediction appeared. Over the last 10 years a number of prediction methods for 3D structures have been developed, first based on bioinformatics and data-mining, and more recently based on a coarse-grained physical representation of the systems. In this review we are going to present the challenges of RNA structure prediction and the main ideas behind bioinformatic approaches and physics-based approaches. We will focus on the description of the more recent physics-based phenomenological models and on how they are built to include the specificity of the interactions of RNA bases, whose role is critical in folding. Through examples from different models, we will point out the strengths of physics-based approaches, which are able not only to predict equilibrium structures, but also to investigate dynamical and thermodynamical behavior, and the open challenges to include more key interactions ruling RNA folding.
Optimal Selling Time of a Stock under Capital Gains Taxes

We investigate the impact of capital gains taxes on optimal investment decisions in a quite simple model. Namely, we consider a risk neutral investor who owns one risky stock from which she assumes that it has a lower expected return than the riskless bank account and determine the optimal stopping time at which she sells the stock to invest the proceeds in the bank account up to the maturity date. In the case of linear taxes and a positive riskless interest rate, the problem is nontrivial because at the selling time the investor has to realize book profits which triggers tax payments. We derive a boundary that is continuous and increasing in time and decreasing in the volatility of the stock such that the investor sells the stock at the first time its price is smaller or equal to this boundary.
Chromatic control in coextruded layered polymer microlenses.

We describe the formation, characterization and theoretical understanding of microlenses comprised of alternating polystyrene and polymethylmethacrylate layers produced by multilayer coextrusion. These lenses are fabricated by photolithography, using a grayscale mask followed by plasma etching, so that the refractive index alternation of the bilayer stack appears across the radius of the microlens. The alternating quarter-wave thick layers form a one-dimensional photonic crystal whose dispersion augments the material dispersion, allowing one to sculpt the chromatic dispersion of the lens by adjusting the layered structure. Using Huygen's principle, we model our experimental measurements of the focal length of these lenses across the reflection band of the multilayer polymer film from which the microlens is fashioned. For a 56 μm diameter multilayered lens of focal length 300 μm, we measured a ∼ 25% variation in the focal length across a shallow, 50 nm-wide reflection band.