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Elastic constants, thermal expansion, magnetostriction and heat capacity measurements were performed with and without applied magnetic field on a single crystal of UIrSi$_3$. The elastic properties were interpreted within the theory of the strain-exchange effect. The exchange-striction model together with the Ising model for the behavior of magnetic localized 5$f$ electrons and itinerant electrons of uranium correctly reproduces the main features of our magneto-acoustic experiments in UIrSi$_3$. Data on thermal expansion and magnetostriction confirm the conclusion that the dominant contribution of the measured temperature and field change in the speed of sound comes from the change in the elastic modulus itself. Based on the analysis of heat capacity measurements in the magnetic field, we explain the significant anomalies at the second-order branch of the phase transition boundary as a manifestation of the tricritical fluctuations, dominating the region below the tricritical point. The outcome confirms the 3D Ising model, at zero and low magnetic fields, with a crossover to the mean-field tricritical behavior at fields close to the tricritical point, where tricritical fluctuations dominate the temperature evolution of the given property., Comment: to be published in PRB

The electronic structure of ThPd$_2$Al$_3$, which is isostructural to the heavy fermion superconductor UPd$_2$Al$_3$, was investigated by photoelectron spectroscopy. The band structure and Fermi surfaces of ThPd$_2$Al$_3$ were obtained by angle-resolved photoelectron spectroscopy (ARPES), and the results were well-explained by the band-structure calculation based on the local density approximation. The comparison between the ARPES spectra and the band-structure calculation suggests that the Fermi surface of ThPd$_2$Al$_3$ mainly consists of the Al $3p$ and Th $6d$ states with a minor contribution from the Pd $4d$ states. The comparison of the band structures between ThPd$_2$Al$_3$ and UPd$_2$Al$_3$ argues that the U $5f$ states form Fermi surfaces in UPd$_2$Al$_3$ through hybridization with the Al $3p$ state in the Al layer, suggesting that the Fermi surface of UPd$_2$Al$_3$ has a strong three-dimensional nature., Comment: accepted to Phys. Rev. B

Most transition-metal trihalides are dimorphic. The representative chromium-based triad, CrCl3, CrBr3, CrI3, is characterized by the low-temperature phase adopting the trigonal BiI3 type while the structure of the high-temperature phase is monoclinic of AlCl3 type (C2/m). The structural transition between the two crystallographic phases is of the first-order type with large thermal hysteresis in CrCl3 and CrI3. We studied crystal structures and structural phase transitions of vanadium-based counterparts VCl3, VBr3, and VI3 by measuring specific heat, magnetization, and X-ray diffraction as functions of temperature and observed an inverse situation. In these cases, the high-temperature phase has a higher symmetry while the low-temperature structure reveals a lower symmetry. The structural phase transition between them shows no measurable hysteresis in contrast to CrX3. Possible relations of the evolution of the ratio c/a of the unit cell parameters, types of crystal structures, and nature of the structural transitions in V and Cr trihalides are discussed., Comment: 11 pages, 6 figures, 1 table. J. Phys.: Condens. Matter (2022)

A search for highly electrically charged objects (HECOs) and magnetic monopoles is presented using 2.2 fb-1 of p - p collision data taken at a centre of mass energy (ECM) of 8 TeV by the MoEDAL detector during LHC's Run-1. The data were collected using MoEDAL's prototype Nuclear Track Detector array and the Trapping Detector array. The results are interpreted in terms of Drell-Yan pair production of stable HECO and monopole pairs with three spin hypotheses (0, 1/2 and 1). The search provides constraints on the direct production of magnetic monopoles carrying one to four Dirac magnetic charges (4gD) and with mass limits ranging from 590 GeV/c^2 to 1 TeV/c^2. Additionally, mass limits are placed on HECOs with charge in the range 10e to 180e, where e is the charge of an electron, for masses between 30 GeV/c^2 and 1 TeV/c^2.

Timepix3 pixel detectors have demonstrated great potential for tracking applications. With $256\times 256$ pixels, 55 $\mathrm{\mu}$m pitch and improved resolution in time (1.56 ns) and energy (2 keV at 60 keV), they have become powerful instruments for characterization of unknown radiation fields. A crucial pre-processing step for such analysis is the determination of particle trajectories in 3D space from individual tracks. This study presents a comprehensive comparison of regression methods that tackle this task under the assumption of track linearity. The proposed methods were first evaluated on a simulation and assessed by their accuracy and computational time. Selected methods were then validated with a real-world dataset, which was measured in a well-known radiation field. Finally, the presented methods were applied to experimental data from the Large Hadron Collider. The best-performing methods achieved a mean absolute error of 1.99{\deg} and 3.90{\deg} in incidence angle $\theta$ and azimuth $\varphi$, respectively. The fastest presented method required a mean computational time of 0.02 ps per track. For all experimental applications, we present angular maps and stopping power spectra., Comment: 9 pages, 6 figures

Rough Volterra volatility models are a progressive and promising field of research in derivative pricing. Although rough fractional stochastic volatility models already proved to be superior in real market data fitting, techniques used in simulation of these models are still inefficient in terms of speed and accuracy. This paper aims to present accurate and efficient tools and techniques for Monte-Carlo simulations for a wide range of rough volatility models. In particular, we compare three commonly used simulation methods: the Cholesky method, the Hybrid scheme, and the rDonsker scheme. We also comment on the implementation of variance reduction techniques. In particular, we show the obstacles of the so-called turbocharging technique whose performance is sometimes counter-productive. To overcome these obstacles, we suggest several modifications.

In this paper, we analyze the robustness and sensitivity of various continuous-time rough Volterra stochastic volatility models in relation to the process of market calibration. Model robustness is examined from two perspectives: the sensitivity of option price estimates and the sensitivity of parameter estimates to changes in the option data structure. The following sensitivity analysis consists of statistical tests to determine whether a given studied model is sensitive to changes in the option data structure based on the distribution of parameter estimates. Empirical study is performed on a data set consisting of Apple Inc. equity options traded on four different days in April and May 2015. In particular, the results for RFSV, rBergomi and $\alpha$RFSV models are provided and compared to the results for Heston, Bates, and AFSVJD models.

The evolution of magnetic properties of isostructural and isoelectronic solid solutions of the superconducting itinerant 5f-electron ferromagnet UCoGe with antiferromagnet UIrGe was studied by magnetization, AC susceptibility, specific heat, and electrical resistivity measurements of a series of UCo1-xIrxGe compounds in polycrystalline and single crystalline form at various temperatures and magnetic fields. Both the weak ferromagnetism and superconductivity in UCoGe were found to have vanished already for very low Ir substitution for Co x = 0.02. The antiferromagnetism of UIrGe is gradually suppressed. This is documented by a rapid decrease in both Neel temperature and the critical field of the metamagnetic transition with decreasing Ir concentration, which both tend to vanish just above x = 0.8. The section of the T-x phase diagram in the range x between 0.02 and 0.8 is dominated by a correlated paramagnetic phase exhibiting very broad bumps in temperature dependencies of b-axis magnetization and specific heat developing with increasing x. For x = 0.24, wide peaks appear in the c-axis thermomagnetic curves due to antiferromagnetic correlations which may eventually lead to frozen incoherent spin configurations at low temperatures. The correlated paramagnetic phase is also accompanied by specific electrical resistivity anomalies. The T-x phase diagram determined for the UCo1-xIrxGe compounds contrasts with the behavior of the related URh1-xIrxGe system, which was reported to exhibit an extended concentration range of stable ferromagnetism in Rh rich compounds and a discontinuous transformation between the ferromagnetic and antiferromagnetic phases at a critical Rh-Ir concentration., Comment: Revised version, Accepted in PRB

Schwinger showed that electrically-charged particles can be produced in a strong electric field by quantum tunnelling through the Coulomb barrier. By electromagnetic duality, if magnetic monopoles (MMs) exist, they would be produced by the same mechanism in a sufficiently strong magnetic field. Unique advantages of the Schwinger mechanism are that its rate can be calculated using semiclassical techniques without relying on perturbation theory, and the finite MM size and strong MM-photon coupling are expected to enhance their production. Pb-Pb heavy-ion collisions at the LHC produce the strongest known magnetic fields in the current Universe, and this article presents the first search for MM production by the Schwinger mechanism. It was conducted by the MoEDAL experiment during the 5.02 TeV/nucleon heavy-ion run at the LHC in November 2018, during which the MoEDAL trapping detectors (MMTs) were exposed to 0.235 nb$^{-1}$ of Pb-Pb collisions. The MMTs were scanned for the presence of magnetic charge using a SQUID magnetometer. MMs with Dirac charges 1$g_D$ $\leq$ $g$ $\leq$ 3$g_D$ and masses up to 75 GeV/c$^2$ were excluded by the analysis. This provides the first lower mass limit for finite-size MMs from a collider search and significantly extends previous mass bounds., Comment: Minor fixes to the authorship list found during production

In this paper we study partial differential equations (PDEs) that can be used to model value adjustments. Different value adjustments denoted generally as xVA are nowadays added to the risk-free financial derivative values and the PDE approach allows their easy incorporation. The aim of this paper is to show how to solve the PDE analytically in the Black-Scholes setting to get new semi-closed formulas that we compare to the widely used Monte-Carlo simulations and to the numerical solutions of the PDE. Particular example of collateral taken as the values from the past will be of interest.

We present an extensive powder and single-crystal neutron scattering investigation of the crystallographic structure and magnetic order of the frustrated metallic $f$-electron magnet UNi$_4$B. We carry out a full refinement of the crystallographic structure and conclude that the low-temperature lattice symmetry is orthorhombic (space group Pmm2; cell parameters: $a = 6.963(4)~\r{A}$ , $b = 14.793(9)~\r{A}$ , $c = 17.126(8)~\r{A}$). We determine the magnetically ordered structure, concluding that below $T_\mathrm{N} = 19.5~\mathrm{K}$ the material undergoes a transition into a partially ordered antiferromagnetic state. The magnetic structure is consistent with the existence of toroidal order in this material. We further test the proposal of a second magnetic transition occurring at $330~\mathrm{mK}$, concluding that the thermodynamic anomalies observed at these temperatures do not reflect modifications of the magnetic structure. Our study provides a consistent picture of the interrelationship of structural and magnetic properties in the frustrated magnet UNi$_4$B previously unresolved., Comment: Submitted to Phys. Rev. B

Despite belonging to a well-studied family of transition metal trihalides, VI_3 has received significant attention only recently. As a hard ferromagnetic van der Waals compound with a large coercivity, it attracted much attention because of its potential use in atomically thin spintronic and optoelectronic devices. However, practical exploration of VI_3 is challenging due to its instability under ambient conditions. Here, we present a comprehensive set of optical, x-ray diffraction, magnetization, and ellipsometric measurements of VI3 and demonstrate that, similarly to the related van der Waals ferromagnet CrI_3, the degradation process is accelerated by the presence of moisture. The VI_3 surface was covered by selected media commonly used in physical measurements to test its stability and lower the degradation rate three times or higher, providing practical information for experimentalists interested. The decomposition study at ambient conditions shows that the VI_3 single crystal can be used for most of the bulk, magnetization, and optical measurements without any noticeable change of physical properties, as the significant degradation appears first after ~ 2 hours of exposition as illustrated, e.g., by the evolution of the ferromagnetic T_1 and T_2 transitions. The ellipsometric measurement demonstrates that even the surface remains optically stable for at least 5 minutes., Comment: 14 pages, 6 figures

A comprehensive study of magnetocrystalline anisotropy of a layered van-der-Waals ferromagnet VI3 was performed. We measured angular dependences of the torque and magnetization with respect to the direction of the applied magnetic field within the "ac" plane perpendicular to and within the basal ab plane, respectively. A two-fold butterfly-like signal was detected by magnetization in the perpendicular "ac" plane. This signal symmetry remains conserved throughout all magnetic regimes as well as through the known structural transition down to the lowest temperatures. The maximum of the magnetization signal and the resulting magnetization easy axis is significantly tilted from the principal c axis by ~40{\deg}. The close relation of the magnetocrystalline anisotropy to the crystal structure was documented. In contrast, a two-fold-like angular signal was detected in the paramagnetic region within the ab plane in the monoclinic phase, which transforms into a six-fold-like signal below the Curie temperature TC. With further cooling, another six-fold-like signal with an angular shift of ~30{\deg} grows approaching TFM. Below TFM, in the triclinic phase, the original six-fold-like signal vanishes, being replaced by a secondary six-fold-like signal with an angular shift of ~30{\deg}., Comment: 14 pages, 10 figures

In mathematical finance, a process of calibrating stochastic volatility (SV) option pricing models to real market data involves a numerical calculation of integrals that depend on several model parameters. This optimization task consists of large number of integral evaluations with high precision and low computational time requirements. However, for some model parameters, many numerical quadrature algorithms fail to meet these requirements. We can observe an enormous increase in function evaluations, serious precision problems and a significant increase of computational time. In this paper we numerically analyse these problems and show that they are especially caused by inaccurately evaluated integrands. We propose a fast regime switching algorithm that tells if it is sufficient to evaluate the integrand in standard double arithmetic or if a higher precision arithmetic has to be used. We compare and recommend numerical quadratures for typical SV models and different parameter values, especially for problematic cases.

The paper investigates strategies for expansion of active set that can be employed by the MPRGP algorithm. The standard MPRGP expansion uses a projected line search in the free gradient direction with a fixed step length. Such a scheme is often too slow to identify the active set, requiring a large number of expansions. We propose to use adaptive step lengths based on the current gradient, which guarantees the decrease of the unconstrained cost function with different gradient-based search directions. Moreover, we also propose expanding the active set by projecting the optimal step for the unconstrained minimization. Numerical experiments demonstrate the benefits of our expansion step modifications on two benchmarks -- contact problem of linear elasticity solved by TFETI and machine learning problems of SVM type, both implemented in PERMON toolbox.

The MoEDAL trapping detector, consists of approximately 800 kg of aluminium volumes. It was exposed during Run-2 of the LHC program to 6.46 fb^-1 of 13 TeV proton-proton collisions at the LHCb interaction point. Evidence for dyons (particles with electric and magnetic charge) captured in the trapping detector was sought by passing the aluminium volumes comprising the detector through a SQUID magnetometer. The presence of a trapped dyon would be signalled by a persistent current induced in the SQUID magnetometer. On the basis of a Drell-Yan production model, we exclude dyons with a magnetic charge ranging up to 5 Dirac charges, and an electric charge up to 200 times the fundamental electric charge for mass limits in the range 790 - 3130 GeV., Comment: arXiv admin note: text overlap with arXiv:1903.08491

A new U-based compound of the U2Rh2Pb, a new compound of the U2T2X series was prepared in a single-crystal form. Its structure was determined as belonging to the tetragonal Mo2FeB2 structure type with the shortest U-U spacing along the c. U2Rh2Pb undergoes an antiferromagnetic transition at TN of 20 K and exhibits an enhanced Sommerfeld coefficient 150 mJ/molK2. In contrast to the two rhodium analogues U2Rh2In and U2Rh2Sn, the easy-magnetization direction is the c with rather low value of the critical field 4.3 T of the metamagnetic transition of a spin-flip type. The observed dependences of TN and Hc on temperature and magnetic field have been used for constructing a magnetic phase diagram. The experimental observations are mostly supported by first-principles calculations., Comment: https://www.sciencedirect.com/science/article/pii/S0925838819347310

Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) posterior probability density functions (PDFs). A plethora of photo-z PDF estimation methodologies abound, producing discrepant results with no consensus on a preferred approach. We present the results of a comprehensive experiment comparing twelve photo-z algorithms applied to mock data produced for The Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). By supplying perfect prior information, in the form of the complete template library and a representative training set as inputs to each code, we demonstrate the impact of the assumptions underlying each technique on the output photo-z PDFs. In the absence of a notion of true, unbiased photo-z PDFs, we evaluate and interpret multiple metrics of the ensemble properties of the derived photo-z PDFs as well as traditional reductions to photo-z point estimates. We report systematic biases and overall over/under-breadth of the photo-z PDFs of many popular codes, which may indicate avenues for improvement in the algorithms or implementations. Furthermore, we raise attention to the limitations of established metrics for assessing photo-z PDF accuracy; though we identify the conditional density estimate (CDE) loss as a promising metric of photo-z PDF performance in the case where true redshifts are available but true photo-z PDFs are not, we emphasize the need for science-specific performancemetrics.

We study the structure and morphological changes of fluids that are in contact with solid composites formed by alternating and microscopically wide stripes of two different materials. One type of the stripes interacts with the fluid via long-ranged Lennard-Jones-like potential and tends to be completely wet, while the other type is purely repulsive and thus tends to be completely dry. We consider closed systems with a fixed number of particles that allows for stabilization of fluid configurations breaking the lateral symmetry of the wall potential. These include liquid morphologies corresponding to a sessile drop that is formed by a sequence of bridging transitions that connect neighboring wet regions adsorbed at the attractive stripes. We study the character of the transitions depending on the wall composition, stripes width, and system size. Using a (classical) nonlocal density functional theory (DFT), we show that the transitions between different liquid morphologies are typically weakly first-order but become rounded if the wavelength of the system is lower than a certain critical value $L_c$. We also argue that in the thermodynamic limit, i.e., for macroscopically large systems, the wall becomes wet via an infinite sequence of first-order bridging transitions that are, however, getting rapidly weaker and weaker and eventually become indistinguishable from a continuous process as the size of the bridging drop increases. Finally, we construct the global phase diagram and study the density dependence of the contact angle of the bridging drops using DFT density profiles and a simple macroscopic theory.

In this paper we perform robustness and sensitivity analysis of several continuous-time stochastic volatility (SV) models with respect to the process of market calibration. The analyses should validate the hypothesis on importance of the jump part in the underlying model dynamics. Also an impact of the long memory parameter is measured for the approximative fractional SV model. For the first time, the robustness of calibrated models is measured using bootstrapping methods on market data and Monte-Carlo filtering techniques. In contrast to several other sensitivity analysis approaches for SV models, the newly proposed methodology does not require independence of calibrated parameters - an assumption that is typically not satisfied in practice. Empirical study is performed on data sets of Apple Inc. equity options traded in April and May 2015.

In this paper we study both analytic and numerical solutions of option pricing equations using systems of orthogonal polynomials. Using a Galerkin-based method, we solve the parabolic partial diferential equation for the Black-Scholes model using Hermite polynomials and for the Heston model using Hermite and Laguerre polynomials. We compare obtained solutions to existing semi-closed pricing formulas. Special attention is paid to the solution of Heston model at the boundary with vanishing volatility.

Timepix and Timepix3 are hybrid pixel detectors ($256\times 256$ pixels), capable of tracking ionizing particles as isolated clusters of pixels. To efficiently analyze such clusters at potentially high rates, we introduce multiple randomized pattern recognition algorithms inspired by computer vision. Offering desirable probabilistic bounds on accuracy and complexity, the presented methods are well-suited for use in real-time applications, and some may even be modified to tackle trans-dimensional problems. In Timepix detectors, which do not support data-driven acquisition, they have been shown to correctly separate clusters of overlapping tracks. In Timepix3 detectors, simultaneous acquisition of Time-of-Arrival (ToA) and Time-over-Threshold (ToT) pixel data enables reconstruction of the depth, transitioning from 2D to 3D point clouds. The presented algorithms have been tested on simulated inputs, test beam data from the Heidelberg Ion therapy Center and the Super Proton Synchrotron and were applied to data acquired in the MoEDAL and ATLAS experiments at CERN., Comment: Presented at Connecting the Dots and Workshop on Intelligent Trackers (CTD/WIT 2019)

With the next-generation Timepix3 hybrid pixel detector, new possibilities and challenges have arisen. The Timepix3 segments active sensor area of 1.98 $cm^2$ into a square matrix of 256 x 256 pixels. In each pixel, the Time of Arrival (ToA, with a time binning of 1.56 $ns$) and Time over Threshold (ToT, energy) are measured simultaneously in a data-driven, i.e. self-triggered, read-out scheme. This contribution presents a framework for data acquisition, real-time clustering, visualization, classification and data saving. All of these tasks can be performed online, directly from multiple readouts through UDP protocol. Clusters are reconstructed on a pixel-by-pixel decision from the stream of not-necessarily chronologically sorted pixel data. To achieve quick spatial pixel-to-cluster matching, non-trivial data structures (quadtree) are utilized. Furthermore, parallelism (i.e multi-threaded architecture) is used to further improve the performance of the framework. Such real-time clustering offers the advantages of online filtering and classification of events. Versatility of the software is ensured by supporting all major operating systems (macOS, Windows and Linux) with both graphical and command-line interfaces. The performance of the real-time clustering and applied filtration methods are demonstrated using data from the Timepix3 network installed in the ATLAS and MoEDAL experiments at CERN., Comment: 4 pages, 3 figures. Proceedings of the CTD/WIT 2019, No. PROC-CTD19-105. Presented at Connecting the Dots and Workshop on Intelligent Trackers (CTD/WIT 2019) Instituto de F\'isica Corpuscular (IFIC), Valencia, Spain April 2-5, 2019

Isogeometric analysis is a recently developed computational approach that integrates finite element analysis directly into design described by non-uniform rational B-splines (NURBS). In this paper we show that price surfaces that occur in option pricing can be easily described by NURBS surfaces. For a class of stochastic volatility models, we develop a methodology for solving corresponding pricing partial integro-differential equations numerically by isogeometric analysis tools and show that a very small number of space discretization steps can be used to obtain sufficiently accurate results. Presented solution by finite element method is especially useful for practitioners dealing with derivatives where closed-form solution is not available.

It is well known in astronomy that propagating non-Gaussian prediction uncertainty in photometric redshift estimates is key to reducing bias in downstream cosmological analyses. Similarly, likelihood-free inference approaches, which are beginning to emerge as a tool for cosmological analysis, require a characterization of the full uncertainty landscape of the parameters of interest given observed data. However, most machine learning (ML) or training-based methods with open-source software target point prediction or classification, and hence fall short in quantifying uncertainty in complex regression and parameter inference settings. As an alternative to methods that focus on predicting the response (or parameters) $\mathbf{y}$ from features $\mathbf{x}$, we provide nonparametric conditional density estimation (CDE) tools for approximating and validating the entire probability density function (PDF) $\mathrm{p}(\mathbf{y}|\mathbf{x})$ of $\mathbf{y}$ given (i.e., conditional on) $\mathbf{x}$. As there is no one-size-fits-all CDE method, the goal of this work is to provide a comprehensive range of statistical tools and open-source software for nonparametric CDE and method assessment which can accommodate different types of settings and be easily fit to the problem at hand. Specifically, we introduce four CDE software packages in $\texttt{Python}$ and $\texttt{R}$ based on ML prediction methods adapted and optimized for CDE: $\texttt{NNKCDE}$, $\texttt{RFCDE}$, $\texttt{FlexCode}$, and $\texttt{DeepCDE}$. Furthermore, we present the $\texttt{cdetools}$ package, which includes functions for computing a CDE loss function for tuning and assessing the quality of individual PDFs, along with diagnostic functions. We provide sample code in $\texttt{Python}$ and $\texttt{R}$ as well as examples of applications to photometric redshift estimation and likelihood-free cosmological inference via CDE., Comment: 27 pages, 7 figures, 4 tables

We consider a nano-patterned planar wall consisting of a periodic array of stripes of width $L$, which are completely wet by liquid (contact angle $\theta=0$), separated by regions of width $D$ which are completely dry (contact angle $\theta=\pi)$. Using microscopic Density Functional Theory we show that in the presence of long-ranged dispersion forces, the wall-gas interface undergoes a first-order wetting transition, at bulk coexistence, as the separation $D$ is reduced to a value $D_w\propto\ln L$, induced by the bridging between neighboring liquid droplets. Associated with this is a line of pre-wetting transitions occurring off coexistence. By varying the stripe width $L$ we show that the pre-wetting line shows universal scaling behaviour and data collapse. This verifies predictions based on mesoscopic models for the scaling properties associated with finite-size effects at complete wetting including the logarithmic singular contribution to the surface free-energy.

In this paper we derive a generic decomposition of the option pricing formula for models with finite activity jumps in the underlying asset price process (SVJ models). This is an extension of the well-known result by Alos (2012) for Heston (1993) SV model. Moreover, explicit approximation formulas for option prices are introduced for a popular class of SVJ models - models utilizing a variance process postulated by Heston (1993). In particular, we inspect in detail the approximation formula for the Bates (1996) model with log-normal jump sizes and we provide a numerical comparison with the industry standard - Fourier transform pricing methodology. For this model, we also reformulate the approximation formula in terms of implied volatilities. The main advantages of the introduced pricing approximations are twofold. Firstly, we are able to significantly improve computation efficiency (while preserving reasonable approximation errors) and secondly, the formula can provide an intuition on the volatility smile behaviour under a specific SVJ model.

The research presented in this article provides an alternative option pricing approach for a class of rough fractional stochastic volatility models. These models are increasingly popular between academics and practitioners due to their surprising consistency with financial markets. However, they bring several challenges alongside. Most noticeably, even simple non-linear financial derivatives as vanilla European options are typically priced by means of Monte-Carlo (MC) simulations which are more computationally demanding than similar MC schemes for standard stochastic volatility models. In this paper, we provide a proof of the prediction law for general Gaussian Volterra processes. The prediction law is then utilized to obtain an adapted projection of the future squared volatility -- a cornerstone of the proposed pricing approximation. Firstly, a decomposition formula for European option prices under general Volterra volatility models is introduced. Then we focus on particular models with rough fractional volatility and we derive an explicit semi-closed approximation formula. Numerical properties of the approximation for a popular model -- the rBergomi model -- are studied and we propose a hybrid calibration scheme which combines the approximation formula alongside MC simulations. This scheme can significantly speed up the calibration to financial markets as illustrated on a set of AAPL options.

Random forests is a common non-parametric regression technique which performs well for mixed-type unordered data and irrelevant features, while being robust to monotonic variable transformations. Standard random forests, however, do not efficiently handle functional data and runs into a curse-of dimensionality when presented with high-resolution curves and surfaces. Furthermore, in settings with heteroskedasticity or multimodality, a regression point estimate with standard errors do not fully capture the uncertainty in our predictions. A more informative quantity is the conditional density p(y | x) which describes the full extent of the uncertainty in the response y given covariates x. In this paper we show how random forests can be efficiently leveraged for conditional density estimation, functional covariates, and multiple responses without increasing computational complexity. We provide open-source software for all procedures with R and Python versions that call a common C++ library., Comment: arXiv admin note: substantial text overlap with arXiv:1804.05753

Continuous-time assessments of game outcomes in sports have become increasingly common in the last decade. In American football, only discrete-time estimates of play value were possible, since the most advanced public football datasets were recorded at the play-by-play level. While measures such as expected points and win probability are useful for evaluating football plays and game situations, there has been no research into how these values change throughout the course of a play. In this work, we make two main contributions: First, we introduce a general framework for continuous-time within-play valuation in the National Football League using player-tracking data. Our modular framework incorporates several modular sub-models, to easily incorporate recent work involving player tracking data in football. Second, we use a long short-term memory recurrent neural network to construct a ball-carrier model to estimate how many yards the ball-carrier is expected to gain from their current position, conditional on the locations and trajectories of the ball-carrier, their teammates and opponents. Additionally, we demonstrate an extension with conditional density estimation so that the expectation of any measure of play value can be calculated in continuous-time, which was never before possible at such a granular level.

Complex phenomena in engineering and the sciences are often modeled with computationally intensive feed-forward simulations for which a tractable analytic likelihood does not exist. In these cases, it is sometimes necessary to estimate an approximate likelihood or fit a fast emulator model for efficient statistical inference; such surrogate models include Gaussian synthetic likelihoods and more recently neural density estimators such as autoregressive models and normalizing flows. To date, however, there is no consistent way of quantifying the quality of such a fit. Here we propose a statistical framework that can distinguish any arbitrary misspecified model from the target likelihood, and that in addition can identify with statistical confidence the regions of parameter as well as feature space where the fit is inadequate. Our validation method applies to settings where simulations are extremely costly and generated in batches or "ensembles" at fixed locations in parameter space. At the heart of our approach is a two-sample test that quantifies the quality of the fit at fixed parameter values, and a global test that assesses goodness-of-fit across simulation parameters. While our general framework can incorporate any test statistic or distance metric, we specifically argue for a new two-sample test that can leverage any regression method to attain high power and provide diagnostics in complex data settings., Comment: 22 pages, 9 Figures, 2 Tables

We study the significance of non-Gaussianity in the likelihood of weak lensing shear two-point correlation functions, detecting significantly non-zero skewness and kurtosis in one-dimensional marginal distributions of shear two-point correlation functions in simulated weak lensing data. We examine the implications in the context of future surveys, in particular LSST, with derivations of how the non-Gaussianity scales with survey area. We show that there is no significant bias in one-dimensional posteriors of $\Omega_{\rm m}$ and $\sigma_{\rm 8}$ due to the non-Gaussian likelihood distributions of shear correlations functions using the mock data ($100$ deg$^{2}$). We also present a systematic approach to constructing approximate multivariate likelihoods with one-dimensional parametric functions by assuming independence or more flexible non-parametric multivariate methods after decorrelating the data points using principal component analysis (PCA). While the use of PCA does not modify the non-Gaussianity of the multivariate likelihood, we find empirically that the one-dimensional marginal sampling distributions of the PCA components exhibit less skewness and kurtosis than the original shear correlation functions.Modeling the likelihood with marginal parametric functions based on the assumption of independence between PCA components thus gives a lower limit for the biases. We further demonstrate that the difference in cosmological parameter constraints between the multivariate Gaussian likelihood model and more complex non-Gaussian likelihood models would be even smaller for an LSST-like survey. In addition, the PCA approach automatically serves as a data compression method, enabling the retention of the majority of the cosmological information while reducing the dimensionality of the data vector by a factor of $\sim$5., Comment: 16 pages, 10 figures, published MNRAS

Using mesoscopic interfacial models and microscopic density functional theory we study fluid adsorption at a dry wall decorated with three completely wet stripes of width $L$ separated by distances $D_1$ and $D_2$. The stripes interact with the fluid with long-range forces inducing a large finite-size contribution to the surface free-energy. We show that this non-extensive free-energy contribution scales with $\ln L$ and drives different types of bridging transition corresponding to the merging of liquid drops adsorbed at neighbouring wetting stripes when the separation between them is molecularly small. We determine the surface phase diagram and show that this exhibits two triple points, where isolated drops, double drops and triple drops coexist. For the symmetric case, $D_1=D_2\equiv D$, our results also confirm that the equilbrium droplet configuration always has the symmetry of the substrate corresponding to either three isolated drops when $D$ is large or a single triple drop when $D$ is small; however, symmetry broken configurations do occur in a metastable part of the phase diagram which lies very close to the equilibrium bridging phase boundary. Implications for phase transitions on other types of patterned surface are considered.

MoEDAL is designed to identify new physics in the form of stable or pseudostable highly ionizing particles produced in high-energy Large Hadron Collider (LHC) collisions. Here we update our previous search for magnetic monopoles in Run 2 using the full trapping detector with almost four times more material and almost twice more integrated luminosity. For the first time at the LHC, the data were interpreted in terms of photon-fusion monopole direct production in addition to the Drell-Yan-like mechanism. The MoEDAL trapping detector, consisting of 794 kg of aluminum samples installed in the forward and lateral regions, was exposed to 4.0 fb$^{-1}$ of 13 TeV proton-proton collisions at the LHCb interaction point and analyzed by searching for induced persistent currents after passage through a superconducting magnetometer. Magnetic charges equal to or above the Dirac charge are excluded in all samples. Monopole spins 0, 1/2 and 1 are considered and both velocity-independent and -dependent couplings are assumed. This search provides the best current laboratory constraints for monopoles with magnetic charges ranging from two to five times the Dirac charge., Comment: 7 pages REVTeX 4, 11 figures, 1 table, matches published version in Physical Review Letters

PAN is a scientific instrument suitable for deep space and interplanetary missions. It can precisely measure and monitor the flux, composition, and direction of highly penetrating particles ($> \sim$100 MeV/nucleon) in deep space, over at least one full solar cycle (~11 years). The science program of PAN is multi- and cross-disciplinary, covering cosmic ray physics, solar physics, space weather and space travel. PAN will fill an observation gap of galactic cosmic rays in the GeV region, and provide precise information of the spectrum, composition and emission time of energetic particle originated from the Sun. The precise measurement and monitoring of the energetic particles is also a unique contribution to space weather studies. PAN will map the flux and composition of penetrating particles, which cannot be shielded effectively, precisely and continuously, providing valuable input for the assessment of the related health risk, and for the development of an adequate mitigation strategy. PAN has the potential to become a standard on-board instrument for deep space human travel. PAN is based on the proven detection principle of a magnetic spectrometer, but with novel layout and detection concept. It will adopt advanced particle detection technologies and industrial processes optimized for deep space application. The device will require limited mass (~20 kg) and power (~20 W) budget. Dipole magnet sectors built from high field permanent magnet Halbach arrays, instrumented in a modular fashion with high resolution silicon strip detectors, allow to reach an energy resolution better than 10\% for nuclei from H to Fe at 1 GeV/n.

The electrical resistance, Hall resistance and thermoelectric power of the Ising-like antiferromagnet UIrSi3 were measured as functions of temperature and magnetic field. We have observed that the unequivocally different characters of first-order and second-order magnetic phase transitions lead to distinctly different magnetotransport properties in the neighborhood of corresponding critical temperatures and magnetic fields, respectively. The magnetic contributions to the electrical and Hall resistivity in the antiferromagnetic state, and the polarized and normal regimes of paramagnetic state are driven by different underlying mechanisms. Results of detailed measurements of magnetotransport in the vicinity of the tricritical point reveal that the Hall-resistivity steps at phase transitions change polarity just at this point. The jumps in field dependences of specific heat, electrical resistivity, Hall resistivity and Seebeck coefficient at the first-order metamagnetic transitions indicate a Fermi surface reconstruction, which is characteristic of a magnetic-field induced Lifshitz transition. The presented results emphasize the usefulness of measurements of electrical- and thermal-transport properties as sensitive probes of magnetic phase transformations in antiferromagnets sometimes hardly detectable by other methods.

In basketball and hockey, state-of-the-art player value statistics are often variants of Adjusted Plus-Minus (APM). But APM hasn't had the same impact in soccer, since soccer games are low scoring with a low number of substitutions. In soccer, perhaps the most comprehensive player value statistics come from video games, and in particular FIFA. FIFA ratings combine the subjective evaluations of over 9000 scouts, coaches, and season-ticket holders into ratings for over 18,000 players. This paper combines FIFA ratings and APM into a single metric, which we call Augmented APM. The key idea is recasting APM into a Bayesian framework, and incorporating FIFA ratings into the prior distribution. We show that Augmented APM predicts better than both standard APM and a model using only FIFA ratings. We also show that Augmented APM decorrelates players that are highly collinear.

The metamagnetic transition between the antiferromagnetic and paramagnetic state in UIrGe has been studied at various temperatures by magnetization, heat capacity and magnetocaloric-effect measurements on a single crystal in static and pulsed magnetic fields applied along the orthorhombic b-axis. A first-order transition is observed at temperatures below 13 K and a second-order one at higher temperatures up to the N\'eel temperature (TN = 16.5 K). The first-order transition is accompanied by a dramatic increase of the Sommerfeld coefficient. Magnetization measurements extended to the paramagnetic range revealed an anomalous S-shape (inflection point at a magnetic field Hm) in magnetization isotherms at temperatures above 13 K and a temperature dependence of susceptibility with a maximum at Tmax well above TN. The lines representing the temperature-induced evolution of Hm and field-induced evolution of Tmax, respectively, are bound for the point in the magnetic phase diagram at which the order of metamagnetic transition changes. A tentative scenario explaining these anomalies by antiferromagnetic correlations or short-range order in the paramagnetic state is discussed., Comment: Accepted to PRB

Approximate Bayesian Computation (ABC) is typically used when the likelihood is either unavailable or intractable but where data can be simulated under different parameter settings using a forward model. Despite the recent interest in ABC, high-dimensional data and costly simulations still remain a bottleneck in some applications. There is also no consensus as to how to best assess the performance of such methods without knowing the true posterior. We show how a nonparametric conditional density estimation (CDE) framework, which we refer to as ABC-CDE, help address three nontrivial challenges in ABC: (i) how to efficiently estimate the posterior distribution with limited simulations and different types of data, (ii) how to tune and compare the performance of ABC and related methods in estimating the posterior itself, rather than just certain properties of the density, and (iii) how to efficiently choose among a large set of summary statistics based on a CDE surrogate loss. We provide theoretical and empirical evidence that justify ABC-CDE procedures that {\em directly} estimate and assess the posterior based on an initial ABC sample, and we describe settings where standard ABC and regression-based approaches are inadequate.

Random forests is a common non-parametric regression technique which performs well for mixed-type data and irrelevant covariates, while being robust to monotonic variable transformations. Existing random forest implementations target regression or classification. We introduce the RFCDE package for fitting random forest models optimized for nonparametric conditional density estimation, including joint densities for multiple responses. This enables analysis of conditional probability distributions which is useful for propagating uncertainty and of joint distributions that describe relationships between multiple responses and covariates. RFCDE is released under the MIT open-source license and can be accessed at https://github.com/tpospisi/rfcde . Both R and Python versions, which call a common C++ library, are available., Comment: Fix URL in Arxiv abstract

We present an extensive study of the ferromagnetic heavy fermion compound U$_4$Ru$_7$Ge$_6$. Measurements of electrical resistivity, specific heat and magnetic properties show that U$_4$Ru$_7$Ge$_6$ orders ferromagnetically at ambient pressure with a Curie temperature $T_{C} = 6.8 \pm 0.3$ K. The low temperature magnetic behavior of this soft ferromagnet is dominated by the excitation of gapless spin-wave modes. Our results on the transport properties of U$_4$Ru$_7$Ge$_6$ under pressures up to $2.49$ GPa suggest that U$_4$Ru$_7$Ge$_6$ has a putative ferromagnetic quantum critical point (QCP) at $P_c \approx 1.7 \pm 0.02$ GPa. In the ordered phase, ferromagnetic magnons scatter the conduction electrons and give rise to a well defined power law temperature dependence in the resistivity. The coefficient of this term is related to the spin-wave stiffness and measurements of the very low temperature resistivity allow to accompany the behavior of this quantity as the the ferromagnetic QCP is approached. We find that the spin-wave stiffness decreases with increasing pressure implying that the transition to the non-magnetic Fermi liquid state is driven by the softening of the magnons. The observed quantum critical behavior of the magnetic stiffness is consistent with the influence of disorder in our system. At quantum criticality ($P = P_c \approx 1.7 \pm 0.02$ GPa), the resistivity shows the behavior expected for an itinerant metallic system near a ferromagnetic QCP., Comment: 10 pages, 1 table, 13 figures

The methodology of single crystal growth of metastable $\beta$-Ti alloy TIMETAL LCB in an optical floating zone furnace is presented in this paper. Chemical compositions of both precursor material and single crystals were checked. It was found that the concentration of base alloying elements did not change significantly during the growth process, while the concentrations of interstitial elements O and N increased. DSC measurement determined that this concentration shift has a slight impact on ongoing phase transformations, as in the single-crystalline material peak associated with $\alpha$ phase precipitation moves by a few degrees to a lower temperature and peak attributed to diffusion controlled growth of $\omega$ particles shifts to a higher temperature. X-ray reciprocal space maps were measured and their simulation showed that the single crystal has a mosaic structure with mean size of mosaic blocks of approximately 60 nm.

Nanosized particles of $\omega$ phase in a $\beta$-Ti alloy were investigated by small-angle X-ray scattering using synchrotron radiation. We demonstrated that the particles are spontaneously weakly ordered in a three-dimensional cubic array along the $\langle 100\rangle$-directions in the $\beta$-Ti matrix. The small-angle scattering data fit well to a three-dimensional short-range-order model; from the fit we determined the evolution of the mean particle size and mean distance between particles during ageing. The self-ordering of the particles is explained by elastic interaction between the particles, since the relative positions of the particles coincide with local minima of the interaction energy. We performed numerical Monte Carlo simulation of the particle ordering and we obtained a good agreement with the experimental data.