The Monte Carlo Program KoralW version 1.51 and The Concurrent Monte Carlo KoralW&YFSWW3 with All Background Graphs and First Order Corrections to W-Pair Production

The version 1.51 of the Monte Carlo (MC) program KoralW for all $e^+e^-\to f_1\bar f_2 f_3\bar f_4$ processes is presented. The most important change since the previous version 1.42 is the facility for writing MC events on the mass storage device and re-processing them later on. In the re-processing one may modify parameters of the Standard Model in order to fit them to experimental data. Another important new feature is a possibility of including complete ${\cal O}(\alpha)$ corrections to double-resonant W-pair component-processes in addition to all background (non-WW) graphs. The inclusion is done with the help of the YFSWW3 MC event generator for fully exclusive differential distributions (event-per-event). Technically, it is done in such a way that YFSWW3 runs concurrently with KoralW as a separate slave process, reading momenta of the MC event generated by KoralW and returning the correction weight to KoralW. KoralW introduces the ${\cal O}(\alpha)$ correction using this weight, and finishes processing the event (rejection due to total MC weight, hadronization, etc.). The communication between KoralW and YFSWW3 is done with the help of the FIFO facility of the UNIX/Linux operating system. This does not require any modifications of the FORTRAN source codes. The resulting Concurrent MC event generator KoralW&YFSWW3 looks from the user's point of view as a regular single MC event generator with all the standard features.Comment: 8 figures, 5 tables, submitted to Comput. Phys. Commu

Multiferroic and Ferroic Topological Order in Ligand-Functionalized Germanene and Arsenene

Two-dimensional (2D) materials that exhibit ferroelectric, ferromagnetic, or topological order have been a major focal topic of nanomaterials research in recent years. The latest efforts in this field explore 2D quantum materials that host multiferroic or concurrent ferroic and topological order. We present a computational discovery of multiferroic state with coexisting ferroelectric and ferromagnetic order in recently synthesized CH2OCH3-functionalized germanene. We show that an electric-field-induced rotation of the ligand CH2OCH3 molecule can serve as the driving mechanism to switch the electric polarization of the ligand molecule, while unpassivated Ge p(z) orbits generate ferromagnetism. Our study also reveals coexisting ferroelectric and topological order in ligand-functionalized arsenene, which possesses a switchable electric polarization and a Dirac transport channel. These findings offer insights into the fundamental physics underlying these coexisting quantum orders and open avenues for achieving states of matter with multiferroic or ferroic-topological order in 2D-layered materials for innovative memory or logic device implementations

Pickup usability dominates: a brief history of mobile text entry research and adoption

Text entry on mobile devices (e.g. phones and PDAs) has been a research challenge since devices shrank below laptop size: mobile devices are simply too small to have a traditional full-size keyboard. There has been a profusion of research into text entry techniques for smaller keyboards and touch screens: some of which have become mainstream, while others have not lived up to early expectations. As the mobile phone industry moves to mainstream touch screen interaction we will review the range of input techniques for mobiles, together with evaluations that have taken place to assess their validity: from theoretical modelling through to formal usability experiments. We also report initial results on iPhone text entry speed

Attention, predictive learning, and the inverse base-rate effect: Evidence from event-related potentials

We report the first electrophysiological investigation of the inverse base-rate effect (IBRE), a robust non-rational bias in predictive learning. In the IBRE, participants learn that one pair of symptoms (AB) predicts a frequently occurring disease, whilst an overlapping pair of symptoms (AC) predicts a rarely occurring disease. Participants subsequently infer that BC predicts the rare disease, a non-rational decision made in opposition to the underlying base rates of the two diseases. Error-driven attention theories of learning state that the IBRE occurs because C attracts more attention than B. On the basis of this account we predicted and observed the occurrence of brain potentials associated with visual attention: a posterior Selection Negativity, and a concurrent anterior Selection Positivity, for C vs. B in a post-training test phase. Error-driven attention theories further predict no Selection Negativity, Selection Positivity or IBRE, for control symptoms matched on frequency to B and C, but for which there was no shared symptom (A) during training. These predictions were also confirmed, and this confirmation discounts alternative explanations of the IBRE based on the relative novelty of B and C. Further, we observed higher response accuracy for B alone than for C alone; this dissociation of response accuracy (B>C) from attentional allocation (C>B) discounts the possibility that the observed attentional difference was caused by the difference in response accuracy

On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling

A multi-fidelity surrogate model for highly nonlinear multiscale problems is proposed. It is based on the introduction of two different surrogate models and an adaptive on-the-fly switching. The two concurrent surrogates are built incrementally starting from a moderate set of evaluations of the full order model. Therefore, a reduced order model (ROM) is generated. Using a hybrid ROM-preconditioned FE solver, additional effective stress-strain data is simulated while the number of samples is kept to a moderate level by using a dedicated and physics-guided sampling technique. Machine learning (ML) is subsequently used to build the second surrogate by means of artificial neural networks (ANN). Different ANN architectures are explored and the features used as inputs of the ANN are fine tuned in order to improve the overall quality of the ML model. Additional ANN surrogates for the stress errors are generated. Therefore, conservative design guidelines for error surrogates are presented by adapting the loss functions of the ANN training in pure regression or pure classification settings. The error surrogates can be used as quality indicators in order to adaptively select the appropriate -- i.e. efficient yet accurate -- surrogate. Two strategies for the on-the-fly switching are investigated and a practicable and robust algorithm is proposed that eliminates relevant technical difficulties attributed to model switching. The provided algorithms and ANN design guidelines can easily be adopted for different problem settings and, thereby, they enable generalization of the used machine learning techniques for a wide range of applications. The resulting hybrid surrogate is employed in challenging multilevel FE simulations for a three-phase composite with pseudo-plastic micro-constituents. Numerical examples highlight the performance of the proposed approach