Discussion

In this contribution aspects of inter-sample input signal behavior are examined. The starting point is that parametric identication always is performed on basis of discrete-time data. This is valid for identication of discrete-time models as well as continuous-time models. The usual assumptions on the input signal are; i) it is band-limited, ii) it is piecewise constant or iii) it is piecewise linear. One point made in this paper is that if a discrete-time model is used, the best possible (in the model structure) adjustment to data is made. This is independent of the assumption on the input signal. However, a transformation of the obtained discrete model to a continuous one is not possible without additional assumptions on the input signal. The other point made is that the frequency functions of the discrete models very well coincides with the frequency functions of the discretized continuous time models and the continuous time transfer function fitted in the frequency domain

W Production in an Improved Parton-Shower Approach

In the description of the production properties of gauge bosons (W+/W-, Z0, gamma) at colliders, the lowest-order graph normally is not sufficient. The contributions of higher orders can be introduced either by an explicit order-by-order matrix-element calculation, by a resummation procedure or by a parton-shower algorithm. Each approach has its advantages and disadvantages. We here introduce a method that allows the parton-shower algorithm to be augmented by higher-order information, thereby offering an economical route to a description of all event properties. It is tested by comparing with the pT spectrum of W bosons at the Tevatron.Comment: 1+9 pages, 5 eps-figures, submitted to Phys. Lett.

Pythia version 7-0.0 - a proof-of-concept version

This document describes the first proof-of-concept version of the Pythia7 program. Pythia7 is a complete re-write of the Pythia program in C++. It is mainly intended to be a replacement for the `Lund' family of event generators, but is also a toolkit with a structure suitable for implementing any event generator model. In this document, the structure of the program is presented both from the user and the developer point of view. It is not intended to be a complete manual, but together with the documentation provided in the distribution, it should be sufficient to start working with the program.Comment: 39 pages, 3 figure

Improving the efficacy of enuresis alarm treatment through early prediction of treatment outcome: a machine learning approach

IntroductionBedwetting, also known as enuresis, is the second most common chronic health problem among children and it affects their everyday life negatively. A first-line treatment option is the enuresis alarm. This method entails the child being awoken by a detector and alarm unit upon urination at night, thereby changing their arousal mechanisms and potentially curing them after 6–8 weeks of consistent therapy. The enuresis alarm treatment has a reported success rate above 50% but requires significant effort from the families involved. Additionally, there is a challenge in identifying early indicators of successful treatment.MethodsThe alarm treatment has been further developed by the company Pjama AB, which, in addition to the alarm, offers a mobile application where users provides data about the patient and information regarding each night throughout the treatment. The wet and dry nights are recorded, in addition to the actual timing of the bedwetting incidents. We used the machine learning model random forest to see if predictions of treatment outcome could be made in early stages of treatment and shorten the evaluation time based on data from 611 patients. This was carried out by using and analyzing data from patients who had used the Pjama application. The patients were split into training and testing groups to evaluate to what extent the algorithm could make predictions every day about whether a patient’s treatment would be successful, partially successful, or unsuccessful.ResultsThe results show that a large number of patient outcomes can already be predicted accurately in the early stages of treatment.DiscussionAccurate predictions enable the correct measures to be taken earlier in the treatment, including increasing motivation, adding pharmacotherapy, or terminating treatment. This has the potential to shorten the treatment in general, and to detect patients who will not respond to the treatment early on, which in turn can improve the lives of children suffering from enuresis. The results show great potential in making the treatment of enuresis more efficient

Lifestyle Factors Are Important Contributors to Subjective Memory Complaints among Patients without Objective Memory Impairment or Positive Neurochemical Biomarkers for Alzheimer’s Disease

Background/Aims: Many patients presenting to a memory disorders clinic for subjective memory complaints do not show objective evidence of decline on neuropsychological data, have nonpathological biomarkers for Alzheimer’s disease, and do not develop a neurodegenerative disorder. Lifestyle variables, including subjective sleep problems and stress, are factors known to affect cognition. Little is known about how these factors contribute to patients’ subjective sense of memory decline. Understanding how lifestyle factors are associated with the subjective sense of failing memory that causes patients to seek a formal evaluation is important both for diagnostic workup purposes and for finding appropriate interventions and treatment for these persons, who are not likely in the early stages of a neurodegenerative disease. The current study investigated specific lifestyle variables, such as sleep and stress, to characterize those patients that are unlikely to deteriorate cognitively. Methods: Two hundred nine patients (mean age 58 years) from a university hospital memory disorders clinic were included. Results: Sleep problems and having much to do distinguished those with subjective, but not objective, memory complaints and non-pathological biomarkers for Alzheimer’s disease. Conclusions: Lifestyle factors including sleep and stress are useful in characterizing subjective memory complaints from objective problems. Inclusion of these variables could potentially improve health care utilization efficiency and guide interventions

High-Energy-Physics Event Generation with PYTHIA 6.1

PYTHIA version 6 represents a merger of the PYTHIA 5, JETSET 7 and SPYTHIA programs, with many improvements. It can be used to generate high-energy-physics `events', i.e. sets of outgoing particles produced in the interactions between two incoming particles. The objective is to provide as accurate as possible a representation of event properties in a wide range of reactions. The underlying physics is not understood well enough to give an exact description; the programs therefore contain a combination of analytical results and various models. The emphasis in this article is on new aspects, but a few words of general introduction are included. Further documentation is available on the web.Comment: 1 + 27 pages, submitted to Computer Physics Communication

Vulnerability analysis of android auto infotainment apps

With over 2 billion active mobile users and a large array of features, Android is the most popular operating system for mobile devices. Android Auto allows such devices to connect with an in-car compatible infotainment system, and it became a popular choice as well. However, as the trend for connecting car dashboard to the Internet or other devices grows, so does the potential for security threats. In this paper, a set of potential security threats are identified, and a static analyzer for the Android Auto infotainment system is presented. All the infotainment apps available in Google Play Store have been checked against that list of possible exposure scenarios. Results show that almost 80% of the apps are potentially vulnerable, out of which 25% poses security threats related to execution of JavaScript

Interleaved Parton Showers and Tuning Prospects

General-purpose Monte Carlo event generators have become important tools in particle physics, allowing the simulation of exclusive hadronic final states. In this article we examine the Pythia 8 generator, in particular focusing on its parton-shower algorithms. Some relevant new additions to the code are introduced, that should allow for a better description of data. We also implement and compare with 2 to 3 real-emission QCD matrix elements, to check how well the shower algorithm fills the phase space away from the soft and collinear regions. A tuning of the generator to Tevatron data is performed for two PDF sets and the impact of first new LHC data is examined