Constraining New Physics with a Positive or Negative Signal of Neutrino-less Double Beta Decay

We investigate numerically how accurately one could constrain the strengths of different short-range contributions to neutrino-less double beta decay in effective field theory. Depending on the outcome of near-future experiments yielding information on the neutrino masses, the corresponding bounds or estimates can be stronger or weaker. A particularly interesting case, resulting in strong bounds, would be a positive signal of neutrino-less double beta decay that is consistent with complementary information from neutrino oscillation experiments, kinematical determinations of the neutrino mass, and measurements of the sum of light neutrino masses from cosmological observations. The keys to more robust bounds are improvements of the knowledge of the nuclear physics involved and a better experimental accuracy.Comment: 23 pages, 3 figures. Minor changes. Matches version published in JHE

Deep Kernels for Optimizing Locomotion Controllers

Sample efficiency is important when optimizing parameters of locomotion controllers, since hardware experiments are time consuming and expensive. Bayesian Optimization, a sample-efficient optimization framework, has recently been widely applied to address this problem, but further improvements in sample efficiency are needed for practical applicability to real-world robots and high-dimensional controllers. To address this, prior work has proposed using domain expertise for constructing custom distance metrics for locomotion. In this work we show how to learn such a distance metric automatically. We use a neural network to learn an informed distance metric from data obtained in high-fidelity simulations. We conduct experiments on two different controllers and robot architectures. First, we demonstrate improvement in sample efficiency when optimizing a 5-dimensional controller on the ATRIAS robot hardware. We then conduct simulation experiments to optimize a 16-dimensional controller for a 7-link robot model and obtain significant improvements even when optimizing in perturbed environments. This demonstrates that our approach is able to enhance sample efficiency for two different controllers, hence is a fitting candidate for further experiments on hardware in the future.Comment: (Rika Antonova and Akshara Rai contributed equally

Multi Detector Fusion of Dynamic TOA Estimation using Kalman Filter

In this paper, we propose fusion of dynamic TOA (time of arrival) from multiple non-coherent detectors like energy detectors operating at sub-Nyquist rate through Kalman filtering. We also show that by using multiple of these energy detectors, we can achieve the performance of a digital matched filter implementation in the AWGN (additive white Gaussian noise) setting. We derive analytical expression for number of energy detectors needed to achieve the matched filter performance. We demonstrate in simulation the validity of our analytical approach. Results indicate that number of energy detectors needed will be high at low SNRs and converge to a constant number as the SNR increases. We also study the performance of the strategy proposed using IEEE 802.15.4a CM1 channel model and show in simulation that two sub-Nyquist detectors are sufficient to match the performance of digital matched filter

On the Reliability of LTE Random Access: Performance Bounds for Machine-to-Machine Burst Resolution Time

Random Access Channel (RACH) has been identified as one of the major bottlenecks for accommodating massive number of machine-to-machine (M2M) users in LTE networks, especially for the case of burst arrival of connection requests. As a consequence, the burst resolution problem has sparked a large number of works in the area, analyzing and optimizing the average performance of RACH. However, the understanding of what are the probabilistic performance limits of RACH is still missing. To address this limitation, in the paper, we investigate the reliability of RACH with access class barring (ACB). We model RACH as a queuing system, and apply stochastic network calculus to derive probabilistic performance bounds for burst resolution time, i.e., the worst case time it takes to connect a burst of M2M devices to the base station. We illustrate the accuracy of the proposed methodology and its potential applications in performance assessment and system dimensioning.Comment: Presented at IEEE International Conference on Communications (ICC), 201

The BFOQ Defense: Title VII’s Concession to Gender Discrimination

Should the BFOQ exception still exist? Because permitting discrimination under Title VII seems fundamentally contrary to the anti-discrimination purpose of the statute, this article questions whether the BFOQ defense is consistent with the aims of Title VII or whether, in actuality, the defense undermines the Act\u27s effectiveness by providing a loophole for employers to participate in the discriminatory practices Title VII seeks to forbid

Closing the Book on Jusen: An Account of the Bad Loan Crisis and a New Chapter for Securitization in Japan

University business incubators (UBIs) are organizations that provide new startup companies with a support environment. However, there are split opinions on the UBIs’ contributions to the startups and the regional economy and, consequently, there are also split opinions on  how to assess UBI performance. According to the resource-based view (RBV), a company’s competitive advantage results from the various resources the company has access to. The biotechnology industry is characterized by high research intensity, weak entrepreneurial and managerial skills of the entrepreneur, huge capital requirements, and long product  evelopment approval processes. Previous research has showed that these characteristics imply certain challenges for new biotech ventures. In this study, these industry specific characteristic and challenges were believed to affect what constitutes successful bioincubation and how bio-incubators’ performance should be assessed. The purpose of this report is, thus, to examine how bio-incubator performance can, and should be, assessed. An existing framework for assessing UBI performance is used as a basis for performing  emistructured interviews with 18 incubator managers in order to examine what performance indicators are perceived as robust for assessing bio-incubator performance. The findings show that the value contributions of bio-incubators mainly include space and network provision, support services, and coaching. The perceived value contributions, in combination with the perceived challenges, imply that it is particularly appropriate to assess bio-incubators performance in terms of Job Creation, Economy Enhancement, Access to Funds, and the Incubator Offer and Internal Environment. However, Job Creation and Economy Enhancement are closely related and are therefore suggested to be merged into a single performance indicator. Hardware and Services, on the other hand, seems to be less relevant for assessing bio-incubator performance as it depends on the incubator’s strategy. The study concludes that there are additional ways of assessing bio-incubator performance, such as shortened time to graduation, links with universities, and the flexibility of the incubator. Further research may include the entrepreneurs’ point of view or use the approach of this study to examine incubator performance in other high-technology industries

A Listing of Current Books

Abstract—We investigate cooperative strategies for relay-aided multi-source multi-destination wireless networks with backhaul support. Each source multicasts information to all destinations using a shared relay. We study cooperative strategies based on different network coding (NC) schemes, namely, finite field NC (FNC), linear NC (LNC), and lattice coding. To further exploit the backhaul connection, we also propose NC-based beam-forming (NBF). We measure the performance in term of achievable rates over Gaussian channels and observe significant gains over a benchmark scheme. The benefit of using backhaul is also clearly demonstrated in most of scenarios. I

Sales and Title and the Proposed Code

Electric powertrain faults that could occur during normal driving can affect the dynamic behaviour of the vehicle and might result in significant course deviations. The severity depends both on the characteristics of the fault itself as well as on how sensitive the vehicle reacts to this type of fault. In this work, a sensitivity study is conducted on the effects of vehicle design parameters, such as geometries and tyre characteristics, and fault characteristics. The vehicle specifications are based on three different parameter sets representing a small city car, a medium-sized sedan and a large passenger car. The evaluation criteria cover the main motions of the vehicle, i.e. longitudinal velocity difference, lateral offset and side slip angle on the rear axle as indicator of the directional stability. A design of experiments approach is applied and the influence on the course deviation is analysed for each studied parameter separately and for all first order combinations. Vehicle parameters of high sensitivity have been found for each criterion. The mass factor is highly relevant for all three motions, while the additional factors wheel base, track width, yaw inertia and vehicle velocity are mainly influencing the lateral and the yaw motion. Changes in the tyre parameters are in general less significant than the vehicle parameters. Among the tyre parameters, the stiffness factor of the tyres on the rear axle has the major influence resulting in a reduction of the course deviation for a stiffer tyre. The fault amplitude is an important fault parameter, together with the fault starting gradient and number of wheels with fault. In this study, it was found that a larger vehicle representing a SUV is more sensitive to these types of faults. To conclude, the result of an electric powertrain fault can cause significant course deviations for all three vehicle types studied.QC 20140909</p

Liking to be in America: Puerto Rico’s Quest for Difference in the United States

The interaction between wind turbines in simple wind farm layouts is investigated with the purpose of observing the influence of wake loss phenomenon on the energy production of downwind turbines. Following an intensive exploration stage about wind farm aerodynamics and wake modeling subjects, several tests cases are designed to represent various wind farm configurations, consisting of different number of wind turbines. These cases are simulated by using DNV GL WindFarmer software which provides the opportunity of performing simulations with two different wake modeling techniques, namely Modified PARK and Eddy Viscosity. Various terrain and ambient turbulence intensity conditions are considered during the test cases. Also three different turbine types having different hub heights, rotor diameters and power-thrust coefficients are used in order to observe the effect of turbine characteristics on wake formation. Besides WindFarmer, WAsP and MATLAB tools are used in some simulation stages in order to generate input data such as wind and terrain conditions or farm layout configurations; and to process the data obtained in the end of these test cases. Simulations which are executed in the presence of a predominant wind direction from a narrow direction bin indicate that, even though there exists no significant interaction between the turbines placed in abreast configurations, successive turbine rows affect each other strongly due to the existence of the wake region of upwind turbines. It is observed that downwind spacing between turbine rows required to recover wake deficit up to a certain level changes depending on terrain and ambient turbulence intensity conditions together with turbine characteristics. For instance increasing surface roughness length (or ambient turbulence intensity) of a given site by keeping all the other parameters constant can provide up to 20% (or 30%) decrease in the required downstream distance to reduce wake loss to 5% level in a simple tandem layout consisting of two wind turbines. Further test cases are executed with various numbers of wind turbines in different configurations to observe the effect of partial, full and multiple wake regions on total farm efficiency. The results obtained from these cases are used in order to have a comparison between several farm layouts and evaluate their advantages and drawbacks

Are We Insane? The Quest for Proportionality in the Discovery Rules of the Federal Rules of Civil Procedure

Atrial fibrillation is a common heart arrhythmia which is characterized by a missing or irregular contraction of the atria. The disease is a risk factor for other more serious diseases and the total medical costs in society are extensive. Therefore it would be beneficial to improve and optimize the prevention and detection of the disease.   Pulse palpation and heart auscultation can facilitate the detection of atrial fibrillation clinically, but the diagnosis is generally confirmed by an ECG examination. Today there are several algorithms that detect atrial fibrillation by analysing an ECG. A common method is to study the heart rate variability (HRV) and by different types of statistical calculations find episodes of atrial fibrillation which deviates from normal sinus rhythm.   Two algorithms for detection of atrial fibrillation have been evaluated in Matlab. One is based on the coefficient of variation and the other uses a logistic regression model. Training and testing of the algorithms were done with data from the Physionet MIT database. Several steps of signal processing were used to remove different types of noise and artefacts before the data could be used.   When testing the algorithms, the CV algorithm performed with a sensitivity of 91,38%, a specificity of 93,93% and accuracy of 92,92%, and the results of the logistic regression algorithm was a sensitivity of 97,23%, specificity of 93,79% and accuracy of 95,39%. The logistic regression algorithm performed better and was chosen for implementation in Java, where it achieved a sensitivity of 97,31%, specificity of 93,47% and accuracy of 95,25%.Förmaksflimmer är en vanlig hjärtrytmrubbning som kännetecknas av en avsaknad eller oregelbunden kontraktion av förmaken. Sjukdomen är en riskfaktor för andra allvarligare sjukdomar och de totala kostnaderna för samhället är betydande. Det skulle därför vara fördelaktigt att effektivisera och förbättra prevention samt diagnostisering av förmaksflimmer.   Kliniskt diagnostiseras förmaksflimmer med hjälp av till exempel pulspalpation och auskultation av hjärtat, men diagnosen brukar fastställas med en EKG-undersökning. Det finns idag flertalet algoritmer för att detektera arytmin genom att analysera ett EKG. En av de vanligaste metoderna är att undersöka variabiliteten av hjärtrytmen (HRV) och utföra olika sorters statistiska beräkningar som kan upptäcka episoder av förmaksflimmer som avviker från en normal sinusrytm.   I detta projekt har två metoder för att detektera förmaksflimmer utvärderats i Matlab, en baseras på beräkningar av variationskoefficienten och den andra använder sig av logistisk regression. EKG som kommer från databasen Physionet MIT används för att träna och testa modeller av algoritmerna. Innan EKG-signalen kan användas måste den behandlas för att ta bort olika typer av brus och artefakter.   Vid test av algoritmen med variationskoefficienten blev resultatet en sensitivitet på 91,38%, en specificitet på 93,93% och en noggrannhet på 92,92%. För logistisk regression blev sensitiviteten 97,23%, specificiteten 93,79% och noggrannheten 95,39%. Algoritmen med logistisk regression presterade bättre och valdes därför för att implementeras i Java, där uppnåddes en sensitivitet på 91,31%, en specificitet på 93,47% och en noggrannhet på 95,25%