Proofs of theorems for the JRSS-B paper `Likelihood ratio tests in linear mixed models with one variance component'

Proofs of theorems for the JRSS-B paper `Likelihood ratio tests in linear mixed models with one variance component

Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches

Synchronization transparency offered by Software Transactional Memory (STM) must not come at the expense of run-time efficiency, thus demanding from the STM-designer the inclusion of mechanisms properly oriented to performance and other quality indexes. Particularly, one core issue to cope with in STM is related to exploiting parallelism while also avoiding thrashing phenomena due to excessive transaction rollbacks, caused by excessively high levels of contention on logical resources, namely concurrently accessed data portions. A means to address run-time efficiency consists in dynamically determining the best-suited level of concurrency (number of threads) to be employed for running the application (or specific application phases) on top of the STM layer. For too low levels of concurrency, parallelism can be hampered. Conversely, over-dimensioning the concurrency level may give rise to the aforementioned thrashing phenomena caused by excessive data contention—an aspect which has reflections also on the side of reduced energy-efficiency. In this chapter we overview a set of recent techniques aimed at building “application-specific” performance models that can be exploited to dynamically tune the level of concurrency to the best-suited value. Although they share some base concepts while modeling the system performance vs the degree of concurrency, these techniques rely on disparate methods, such as machine learning or analytic methods (or combinations of the two), and achieve different tradeoffs in terms of the relation between the precision of the performance model and the latency for model instantiation. Implications of the different tradeoffs in real-life scenarios are also discussed

Partonic Scattering Cross Sections in the QCD Medium

A medium modified gluon propagator is used to evaluate the scattering cross section for the process gg-->gg in the QCD medium by performing an explicit sum over the polarizations of the gluons. We incorporate a magnetic sreening mass from a non-perturbative study. It is shown that the medium modified cross section is finite, divergence free, and is independent of any ad-hoc momentum transfer cut-off parameters. The medium modified finite cross sections are necessary for a realistic investigation of the production and equilibration of the minijet plasma expected at RHIC and LHC

Elastic Convection in Vibrated Viscoplastic Fluids

We observe a new type of behavior in a shear thinning yield stress fluid: freestanding convection rolls driven by vertical oscillation. The convection occurs without the constraint of container boundaries yet the diameter of the rolls is spontaneously selected for a wide range of parameters. The transition to the convecting state occurs without hysteresis when the amplitude of the plate acceleration exceeds a critical value. We find that a non-dimensional stress, the stress due to the inertia of the fluid normalized by the yield stress, governs the onset of the convective motion.Comment: 4 pages, 6 figure

Theory and Phenomenology of Vector Mesons in Medium

Electromagnetic probes promise to be direct messengers of (spectral properties of) hot and dense matter formed in heavy-ion collisions, even at soft momentum transfers essential for characterizing possible phase transitions. We examine how far we have progressed toward this goal by highlighting recent developments, and trying to establish connections between lattice QCD, effective hadronic models and phenomenology of dilepton production.Comment: 8 pages latex incl. 12 ps/eps files; invited plenary talk at Quark Matter 2006 conference, Shanghai (China), Nov. 14-20, 200

Force Sensing in an Optomechanical System with Feedback-Controlled In-Loop Light

Quantum control techniques applied at macroscopic scales provide us with opportunities in fundamental physics and practical applications. Among them, measurement-based feedback allows efficient control of optomechanical systems and quantum-enhanced sensing. In this paper, we propose a near-resonant narrow-band force sensor with extremely low optically added noise in a membrane in the middle optomechanical system subject to a feedback-controlled in-loop light. The membrane's intrinsic motion consisting of zero-point motion and thermal motion is affected by the added noise of measurement due to the backaction noise and imprecision noise. We show that, in the optimal low-noise regime, the system is analogous to an optomechanical system containing a near quantum-limited optical parametric amplifier coupled to an engineered reservoir interacting with the cavity. Therefore, the feedback loop enhances the mechanical response of the system to the input while keeping the optically added noise of measurement below the standard quantum limit. Moreover, the system based on feedback offers a much larger amplification bandwidth than the same system with no feedback. Without the need to hybridize it with other quantum systems or introduce nonlinearities, our force sensor may have broad applications ranging from biology and medicine to gravitational wave detection and tests of fundamental physics

Functional generalized additive models

We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·, ·) is an unknown regression function and X(t) is a functional covariate. Rather than having an additive model in a finite number of principal components as by Müller and Yao (2008), our model incorporates the functional predictor directly and thus our model can be viewed as the natural functional extension of generalized additive models. We estimate F(·, ·) using tensor-product B-splines with roughness penalties. A pointwise quantile transformation of the functional predictor is also considered to ensure each tensor-product B-spline has observed data on its support. The methods are evaluated using simulated data and their predictive performance is compared with other competing scalar-on-function regression alternatives. We illustrate the usefulness of our approach through an application to brain tractography, where X(t) is a signal from diffusion tensor imaging at position, t, along a tract in the brain. In one example, the response is disease-status (case or control) and in a second example, it is the score on a cognitive test. The FGAM is implemented in R in the refund package. There are additional supplementary materials available online. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America