1,769 research outputs found
EffiTest: Efficient Delay Test and Statistical Prediction for Configuring Post-silicon Tunable Buffers
At nanometer manufacturing technology nodes, process variations significantly
affect circuit performance. To combat them, post- silicon clock tuning buffers
can be deployed to balance timing bud- gets of critical paths for each
individual chip after manufacturing. The challenge of this method is that path
delays should be mea- sured for each chip to configure the tuning buffers
properly. Current methods for this delay measurement rely on path-wise
frequency stepping. This strategy, however, requires too much time from ex-
pensive testers. In this paper, we propose an efficient delay test framework
(EffiTest) to solve the post-silicon testing problem by aligning path delays
using the already-existing tuning buffers in the circuit. In addition, we only
test representative paths and the delays of other paths are estimated by
statistical delay prediction. Exper- imental results demonstrate that the
proposed method can reduce the number of frequency stepping iterations by more
than 94% with only a slight yield loss.Comment: ACM/IEEE Design Automation Conference (DAC), June 201
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
Recommendation systems are ubiquitous and impact many domains; they have the
potential to influence product consumption, individuals' perceptions of the
world, and life-altering decisions. These systems are often evaluated or
trained with data from users already exposed to algorithmic recommendations;
this creates a pernicious feedback loop. Using simulations, we demonstrate how
using data confounded in this way homogenizes user behavior without increasing
utility
Using audit and feedback to increase clinician adherence to clinical practice guidelines in brain injury rehabilitation: v
ObjectiveThis study evaluated whether frequent (fortnightly) audit and feedback cycles over a sustained period of time (>12 months) increased clinician adherence to recommended guidelines in acquired brain injury rehabilitation.DesignA before and after study design.SettingA metropolitan inpatient brain injury rehabilitation unit.ParticipantsClinicians; medical, nursing and allied health staff.InterventionsFortnightly cycles of audit and feedback for 14 months. Each fortnight, medical file and observational audits were completed against 114 clinical indicators.Main outcome measureAdherence to guideline indicators before and after intervention, calculated by proportions, Mann-Whitney U and Chi square analysis.ResultsClinical and statistical significant improvements in median clinical indicator adherence were found immediately following the audit and feedback program from 38.8% (95% CI 34.3 to 44.4) to 83.6% (95% CI 81.8 to 88.5). Three months after cessation of the intervention, median adherence had decreased from 82.3% to 76.6% (95% CI 72.7 to 83.3, pConclusionA fortnightly audit and feedback program increased cliniciansâ adherence to guideline recommendations in an inpatient acquired brain injury rehabilitation setting. We propose future studies build on the evidence-based method used in the present study to determine effectiveness and develop an implementation toolkit for scale-up.</div
High-Dimensional Inference with the generalized Hopfield Model: Principal Component Analysis and Corrections
We consider the problem of inferring the interactions between a set of N
binary variables from the knowledge of their frequencies and pairwise
correlations. The inference framework is based on the Hopfield model, a special
case of the Ising model where the interaction matrix is defined through a set
of patterns in the variable space, and is of rank much smaller than N. We show
that Maximum Lik elihood inference is deeply related to Principal Component
Analysis when the amp litude of the pattern components, xi, is negligible
compared to N^1/2. Using techniques from statistical mechanics, we calculate
the corrections to the patterns to the first order in xi/N^1/2. We stress that
it is important to generalize the Hopfield model and include both attractive
and repulsive patterns, to correctly infer networks with sparse and strong
interactions. We present a simple geometrical criterion to decide how many
attractive and repulsive patterns should be considered as a function of the
sampling noise. We moreover discuss how many sampled configurations are
required for a good inference, as a function of the system size, N and of the
amplitude, xi. The inference approach is illustrated on synthetic and
biological data.Comment: Physical Review E: Statistical, Nonlinear, and Soft Matter Physics
(2011) to appea
Statistical Regularization of Deformation Fields for Atlas-Based Segmentation of Bone Scintigraphy Images
Spatially Resolved Mapping of Local Polarization Dynamics in an Ergodic Phase of Ferroelectric Relaxor
Spatial variability of polarization relaxation kinetics in relaxor
ferroelectric 0.9Pb(Mg1/3Nb2/3)O3-0.1PbTiO3 is studied using time-resolved
Piezoresponse Force Microscopy. Local relaxation attributed to the
reorientation of polar nanoregions is shown to follow stretched exponential
dependence, exp(-(t/tau)^beta), with beta~~0.4, much larger than the
macroscopic value determined from dielectric spectra (beta~~0.09). The spatial
inhomogeneity of relaxation time distributions with the presence of 100-200 nm
"fast" and "slow" regions is observed. The results are analyzed to map the
Vogel-Fulcher temperatures on the nanoscale.Comment: 23 pages, 4 figures, supplementary materials attached; to be
submitted to Phys. Rev. Let
A nonparametric approach to detecting changes in variance in locally stationary time series
This paper proposes a nonparametric approach to detecting changes in variance within a time series that we demonstrate is resilient to departures from the assumption of normality or presence of outliers. Our method is founded on a local estimate of the variance provided by the locally stationary wavelet framework. Within this setting, the structure of this local estimate of the variance will be piecewise constant if a time series has piecewise constant variance. Consequently, changes in the variance of a time series can be detected in a nonparametric setting. In addition, using a simulation study, we explore the robustness of our approach against the typical assumption of normality and presence of outliers. We illustrate the application of the approach to changes in variability of wind speeds at a location in the United Kingdom
Extracting quantum dynamics from genetic learning algorithms through principal control analysis
Genetic learning algorithms are widely used to control ultrafast optical
pulse shapes for photo-induced quantum control of atoms and molecules. An
unresolved issue is how to use the solutions found by these algorithms to learn
about the system's quantum dynamics. We propose a simple method based on
covariance analysis of the control space, which can reveal the degrees of
freedom in the effective control Hamiltonian. We have applied this technique to
stimulated Raman scattering in liquid methanol. A simple model of two-mode
stimulated Raman scattering is consistent with the results.Comment: 4 pages, 5 figures. Presented at coherent control Ringberg conference
200
Data-adaptive harmonic spectra and multilayer Stuart-Landau models
Harmonic decompositions of multivariate time series are considered for which
we adopt an integral operator approach with periodic semigroup kernels.
Spectral decomposition theorems are derived that cover the important cases of
two-time statistics drawn from a mixing invariant measure.
The corresponding eigenvalues can be grouped per Fourier frequency, and are
actually given, at each frequency, as the singular values of a cross-spectral
matrix depending on the data. These eigenvalues obey furthermore a variational
principle that allows us to define naturally a multidimensional power spectrum.
The eigenmodes, as far as they are concerned, exhibit a data-adaptive character
manifested in their phase which allows us in turn to define a multidimensional
phase spectrum.
The resulting data-adaptive harmonic (DAH) modes allow for reducing the
data-driven modeling effort to elemental models stacked per frequency, only
coupled at different frequencies by the same noise realization. In particular,
the DAH decomposition extracts time-dependent coefficients stacked by Fourier
frequency which can be efficiently modeled---provided the decay of temporal
correlations is sufficiently well-resolved---within a class of multilayer
stochastic models (MSMs) tailored here on stochastic Stuart-Landau oscillators.
Applications to the Lorenz 96 model and to a stochastic heat equation driven
by a space-time white noise, are considered. In both cases, the DAH
decomposition allows for an extraction of spatio-temporal modes revealing key
features of the dynamics in the embedded phase space. The multilayer
Stuart-Landau models (MSLMs) are shown to successfully model the typical
patterns of the corresponding time-evolving fields, as well as their statistics
of occurrence.Comment: 26 pages, double columns; 15 figure
Data mining: a tool for detecting cyclical disturbances in supply networks.
Disturbances in supply chains may be either exogenous or endogenous. The ability automatically to detect, diagnose, and distinguish between the causes of disturbances is of prime importance to decision makers in order to avoid uncertainty. The spectral principal component analysis (SPCA) technique has been utilized to distinguish between real and rogue disturbances in a steel supply network. The data set used was collected from four different business units in the network and consists of 43 variables; each is described by 72 data points. The present paper will utilize the same data set to test an alternative approach to SPCA in detecting the disturbances. The new approach employs statistical data pre-processing, clustering, and classification learning techniques to analyse the supply network data. In particular, the incremental k-means
clustering and the RULES-6 classification rule-learning algorithms, developed by the present authorsâ team, have been applied to identify important patterns in the data set. Results show that the proposed approach has the capability automatically to detect and characterize network-wide cyclical disturbances and generate hypotheses about their root cause
- âŠ