1,380 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
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
Principal component analysis - an efficient tool for variable stars diagnostics
We present two diagnostic methods based on ideas of Principal Component
Analysis and demonstrate their efficiency for sophisticated processing of
multicolour photometric observations of variable objects.Comment: 8 pages, 4 figures. Published alread
Principal Component Analysis with Noisy and/or Missing Data
We present a method for performing Principal Component Analysis (PCA) on
noisy datasets with missing values. Estimates of the measurement error are used
to weight the input data such that compared to classic PCA, the resulting
eigenvectors are more sensitive to the true underlying signal variations rather
than being pulled by heteroskedastic measurement noise. Missing data is simply
the limiting case of weight=0. The underlying algorithm is a noise weighted
Expectation Maximization (EM) PCA, which has additional benefits of
implementation speed and flexibility for smoothing eigenvectors to reduce the
noise contribution. We present applications of this method on simulated data
and QSO spectra from the Sloan Digital Sky Survey.Comment: Accepted for publication in PASP; v2 with minor updates, mostly to
bibliograph
Significance analysis and statistical mechanics: an application to clustering
This paper addresses the statistical significance of structures in random
data: Given a set of vectors and a measure of mutual similarity, how likely
does a subset of these vectors form a cluster with enhanced similarity among
its elements? The computation of this cluster p-value for randomly distributed
vectors is mapped onto a well-defined problem of statistical mechanics. We
solve this problem analytically, establishing a connection between the physics
of quenched disorder and multiple testing statistics in clustering and related
problems. In an application to gene expression data, we find a remarkable link
between the statistical significance of a cluster and the functional
relationships between its genes.Comment: to appear in Phys. Rev. Let
Coarse-grained dynamics of an activity bump in a neural field model
We study a stochastic nonlocal PDE, arising in the context of modelling
spatially distributed neural activity, which is capable of sustaining
stationary and moving spatially-localized ``activity bumps''. This system is
known to undergo a pitchfork bifurcation in bump speed as a parameter (the
strength of adaptation) is changed; yet increasing the noise intensity
effectively slowed the motion of the bump. Here we revisit the system from the
point of view of describing the high-dimensional stochastic dynamics in terms
of the effective dynamics of a single scalar "coarse" variable. We show that
such a reduced description in the form of an effective Langevin equation
characterized by a double-well potential is quantitatively successful. The
effective potential can be extracted using short, appropriately-initialized
bursts of direct simulation. We demonstrate this approach in terms of (a) an
experience-based "intelligent" choice of the coarse observable and (b) an
observable obtained through data-mining direct simulation results, using a
diffusion map approach.Comment: Corrected aknowledgement
Nonequilibrium Invariant Measure under Heat Flow
We provide an explicit representation of the nonequilibrium invariant measure
for a chain of harmonic oscillators with conservative noise in the presence of
stationary heat flow. By first determining the covariance matrix, we are able
to express the measure as the product of Gaussian distributions aligned along
some collective modes that are spatially localized with power-law tails.
Numerical studies show that such a representation applies also to a purely
deterministic model, the quartic Fermi-Pasta-Ulam chain
Mesoscopic Model for Free Energy Landscape Analysis of DNA sequences
A mesoscopic model which allows us to identify and quantify the strength of
binding sites in DNA sequences is proposed. The model is based on the
Peyrard-Bishop-Dauxois model for the DNA chain coupled to a Brownian particle
which explores the sequence interacting more importantly with open base pairs
of the DNA chain. We apply the model to promoter sequences of different
organisms. The free energy landscape obtained for these promoters shows a
complex structure that is strongly connected to their biological behavior. The
analysis method used is able to quantify free energy differences of sites
within genome sequences.Comment: 7 pages, 5 figures, 1 tabl
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 authors’ 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
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
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