42,992 research outputs found
Signal Recovery From 1-Bit Quantized Noisy Samples via Adaptive Thresholding
In this paper, we consider the problem of signal recovery from 1-bit noisy
measurements. We present an efficient method to obtain an estimation of the
signal of interest when the measurements are corrupted by white or colored
noise. To the best of our knowledge, the proposed framework is the pioneer
effort in the area of 1-bit sampling and signal recovery in providing a unified
framework to deal with the presence of noise with an arbitrary covariance
matrix including that of the colored noise. The proposed method is based on a
constrained quadratic program (CQP) formulation utilizing an adaptive
quantization thresholding approach, that further enables us to accurately
recover the signal of interest from its 1-bit noisy measurements. In addition,
due to the adaptive nature of the proposed method, it can recover both fixed
and time-varying parameters from their quantized 1-bit samples.Comment: This is a pre-print version of the original conference paper that has
been accepted at the 2018 IEEE Asilomar Conference on Signals, Systems, and
Computer
About Adaptive Coding on Countable Alphabets: Max-Stable Envelope Classes
In this paper, we study the problem of lossless universal source coding for
stationary memoryless sources on countably infinite alphabets. This task is
generally not achievable without restricting the class of sources over which
universality is desired. Building on our prior work, we propose natural
families of sources characterized by a common dominating envelope. We
particularly emphasize the notion of adaptivity, which is the ability to
perform as well as an oracle knowing the envelope, without actually knowing it.
This is closely related to the notion of hierarchical universal source coding,
but with the important difference that families of envelope classes are not
discretely indexed and not necessarily nested.
Our contribution is to extend the classes of envelopes over which adaptive
universal source coding is possible, namely by including max-stable
(heavy-tailed) envelopes which are excellent models in many applications, such
as natural language modeling. We derive a minimax lower bound on the redundancy
of any code on such envelope classes, including an oracle that knows the
envelope. We then propose a constructive code that does not use knowledge of
the envelope. The code is computationally efficient and is structured to use an
{E}xpanding {T}hreshold for {A}uto-{C}ensoring, and we therefore dub it the
\textsc{ETAC}-code. We prove that the \textsc{ETAC}-code achieves the lower
bound on the minimax redundancy within a factor logarithmic in the sequence
length, and can be therefore qualified as a near-adaptive code over families of
heavy-tailed envelopes. For finite and light-tailed envelopes the penalty is
even less, and the same code follows closely previous results that explicitly
made the light-tailed assumption. Our technical results are founded on methods
from regular variation theory and concentration of measure
Semiparametric Exponential Families for Heavy-Tailed Data
We propose a semiparametric method for fitting the tail of a heavy-tailed
population given a relatively small sample from that population and a larger
sample from a related background population. We model the tail of the small
sample as an exponential tilt of the better-observed large-sample tail, using a
robust sufficient statistic motivated by extreme value theory. In particular,
our method induces an estimator of the small-population mean, and we give
theoretical and empirical evidence that this estimator outperforms methods that
do not use the background sample. We demonstrate substantial efficiency gains
over competing methods in simulation and on data from a large controlled
experiment conducted by Facebook.Comment: To appear in Biometrik
Assurances in Software Testing: A Roadmap
As researchers, we already understand how to make testing more effective and
efficient at finding bugs. However, as fuzzing (i.e., automated testing)
becomes more widely adopted in practice, practitioners are asking: Which
assurances does a fuzzing campaign provide that exposes no bugs? When is it
safe to stop the fuzzer with a reasonable residual risk? How much longer should
the fuzzer be run to achieve sufficient coverage?
It is time for us to move beyond the innovation of increasingly sophisticated
testing techniques, to build a body of knowledge around the explication and
quantification of the testing process, and to develop sound methodologies to
estimate and extrapolate these quantities with measurable accuracy. In our
vision of the future practitioners leverage a rich statistical toolset to
assess residual risk, to obtain statistical guarantees, and to analyze the
cost-benefit trade-off for ongoing fuzzing campaigns. We propose a general
framework as a first starting point to tackle this fundamental challenge and
discuss a large number of concrete opportunities for future research.Comment: Accepted at ICSE'19 NIER. Extended version. 5 pages + reference
Sequential Score Adaptation with Extreme Value Theory for Robust Railway Track Inspection
Periodic inspections are necessary to keep railroad tracks in state of good
repair and prevent train accidents. Automatic track inspection using machine
vision technology has become a very effective inspection tool. Because of its
non-contact nature, this technology can be deployed on virtually any railway
vehicle to continuously survey the tracks and send exception reports to track
maintenance personnel. However, as appearance and imaging conditions vary,
false alarm rates can dramatically change, making it difficult to select a good
operating point. In this paper, we use extreme value theory (EVT) within a
Bayesian framework to optimally adjust the sensitivity of anomaly detectors. We
show that by approximating the lower tail of the probability density function
(PDF) of the scores with an Exponential distribution (a special case of the
Generalized Pareto distribution), and using the Gamma conjugate prior learned
from the training data, it is possible to reduce the variability in false alarm
rate and improve the overall performance. This method has shown an increase in
the defect detection rate of rail fasteners in the presence of clutter (at PFA
0.1%) from 95.40% to 99.26% on the 85-mile Northeast Corridor (NEC) 2012-2013
concrete tie dataset.Comment: To be presented at the 3rd Workshop on Computer Vision for Road Scene
Understanding and Autonomous Driving (CVRSUAD 2015
Managing catastrophic changes in a collective
We address the important practical issue of understanding, predicting and
eventually controlling catastrophic endogenous changes in a collective. Such
large internal changes arise as macroscopic manifestations of the microscopic
dynamics, and their presence can be regarded as one of the defining features of
an evolving complex system. We consider the specific case of a multi-agent
system related to the El Farol bar model, and show explicitly how the
information concerning such large macroscopic changes becomes encoded in the
microscopic dynamics. Our findings suggest that these large endogenous changes
can be avoided either by pre-design of the collective machinery itself, or in
the post-design stage via continual monitoring and occasional `vaccinations'.Comment: A contribution to the Workshop on Collectives and the Design of
Complex Systems, organized by David Wolpert and Kagan Tumer, at NASA Ames
Research Center, CA, August (2002
Adaptive Decision Feedback Detection with Parallel Interference Cancellation and Constellation Constraints for Multi-Antenna Systems
In this paper, a novel low-complexity adaptive decision feedback detection
with parallel decision feedback and constellation constraints (P-DFCC) is
proposed for multiuser MIMO systems. We propose a constrained constellation map
which introduces a number of selected points served as the feedback candidates
for interference cancellation. By introducing a reliability checking, a higher
degree of freedom is introduced to refine the unreliable estimates. The P-DFCC
is followed by an adaptive receive filter to estimate the transmitted symbol.
In order to reduce the complexity of computing the filters with time-varying
MIMO channels, an adaptive recursive least squares (RLS) algorithm is employed
in the proposed P-DFCC scheme. An iterative detection and decoding (Turbo)
scheme is considered with the proposed P-DFCC algorithm. Simulations show that
the proposed technique has a complexity comparable to the conventional parallel
decision feedback detector while it obtains a performance close to the maximum
likelihood detector at a low to medium SNR range.Comment: 10 figure
Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement
Low-light image enhancement is a promising solution to tackle the problem of
insufficient sensitivity of human vision system (HVS) to perceive information
in low light environments. Previous Retinex-based works always accomplish
enhancement task by estimating light intensity. Unfortunately, single light
intensity modelling is hard to accurately simulate visual perception
information, leading to the problems of imbalanced visual photosensitivity and
weak adaptivity. To solve these problems, we explore the precise relationship
between light source and visual perception and then propose the visual
perception (VP) model to acquire a precise mathematical description of visual
perception. The core of VP model is to decompose the light source into light
intensity and light spatial distribution to describe the perception process of
HVS, offering refinement estimation of illumination and reflectance. To reduce
complexity of the estimation process, we introduce the rapid and adaptive
and functions to build an illumination and
reflectance estimation scheme. Finally, we present a optimal determination
strategy, consisting of a \emph{cycle operation} and a \emph{comparator}.
Specifically, the \emph{comparator} is responsible for determining the optimal
enhancement results from multiple enhanced results through implementing the
\emph{cycle operation}. By coordinating the proposed VP model, illumination and
reflectance estimation scheme, and the optimal determination strategy, we
propose a rapid and adaptive framework for low-light image enhancement.
Extensive experiment results demenstrate that the proposed method achieves
better performance in terms of visual comparison, quantitative assessment, and
computational efficiency, compared with the currently state-of-the-arts.Comment: Due to the limitation "The abstract field cannot be longer than 1,920
characters", the abstract here is shorter than that in the PDF fil
Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures
The joint Value at Risk (VaR) and expected shortfall (ES) quantile regression
model of Taylor (2017) is extended via incorporating a realized measure, to
drive the tail risk dynamics, as a potentially more efficient driver than daily
returns. Both a maximum likelihood and an adaptive Bayesian Markov Chain Monte
Carlo method are employed for estimation, whose properties are assessed and
compared via a simulation study; results favour the Bayesian approach, which is
subsequently employed in a forecasting study of seven market indices and two
individual assets. The proposed models are compared to a range of parametric,
non-parametric and semi-parametric models, including GARCH, Realized-GARCH and
the joint VaR and ES quantile regression models in Taylor (2017). The
comparison is in terms of accuracy of one-day-ahead Value-at-Risk and Expected
Shortfall forecasts, over a long forecast sample period that includes the
global financial crisis in 2007-2008. The results favor the proposed models
incorporating a realized measure, especially when employing the sub-sampled
Realized Variance and the sub-sampled Realized Range.Comment: 36 pages, 5 figures. arXiv admin note: substantial text overlap with
arXiv:1612.0848
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits
Monte Carlo (MC) permutation test is considered the gold standard for
statistical hypothesis testing, especially when standard parametric assumptions
are not clear or likely to fail. However, in modern data science settings where
a large number of hypothesis tests need to be performed simultaneously, it is
rarely used due to its prohibitive computational cost. In genome-wide
association studies, for example, the number of hypothesis tests is around
while the number of MC samples for each test could be greater than
, totaling more than = samples. In this paper, we propose
Adaptive MC multiple Testing (AMT) to estimate MC p-values and control false
discovery rate in multiple testing. The algorithm outputs the same result as
the standard full MC approach with high probability while requiring only
samples. This sample complexity is shown to be optimal.
On a Parkinson GWAS dataset, the algorithm reduces the running time from 2
months for full MC to an hour. The AMT algorithm is derived based on the theory
of multi-armed bandits
- …