12,390 research outputs found
Detection of treatment effects by covariate-adjusted expected shortfall
The statistical tests that are commonly used for detecting mean or median
treatment effects suffer from low power when the two distribution functions
differ only in the upper (or lower) tail, as in the assessment of the Total
Sharp Score (TSS) under different treatments for rheumatoid arthritis. In this
article, we propose a more powerful test that detects treatment effects through
the expected shortfalls. We show how the expected shortfall can be adjusted for
covariates, and demonstrate that the proposed test can achieve a substantial
sample size reduction over the conventional tests on the mean effects.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS347 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data
Feature selection has been studied widely in the literature. However, the
efficacy of the selection criteria for low sample size applications is
neglected in most cases. Most of the existing feature selection criteria are
based on the sample similarity. However, the distance measures become
insignificant for high dimensional low sample size (HDLSS) data. Moreover, the
variance of a feature with a few samples is pointless unless it represents the
data distribution efficiently. Instead of looking at the samples in groups, we
evaluate their efficiency based on pairwise fashion. In our investigation, we
noticed that considering a pair of samples at a time and selecting the features
that bring them closer or put them far away is a better choice for feature
selection. Experimental results on benchmark data sets demonstrate the
effectiveness of the proposed method with low sample size, which outperforms
many other state-of-the-art feature selection methods.Comment: European Signal Processing Conference 201
Invisible Higgs boson, continuous mass fields and unHiggs mechanism
We explore the consequences of an electroweak symmetry breaking sector which
exhibits approximately scale invariant dynamics -- i.e., nontrivial fixed point
behavior, as in unparticle models. One can think of an unHiggs as a composite
Higgs boson with a continuous mass distribution. We find it convenient to
represent the unHiggs in terms of a Kallen-Lehmann spectral function, from
which it is simple to verify the generation of gauge boson and fermion masses,
and unitarization of WW scattering. We show that a spectral function with broad
support, which corresponds to approximate fixed point behavior over an extended
range of energy, can lead to an effectively invisible Higgs particle, whose
decays at LEP or LHC could be obscured by background.Comment: 8 page
Learning to infer: RL-based search for DNN primitive selection on Heterogeneous Embedded Systems
Deep Learning is increasingly being adopted by industry for computer vision
applications running on embedded devices. While Convolutional Neural Networks'
accuracy has achieved a mature and remarkable state, inference latency and
throughput are a major concern especially when targeting low-cost and low-power
embedded platforms. CNNs' inference latency may become a bottleneck for Deep
Learning adoption by industry, as it is a crucial specification for many
real-time processes. Furthermore, deployment of CNNs across heterogeneous
platforms presents major compatibility issues due to vendor-specific technology
and acceleration libraries. In this work, we present QS-DNN, a fully automatic
search based on Reinforcement Learning which, combined with an inference engine
optimizer, efficiently explores through the design space and empirically finds
the optimal combinations of libraries and primitives to speed up the inference
of CNNs on heterogeneous embedded devices. We show that, an optimized
combination can achieve 45x speedup in inference latency on CPU compared to a
dependency-free baseline and 2x on average on GPGPU compared to the best vendor
library. Further, we demonstrate that, the quality of results and time
"to-solution" is much better than with Random Search and achieves up to 15x
better results for a short-time search
Provably Efficient Adaptive Scheduling for Parallel Jobs
Scheduling competing jobs on multiprocessors has always been an important issue for parallel and distributed systems. The challenge is to ensure global, system-wide efficiency while offering a level of fairness to user jobs. Various degrees of successes have been achieved over the years. However, few existing schemes address both efficiency and fairness over a wide range of work loads. Moreover, in order to obtain analytical results, most of them require prior information about jobs, which may be difficult to obtain in real applications.
This paper presents two novel adaptive scheduling algorithms -- GRAD for centralized scheduling, and WRAD for distributed scheduling. Both GRAD and WRAD ensure fair allocation under all levels of workload, and they offer provable efficiency without requiring prior information of job's parallelism. Moreover, they provide effective control over the scheduling overhead and ensure efficient utilization of processors. To the best of our knowledge, they are the first non-clairvoyant scheduling algorithms that offer such guarantees. We also believe that our new approach of resource request-allotment protocol deserves further exploration.
Specifically, both GRAD and WRAD are O(1)-competitive with respect to mean response time for batched jobs, and O(1)-competitive with respect to makespan for non-batched jobs with arbitrary release times. The simulation results show that, for non-batched jobs, the makespan produced by GRAD is no more than 1.39 times of the optimal on average and it never exceeds 4.5 times. For batched jobs, the mean response time produced by GRAD is no more than 2.37 times of the optimal on average, and it never exceeds 5.5 times.Singapore-MIT Alliance (SMA
Is anterior N2 enhancement a reliable electrophysiological index of concealed information?
publisher: Elsevier articletitle: Is anterior N2 enhancement a reliable electrophysiological index of concealed information? journaltitle: NeuroImage articlelink: http://dx.doi.org/10.1016/j.neuroimage.2016.08.042 content_type: article copyright: © 2016 Elsevier Inc. All rights reserved
Nonlinear magnetotransport shaped by Fermi surface topology and convexity in WTe2
The nature of Fermi surface defines the physical properties of conductors and
many physical phenomena can be traced to its shape. Although the recent
discovery of a current-dependent nonlinear magnetoresistance in spin-polarized
non-magnetic materials has attracted considerable attention in spintronics,
correlations between this phenomenon and the underlying fermiology remain
unexplored. Here, we report the observation of nonlinear magnetoresistance at
room temperature in a semimetal WTe2, with an interesting temperature-driven
inversion. Theoretical calculations reproduce the nonlinear transport
measurements and allow us to attribute the inversion to temperature-induced
changes in Fermi surface convexity. We also report a large anisotropy of
nonlinear magnetoresistance in WTe2, due to its low symmetry of Fermi surfaces.
The good agreement between experiments and theoretical modeling reveals the
critical role of Fermi surface topology and convexity on the nonlinear
magneto-response. These results lay a new path to explore ramifications of
distinct fermiology for nonlinear transport in condensed-matter
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