238 research outputs found
Shot noise of spin current and spin transfer torque
We report the theoretical investigation of noise spectrum of spin current and
spin transfer torque for non-colinear spin polarized transport in a spin-valve
device which consists of normal scattering region connected by two
ferromagnetic electrodes. Our theory was developed using non-equilibrium
Green's function method and general non-linear and
relations were derived as a function of angle between magnetization of
two leads. We have applied our theory to a quantum dot system with a resonant
level coupled with two ferromagnetic electrodes. It was found that for the MNM
system, the auto-correlation of spin current is enough to characterize the
fluctuation of spin current. For a system with three ferromagnetic layers,
however, both auto-correlation and cross-correlation of spin current are needed
to characterize the noise spectrum of spin current. Furthermore, the spin
transfer torque and the torque noise were studied for the MNM system. For a
quantum dot with a resonant level, the derivative of spin torque with respect
to bias voltage is proportional to when the system is far away
from the resonance. When the system is near the resonance, the spin transfer
torque becomes non-sinusoidal function of . The derivative of noise
spectrum of spin transfer torque with respect to the bias voltage
behaves differently when the system is near or far away from the resonance.
Specifically, the differential shot noise of spin transfer torque is a
concave function of near the resonance while it becomes convex
function of far away from resonance. For certain bias voltages, the
period becomes instead of . For small , it
was found that the differential shot noise of spin transfer torque is very
sensitive to the bias voltage and the other system parameters.Comment: 15pages, 6figure
In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer
Enabling machine learning classifiers to defer their decision to a downstream
expert when the expert is more accurate will ensure improved safety and
performance. This objective can be achieved with the learning-to-defer
framework which aims to jointly learn how to classify and how to defer to the
expert. In recent studies, it has been theoretically shown that popular
estimators for learning to defer parameterized with softmax provide unbounded
estimates for the likelihood of deferring which makes them uncalibrated.
However, it remains unknown whether this is due to the widely used softmax
parameterization and if we can find a softmax-based estimator that is both
statistically consistent and possesses a valid probability estimator. In this
work, we first show that the cause of the miscalibrated and unbounded estimator
in prior literature is due to the symmetric nature of the surrogate losses used
and not due to softmax. We then propose a novel statistically consistent
asymmetric softmax-based surrogate loss that can produce valid estimates
without the issue of unboundedness. We further analyze the non-asymptotic
properties of our method and empirically validate its performance and
calibration on benchmark datasets.Comment: NeurIPS 202
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error
Estimating the generalization performance is practically challenging on
out-of-distribution (OOD) data without ground truth labels. While previous
methods emphasize the connection between distribution difference and OOD
accuracy, we show that a large domain gap not necessarily leads to a low test
accuracy. In this paper, we investigate this problem from the perspective of
feature separability, and propose a dataset-level score based upon feature
dispersion to estimate the test accuracy under distribution shift. Our method
is inspired by desirable properties of features in representation learning:
high inter-class dispersion and high intra-class compactness. Our analysis
shows that inter-class dispersion is strongly correlated with the model
accuracy, while intra-class compactness does not reflect the generalization
performance on OOD data. Extensive experiments demonstrate the superiority of
our method in both prediction performance and computational efficiency
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
Modern neural networks are known to give overconfident prediction for
out-of-distribution inputs when deployed in the open world. It is common
practice to leverage a surrogate outlier dataset to regularize the model during
training, and recent studies emphasize the role of uncertainty in designing the
sampling strategy for outlier dataset. However, the OOD samples selected solely
based on predictive uncertainty can be biased towards certain types, which may
fail to capture the full outlier distribution. In this work, we empirically
show that diversity is critical in sampling outliers for OOD detection
performance. Motivated by the observation, we propose a straightforward and
novel sampling strategy named DOS (Diverse Outlier Sampling) to select diverse
and informative outliers. Specifically, we cluster the normalized features at
each iteration, and the most informative outlier from each cluster is selected
for model training with absent category loss. With DOS, the sampled outliers
efficiently shape a globally compact decision boundary between ID and OOD data.
Extensive experiments demonstrate the superiority of DOS, reducing the average
FPR95 by up to 25.79% on CIFAR-100 with TI-300K
Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition
Human Activity Recognition (HAR) models often suffer from performance
degradation in real-world applications due to distribution shifts in activity
patterns across individuals. Test-Time Adaptation (TTA) is an emerging learning
paradigm that aims to utilize the test stream to adjust predictions in
real-time inference, which has not been explored in HAR before. However, the
high computational cost of optimization-based TTA algorithms makes it
intractable to run on resource-constrained edge devices. In this paper, we
propose an Optimization-Free Test-Time Adaptation (OFTTA) framework for
sensor-based HAR. OFTTA adjusts the feature extractor and linear classifier
simultaneously in an optimization-free manner. For the feature extractor, we
propose Exponential DecayTest-time Normalization (EDTN) to replace the
conventional batch normalization (CBN) layers. EDTN combines CBN and Test-time
batch Normalization (TBN) to extract reliable features against domain shifts
with TBN's influence decreasing exponentially in deeper layers. For the
classifier, we adjust the prediction by computing the distance between the
feature and the prototype, which is calculated by a maintained support set. In
addition, the update of the support set is based on the pseudo label, which can
benefit from reliable features extracted by EDTN. Extensive experiments on
three public cross-person HAR datasets and two different TTA settings
demonstrate that OFTTA outperforms the state-of-the-art TTA approaches in both
classification performance and computational efficiency. Finally, we verify the
superiority of our proposed OFTTA on edge devices, indicating possible
deployment in real applications. Our code is available at
\href{https://github.com/Claydon-Wang/OFTTA}{this https URL}.Comment: To be presented at UbiComp 2024; Accepted by Proceedings of the ACM
on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT
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