112 research outputs found
C*-extreme Entanglement Breaking Maps On Operator Systems
Let denote the set of all unital entanglement breaking (UEB)
linear maps defined on an operator system and, mapping
into . As it turns out, the set is not only convex in the
classical sense but also in a quantum sense, namely it is -convex. The
main objective of this article is to describe the -extreme points of this
set . By observing that every EB map defined on the operator system
dilates to a positive map with commutative range and also extends
to an EB map on , We show that the -extreme points of the set
are precisely the UEB maps that are maximal in the sense of
Arveson (\cite{A} and \cite{A69}) and that they are also exactly the linear
extreme points of the set with commutative range. We also
determine their explicit structure, thereby obtaining operator system
generalizations of the analogous structure theorem and the Krein-Milman type
theorem given in \cite{BDMS}. As a consequence, we show that -extreme
(UEB) maps in extend to -extreme UEB maps on the full
algebra. Finally, we obtain an improved version of the main result in
\cite{BDMS}, which contains various characterizations of -extreme UEB maps
between the algebras and .Comment: This is part of the second named author's ongoing doctoral thesis
work. Comments and feedback are welcom
Towards Improved Input Masking for Convolutional Neural Networks
The ability to remove features from the input of machine learning models is
very important to understand and interpret model predictions. However, this is
non-trivial for vision models since masking out parts of the input image
typically causes large distribution shifts. This is because the baseline color
used for masking (typically grey or black) is out of distribution. Furthermore,
the shape of the mask itself can contain unwanted signals which can be used by
the model for its predictions. Recently, there has been some progress in
mitigating this issue (called missingness bias) in image masking for vision
transformers. In this work, we propose a new masking method for CNNs we call
layer masking in which the missingness bias caused by masking is reduced to a
large extent. Intuitively, layer masking applies a mask to intermediate
activation maps so that the model only processes the unmasked input. We show
that our method (i) is able to eliminate or minimize the influence of the mask
shape or color on the output of the model, and (ii) is much better than
replacing the masked region by black or grey for input perturbation based
interpretability techniques like LIME. Thus, layer masking is much less
affected by missingness bias than other masking strategies. We also demonstrate
how the shape of the mask may leak information about the class, thus affecting
estimates of model reliance on class-relevant features derived from input
masking. Furthermore, we discuss the role of data augmentation techniques for
tackling this problem, and argue that they are not sufficient for preventing
model reliance on mask shape. The code for this project is publicly available
at https://github.com/SriramB-98/layer_maskingComment: 29 pages, 19 figures. Accepted at ICCV 202
The co-optimization of floral display and nectar reward
In most insect-pollinated flowers, pollinators cannot detect the presence of nectar without entering the flower. Therefore, flowers may cheat by not producing nectar and may still get pollinated. Earlier studies supported this 'cheater flower' hypothesis and suggested that the cost saving by cheater flowers could be the most predominant selective force in the evolution of nectarless flowers. Previous models as well as empirical studies have addressed the problem of optimizing the proportion of nectarless and nectarful flowers. However, there has been no attempt to optimize the investment in nectar production along with that in floral display. One of the key questions that arises is whether the floral display will evolve to be an honest indicator of nectar reward. We use a mathematical model to cooptimize the investments in nectar and floral display in order to achieve maximum reproductive success. The model assumes that pollinators rely on a relative rather than an absolute judgement of reward. A conspicuous floral display attracts naive pollinators on the one hand and enhances pollinator learning on the other. We show that under these assumptions, plant-pollinator co-evolution leads to honest signalling, i.e. a positive correlation between display and reward
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