565 research outputs found
On the Limited Communication Analysis and Design for Decentralized Estimation
This paper pertains to the analysis and design of decentralized estimation
schemes that make use of limited communication. Briefly, these schemes equip
the sensors with scalar states that iteratively merge the measurements and the
state of other sensors to be used for state estimation. Contrarily to commonly
used distributed estimation schemes, the only information being exchanged are
scalars, there is only one common time-scale for communication and estimation,
and the retrieval of the state of the system and sensors is achieved in
finite-time. We extend previous work to a more general setup and provide
necessary and sufficient conditions required for the communication between the
sensors that enable the use of limited communication decentralized
estimation~schemes. Additionally, we discuss the cases where the sensors are
memoryless, and where the sensors might not have the capacity to discern the
contributions of other sensors. Based on these conditions and the fact that
communication channels incur a cost, we cast the problem of finding the minimum
cost communication graph that enables limited communication decentralized
estimation schemes as an integer programming problem.Comment: Updates on the paper in CDC 201
The Variational Homoencoder: Learning to learn high capacity generative models from few examples
Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot
classification, conditional and unconditional generation) as inference within a
single generative model. However, when this generative model is expressed as a
powerful neural network such as a PixelCNN, we show that existing learning
techniques typically fail to effectively use latent variables. To address this,
we develop a modification of the Variational Autoencoder in which encoded
observations are decoded to new elements from the same class. This technique,
which we call a Variational Homoencoder (VHE), produces a hierarchical latent
variable model which better utilises latent variables. We use the VHE framework
to learn a hierarchical PixelCNN on the Omniglot dataset, which outperforms all
existing models on test set likelihood and achieves strong performance on
one-shot generation and classification tasks. We additionally validate the VHE
on natural images from the YouTube Faces database. Finally, we develop
extensions of the model that apply to richer dataset structures such as
factorial and hierarchical categories.Comment: UAI 2018 oral presentatio
Decentralized Observability with Limited Communication between Sensors
In this paper, we study the problem of jointly retrieving the state of a
dynamical system, as well as the state of the sensors deployed to estimate it.
We assume that the sensors possess a simple computational unit that is capable
of performing simple operations, such as retaining the current state and model
of the system in its memory.
We assume the system to be observable (given all the measurements of the
sensors), and we ask whether each sub-collection of sensors can retrieve the
state of the underlying physical system, as well as the state of the remaining
sensors. To this end, we consider communication between neighboring sensors,
whose adjacency is captured by a communication graph. We then propose a linear
update strategy that encodes the sensor measurements as states in an augmented
state space, with which we provide the solution to the problem of retrieving
the system and sensor states.
The present paper contains three main contributions. First, we provide
necessary and sufficient conditions to ensure observability of the system and
sensor states from any sensor. Second, we address the problem of adding
communication between sensors when the necessary and sufficient conditions are
not satisfied, and devise a strategy to this end. Third, we extend the former
case to include different costs of communication between sensors. Finally, the
concepts defined and the method proposed are used to assess the state of an
example of approximate structural brain dynamics through linearized
measurements.Comment: 15 pages, 5 figures, extended version of paper accepted at IEEE
Conference on Decision and Control 201
Cloud-based Quadratic Optimization with Partially Homomorphic Encryption
The development of large-scale distributed control systems has led to the
outsourcing of costly computations to cloud-computing platforms, as well as to
concerns about privacy of the collected sensitive data. This paper develops a
cloud-based protocol for a quadratic optimization problem involving multiple
parties, each holding information it seeks to maintain private. The protocol is
based on the projected gradient ascent on the Lagrange dual problem and
exploits partially homomorphic encryption and secure multi-party computation
techniques. Using formal cryptographic definitions of indistinguishability, the
protocol is shown to achieve computational privacy, i.e., there is no
computationally efficient algorithm that any involved party can employ to
obtain private information beyond what can be inferred from the party's inputs
and outputs only. In order to reduce the communication complexity of the
proposed protocol, we introduced a variant that achieves this objective at the
expense of weaker privacy guarantees. We discuss in detail the computational
and communication complexity properties of both algorithms theoretically and
also through implementations. We conclude the paper with a discussion on
computational privacy and other notions of privacy such as the non-unique
retrieval of the private information from the protocol outputs
Neuromarketing for a better understanding of consumer needs and emotions
In this paper we are talking about the fact that marketing and publicity specialists have become aware of the limitations of traditional market research methods for decades, but only in recent years science has allowed the development of a more effective mechanism by which consumers' thoughts can be deciphered: neuromarketing. This term refers to the use of techniques developed by cognitive neuroscience and psychology specialists to analyze and understand people's reactions to products and promotions, which allows refining marketing efforts to make them more effective. In the article we are talking about the tools used for this purpose, which include magnetic resonance imaging (MRI), brain scanners that identify brain parts that react to different stimuli, and electroencephalography (EEG), devices that measure electrical activity in the brain. By tracking brain reactions to different stimuli, researchers can discover the marketing mechanisms that are most likely to lead to the desired outcome: selling the product. For this, in parallel with the EEG measurements, an eye-tracking device is used, which allows the exact identification of the stimulus that produces the reaction from that moment. Also, some neuromarketing companies also use GSR (galvanic skin response) sensors to measure the electrical conductivity of the skin, which is another element that provides information about the consumer's response to various commercial messages. The purpose of our article is to show the role played by neuromarketing in the correct understanding of consumer needs, words and emotions
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