2,692 research outputs found
Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization
Protecting vast quantities of data poses a daunting challenge for the growing
number of organizations that collect, stockpile, and monetize it. The ability
to distinguish data that is actually needed from data collected "just in case"
would help these organizations to limit the latter's exposure to attack. A
natural approach might be to monitor data use and retain only the working-set
of in-use data in accessible storage; unused data can be evicted to a highly
protected store. However, many of today's big data applications rely on machine
learning (ML) workloads that are periodically retrained by accessing, and thus
exposing to attack, the entire data store. Training set minimization methods,
such as count featurization, are often used to limit the data needed to train
ML workloads to improve performance or scalability. We present Pyramid, a
limited-exposure data management system that builds upon count featurization to
enhance data protection. As such, Pyramid uniquely introduces both the idea and
proof-of-concept for leveraging training set minimization methods to instill
rigor and selectivity into big data management. We integrated Pyramid into
Spark Velox, a framework for ML-based targeting and personalization. We
evaluate it on three applications and show that Pyramid approaches
state-of-the-art models while training on less than 1% of the raw data
EEG Classification based on Image Configuration in Social Anxiety Disorder
The problem of detecting the presence of Social Anxiety Disorder (SAD) using
Electroencephalography (EEG) for classification has seen limited study and is
addressed with a new approach that seeks to exploit the knowledge of EEG sensor
spatial configuration. Two classification models, one which ignores the
configuration (model 1) and one that exploits it with different interpolation
methods (model 2), are studied. Performance of these two models is examined for
analyzing 34 EEG data channels each consisting of five frequency bands and
further decomposed with a filter bank. The data are collected from 64 subjects
consisting of healthy controls and patients with SAD. Validity of our
hypothesis that model 2 will significantly outperform model 1 is borne out in
the results, with accuracy -- higher for model 2 for each machine
learning algorithm we investigated. Convolutional Neural Networks (CNN) were
found to provide much better performance than SVM and kNNs
Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates
This paper presents a novel approach for indoor acoustic source localization
using microphone arrays and based on a Convolutional Neural Network (CNN). The
proposed solution is, to the best of our knowledge, the first published work in
which the CNN is designed to directly estimate the three dimensional position
of an acoustic source, using the raw audio signal as the input information
avoiding the use of hand crafted audio features. Given the limited amount of
available localization data, we propose in this paper a training strategy based
on two steps. We first train our network using semi-synthetic data, generated
from close talk speech recordings, and where we simulate the time delays and
distortion suffered in the signal that propagates from the source to the array
of microphones. We then fine tune this network using a small amount of real
data. Our experimental results show that this strategy is able to produce
networks that significantly improve existing localization methods based on
\textit{SRP-PHAT} strategies. In addition, our experiments show that our CNN
method exhibits better resistance against varying gender of the speaker and
different window sizes compared with the other methods.Comment: 18 pages, 3 figures, 8 table
Real-time neural signal processing and low-power hardware co-design for wireless implantable brain machine interfaces
Intracortical Brain-Machine Interfaces (iBMIs) have advanced significantly over the past
two decades, demonstrating their utility in various aspects, including neuroprosthetic control
and communication. To increase the information transfer rate and improve the devices’
robustness and longevity, iBMI technology aims to increase channel counts to access more
neural data while reducing invasiveness through miniaturisation and avoiding percutaneous
connectors (wired implants). However, as the number of channels increases, the raw data
bandwidth required for wireless transmission also increases becoming prohibitive, requiring
efficient on-implant processing to reduce the amount of data through data compression or
feature extraction.
The fundamental aim of this research is to develop methods for high-performance neural spike processing co-designed within low-power hardware that is scaleable for real-time
wireless BMI applications. The specific original contributions include the following:
Firstly, a new method has been developed for hardware-efficient spike detection, which
achieves state-of-the-art spike detection performance and significantly reduces the hardware
complexity. Secondly, a novel thresholding mechanism for spike detection has been introduced. By incorporating firing rate information as a key determinant in establishing the spike
detection threshold, we have improved the adaptiveness of spike detection. This eventually
allows the spike detection to overcome the signal degradation that arises due to scar tissue
growth around the recording site, thereby ensuring enduringly stable spike detection results.
The long-term decoding performance, as a consequence, has also been improved notably.
Thirdly, the relationship between spike detection performance and neural decoding accuracy has been investigated to be nonlinear, offering new opportunities for further reducing
transmission bandwidth by at least 30% with minor decoding performance degradation.
In summary, this thesis presents a journey toward designing ultra-hardware-efficient spike
detection algorithms and applying them to reduce the data bandwidth and improve neural
decoding performance. The software-hardware co-design approach is essential for the next
generation of wireless brain-machine interfaces with increased channel counts and a highly
constrained hardware budget.
The fundamental aim of this research is to develop methods for high-performance neural spike processing co-designed within low-power hardware that is scaleable for real-time wireless BMI applications. The specific original contributions include the following:
Firstly, a new method has been developed for hardware-efficient spike detection, which achieves state-of-the-art spike detection performance and significantly reduces the hardware complexity. Secondly, a novel thresholding mechanism for spike detection has been introduced. By incorporating firing rate information as a key determinant in establishing the spike detection threshold, we have improved the adaptiveness of spike detection. This eventually allows the spike detection to overcome the signal degradation that arises due to scar tissue growth around the recording site, thereby ensuring enduringly stable spike detection results. The long-term decoding performance, as a consequence, has also been improved notably. Thirdly, the relationship between spike detection performance and neural decoding accuracy has been investigated to be nonlinear, offering new opportunities for further reducing transmission bandwidth by at least 30\% with only minor decoding performance degradation.
In summary, this thesis presents a journey toward designing ultra-hardware-efficient spike detection algorithms and applying them to reduce the data bandwidth and improve neural decoding performance. The software-hardware co-design approach is essential for the next generation of wireless brain-machine interfaces with increased channel counts and a highly constrained hardware budget.Open Acces
- …