63 research outputs found
A novel symbolization technique for time-series outlier detection
The detection of outliers in time series data is a core component of many data-mining applications and broadly applied in industrial applications. In large data sets algorithms that are efficient in both time and space are required. One area where speed and storage costs can be reduced is via symbolization as a pre-processing step, additionally opening up the use of an array of discrete algorithms. With this common pre-processing step in mind, this work highlights that (1) existing symbolization approaches are designed to address problems other than outlier detection and are hence sub-optimal and (2) use of off-the-shelf symbolization techniques can therefore lead to significant unnecessary data corruption and potential performance loss when outlier detection is a key aspect of the data mining task at hand. Addressing this a novel symbolization method is motivated specifically targeting the end use application of outlier detection. The method is empirically shown to outperform existing approaches
On the Computational Entanglement of Distant Features in Adversarial Machine Learning
Adversarial examples in machine learning has emerged as a focal point of
research due to their remarkable ability to deceive models with seemingly
inconspicuous input perturbations, potentially resulting in severe
consequences. In this study, we undertake a thorough investigation into the
emergence of adversarial examples, a phenomenon that can, in principle,
manifest in a wide range of machine learning models. Through our research, we
unveil a new notion termed computational entanglement, with its ability to
entangle distant features, display perfect correlations or anti-correlations
regardless to their spatial separation, significantly contributes to the
emergence of adversarial examples. We illustrate how computational entanglement
aligns with relativistic effects such as time dilation and length contraction
to feature pair, ultimately resulting in the convergence of their angle
differences and distances towards zero, signifying perfect correlation, or
towards maximum, indicating perfect anti-correlation.Comment: The latest version has titled update
Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant
In this work, we consider applying machine learning to the analysis and
compression of audio signals in the context of monitoring elephants in
sub-Saharan Africa. Earth's biodiversity is increasingly under threat by
sources of anthropogenic change (e.g. resource extraction, land use change, and
climate change) and surveying animal populations is critical for developing
conservation strategies. However, manually monitoring tropical forests or deep
oceans is intractable. For species that communicate acoustically, researchers
have argued for placing audio recorders in the habitats as a cost-effective and
non-invasive method, a strategy known as passive acoustic monitoring (PAM). In
collaboration with conservation efforts, we construct a large labeled dataset
of passive acoustic recordings of the African Forest Elephant via
crowdsourcing, compromising thousands of hours of recordings in the wild. Using
state-of-the-art techniques in artificial intelligence we improve upon
previously proposed methods for passive acoustic monitoring for classification
and segmentation. In real-time detection of elephant calls, network bandwidth
quickly becomes a bottleneck and efficient ways to compress the data are
needed. Most audio compression schemes are aimed at human listeners and are
unsuitable for low-frequency elephant calls. To remedy this, we provide a novel
end-to-end differentiable method for compression of audio signals that can be
adapted to acoustic monitoring of any species and dramatically improves over
naive coding strategies
Classical communication over a quantum interference channel
Calculating the capacity of interference channels is a notorious open problem in classical information theory. Such channels have two senders and two receivers, and each sender would like to communicate with a partner receiver. The capacity of such channels is known exactly in the settings of very strong and strong interference, while the Han-Kobayashi coding strategy gives the best known achievable rate region in the general case. Here, we introduce and study the quantum interference channel, a natural generalization of the interference channel to the setting of quantum information theory. We restrict ourselves for the most part to channels with two classical inputs and two quantum outputs in order to simplify the presentation of our results (though generalizations of our results to channels with quantum inputs are straightforward). We are able to determine the exact classical capacity of this channel in the settings of very strong and strong interference, by exploiting Winter\u27s successive decoding strategy and a novel two-sender quantum simultaneous decoder, respectively. We provide a proof that a Han-Kobayashi strategy is achievable with Holevo information rates, up to a conjecture regarding the existence of a three-sender quantum simultaneous decoder. This conjecture holds for a special class of quantum multiple-access channels with average output states that commute, and we discuss some other variations of the conjecture that hold. Finally, we detail a connection between the quantum interference channel and prior work on the capacity of bipartite unitary gates. © 2012 IEEE
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