18,626 research outputs found
Quantum Hopfield neural network
Quantum computing allows for the potential of significant advancements in
both the speed and the capacity of widely used machine learning techniques.
Here we employ quantum algorithms for the Hopfield network, which can be used
for pattern recognition, reconstruction, and optimization as a realization of a
content-addressable memory system. We show that an exponentially large network
can be stored in a polynomial number of quantum bits by encoding the network
into the amplitudes of quantum states. By introducing a classical technique for
operating the Hopfield network, we can leverage quantum algorithms to obtain a
quantum computational complexity that is logarithmic in the dimension of the
data. We also present an application of our method as a genetic sequence
recognizer.Comment: 13 pages, 3 figures, final versio
Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition
In this paper we present a system for the detection of immunogold particles
and a Transfer Learning (TL) framework for the recognition of these immunogold
particles. Immunogold particles are part of a high-magnification method for the
selective localization of biological molecules at the subcellular level only
visible through Electron Microscopy. The number of immunogold particles in the
cell walls allows the assessment of the differences in their compositions
providing a tool to analise the quality of different plants. For its
quantization one requires a laborious manual labeling (or annotation) of images
containing hundreds of particles. The system that is proposed in this paper can
leverage significantly the burden of this manual task.
For particle detection we use a LoG filter coupled with a SDA. In order to
improve the recognition, we also study the applicability of TL settings for
immunogold recognition. TL reuses the learning model of a source problem on
other datasets (target problems) containing particles of different sizes. The
proposed system was developed to solve a particular problem on maize cells,
namely to determine the composition of cell wall ingrowths in endosperm
transfer cells. This novel dataset as well as the code for reproducing our
experiments is made publicly available.
We determined that the LoG detector alone attained more than 84\% of accuracy
with the F-measure. Developing immunogold recognition with TL also provided
superior performance when compared with the baseline models augmenting the
accuracy rates by 10\%
Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning
We present ChromAlignNet, a deep learning model for alignment of peaks in Gas
Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's
retention time (RT) may not stay fixed across multiple chromatograms. To use
GC-MS data for biomarker discovery requires alignment of identical analyte's RT
from different samples. Current methods of alignment are all based on a set of
formal, mathematical rules. We present a solution to GC-MS alignment using deep
learning neural networks, which are more adept at complex, fuzzy data sets. We
tested our model on several GC-MS data sets of various complexities and
analysed the alignment results quantitatively. We show the model has very good
performance (AUC for simple data sets and AUC for very
complex data sets). Further, our model easily outperforms existing algorithms
on complex data sets. Compared with existing methods, ChromAlignNet is very
easy to use as it requires no user input of reference chromatograms and
parameters. This method can easily be adapted to other similar data such as
those from liquid chromatography. The source code is written in Python and
available online
SchNet - a deep learning architecture for molecules and materials
Deep learning has led to a paradigm shift in artificial intelligence,
including web, text and image search, speech recognition, as well as
bioinformatics, with growing impact in chemical physics. Machine learning in
general and deep learning in particular is ideally suited for representing
quantum-mechanical interactions, enabling to model nonlinear potential-energy
surfaces or enhancing the exploration of chemical compound space. Here we
present the deep learning architecture SchNet that is specifically designed to
model atomistic systems by making use of continuous-filter convolutional
layers. We demonstrate the capabilities of SchNet by accurately predicting a
range of properties across chemical space for \emph{molecules and materials}
where our model learns chemically plausible embeddings of atom types across the
periodic table. Finally, we employ SchNet to predict potential-energy surfaces
and energy-conserving force fields for molecular dynamics simulations of small
molecules and perform an exemplary study of the quantum-mechanical properties
of C-fullerene that would have been infeasible with regular ab initio
molecular dynamics
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