612 research outputs found
Intestinal Parasites Classification Using Deep Belief Networks
Currently, approximately billion people are infected by intestinal
parasites worldwide. Diseases caused by such infections constitute a public
health problem in most tropical countries, leading to physical and mental
disorders, and even death to children and immunodeficient individuals. Although
subjected to high error rates, human visual inspection is still in charge of
the vast majority of clinical diagnoses. In the past years, some works
addressed intelligent computer-aided intestinal parasites classification, but
they usually suffer from misclassification due to similarities between
parasites and fecal impurities. In this paper, we introduce Deep Belief
Networks to the context of automatic intestinal parasites classification.
Experiments conducted over three datasets composed of eggs, larvae, and
protozoa provided promising results, even considering unbalanced classes and
also fecal impurities
The Impact of 18 Ancestral and Horizontally-Acquired Regulatory Proteins upon the Transcriptome and sRNA Landscape of Salmonella enterica serovar Typhimurium
Article Authors Metrics Comments Media Coverage Abstract Author Summary Introduction Results and Discussion Materials and Methods Supporting Information Acknowledgments Author Contributions References Reader Comments (0) Media Coverage (0) Figures Abstract We know a great deal about the genes used by the model pathogen Salmonella enterica serovar Typhimurium to cause disease, but less about global gene regulation. New tools for studying transcripts at the single nucleotide level now offer an unparalleled opportunity to understand the bacterial transcriptome, and expression of the small RNAs (sRNA) and coding genes responsible for the establishment of infection. Here, we define the transcriptomes of 18 mutants lacking virulence-related global regulatory systems that modulate the expression of the SPI1 and SPI2 Type 3 secretion systems of S. Typhimurium strain 4/74. Using infection-relevant growth conditions, we identified a total of 1257 coding genes that are controlled by one or more regulatory system, including a sub-class of genes that reflect a new level of cross-talk between SPI1 and SPI2. We directly compared the roles played by the major transcriptional regulators in the expression of sRNAs, and discovered that the RpoS (σ38) sigma factor modulates the expression of 23% of sRNAs, many more than other regulatory systems. The impact of the RNA chaperone Hfq upon the steady state levels of 280 sRNA transcripts is described, and we found 13 sRNAs that are co-regulated with SPI1 and SPI2 virulence genes. We report the first example of an sRNA, STnc1480, that is subject to silencing by H-NS and subsequent counter-silencing by PhoP and SlyA. The data for these 18 regulatory systems is now available to the bacterial research community in a user-friendly online resource, SalComRegulon
WiSeBE: Window-based Sentence Boundary Evaluation
Sentence Boundary Detection (SBD) has been a major research topic since
Automatic Speech Recognition transcripts have been used for further Natural
Language Processing tasks like Part of Speech Tagging, Question Answering or
Automatic Summarization. But what about evaluation? Do standard evaluation
metrics like precision, recall, F-score or classification error; and more
important, evaluating an automatic system against a unique reference is enough
to conclude how well a SBD system is performing given the final application of
the transcript? In this paper we propose Window-based Sentence Boundary
Evaluation (WiSeBE), a semi-supervised metric for evaluating Sentence Boundary
Detection systems based on multi-reference (dis)agreement. We evaluate and
compare the performance of different SBD systems over a set of Youtube
transcripts using WiSeBE and standard metrics. This double evaluation gives an
understanding of how WiSeBE is a more reliable metric for the SBD task.Comment: In proceedings of the 17th Mexican International Conference on
Artificial Intelligence (MICAI), 201
Non invasive in vivo investigation of hepatobiliary structure and function in STII medaka (Oryzias latipes): methodology and applications
Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces
After the tremendous development of neural networks trained by
backpropagation, it is a good time to develop other algorithms for training
neural networks to gain more insights into networks. In this paper, we propose
a new algorithm for training feedforward neural networks which is fairly faster
than backpropagation. This method is based on projection and reconstruction
where, at every layer, the projected data and reconstructed labels are forced
to be similar and the weights are tuned accordingly layer by layer. The
proposed algorithm can be used for both input and feature spaces, named as
backprojection and kernel backprojection, respectively. This algorithm gives an
insight to networks with a projection-based perspective. The experiments on
synthetic datasets show the effectiveness of the proposed method.Comment: Accepted (to appear) in International Conference on Image Analysis
and Recognition (ICIAR) 2020, Springe
CubeNet: Equivariance to 3D Rotation and Translation
3D Convolutional Neural Networks are sensitive to transformations applied to
their input. This is a problem because a voxelized version of a 3D object, and
its rotated clone, will look unrelated to each other after passing through to
the last layer of a network. Instead, an idealized model would preserve a
meaningful representation of the voxelized object, while explaining the
pose-difference between the two inputs. An equivariant representation vector
has two components: the invariant identity part, and a discernable encoding of
the transformation. Models that can't explain pose-differences risk "diluting"
the representation, in pursuit of optimizing a classification or regression
loss function.
We introduce a Group Convolutional Neural Network with linear equivariance to
translations and right angle rotations in three dimensions. We call this
network CubeNet, reflecting its cube-like symmetry. By construction, this
network helps preserve a 3D shape's global and local signature, as it is
transformed through successive layers. We apply this network to a variety of 3D
inference problems, achieving state-of-the-art on the ModelNet10 classification
challenge, and comparable performance on the ISBI 2012 Connectome Segmentation
Benchmark. To the best of our knowledge, this is the first 3D rotation
equivariant CNN for voxel representations.Comment: Preprin
Physics-Informed Echo State Networks for Chaotic Systems Forecasting
We propose a physics-informed Echo State Network (ESN) to predict the
evolution of chaotic systems. Compared to conventional ESNs, the
physics-informed ESNs are trained to solve supervised learning tasks while
ensuring that their predictions do not violate physical laws. This is achieved
by introducing an additional loss function during the training of the ESNs,
which penalizes non-physical predictions without the need of any additional
training data. This approach is demonstrated on a chaotic Lorenz system, where
the physics-informed ESNs improve the predictability horizon by about two
Lyapunov times as compared to conventional ESNs. The proposed framework shows
the potential of using machine learning combined with prior physical knowledge
to improve the time-accurate prediction of chaotic dynamical systems.Comment: 7 pages, 3 figure
Physics-Informed Echo State Networks for Chaotic Systems Forecasting
We propose a physics-informed Echo State Network (ESN)
to predict the evolution of chaotic systems. Compared to conventional
ESNs, the physics-informed ESNs are trained to solve supervised learning
tasks while ensuring that their predictions do not violate physical laws.
This is achieved by introducing an additional loss function during the
training of the ESNs, which penalizes non-physical predictions without
the need of any additional training data. This approach is demonstrated
on a chaotic Lorenz system, where the physics-informed ESNs improve
the predictability horizon by about two Lyapunov times as compared to
conventional ESNs. The proposed framework shows the potential of using
machine learning combined with prior physical knowledge to improve the
time-accurate prediction of chaotic dynamical systems
Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks
Recurrent neural networks (RNNs) are widely used in computational
neuroscience and machine learning applications. In an RNN, each neuron computes
its output as a nonlinear function of its integrated input. While the
importance of RNNs, especially as models of brain processing, is undisputed, it
is also widely acknowledged that the computations in standard RNN models may be
an over-simplification of what real neuronal networks compute. Here, we suggest
that the RNN approach may be made both neurobiologically more plausible and
computationally more powerful by its fusion with Bayesian inference techniques
for nonlinear dynamical systems. In this scheme, we use an RNN as a generative
model of dynamic input caused by the environment, e.g. of speech or kinematics.
Given this generative RNN model, we derive Bayesian update equations that can
decode its output. Critically, these updates define a 'recognizing RNN' (rRNN),
in which neurons compute and exchange prediction and prediction error messages.
The rRNN has several desirable features that a conventional RNN does not have,
for example, fast decoding of dynamic stimuli and robustness to initial
conditions and noise. Furthermore, it implements a predictive coding scheme for
dynamic inputs. We suggest that the Bayesian inversion of recurrent neural
networks may be useful both as a model of brain function and as a machine
learning tool. We illustrate the use of the rRNN by an application to the
online decoding (i.e. recognition) of human kinematics
Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions
The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions
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