48 research outputs found
RGMQL: scalable and interoperable computing of heterogeneous omics big data and metadata in R/Bioconductor
Heterogeneous omics data, increasingly collected through high-throughput technologies, can contain hidden answers to very important and still unsolved biomedical questions. Their integration and processing are crucial mostly for tertiary analysis of Next Generation Sequencing data, although suitable big data strategies still address mainly primary and secondary analysis. Hence, there is a pressing need for algorithms specifically designed to explore big omics datasets, capable of ensuring scalability and interoperability, possibly relying on high-performance computing infrastructures
Handwritten Text Generation from Visual Archetypes
Generating synthetic images of handwritten text in a writer-specific style is
a challenging task, especially in the case of unseen styles and new words, and
even more when these latter contain characters that are rarely encountered
during training. While emulating a writer's style has been recently addressed
by generative models, the generalization towards rare characters has been
disregarded. In this work, we devise a Transformer-based model for Few-Shot
styled handwritten text generation and focus on obtaining a robust and
informative representation of both the text and the style. In particular, we
propose a novel representation of the textual content as a sequence of dense
vectors obtained from images of symbols written as standard GNU Unifont glyphs,
which can be considered their visual archetypes. This strategy is more suitable
for generating characters that, despite having been seen rarely during
training, possibly share visual details with the frequently observed ones. As
for the style, we obtain a robust representation of unseen writers' calligraphy
by exploiting specific pre-training on a large synthetic dataset. Quantitative
and qualitative results demonstrate the effectiveness of our proposal in
generating words in unseen styles and with rare characters more faithfully than
existing approaches relying on independent one-hot encodings of the characters.Comment: Accepted at CVPR202
Supervised Relevance-Redundancy assessments for feature selection in omics-based classification scenarios
Background and objective: Many classification tasks in translational bioinformatics and genomics are characterized by the high dimensionality of potential features and unbalanced sample distribution among classes. This can affect classifier robustness and increase the risk of overfitting, curse of dimensionality and generalization leaks; furthermore and most importantly, this can prevent obtaining adequate patient stratification required for precision medicine in facing complex diseases, like cancer. Setting up a feature selection strategy able to extract only proper predictive features by removing irrelevant, redundant, and noisy ones is crucial to achieving valuable results on the desired task. Methods: We propose a new feature selection approach, called ReRa, based on supervised Relevance-Redundancy assessments. ReRa consists of a customized step of relevance-based filtering, to identify a reduced subset of meaningful features, followed by a supervised similarity-based procedure to minimize redundancy. This latter step innovatively uses a combination of global and class-specific similarity assessments to remove redundant features while preserving those differentiated across classes, even when these classes are strongly unbalanced. Results: We compared ReRa with several existing feature selection methods to obtain feature spaces on which performing breast cancer patient subtyping using several classifiers: we considered two use cases based on gene or transcript isoform expression. In the vast majority of the assessed scenarios, when using ReRa-selected feature spaces, the performances were significantly increased compared to simple feature filtering, LASSO regularization, or even MRmr - another Relevance-Redundancy method. The two use cases represent an insightful example of translational application, taking advantage of ReRa capabilities to investigate and enhance a clinically-relevant patient stratification task, which could be easily applied also to other cancer types and diseases. Conclusions: ReRa approach has the potential to improve the performance of machine learning models used in an unbalanced classification scenario. Compared to another Relevance-Redundancy approach like MRmr, ReRa does not require tuning the number of preserved features, ensures efficiency and scalability over huge initial dimensionalities and allows re-evaluation of all previously selected features at each iteration of the redundancy assessment, to ultimately preserve only the most relevant and class-differentiated features
Boosting Modern and Historical Handwritten Text Recognition with Deformable Convolutions
Handwritten Text Recognition (HTR) in free-layout pages is a challenging image understanding task that can provide a relevant boost to the digitization of handwritten documents and reuse of their content. The task becomes even more challenging when dealing with historical documents due to the variability of the writing style and degradation of the page quality. State-of-the-art HTR approaches typically couple recurrent structures for sequence modeling with Convolutional Neural Networks for visual feature extraction. Since convolutional kernels are defined on fixed grids and focus on all input pixels independently while moving over the input image, this strategy disregards the fact that handwritten characters can vary in shape, scale, and orientation even within the same document and that the ink pixels are more relevant than the background ones. To cope with these specific HTR difficulties, we propose to adopt deformable convolutions, which can deform depending on the input at hand and better adapt to the geometric variations of the text. We design two deformable architectures and conduct extensive experiments on both modern and historical datasets. Experimental results confirm the suitability of deformable convolutions for the HTR task
HWD: A Novel Evaluation Score for Styled Handwritten Text Generation
Styled Handwritten Text Generation (Styled HTG) is an important task in
document analysis, aiming to generate text images with the handwriting of given
reference images. In recent years, there has been significant progress in the
development of deep learning models for tackling this task. Being able to
measure the performance of HTG models via a meaningful and representative
criterion is key for fostering the development of this research topic. However,
despite the current adoption of scores for natural image generation evaluation,
assessing the quality of generated handwriting remains challenging. In light of
this, we devise the Handwriting Distance (HWD), tailored for HTG evaluation. In
particular, it works in the feature space of a network specifically trained to
extract handwriting style features from the variable-lenght input images and
exploits a perceptual distance to compare the subtle geometric features of
handwriting. Through extensive experimental evaluation on different word-level
and line-level datasets of handwritten text images, we demonstrate the
suitability of the proposed HWD as a score for Styled HTG. The pretrained model
used as backbone will be released to ease the adoption of the score, aiming to
provide a valuable tool for evaluating HTG models and thus contributing to
advancing this important research area.Comment: Accepted at BMVC202
Out of the Box: Embodied Navigation in the Real World
The research field of Embodied AI has witnessed substantial progress in visual navigation and exploration thanks to powerful simulating platforms and the availability of 3D data of indoor and photorealistic environments. These two factors have opened the doors to a new generation of intelligent agents capable of achieving nearly perfect PointGoal Navigation. However, such architectures are commonly trained with millions, if not billions, of frames and tested in simulation. Together with great enthusiasm, these results yield a question: how many researchers will effectively benefit from these advances?
In this work, we detail how to transfer the knowledge acquired in simulation into the real world. To that end, we describe the architectural discrepancies that damage the Sim2Real adaptation ability of models trained on the Habitat simulator and propose a novel solution tailored towards the deployment in real-world scenarios. We then deploy our models on a LoCoBot, a Low-Cost Robot equipped with a single Intel RealSense camera. Different from previous work, our testing scene is unavailable to the agent in simulation. The environment is also inaccessible to the agent beforehand, so it cannot count on scene-specific semantic priors. In this way, we reproduce a setting in which a research group (potentially from other fields) needs to employ the agent visual navigation capabilities as-a-Service. Our experiments indicate that it is possible to achieve satisfying results when deploying the obtained model in the real world
Focus on Impact: Indoor Exploration with Intrinsic Motivation
Exploration of indoor environments has recently experienced a significant
interest, also thanks to the introduction of deep neural agents built in a
hierarchical fashion and trained with Deep Reinforcement Learning (DRL) on
simulated environments. Current state-of-the-art methods employ a dense
extrinsic reward that requires the complete a priori knowledge of the layout of
the training environment to learn an effective exploration policy. However,
such information is expensive to gather in terms of time and resources. In this
work, we propose to train the model with a purely intrinsic reward signal to
guide exploration, which is based on the impact of the robot's actions on its
internal representation of the environment. So far, impact-based rewards have
been employed for simple tasks and in procedurally generated synthetic
environments with countable states. Since the number of states observable by
the agent in realistic indoor environments is non-countable, we include a
neural-based density model and replace the traditional count-based
regularization with an estimated pseudo-count of previously visited states. The
proposed exploration approach outperforms DRL-based competitors relying on
intrinsic rewards and surpasses the agents trained with a dense extrinsic
reward computed with the environment layouts. We also show that a robot
equipped with the proposed approach seamlessly adapts to point-goal navigation
and real-world deployment.Comment: Published in IEEE Robotics and Automation Letters. To appear in ICRA
202
Spot the Difference: A Novel Task for Embodied Agents in Changing Environments
Embodied AI is a recent research area that aims at creating intelligent
agents that can move and operate inside an environment. Existing approaches in
this field demand the agents to act in completely new and unexplored scenes.
However, this setting is far from realistic use cases that instead require
executing multiple tasks in the same environment. Even if the environment
changes over time, the agent could still count on its global knowledge about
the scene while trying to adapt its internal representation to the current
state of the environment. To make a step towards this setting, we propose Spot
the Difference: a novel task for Embodied AI where the agent has access to an
outdated map of the environment and needs to recover the correct layout in a
fixed time budget. To this end, we collect a new dataset of occupancy maps
starting from existing datasets of 3D spaces and generating a number of
possible layouts for a single environment. This dataset can be employed in the
popular Habitat simulator and is fully compliant with existing methods that
employ reconstructed occupancy maps during navigation. Furthermore, we propose
an exploration policy that can take advantage of previous knowledge of the
environment and identify changes in the scene faster and more effectively than
existing agents. Experimental results show that the proposed architecture
outperforms existing state-of-the-art models for exploration on this new
setting.Comment: Accepted by 26TH International Conference on Pattern Recognition
(ICPR 2022