120,219 research outputs found
A cascade approach to complex-type classification
The work detailed in this paper describes a 2-step cascade approach for the classification of complex-type nominals. We describe an experiment that demonstrates how a cascade approach performs when the task consists in distinguishing nominals from a given complex-type from any other noun in the language. Overall, our classifier successfully identifies very specific and not highly frequent lexical items such as complex-types with high accuracy, and distinguishes them from those instances that are not complex types by using lexico-syntactic patterns indicative of the semantic classes corresponding to each of the individual sense components of the complex type. Although there is still room for improvement with regard to the coverage of the classifiers developed, the cascade approach increases the precision of classification of the complex-type nouns that are covered in the experiment presented.info:eu-repo/semantics/publishedVersio
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
Chest X-ray is one of the most accessible medical imaging technique for
diagnosis of multiple diseases. With the availability of ChestX-ray14, which is
a massive dataset of chest X-ray images and provides annotations for 14
thoracic diseases; it is possible to train Deep Convolutional Neural Networks
(DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we
experiment a set of deep learning models and present a cascaded deep neural
network that can diagnose all 14 pathologies better than the baseline and is
competitive with other published methods. Our work provides the quantitative
results to answer following research questions for the dataset: 1) What loss
functions to use for training DCNN from scratch on ChestX-ray14 dataset that
demonstrates high class imbalance and label co occurrence? 2) How to use
cascading to model label dependency and to improve accuracy of the deep
learning model?Comment: Submitted to CVPR 201
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
The design of complexity-aware cascaded detectors, combining features of very
different complexities, is considered. A new cascade design procedure is
introduced, by formulating cascade learning as the Lagrangian optimization of a
risk that accounts for both accuracy and complexity. A boosting algorithm,
denoted as complexity aware cascade training (CompACT), is then derived to
solve this optimization. CompACT cascades are shown to seek an optimal
trade-off between accuracy and complexity by pushing features of higher
complexity to the later cascade stages, where only a few difficult candidate
patches remain to be classified. This enables the use of features of vastly
different complexities in a single detector. In result, the feature pool can be
expanded to features previously impractical for cascade design, such as the
responses of a deep convolutional neural network (CNN). This is demonstrated
through the design of a pedestrian detector with a pool of features whose
complexities span orders of magnitude. The resulting cascade generalizes the
combination of a CNN with an object proposal mechanism: rather than a
pre-processing stage, CompACT cascades seamlessly integrate CNNs in their
stages. This enables state of the art performance on the Caltech and KITTI
datasets, at fairly fast speeds
Streamlining collection of training samples for object detection and classification in video
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Studies of Single-Molecule Dynamics in Microorganisms
Fluorescence microscopy is one of the most extensively used techniques in the life sciences. Considering the non-invasive sample preparation, enabling live-cell compliant imaging, and the specific fluorescence labeling, allowing for a specific visualization of virtually any cellular compound, it is possible to localize even a single molecule in living cells. This makes modern fluorescence microscopy a powerful toolbox.
In the recent decades, the development of new, "super-resolution" fluorescence microscopy techniques, which surpass the diffraction limit, revolutionized the field. Single-Molecule Localization Microscopy (SMLM) is a class of super-resolution microscopy methods and it enables resolution of down to tens of nanometers. SMLM methods like Photoactivated Localization Microscopy (PALM), (direct) Stochastic Optical Reconstruction Microscopy ((d)STORM), Ground-State Depletion followed by Individual Molecule Return (GSDIM) and Point Accumulation for Imaging in Nanoscale Topography (PAINT) have allowed to investigate both, the intracellular spatial organization of proteins and to observe their real-time dynamics at the single-molecule level in live cells.
The focus of this thesis was the development of novel tools and strategies for live-cell SingleParticle Tracking PALM (sptPALM) imaging and implementing them for biological research. In the first part of this thesis, I describe the development of new Photoconvertible Fluorescent Proteins (pcFPs) which are optimized for sptPALM lowering the phototoxic damage caused by the imaging procedure. Furthermore, we show that we can utilize them together with Photoactivatable Fluorescent Proteins (paFPs) to enable multi-target labeling and read-out in a single color channel, which significantly simplifies the sample preparation and imaging routines as well as data analysis of multi-color PALM imaging of live cells.
In parallel to developing new fluorescent proteins, I developed a high throughput data analysis pipeline. I have implemented this pipeline in my second project, described in the second part of this thesis, where I have investigated the protein organization and dynamics of the CRISPR-Cas antiviral defense mechanism of bacteria in vivo at a high spatiotemporal level with the sptPALM approach. I was successful to show the differences in the target search dynamics of the CRISPR effector complexes as well as of single Cas proteins for different target complementarities. I have also first data describing longer-lasting bound-times between effector complex and their potential targets in vivo, for which only in vitro data has been available till today.
In summary, this thesis is a significant contribution for both, the advances of current sptPALM imaging methods, as well as for the understanding of the native behavior of CRISPR-Cas systems in vivo
Influence of augmented humans in online interactions during voting events
The advent of the digital era provided a fertile ground for the development
of virtual societies, complex systems influencing real-world dynamics.
Understanding online human behavior and its relevance beyond the digital
boundaries is still an open challenge. Here we show that online social
interactions during a massive voting event can be used to build an accurate map
of real-world political parties and electoral ranks. We provide evidence that
information flow and collective attention are often driven by a special class
of highly influential users, that we name "augmented humans", who exploit
thousands of automated agents, also known as bots, for enhancing their online
influence. We show that augmented humans generate deep information cascades, to
the same extent of news media and other broadcasters, while they uniformly
infiltrate across the full range of identified groups. Digital augmentation
represents the cyber-physical counterpart of the human desire to acquire power
within social systems.Comment: 11 page
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