Regional Data Archiving and Management for Northeast Illinois

This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the regional transportation agencies, from both technical and business perspectives, about building such a comprehensive transportation information system. Several implementation alternatives are identified and analyzed. This research is carried out in three phases. In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect information on all data elements that they store, including the format, system, and granularity. Their perception of a data archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the hardware, software, labor, and resource requirements. We also identify possible revenue opportunities. A few implementation options for the archive system are summarized in this report; namely: 1. System hosted by a partnering agency 2. System contracted to a university 3. System contracted to a national laboratory 4. System outsourced to a service provider The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe

The UNC-83/UNC-84 LINC members are required for body wall muscle nuclei positioning in C. elegans

From a mutagenesis screen in the nematode C. elegans we isolated the mutant bar18, showing an accumulation of muscle cell nuclei around the posterior pharyngeal bulb of the worm. Quantification of the overall amount of body wall muscle nuclei, based on the muscle-specific reporter myo-3p::gfp::NLS, revealed that the number of nuclei in bar18 mutants is unchanged compared to WT worms. The accumulation of muscle nuclei around the posterior pharyngeal bulb is due to a positioning defect, which can be precisely quantified by subdividing the worm into head, neck, and posterior body segments. Whole-genome sequencing revealed that bar18 animals carry a mutation in the KASH-domain gene unc-83 causing a premature STOP. An additional unc-83 mutant allele recapitulates the phenotype, as does a mutant allele of UNC-84, a SUN-domain containing protein that interacts with UNC-83. UNC-83 and UNC-84 belong to a Linker of Nucleoskeleton and Cytoskeletonnuclear (LINC) complex that bridges the nuclear lamina with the cytoskeleton. SUN and KASH domain proteins are conserved in mammals and mutations in the corresponding genes have been linked to cancer, autism, muscular dystrophy and other diseases. Additionally, LINC complexes that function in nuclear migration have also been identified in mammals. We were able to rescue the unc-83 mutant phenotype by expressing the WT gene under a muscle-specific (myo-3p) promoter, demonstrating that the effect is cell autonomous. Mutations in either unc-83 or unc-84 have previously been linked to nuclear migration defects in P cells, intestinal cells and hyp7 hypodermal precursors but not in muscles. Whether the mis-positioning of muscle nuclei is due to migration or anchoring defects still needs to be determined

Perceptual Visibility Model for Temporal Contrast Changes in Periphery

Modeling perception is critical for many applications and developments in computer graphics to optimize and evaluate content generation techniques. Most of the work to date has focused on central (foveal) vision. However, this is insufficient for novel wide-field-of-view display devices, such as virtual and augmented reality headsets. Furthermore, the perceptual models proposed for the fovea do not readily extend to the off-center, peripheral visual field, where human perception is drastically different. In this paper, we focus on modeling the temporal aspect of visual perception in the periphery. We present new psychophysical experiments that measure the sensitivity of human observers to different spatio-temporal stimuli across a wide field of view. We use the collected data to build a perceptual model for the visibility of temporal changes at different eccentricities in complex video content. Finally, we discuss, demonstrate, and evaluate several problems that can be addressed using our technique. First, we show how our model enables injecting new content into the periphery without distracting the viewer, and we discuss the link between the model and human attention. Second, we demonstrate how foveated rendering methods can be evaluated and optimized to limit the visibility of temporal aliasing

Transdifferentiation: do transition states lie on the path of development?

The direct conversion of one differentiated cell fate into another identity is a process known as Transdifferentiation. During Transdifferentiation, cells pass through intermediate states that are not well understood. Given the potential application of transdifferentiation in regenerative medicine and disease modeling, a better understanding of intermediate states is crucial to avoid uncontrolled conversion or proliferation, which pose a risk for patients. Researchers have begun to analyze the transcriptomes of donor, converting and target cells of Transdifferentiation with single cell resolution to compare transitional states to those found along the path of development. Here, we review examples of Transdifferentiation in a range of model systems and organisms. We propose that cells pass either through a mixed, unspecific intermediate or progenitor-like state during Transdifferentiation, which, to varying degrees, resemble states seen during development

Component-based Attention for Large-scale Trademark Retrieval

The demand for large-scale trademark retrieval (TR) systems has significantly increased to combat the rise in international trademark infringement. Unfortunately, the ranking accuracy of current approaches using either hand-crafted or pre-trained deep convolution neural network (DCNN) features is inadequate for large-scale deployments. We show in this paper that the ranking accuracy of TR systems can be significantly improved by incorporating hard and soft attention mechanisms, which direct attention to critical information such as figurative elements and reduce attention given to distracting and uninformative elements such as text and background. Our proposed approach achieves state-of-the-art results on a challenging large-scale trademark dataset.Comment: Fix typos related to authors' informatio

MTRNet: A Generic Scene Text Eraser

Text removal algorithms have been proposed for uni-lingual scripts with regular shapes and layouts. However, to the best of our knowledge, a generic text removal method which is able to remove all or user-specified text regions regardless of font, script, language or shape is not available. Developing such a generic text eraser for real scenes is a challenging task, since it inherits all the challenges of multi-lingual and curved text detection and inpainting. To fill this gap, we propose a mask-based text removal network (MTRNet). MTRNet is a conditional adversarial generative network (cGAN) with an auxiliary mask. The introduced auxiliary mask not only makes the cGAN a generic text eraser, but also enables stable training and early convergence on a challenging large-scale synthetic dataset, initially proposed for text detection in real scenes. What's more, MTRNet achieves state-of-the-art results on several real-world datasets including ICDAR 2013, ICDAR 2017 MLT, and CTW1500, without being explicitly trained on this data, outperforming previous state-of-the-art methods trained directly on these datasets.Comment: Presented at ICDAR2019 Conferenc

Noise-based Enhancement for Foveated Rendering

Human visual sensitivity to spatial details declines towards the periphery. Novel image synthesis techniques, so-called foveated rendering, exploit this observation and reduce the spatial resolution of synthesized images for the periphery, avoiding the synthesis of high-spatial-frequency details that are costly to generate but not perceived by a viewer. However, contemporary techniques do not make a clear distinction between the range of spatial frequencies that must be reproduced and those that can be omitted. For a given eccentricity, there is a range of frequencies that are detectable but not resolvable. While the accurate reproduction of these frequencies is not required, an observer can detect their absence if completely omitted. We use this observation to improve the performance of existing foveated rendering techniques. We demonstrate that this specific range of frequencies can be efficiently replaced with procedural noise whose parameters are carefully tuned to image content and human perception. Consequently, these fre- quencies do not have to be synthesized during rendering, allowing more aggressive foveation, and they can be replaced by noise generated in a less expensive post-processing step, leading to improved performance of the ren- dering system. Our main contribution is a perceptually-inspired technique for deriving the parameters of the noise required for the enhancement and its calibration. The method operates on rendering output and runs at rates exceeding 200 FPS at 4K resolution, making it suitable for integration with real-time foveated rendering systems for VR and AR devices. We validate our results and compare them to the existing contrast enhancement technique in user experiments