Lifting GIS Maps into Strong Geometric Context for Scene Understanding

Contextual information can have a substantial impact on the performance of visual tasks such as semantic segmentation, object detection, and geometric estimation. Data stored in Geographic Information Systems (GIS) offers a rich source of contextual information that has been largely untapped by computer vision. We propose to leverage such information for scene understanding by combining GIS resources with large sets of unorganized photographs using Structure from Motion (SfM) techniques. We present a pipeline to quickly generate strong 3D geometric priors from 2D GIS data using SfM models aligned with minimal user input. Given an image resectioned against this model, we generate robust predictions of depth, surface normals, and semantic labels. We show that the precision of the predicted geometry is substantially more accurate other single-image depth estimation methods. We then demonstrate the utility of these contextual constraints for re-scoring pedestrian detections, and use these GIS contextual features alongside object detection score maps to improve a CRF-based semantic segmentation framework, boosting accuracy over baseline models

A Constrained Finite-State Morphotactics for Korean

PACLIC 19 / Taipei, taiwan / December 1-3, 200

Ultrafast Chemical Exchange Dynamics of Hydrogen Bonds Observed via Isonitrile Infrared Sensors: Implications for Biomolecular Studies

Local probes are indispensable to study protein structure and dynamics with site-specificity. The isonitrile functional group is a highly sensitive and H-bonding interaction-specific probe. Isonitriles exhibit large spectral shifts and transition dipole moment changes upon H-bonding while being weakly affected by solvent polarity. These unique properties allow a clear separation of distinct subpopulations of interacting species and an elucidation of their ultrafast dynamics with two-dimensional infrared (2D-IR) spectroscopy. Here, we apply 2D-IR to quantify the picosecond chemical exchange dynamics of solute–solvent complexes forming between isonitrile-derivatized alanine and fluorinated ethanol, where the degree of fluorination controls their H-bond-donating ability. We show that the molecules undergo faster exchange in the presence of more acidic H-bond donors, indicating that the exchange process is primarily dependent on the nature of solvent–solvent interactions. We foresee isonitrile as a highly promising probe for studying of H-bonds dynamics in the active site of enzymes. © 2019 American Chemical Society11sciescopu

Unveiling the pathway to Z-DNA in the protein-induced B–Z transition

Left-handed Z-DNA is an extraordinary conformation of DNA, which can form by special sequences under specific biological, chemical or physical conditions. Human ADAR1, prototypic Z-DNA binding protein (ZBP), binds to Z-DNA with high affinity. Utilizing single-molecule FRET assays for Z-DNA forming sequences embedded in a long inactive DNA, we measure thermodynamic populations of ADAR1-bound DNA conformations in both GC and TG repeat sequences. Based on a statistical physics model, we determined quantitatively the affinities of ADAR1 to both Z-form and B-form of these sequences. We also reported what pathways it takes to induce the B–Z transition in those sequences. Due to the high junction energy, an intermediate B* state has to accumulate prior to the B–Z transition. Our study showing the stable B* state supports the active picture for the protein-induced B–Z transition that occurs under a physiological setting. (c)The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research

Configurationality Parameter in Korean and its Computational Implication : An HPSG Approach

This paper has the following two purposes. First, I will show that the configurational hypothesis provides a good basis for the adequate description of the so-called "case conversion " phenomena involved in nominalization and causativization in Korean. With respect to the case conversions, I will discus

Geometric Reconstruction for Visual data Interpretation

Reconstruction happens in the human brain every day. When humans watch their surrounding scene, they effortlessly infer dynamic representations of scene geometry from sequences of images. This higher dimensional reconstruction not only helps to interpret the input data but also provides an important basis for performing complex, higher level tasks. In spite of the importance of scene reconstruction, it has been considered a difficult task since the amount of information in the input image (2D) is insufficient to fully reconstruct scene geometry (3D). Performing such a task clearly requires the use of prior knowledge. In this thesis, we explore the advantages of machine learning-based techniques in order to reconstruct geometric information from images or videos. We utilize deep neural networks and probabilistic models and demonstrate their effectiveness in reconstructing geometric information.As the first part of this thesis, we estimate 2D motion flow from video, leveraging constraints of camera ego-motion and scene geometry. From a sequence of images, we first predict relative ego-motion between the input frames, and then reconstruct the camera trajectory. By considering the cycle consistency between 2D motion, depth and camera ego-motion, we train a model to reconstruct scene depth without additional supervision. These processes are trained using a self- supervised end-to-end convolutional neural network (CNN) architecture with motion field driven photometric consistency loss. To minimize accumulated error from imperfect local estimates, we predict relative reliability scores between every connected pair of input frames and then utilize them in global refinement.In the second part, we reconstruct spatio-temporal 4D model from a set of 3D models. From a set of multiple 3D models, we optimize transformation parameters from each model space to global space using a Gaussian mixture to model point observations. We optimize alignment parameters using expectation-maximization algorithm and estimate the temporal extent for each 3D patches by maximizing expected posterior probability over time. This spatial-temporal model allows us to perform object segmentation as well as infer the existence of occluded objects