Rensselaer Polytechnic Institute

About: Rensselaer Polytechnic Institute is a education organization based out in Troy, New York, United States. It is known for research contribution in the topics: Terahertz radiation & Population. The organization has 19024 authors who have published 39922 publications receiving 1414699 citations. The organization is also known as: RPI & Rensselaer Institute.

A Joint Neural Model for Information Extraction with Global Features.

Abstract: Most existing joint neural models for Information Extraction (IE) use local task-specific classifiers to predict labels for individual instances (e.g., trigger, relation) regardless of their interactions. For example, a victim of a die event is likely to be a victim of an attack event in the same sentence. In order to capture such cross-subtask and cross-instance inter-dependencies, we propose a joint neural framework, OneIE, that aims to extract the globally optimal IE result as a graph from an input sentence. OneIE performs end-to-end IE in four stages: (1) Encoding a given sentence as contextualized word representations; (2) Identifying entity mentions and event triggers as nodes; (3) Computing label scores for all nodes and their pairwise links using local classifiers; (4) Searching for the globally optimal graph with a beam decoder. At the decoding stage, we incorporate global features to capture the cross-subtask and cross-instance interactions. Experiments show that adding global features improves the performance of our model and achieves new state of-the-art on all subtasks. In addition, as OneIE does not use any language-specific feature, we prove it can be easily applied to new languages or trained in a multilingual manner.

Inkjet-bioprinted acrylated peptides and PEG hydrogel with human mesenchymal stem cells promote robust bone and cartilage formation with minimal printhead clogging.

Abstract: Inkjet bioprinting is one of the most promising additive manufacturing approaches for tissue fabrication with the advantages of high speed, high resolution, and low cost. The limitation of this technology is the potential damage to the printed cells and frequent clogging of the printhead. Here we developed acrylated peptides and co-printed with acrylated poly(ethylene glycol) (PEG) hydrogel with simultaneous photopolymerization. At the same time, the bone marrow-derived human mesenchymal stem cells (hMSCs) were precisely printed during the scaffold fabrication process so the cells were delivered simultaneously with minimal UV exposure. The multiple steps of scaffold synthesis and cell encapsulation were successfully combined into one single step using bioprinting. The resulted peptide-conjugated PEG scaffold demonstrated excellent biocompatibility, with a cell viability of 87.9 ± 5.3%. Nozzle clogging was minimized due to the low viscosity of the PEG polymer. With osteogenic and chondrogenic differentiation, the bioprinted bone and cartilage tissue demonstrated excellent mineral and cartilage matrix deposition, as well as significantly increased mechanical properties. Strikingly, the bioprinted PEG-peptide scaffold dramatically inhibited hMSC hypertrophy during chondrogenic differentiation. Collectively, bioprinted PEG-peptide scaffold and hMSCs significantly enhanced osteogenic and chondrogenic differentiation for robust bone and cartilage formation with minimal printhead clogging.

Registration of Challenging Image Pairs: Initialization, Estimation, and Decision

Abstract: Our goal is an automated 2D-image-pair registration algorithm capable of aligning images taken of a wide variety of natural and man-made scenes as well as many medical images. The algorithm should handle low overlap, substantial orientation and scale differences, large illumination variations, and physical changes in the scene. An important component of this is the ability to automatically reject pairs that have no overlap or have too many differences to be aligned well. We propose a complete algorithm including techniques for initialization, for estimating transformation parameters, and for automatically deciding if an estimate is correct. Keypoints extracted and matched between images are used to generate initial similarity transform estimates, each accurate over a small region. These initial estimates are rank-ordered and tested individually in succession. Each estimate is refined using the Dual-Bootstrap ICP algorithm, driven by matching of multiscale features. A three-part decision criteria, combining measurements of alignment accuracy, stability in the estimate, and consistency in the constraints, determines whether the refined transformation estimate is accepted as correct. Experimental results on a data set of 22 challenging image pairs show that the algorithm effectively aligns 19 of the 22 pairs and rejects 99.8 percent of the misalignments that occur when all possible pairs are tried. The algorithm substantially out-performs algorithms based on keypoint matching alone.

Carbon Nanotubes as High-Pressure Cylinders and Nanoextruders

Abstract: Closed-shell carbon nanostructures, such as carbon onions, have been shown to act as self-contracting high-pressure cells under electron irradiation. We report that controlled irradiation of multiwalled carbon nanotubes can cause large pressure buildup within the nanotube cores that can plastically deform, extrude, and break solid materials that are encapsulated inside the core. We further showed by atomistic simulations that the internal pressure inside nanotubes can reach values higher than 40 gigapascals. Nanotubes can thus be used as robust nanoscale jigs for extruding and deforming hard nanomaterials and for modifying their properties, as well as templates for the study of individual nanometer-sized crystals under high pressure.