Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Cognitive radio & Wireless network. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

3D Printed Cartilage-Like Tissue Constructs with Spatially Controlled Mechanical Properties

Abstract: Developing biomimetic cartilaginous tissues that support locomotion while maintaining chondrogenic behavior is a major challenge in the tissue engineering field. Specifically, while locomotive forces demand tissues with strong mechanical properties, chondrogenesis requires a soft microenvironment. To address this challenge, 3D cartilage-like tissue is bioprinted using two biomaterials with different mechanical properties: a hard biomaterial to reflect the macromechanical properties of native cartilage, and a soft biomaterial to create a chondrogenic microenvironment. To this end, a hard biomaterial (MPa order compressive modulus) composed of an interpenetrating polymer network (IPN) of polyethylene glycol (PEG) and alginate hydrogel is developed as an extracellular matrix (ECM) with self-healing properties, but low diffusive capacity. Within this bath supplemented with thrombin, fibrinogen containing human mesenchymal stem cell (hMSC) spheroids is bioprinted forming fibrin, as the soft biomaterial (kPa order compressive modulus) to simulate cartilage's pericellular matrix and allow a fast diffusion of nutrients. The bioprinted hMSC spheroids improve viability and chondrogenic-like behavior without adversely affecting the macromechanical properties of the tissue. Therefore, the ability to print locally soft and cell stimulating microenvironments inside of a mechanically robust hydrogel is demonstrated, thereby uncoupling the micro- and macromechanical properties of the 3D printed tissues such as cartilage.

Rotational and vibrational temperature measurements in a high-pressure cylindrical dielectric barrier discharge (C-DBD)

Abstract: The rotational (TR) and vibrational (Tv) temperatures of N2 molecules were measured in a high-pressure cylindrical dielectric barrier discharge (C-DBD) source in Ne with trace amounts (0.02 %) of N2 and dry air excited by radio-frequency (rf) power. Both TR and Tv of the N2 molecules in the C 3Πu state were determined from an emission spectroscopic analysis the 2nd positive system (C 3Πu B3Πg). Gas temperatures were inferred from the measured rotational temperatures. As a function of pressure, the rotational temperature is essentially constant at about 360 K in the range from 200 Torr to 600 Torr (at 30W rf power) and increases slightly with increasing rf power at constant pressure. As one would expect, vibrational temperature measurements revealed significantly higher temperatures. The vibrational temperature decreases with pressure from 3030 K at 200 Torr to 2270 K at 600 Torr (at 30 W rf power). As a function of rf power, the vibrational temperature increases from 2520 K at 20 W to 2940 K at 60 W (at 400 Torr). Both TR and Tv also show a dependence on the excitation frequency at the two frequencies that we studied, 400 kHz and 13.56 MHz. Adding trace amounts of air instead of N2 to the Ne in the discharge resulted in higher TR and Tv values and in a different pressure dependence of the rotational and vibrational temperatures. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds

Abstract: Kernel $k$-means clustering can correctly identify and extract a far more varied collection of cluster structures than the linear $k$-means clustering algorithm. However, kernel $k$-means clustering is computationally expensive when the non-linear feature map is high-dimensional and there are many input points. Kernel approximation, e.g., the Nystrom method, has been applied in previous works to approximately solve kernel learning problems when both of the above conditions are present. This work analyzes the application of this paradigm to kernel $k$-means clustering, and shows that applying the linear $k$-means clustering algorithm to $\frac{k}{\epsilon} (1 + o(1))$ features constructed using a so-called rank-restricted Nystrom approximation results in cluster assignments that satisfy a $1 + \epsilon$ approximation ratio in terms of the kernel $k$-means cost function, relative to the guarantee provided by the same algorithm without the use of the Nystrom method. As part of the analysis, this work establishes a novel $1 + \epsilon$ relative-error trace norm guarantee for low-rank approximation using the rank-restricted Nystrom approximation. Empirical evaluations on the $8.1$ million instance MNIST8M dataset demonstrate the scalability and usefulness of kernel $k$-means clustering with Nystrom approximation. This work argues that spectral clustering using Nystrom approximation---a popular and computationally efficient, but theoretically unsound approach to non-linear clustering---should be replaced with the efficient and theoretically sound combination of kernel $k$-means clustering with Nystrom approximation. The superior performance of the latter approach is empirically verified.