Self-organized Ge nanocrystals embedded in HfAlO fabricated by pulsed-laser deposition and application to floating gate memory

2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Free-hand sketch synthesis with deformable stroke models

We present a generative model which can automatically summarize the stroke composition of free-hand sketches of a given category. When our model is fit to a collection of sketches with similar poses, it discovers and learns the structure and appearance of a set of coherent parts, with each part represented by a group of strokes. It represents both consistent (topology) as well as diverse aspects (structure and appearance variations) of each sketch category. Key to the success of our model are important insights learned from a comprehensive study performed on human stroke data. By fitting this model to images, we are able to synthesize visually similar and pleasant free-hand sketches

Fast and Accurate Lung Tumor Spotting and Segmentation for Boundary Delineation on CT Slices In A Coarse-To-Fine Framework

Label noise and class imbalance are two of the critical challenges when training image-based deep neural networks, especially in the biomedical image processing domain. Our work focuses on how to address the two challenges effectively and accurately in the task of lesion segmentation from biomedical/medical images. To address the pixel-level label noise problem, we propose an advanced transfer training and learning approach with a detailed DICOM pre-processing method. To address the tumor/non-tumor class imbalance problem, we exploit a self-adaptive fully convolutional neural network with an automated weight distribution mechanism to spot the Radiomics lung tumor regions accurately. Furthermore, an improved conditional random field method is employed to obtain sophisticated lung tumor contour delineation and segmentation. Finally, our approach has been evaluated using several well-known evaluation metrics on the Lung Tumor segmentation dataset used in the 2018 IEEE VIP-CUP Challenge. Experimental results show that our weakly supervised learning algorithm outperforms other deep models and state-of-the-art approache

Bjorken Flow, Plasma Instabilities, and Thermalization

At asymptotically high energies, thermalization in heavy ion collisions can be described via weak-coupling QCD. We present a complete treatment of how thermalization proceeds, at the parametric weak-coupling level. We show that plasma instabilities dominate the dynamics, from immediately after the collision until well after the plasma becomes nearly in equilibrium. Initially they drive the system close to isotropy, but Bjorken expansion and increasing diluteness makes the system again become more anisotropic. At time \tau ~ \alpha^(-12/5) Q^(-1) the dynamics become dominated by a nearly-thermal bath; and at time \tau ~ \alpha^(-5/2) Q^(-1)$ the bath comes to dominate the energy density, completing thermalization. After this time there is a nearly isotropic and thermal Quark-Gluon Plasma.Comment: 22 pages, 5 figure

Hierarchical information clustering by means of topologically embedded graphs

We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.Comment: 33 Pages, 18 Figures, 5 Table

New directions in cellular therapy of cancer: a summary of the summit on cellular therapy for cancer

A summit on cellular therapy for cancer discussed and presented advances related to the use of adoptive cellular therapy for melanoma and other cancers. The summit revealed that this field is advancing rapidly. Conventional cellular therapies, such as tumor infiltrating lymphocytes (TIL), are becoming more effective and more available. Gene therapy is becoming an important tool in adoptive cell therapy. Lymphocytes are being engineered to express high affinity T cell receptors (TCRs), chimeric antibody-T cell receptors (CARs) and cytokines. T cell subsets with more naïve and stem cell-like characteristics have been shown in pre-clinical models to be more effective than unselected populations and it is now possible to reprogram T cells and to produce T cells with stem cell characteristics. In the future, combinations of adoptive transfer of T cells and specific vaccination against the cognate antigen can be envisaged to further enhance the effectiveness of these therapies

Accounting Problems Under the Excess Profits Tax

DNA vaccines based on subunits from pathogens have several advantages over other vaccine strategies. DNA vaccines can easily be modified, they show good safety profiles, are stable and inexpensive to produce, and the immune response can be focused to the antigen of interest. However, the immunogenicity of DNA vaccines which is generally quite low needs to be improved. Electroporation and co-delivery of genetically encoded immune adjuvants are two strategies aiming at increasing the efficacy of DNA vaccines. Here, we have examined whether targeting to antigen-presenting cells (APC) could increase the immune response to surface envelope glycoprotein (Env) gp120 from Human Immunodeficiency Virus type 1 (HIV- 1). To target APC, we utilized a homodimeric vaccine format denoted vaccibody, which enables covalent fusion of gp120 to molecules that can target APC. Two molecules were tested for their efficiency as targeting units: the antibody-derived single chain Fragment variable (scFv) specific for the major histocompatilibility complex (MHC) class II I-E molecules, and the CC chemokine ligand 3 (CCL3). The vaccines were delivered as DNA into muscle of mice with or without electroporation. Targeting of gp120 to MHC class II molecules induced antibodies that neutralized HIV-1 and that persisted for more than a year after one single immunization with electroporation. Targeting by CCL3 significantly increased the number of HIV-1 gp120-reactive CD8(+) T cells compared to non-targeted vaccines and gp120 delivered alone in the absence of electroporation. The data suggest that chemokines are promising molecular adjuvants because small amounts can attract immune cells and promote immune responses without advanced equipment such as electroporation.Funding Agencies|Research Council of Norway; Odd Fellow</p

Investigation of Ge nanocrytals in a metal-insulator-semiconductor structure with a HfO₂/SiO₂stack as the tunnel dielectric

Author name used in this publication: Weili LiuAuthor name used in this publication: J. Y. DaiAuthor name used in this publication: P. F. LeeAuthor name used in this publication: Zhitang Song2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe