The use of images and descriptive words for the development of an image database for product designers

This research aims to understand the role images currently play within the design process, in order to develop a classification of image types and reference keywords to construct an electronic image database for professional use in product design. Images play an important role in the design process, both in defining the context for designs and in informing the creation of individual design. They are also used to communicate with clients, to understand consumers, to assist in expressing the themes of the project, to understand the related environments, or to search for inspiration or functional solutions. Designers usually have their own collections of images, however for each project they still spend a significant amount of time searching images, either looking within their own collection or searching for new images. This study is based on the assumption that there is a structure that can show the relationship between the image itself and the information it conveys and can be used to develop the database. A product-image database will enable designers to consult images more easily and this will also facilitate communication of visual ideas among designers or between designers and their clients, thus augmenting its potential value in the professional design process. Also, the value of an image may be enhanced by applying its linguistic associations through descriptions and keywords which identify and interpret its content. Through a series of interviews, workshops, and understanding relevant issues, such as design method, linguistic theory, perception psychology and so on, a prototype database system was developed. It was developed based on three information divisions: SPECIFICATION, CHARACTERISTIC, and EMOTION. The three divisions construct a model of the information which an image conveys. The database prototype was tested and evaluated by groups of students and professional designers. The results showed that users understand the concept and working of the database and appreciated its value. They also indicated that the CHARACTERISTIC division was most valuable as it allows users to record images through their recollection of feelings

A Comparative Study on Spin-Orbit Torque Efficiencies from W/ferromagnetic and W/ferrimagnetic Heterostructures

It has been shown that W in its resistive form possesses the largest spin-Hall ratio among all heavy transition metals, which makes it a good candidate for generating efficient dampinglike spin-orbit torque (DL-SOT) acting upon adjacent ferromagnetic or ferrimagnetic (FM) layer. Here we provide a systematic study on the spin transport properties of W/FM magnetic heterostructures with the FM layer being ferromagnetic Co$_{20}$Fe$_{60}$B$_{20}$ or ferrimagnetic Co$_{63}$Tb$_{37}$ with perpendicular magnetic anisotropy. The DL-SOT efficiency $|\xi_{DL}|$, which is characterized by a current-induced hysteresis loop shift method, is found to be correlated to the microstructure of W buffer layer in both W/Co$_{20}$Fe$_{60}$B$_{20}$ and W/Co$_{63}$Tb$_{37}$ systems. Maximum values of $|\xi_{DL}|\approx 0.144$ and $|\xi_{DL}|\approx 0.116$ are achieved when the W layer is partially amorphous in the W/Co$_{20}$Fe$_{60}$B$_{20}$ and W/Co$_{63}$Tb$_{37}$ heterostructures, respectively. Our results suggest that the spin Hall effect from resistive phase of W can be utilized to effectively control both ferromagnetic and ferrimagnetic layers through a DL-SOT mechanism

Transthyretin Stimulates Tumor Growth through Regulation of Tumor, Immune, and Endothelial Cells

Early detection of lung cancer offers an important opportunity to decrease mortality while it is still treatable and curable. Thirteen secretory proteins that are Stat3 downstream gene products were identified as a panel of biomarkers for lung cancer detection in human sera. This panel of biomarkers potentially differentiates different types of lung cancer for classification. Among them, the transthyretin (TTR) concentration was highly increased in human serum of lung cancer patients. TTR concentration was also induced in the serum, bronchoalveolar lavage fluid, alveolar type II epithelial cells, and alveolar myeloid cells of the CCSP-rtTA/(tetO)7-Stat3C lung tumor mouse model. Recombinant TTR stimulated lung tumor cell proliferation and growth, which were mediated by activation of mitogenic and oncogenic molecules. TTR possesses cytokine functions to stimulate myeloid cell differentiation, which are known to play roles in tumor environment. Further analyses showed that TTR treatment enhanced the reactive oxygen species production in myeloid cells and enabled them to become functional myeloid-derived suppressive cells. TTR demonstrated a great influence on a wide spectrum of endothelial cell functions to control tumor and immune cell migration and infiltration. TTR-treated endothelial cells suppressed T cell proliferation. Taken together, these 13 Stat3 downstream inducible secretory protein biomarkers potentially can be used for lung cancer diagnosis, classification, and as clinical targets for lung cancer personalized treatment if their expression levels are increased in a given lung cancer patient in the blood

Modulation of inositol polyphosphate levels regulates neuronal differentiation

The binding of neurotrophins to tropomyosin receptor kinase receptors initiates several signaling pathways, including the activation of phospholipase C-γ, which promotes the release of diacylglycerol and inositol 1,4,5-trisphosphate (IP3). In addition to recycling back to inositol, IP3 serves as a precursor for the synthesis of higher phosphorylated inositols, such as inositol 1,3,4,5,6-pentakisphosphate (IP5) and inositol hexakisphosphate (IP6). Previous studies on the effect of neurotrophins on inositol signaling were limited to the analysis of IP 3 and its dephosphorylation products. Here we demonstrate that nerve growth factor (NGF) regulates the levels of IP5 and IP6 during PC12 differentiation. Furthermore, both NGF and brain-derived neurotrophic factor alter IP5 and IP6 intracellular ratio in differentiated PC12 cells and primary neurons. Neurotrophins specifically regulate the expression of IP5-2 kinase (IP5-2K), which phosphorylates IP5 into IP6. IP5-2K is rapidly induced after NGF treatment, but its transcriptional levels sharply decrease in fully differentiated PC12 cells. Reduction of IP5-2K protein levels by small interfering RNA has an effect on the early stages of PC12 cell differentiation, whereas fully differentiated cells are not affected. Conversely, perturbation of IP5-2K levels by overexpression suggests that both differentiated PC12 cells and sympathetic neurons require low levels of the enzyme for survival. Therefore maintaining appropriate intracellular levels of inositol polyphosphates is necessary for neuronal survival and differentiation. © 2013 Loss et al

Improvement of LiDAR Data Accuracy Using 12 Parameter Affine Transformation

LiDAR data in a local coordinate system may need to be georeferenced and converted into a geographic or projected system. In coordinate transformation, the 7-parameter Helmet transformation method is usually used in measurements to eliminate the systematic errors made by a laser scanner. However, 7-parameter coordinate transformation assumes that there is only one scale error in all of the systematic errors. This study used 12 parameter affine transformation for coordinate transformation of airborne LiDAR data and terrestrial LiDAR data. The LiDAR data accuracy results upon 6-parameter similarity transformation, 7-parameter similarity transformation, and 12-parameter affine transformation were compared. The results showed that using 12-parameter affine transformation the airborne LiDAR and terrestrial LiDAR data have 2-3 times greater accuracy than do 7-parameter or 6-parameter transformations

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training