Orthogonality of information structures

Summary In the mathematical framework of data spaces the paper develops some important general principles of information structuring. These principles are related to the notions of redundancy of information, completeness of a set of access paths, information sharing and compounding, and virtual access to information. The results are relevant to both sequential and concurrent processing.This research is supported in part by the Office of Naval Research under Contract No. N00014-77-C-0536 through the University of Southern CaliforniaA version of this paper was presented at the Conference on Theoretical Computer Science, University of Waterloo, Waterloo, Ontario, Canada, Aug. 15–17, 1977     

Tunable microfluidic microlenses

A novel type of liquid microlens, bounded by a microfabricated, distensible membrane and activated by a microfluidic liquid-handling system, is presented. By use of an elastomer membrane fabricated by spin coating onto a dry-etched silicon substrate, the liquid-filled cavity acts as a lens whereby applied pressure changes the membrane distension and thus the focal length. Both plano-convex and plano-concave lenses, individual elements as well as arrays, were fabricated and tested. The lens surface roughness was seen to be approximately 9 nm rms, and the focal length could be tuned from 1 to 18 mm. This lens represents a robust, self-contained tunable optical structure suitable for use in, for example, a medical environment.     

Importance-driven feature enhancement in volume visualization

This paper presents importance-driven feature enhancement as a technique for the automatic generation of cut-away and ghosted views out of volumetric data. The presented focus+context approach removes or suppresses less important parts of a scene to reveal more important underlying information. However, less important parts are fully visible in those regions, where important visual information is not lost, i.e., more relevant features are not occluded. Features within the volumetric data are first classified according to a new dimension, denoted as object importance. This property determines which structures should be readily discernible and which structures are less important. Next, for each feature, various representations (levels of sparseness) from a dense to a sparse depiction are defined. Levels of sparseness define a spectrum of optical properties or rendering styles. The resulting image is generated by ray-casting and combining the intersected features proportional to their importance (importance compositing). The paper includes an extended discussion on several possible schemes for levels of sparseness specification. Furthermore, different approaches to importance compositing are treated.     

Discovery of Riociguat (BAY 63‐2521): A Potent,Oral Stimulator of Soluble Guanylate Cyclase for the Treatment of Pulmonary Hypertension

Direct stimulation of soluble guanylate cyclase (sGC) represents a promising therapeutic strategy for the treatment of a range of diseases, including the severely disabling pulmonary hypertension (PH). Optimization of the unfavorable DMPK profile of previous sGC stimulators provided riociguat, which is currently being investigated in phase III clinical trials for the oral treatment of PH.

     

Novel Nanoencapsulation Technology and its Potential Role in Bile Acid-Based Targeted Gene Delivery to the Inner Ear

Hearing loss impacts a large proportion of the global population. Damage to the inner ear, in particular the sensitive hair cells, can impact individuals for the rest of their lives. There are very limited options for interventions after damage to these cells has occurred. Targeted gene delivery may provide an effective means to trigger appropriate differentiation of progenitor cells for effective replacement of these sensitive hair cells. There are several hurdles that need to be overcome to effectively deliver these genes. Nanoencapsulation technology has previously been used for the delivery of pharmaceuticals, proteins and nucleic acids, and may provide an effective means of delivering genes to trigger appropriate differentiation. This review investigates the background of hearing loss, current advancements and pitfalls of gene delivery, and how nanoencapsulation may be useful.     

Automated design of error-resilient and hardware-efficient deep neural networks

Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as autonomous vehicles, demands a reliable and efficient execution on hardware. The design of the neural architecture has a large influence on the achievable efficiency and bit error resilience of the network on hardware. Since there are numerous design choices for the architecture of DNNs, with partially opposing effects on the preferred characteristics (such as small error rates at low latency), multi-objective optimization strategies are necessary. In this paper, we develop an evolutionary optimization technique for the automated design of hardware-optimized DNN architectures. For this purpose, we derive a set of inexpensively computable objective functions, which enable the fast evaluation of DNN architectures with respect to their hardware efficiency and error resilience. We observe a strong correlation between predicted error resilience and actual measurements obtained from fault injection simulations. Furthermore, we analyze two different quantization schemes for efficient DNN computation and find one providing a significantly higher error resilience compared to the other. Finally, a comparison of the architectures provided by our algorithm with the popular MobileNetV2 and NASNet-A models reveals an up to seven times improved bit error resilience of our models. We are the first to combine error resilience, efficiency, and performance optimization in a neural architecture search framework.

     

A mortar formulation for frictionless line-to-line beam contact

Multibody System Dynamics - This paper describes the quasi-static formulation of frictionless line contact between flexible beams by employing the mortar finite element approach. Contact...     

Synthesis and Characterization of Anti-fungus,Anti-corrosion and Self-cleaning Hybrid Nanocomposite Coatings Based on Sol–Gel Process

Anti-corrosion, anti-fungus, and self-cleaning properties of coatings containing ZnO–TiO₂, SiO₂–TiO₂ and SiO₂/TiO₂/ZnO nanoparticles synthesized based on sol–gel precursors using tetra methoxysilane, 3-glycidoxypropyl trimethoxysilane, tetra (n-butyl orthotitanate) and zinc acetate dihydrate were investigated by FESEM, EDAX and TEM analyses. Results indicated uniform dispersion of inorganic nanoparticles in the range of 20–40 nm in size. Anti-corrosion property of the hybrid coating was characterized by EIS measurements and parametrically analyzed in an equivalent circuit when the coating was exposed to salt solution. Results showed that, ZnO and TiO₂ nanoparticles enhance anti-corrosion property of the hybrid coatings. Anti-fungus and anti-bacterial properties of the coatings were determined by diameter of inhibition zone and inhabitation of bacterial growth, respectively. The coating containing ZnO and TiO₂ nanoparticles showed anti-fungus and anti-bacterial properties which were related to their photocatalytic properties. Degradation of methylene blue in aqueous solution was determined by UV–Visible tests which indicated self-cleaning property of the coatings containing ZnO and TiO₂ nanoparticles.