A comparative analysis of the value of information in a continuous time market model with partial information: the cases of log-utility and CRRA

We study the question what value an agent in a generalized Black-Scholes model with partial information attributes to the complementary information. To do this, we study the utility maximization problems from terminal wealth for the two cases partial information and full information. We assume that the drift term of the risky asset is a dynamic process of general linear type and that the two levels of observation correspond to whether this drift term is observable or not. Applying methods from stochastic filtering theory we derive an analytical tractable formula for the value of information in the case of logarithmic utility. For the case of constant relative risk aversion (CRRA) we derive a semianalytical formula, which uses as an input the numerical solution of a system of ODEs. For both cases we present a comparative analysis

Memristor-based Random Access Memory: The delayed switching effect could revolutionize memory design

Memristor’s on/off resistance can naturally store binary bits for non-volatile memories. In this work, we found that memristor’s another peculiar feature that the switching takes place with a time delay (we name it “the delayed switching”) can be used to selectively address any desired memory cell in a crossbar array. The analysis shows this is a must-be in a memristor with a piecewise-linear ?-q curve. A “circuit model”-based experiment has verified the delayed switching feature. It is demonstrated that memristors can be packed at least twice as densely as semiconductors, achieving a significant breakthrough in storage density

Nanosecond Dynamics of Single-Molecule Fluorescence Resonance Energy Transfer

Motivated by recent experiments on photon statistics from individual dye pairs planted on biomolecules and coupled by fluorescence resonance energy transfer (FRET), we show here that the FRET dynamics can be modelled by Gaussian random processes with colored noise. Using Monte-Carlo numerical simulations, the photon intensity correlations from the FRET pairs are calculated, and are turned out to be very close to those observed in experiment. The proposed stochastic description of FRET is consistent with existing theories for microscopic dynamics of the biomolecule that carries the FRET coupled dye pairs.Comment: 8 pages, 1 figure. accepted to J.Phys.Chem.

Micro-device for coupling, multiplexing and demultiplexing using elliptical-core two-mode fiber

We propose and demonstrate experimentally a fiber optic micro-device that is capable of tunably splitting, multiplexing, and demultiplexing optical signals using elliptical-core two-mode optical fiber. A crosstalk of 15 dB with an insertion loss of 1.2 dB was obtained

Analysis of electromagnetic interference from power system processing and transmission components for Space Station Freedom

The goal is to analyze the potential effects of electromagnetic interference (EMI) originating from power system processing and transmission components for Space Station Freedom.The approach consists of four steps: (1) develop analytical tools (models and computer programs); (2) conduct parameterization studies; (3) predict the global space station EMI environment; and (4) provide a basis for modification of EMI standards

Neural Wireframe Renderer: Learning Wireframe to Image Translations

In architecture and computer-aided design, wireframes (i.e., line-based models) are widely used as basic 3D models for design evaluation and fast design iterations. However, unlike a full design file, a wireframe model lacks critical information, such as detailed shape, texture, and materials, needed by a conventional renderer to produce 2D renderings of the objects or scenes. In this paper, we bridge the information gap by generating photo-realistic rendering of indoor scenes from wireframe models in an image translation framework. While existing image synthesis methods can generate visually pleasing images for common objects such as faces and birds, these methods do not explicitly model and preserve essential structural constraints in a wireframe model, such as junctions, parallel lines, and planar surfaces. To this end, we propose a novel model based on a structure-appearance joint representation learned from both images and wireframes. In our model, structural constraints are explicitly enforced by learning a joint representation in a shared encoder network that must support the generation of both images and wireframes. Experiments on a wireframe-scene dataset show that our wireframe-to-image translation model significantly outperforms the state-of-the-art methods in both visual quality and structural integrity of generated images.Comment: ECCV 202

Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation

Image-to-image translation has been made much progress with embracing Generative Adversarial Networks (GANs). However, it's still very challenging for translation tasks that require high quality, especially at high-resolution and photorealism. In this paper, we present Discriminative Region Proposal Adversarial Networks (DRPAN) for high-quality image-to-image translation. We decompose the procedure of image-to-image translation task into three iterated steps, first is to generate an image with global structure but some local artifacts (via GAN), second is using our DRPnet to propose the most fake region from the generated image, and third is to implement "image inpainting" on the most fake region for more realistic result through a reviser, so that the system (DRPAN) can be gradually optimized to synthesize images with more attention on the most artifact local part. Experiments on a variety of image-to-image translation tasks and datasets validate that our method outperforms state-of-the-arts for producing high-quality translation results in terms of both human perceptual studies and automatic quantitative measures.Comment: ECCV 201

Audio Event Detection using Weakly Labeled Data

Acoustic event detection is essential for content analysis and description of multimedia recordings. The majority of current literature on the topic learns the detectors through fully-supervised techniques employing strongly labeled data. However, the labels available for majority of multimedia data are generally weak and do not provide sufficient detail for such methods to be employed. In this paper we propose a framework for learning acoustic event detectors using only weakly labeled data. We first show that audio event detection using weak labels can be formulated as an Multiple Instance Learning problem. We then suggest two frameworks for solving multiple-instance learning, one based on support vector machines, and the other on neural networks. The proposed methods can help in removing the time consuming and expensive process of manually annotating data to facilitate fully supervised learning. Moreover, it can not only detect events in a recording but can also provide temporal locations of events in the recording. This helps in obtaining a complete description of the recording and is notable since temporal information was never known in the first place in weakly labeled data.Comment: ACM Multimedia 201