A method of classification for multisource data in remote sensing based on interval-valued probabilities

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method

Real Interest Rate Linkages in the Pacific Basin Region

This paper examines the linkage of real interest rates of a group of Pacific-Basin countries with a focus on East Asia. We consider monthly real interest rates of the US, Japan, Korea, Singapore, and Thailand from 1980 and 2004. The impulse response analysis and half-life estimation are conducted in a multivariate setting, adopting the bias-corrected bootstrap as a means of statistical inference. It is found that the degree of capital market integration has increased after the Asian financial crisis in 1997. The evidence suggests that the crisis has substantially changed the nature of the short run interactions among the real interest rates. Before the crisis, both the US and Japanese capital markets dominated the region. However, after the crisis, the dominance of the Japanese market has completely disappeared, while the US remains as a sole dominant player.Financial crisis, Bias-correction, Bootstrapping, Capital market Integration, Half-life, Impulse response analysis, Vector autoregression.

International linkage of real interest rates: the case of East Asian countries

This paper examines linkage of real interest rates for a group of selected countries in East Asia. The countries under study include Japan, Korea, Singapore, Malaysia and Thailand. The long run relationship is tested and estimated using the conitegration analysis. We also have conducted the impulse response analysis based on unrestricted vector autoregression, using the bias-corrected wild bootstrap for statistical inference. Our results show that (1) there exists a long run equilibrium relationship, (2) there are interesting short run dynamic interactions, in which Singapore, Malaysia and Thailand play the role of equilibrating factorFinancial linkage; Real interest rate parity; Cointegration analysis; Wild bootstrap

Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks

Deep networks consume a large amount of memory by their nature. A natural question arises can we reduce that memory requirement whilst maintaining performance. In particular, in this work we address the problem of memory efficient learning for multiple tasks. To this end, we propose a novel network architecture producing multiple networks of different configurations, termed deep virtual networks (DVNs), for different tasks. Each DVN is specialized for a single task and structured hierarchically. The hierarchical structure, which contains multiple levels of hierarchy corresponding to different numbers of parameters, enables multiple inference for different memory budgets. The building block of a deep virtual network is based on a disjoint collection of parameters of a network, which we call a unit. The lowest level of hierarchy in a deep virtual network is a unit, and higher levels of hierarchy contain lower levels' units and other additional units. Given a budget on the number of parameters, a different level of a deep virtual network can be chosen to perform the task. A unit can be shared by different DVNs, allowing multiple DVNs in a single network. In addition, shared units provide assistance to the target task with additional knowledge learned from another tasks. This cooperative configuration of DVNs makes it possible to handle different tasks in a memory-aware manner. Our experiments show that the proposed method outperforms existing approaches for multiple tasks. Notably, ours is more efficient than others as it allows memory-aware inference for all tasks.Comment: CVPR 201

Method of Classification for Multisource Data in Remote Sensing Based on Interval-VaIued Probabilities

This work was supported by NASA Grant No. NAGW-925 “Earth Observation Research - Using Multistage EOS-Iike Data” (Principal lnvestigators: David A. Landgrebe and Chris Johannsen). The Anderson River SAR/MSS data set was acquired, preprocessed, and loaned to us by the Canada Centre for Remote Sensing, Department of Energy Mines, and Resources, of the Government of Canada. The importance of utilizing multisource data in ground-cover^ classification lies in the fact that improvements in classification accuracy can be achieved at the expense of additional independent features provided by separate sensors. However, it should be recognized that information and knowledge from most available data sources in the real world are neither certain nor complete. We refer to such a body of uncertain, incomplete, and sometimes inconsistent information as “evidential information.” The objective of this research is to develop a mathematical framework within which various applications can be made with multisource data in remote sensing and geographic information systems. The methodology described in this report has evolved from “evidential reasoning,” where each data source is considered as providing a body of evidence with a certain degree of belief. The degrees of belief based on the body of evidence are represented by “interval-valued (IV) probabilities” rather than by conventional point-valued probabilities so that uncertainty can be embedded in the measures. There are three fundamental problems in the muItisource data analysis based on IV probabilities: (1) how to represent bodies of evidence by IV probabilities, (2) how to combine IV probabilities to give an overall assessment of the combined body of evidence, and (3) how to make a decision when the statistical evidence is given by IV probabilities. This report first introduces an axiomatic approach to IV probabilities, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach the report focuses on representation of statistical evidence by IV probabilities and combination of multiple bodies of evidence. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. This report also focuses on the development of decision rules over IV probabilities from the viewpoint of statistical pattern recognition The proposed method, so called “evidential reasoning” method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data* Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor, in each case, a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a Divide-and-Combine process, the method is able to utilize more features than the conventional Maximum Likelihood method

A technique for chronic repuncture micropuncture of dog kidney

The precision of kidney micropuncture for exploring changes in glomerular and tubular function was strikingly enhanced by the introduction of the repuncture and re-collection technique [1], thus permitting the use of each tubule as its own control. This procedure has allowed detection of changes in function that were not appreciated previously. However, the technical design of re-collection micropuncture is such that it is mainly applicable to the study of rapid and usually massive changes in homeostasis; exploration of the renal response to chronic, more physiologic maneuvers has been limited to clearance and non-repuncture micropuncture studies.This paper describes a technique for chronic repuncture micropuncture that allows repeat proximal tubule sampling in dogs at an interval of up to 14 days

Development of flashlamp-pumped Q-switched Ho:Tm:Cr:YAG lasers for mid-infrared LIDAR application

A flashlamp-pumped 2.1 micron Ho:Tm:Cr:YAG laser was studied for both normal mode and Q-switched operations under a wide variety of experimental conditions in order to optimize performance. Laser output energy, slope efficiency, threshold and pulselength were determined as a function of operating temperature, output mirror reflectivity, input electrical energy and Q-switch opening time. The measured normal-mode laser thresholds of a Ho(3+) (0.45 atomic percent):Tm(3+) (2.5 atomic percent):Cr(3+) (0.8 atomic percent):YAG crystal ranged form 26 to 50 J between 120 and 200 K with slope efficiencies up to 0.36 percent with a 60 percent reflective output mirror. Under Q-switched operation the slope efficiency was 90 percent of the normal-mode result. Development of solid state lasers with Ho(3+), Tm(3+) and/or Er(3+) doped crystals has been pursued by NASA for eye-dafe mid-infrared LIDAR (light detection and ranging) application. As a part of the project, the authors have been working on evaluating Ho(3+):Tm(3+):Cr(3+):YAG crystals for normal-mode and Q-switched 2.1 micron laser operations in order to determine an optimum Tm(3+) concentration under flashlamp pumping conditions. Lasing properties of the Ho(3+) in the mid-infrared region have been studied by many research groups since the early 1960's. However, the technology of those lasers is still premature for lidar application. In order to overcome the inefficiency related to narrow absorption bands of the Ho(3+), Tm(3+) and Er(3+), the erbium has been replaced by chromium. The improvement in flashlamp-pumped Ho(3+) laser efficiency has been demonstrated recently by several research groups by utilizing the broad absorption spectrum of Cr(3+) which covers the flashlamp's emission spectrum. Efficient energy transfer to the Tm(3+) and then the Ho(3+) occurs subsequently. It is known that high Tm(3+) concentration and low Ho(3+) concentration are preferred to achieve a quantum efficiency approaching two and to avoid large reabsorption losses. However, determination of the optimum Tm(3+) concentration required to ensure efficient energy transfer from Cr(3+) to Tm(3+) and from Tm(3+) to Ho(3+) has not been made in the Ho:Tm:CR:YAG crystal. The results obtained so far are given

Electron impact ionization loading of a surface electrode ion trap

We demonstrate a method for loading surface electrode ion traps by electron impact ionization. The method relies on the property of surface electrode geometries that the trap depth can be increased at the cost of more micromotion. By introducing a buffer gas, we can counteract the rf heating assocated with the micromotion and benefit from the larger trap depth. After an initial loading of the trap, standard compensation techniques can be used to cancel the stray fields resulting from charged dielectric and allow for the loading of the trap at ultra-high vacuum.Comment: 4 pages, 5 eps figures. Shift in focus, minor correction

Making ARPES Measurements on Corrugated Monolayer Crystals: Suspended Exfoliated Single-Crystal Graphene

Free-standing exfoliated monolayer graphene is an ultra-thin flexible membrane, which exhibits out of plane deformation or corrugation. In this paper, a technique is described to measure the band structure of such free-standing graphene by angle-resolved photoemission. Our results show that photoelectron coherence is limited by the crystal corrugation. However, by combining surface morphology measurements of the graphene roughness with angle-resolved photoemission, energy dependent quasiparticle lifetime and bandstructure measurements can be extracted. Our measurements rely on our development of an analytical formulation for relating the crystal corrugation to the photoemission linewidth. Our ARPES measurements show that, despite significant deviation from planarity of the crystal, the electronic structure of exfoliated suspended graphene is nearly that of ideal, undoped graphene; we measure the Dirac point to be within 25 meV of $E_F$ . Further, we show that suspended graphene behaves as a marginal Fermi-liquid, with a quasiparticle lifetime which scales as $(E - E_F)^{-1}$; comparison with other graphene and graphite data is discussed

Pulsed ultrasound-modulated optical tomography using spectral hole-burning

We present a novel optical quantum sensor using spectral hole-burning for detecting signals in ultrasound-modulated optical tomography. In this technique, we utilize the capability of sub-MHz spectral filtering afforded by a spectral hole burning crystal to select the desired spectral component from the ultrasound-modulated diffuse light. This technique is capable of providing a large etendue, processing a large number of speckles in parallel, tolerating speckle decorrelation, and imaging in real-time. Experimental results are presented