Integration of prior knowledge of measurement noise in kernel density classification

Samples can be measured with different precisions and reliabilities in different experiments, or even within the same experiment. These varying levels of measurement noise may deteriorate the performance of a pattern recognition system, if not treated with care. Here we seek to investigate the benefit of incorporating prior knowledge about measurement noise into system construction. We propose a kernel density classifier which integrates such prior knowledge. Instead of using an identical kernel for each sample, we transform the prior knowledge into a distinct kernel for each sample. The integration procedure is straightforward and easy to interpret. In addition, we show how to estimate the diverse measurement noise levels in a real world dataset. Compared to the basic methods, the new kernel density classifier can give a significantly better classification performance. As expected, this improvement is more obvious for small sample size datasets and large number of features.     

The Effects of H2O, CO and CO2 on the H2 Permeance and Surface Characteristics of 1 mm Thick Pd80wt%Cu Membranes

The hydrogen permeance of 1 mm-thick Pd_80wt%Cu foils was measured in the presence of equimolar mixtures of H₂ with CO, CO₂ or H₂O over the temperature and total pressure ranges of 623–1,173 K and 0.62–2.86 MPa, respectively. In all cases, permeance losses at 623 and 738 K were very modest. At higher temperatures, more significant decreases in membrane permeance were observed with the highest reduction of about 50% occurring when macroscopic carbon deposition occurred on the membrane surface during H₂–CO exposure at 908 K. The more worrisome effects of exposure to these gases, however, were the micron-scale surface defects observed at 908 and 1,038 K. Although the 1 mm thick disk membranes retained their mechanical integrity, such defects could cause catastrophic failure of ultra-thin, Pd–Cu membranes (1–5 μm thick) deposited on porous substrates.     

Stochastic molecular descriptors for polymers. 4. Study of complex mixtures with topological indices of mass spectra spiral and star networks: The blood proteome case

The Quantitative Structure-Property Relationships (QSPRs) based on Graph or Network Theory are important for predicting the properties of polymeric systems. In the three previous papers of this series (Polymer 45 (2004) 3845-3853; Polymer 46 (2005) 2791-2798; and Polymer 46 (2005) 6461-6473) we focused on the uses of molecular graph parameters called topological indices (TIs) to link the structure of polymers with their biological properties. However, there has been little effort to extend these TIs to the study of complex mixtures of artificial polymers or biopolymers such as nucleic acids and proteins. In this sense, Blood Proteome (BP) is one of the most important and complex mixtures containing protein polymers. For instance, outcomes obtained by Mass Spectrometry (MS) analysis of BP are very useful for the early detection of diseases and drug-induced toxicities. Here, we use two Spiral and Star Network representations of the MS outcomes and defined a new type of TIs. The new TIs introduced here are the spectral moments (π_k) of the stochastic matrix associated to the Spiral graph and describe non-linear relationships between the different regions of the MS characteristic of BP. We used the MARCH-INSIDE approach to calculate the π_k(SN) of different BP samples and S2SNet to determine several Star graph TIs. In the second step, we develop the corresponding Quantitative Proteome-Property Relationship (QPPR) models using the Linear Discriminant Analysis (LDA). QPPRs are the analogues of QSPRs in the case of complex biopolymer mixtures. Specifically, the new QPPRs derived here may be used to detect drug-induced cardiac toxicities from BP samples. Different Machine Learning classification algorithms were used to fit the QPPRs based on π_k(SN), showing J48 decision tree classifier to have the best performance. These results suggest that the present approach captures important features of the complex biopolymers mixtures and opens new opportunities to the application of the idea supporting classic QSPRs in polymer sciences.     

智能化使汽车更安全

新的特性必须更加智能,在增加价值的同时不会分散驾驶者的注意力和增加事故的风险。     

Results of a User Study on 2D Hurricane Visualization

We present the results from a user study looking at the ability of observers to mentally integrate wind direction and magnitude over a vector field. The data set chosen for the study is an MM5 (PSU/NCAR Mesoscale Model) simulation of Hurricane Lili over the Gulf of Mexico as it approaches the southeastern United States. Nine observers participated in the study. This study investigates the effect of layering on the observer's ability to determine the magnitude and direction of a vector field. We found a tendency for observers to underestimate the magnitude of the vectors and a counter‐clockwise bias when determining the average direction of a vector field. We completed an additional study with two observers to try to uncover the source of the counter‐clockwise bias. These results have direct implications to atmospheric scientists, but may also be able to be applied to other fields that use 2D vector fields.     

Power management in networked devices

Networks are one of the most significant developments in computing and a hallmark of modern society. However, along with increasing efficiency and productivity, both at home and in the workplace, networks have costs. One cost is the additional energy that electronic devices consume when attached to networks. Power management, a standard feature of modern PCs, was primarily developed to increase battery lifetime in laptop PCs, which historically were not network-connected when using battery power. Today, however, many laptops are connected to a network - typically a Wi-Fi network - as are the majority of desktop computers. Three key drivers of energy use are induced consumption by devices prevented by network connections from entering low-power states, increasing link data rates that inherently consume more energy for the network interfaces, and proliferation of network-connected displays that actively update and display data when no one is present.     

Surface segregation studied by low-energy ion scattering: experiment and numerical simulation

The changes in surface composition of metallic alloys caused by segregation can be very efficiently studied by low-energy ion scattering (LEIS) due to the specific surface sensitivity of this technique. Investigations of single-crystal surfaces of ordered alloys are of particular interest because they provide the possibility to investigate the interplay between segregation effects and the order-disorder phase transition when passing through the transition temperature. Exemplifying these effects for bimetallic alloys we consider in particular the CuAu-system.For the quantitative interpretation of energy and angle resolved LEIS intensity distributions we compare experimental results with those from numerical simulations using the MARLOWE code which we extended with a detailed trajectory analysis. This allows us to apply various discrimination criteria, such as number of collisions, distance of closest approach, identification of the scattering crystal layer, total path length, etc. On this basis structural effects, ion survival probabilities and the influence of thermal vibrations can be studied.We demonstrate this potential by using CuAu(1 0 0) as a special example. The scattering potential parameters were calibrated with elemental single crystals of known structures and the anisotropic Debye temperatures taken from the literature showed good agreement, neutralization was of minor importance in this case. Our procedure could be successfully used for the quantitative analysis of the composition of the first and second layer as a function of temperature. These results are in good agreement with theoretical predictions.     

A logical approach to program analysis

This paper describesMicroScope, a framework for developing analysis tools for Lisp programs. MicroScope uses a knowledge-intensive approach for program representation and analysis. The analysis tools share a common object oriented program database, and a common Prolog inference engine. The use of Prolog and a declarative representation for programs permits sharing of information, and provides high bandwidth communication between diverse analysis tools. It also supports program specification and debugging activities in the same framework. Extensions to Prolog to support analysis are described, and two tools, theCritic and theExpector, are presented.This work supported in part by Hewlett-Packard Company, the National Science foundation Under Grant Number MCS81-21750 and the Defense Advanced Research Projects Agency under contract number DAAK11-84-K-0017.     

Self-monitoring processes and Holland Vocational Preferences among college students.

Examined the association between self-monitoring and occupational preferences. 237 undergraduates completed Snyder and Gangestad's (1986) revised self-monitoring scale and Holland's (1977) Vocational Preference Inventory (VPI). Regression analyses showed that men who valued congruence between their beliefs and behavior (i.e., low self-monitors) preferred social occupations. Men who were inclined to modify their behavior to fit social situations (i.e., high self-monitors) preferred enterprising occupations. In contrast, high self-monitoring women preferred artistic occupations. It is argued that these gender-neutral occupations permit high self-monitoring women to use their social skills but without the constraints found in male dominated enterprising occupations. Results support the assumption that people prefer occupations compatible with their social skills. Implications for career counseling and further research are noted. (PsycINFO Database Record (c) 2010 APA, all rights reserved)