Measurement of substrate thermal resistance using DNA denaturation temperature

Heat Transfer and Thermal Management have become important aspects of the developing field of uTAS systems particularly in the application of the the uTAS philosophy to thermally driven analysis techniques such as PCR. Due to the development of flowing PCR thermocyclers in the field of uTAS, the authors have previously developed a melting curve analysis technique that is compatible with these flowing PCR thermocyclers. In this approach a linear temperature gradient is induced along a sample carrying microchannel. Any flow passing through the microchannel is subject to linear heating. Fluorescent monitoring of DNA in the flow results in the generation of DNA melting curve plots. This works presents an experimental technique where DNA melting curve analysis is used to measure the thermal resistance of microchannel substrates. DNA in solution is tested at a number of different ramp rates and the di®erent apparent denaturation temperatures measured are used to infer the thermal resistance of the microchannel substrates. The apparent variation in denaturation temperature is found to be linearly proportional to flow ramp rate. Providing knowledge of the microchannel diameter and a non-varying cross-section in the direction of heat flux the thermal resistance measurement technique is independent of knowledge of substrate dimensions, contact surface quality and substrate composition/material properties. In this approach to microchannel DNA melting curve analysis the difference between the measured and actual denaturation temperatures is proportional to the substrate thermal resistance and the ramp-rate seen by the sample. Therefore quantitative knowledge of the substrate thermal resistance is required when using this technique to measure accurately DNA denaturation temperatur

Pseudospectral Model Predictive Control under Partially Learned Dynamics

Trajectory optimization of a controlled dynamical system is an essential part of autonomy, however many trajectory optimization techniques are limited by the fidelity of the underlying parametric model. In the field of robotics, a lack of model knowledge can be overcome with machine learning techniques, utilizing measurements to build a dynamical model from the data. This paper aims to take the middle ground between these two approaches by introducing a semi-parametric representation of the underlying system dynamics. Our goal is to leverage the considerable information contained in a traditional physics based model and combine it with a data-driven, non-parametric regression technique known as a Gaussian Process. Integrating this semi-parametric model with model predictive pseudospectral control, we demonstrate this technique on both a cart pole and quadrotor simulation with unmodeled damping and parametric error. In order to manage parametric uncertainty, we introduce an algorithm that utilizes Sparse Spectrum Gaussian Processes (SSGP) for online learning after each rollout. We implement this online learning technique on a cart pole and quadrator, then demonstrate the use of online learning and obstacle avoidance for the dubin vehicle dynamics.Comment: Accepted but withdrawn from AIAA Scitech 201

Efficient maximum likelihood estimation for L\'{e}vy-driven Ornstein-Uhlenbeck processes

We consider the problem of efficient estimation of the drift parameter of an Ornstein-Uhlenbeck type process driven by a L\'{e}vy process when high-frequency observations are given. The estimator is constructed from the time-continuous likelihood function that leads to an explicit maximum likelihood estimator and requires knowledge of the continuous martingale part. We use a thresholding technique to approximate the continuous part of the process. Under suitable conditions, we prove asymptotic normality and efficiency in the H\'{a}jek-Le Cam sense for the resulting drift estimator. Finally, we investigate the finite sample behavior of the method and compare our approach to least squares estimation.Comment: Published in at http://dx.doi.org/10.3150/13-BEJ510 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Towards Interpretable Deep Learning Models for Knowledge Tracing

As an important technique for modeling the knowledge states of learners, the traditional knowledge tracing (KT) models have been widely used to support intelligent tutoring systems and MOOC platforms. Driven by the fast advancements of deep learning techniques, deep neural network has been recently adopted to design new KT models for achieving better prediction performance. However, the lack of interpretability of these models has painfully impeded their practical applications, as their outputs and working mechanisms suffer from the intransparent decision process and complex inner structures. We thus propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model by backpropagating the relevance from the model's output layer to its input layer. The experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions, and partially validate the computed relevance scores from both question level and concept level. We believe it can be a solid step towards fully interpreting the DLKT models and promote their practical applications in the education domain

Spectral Attention-Driven Intelligent Target Signal Identification on a Wideband Spectrum

This paper presents a spectral attention-driven reinforcement learning based intelligent method for effective and efficient detection of important signals in a wideband spectrum. In the work presented in this paper, it is assumed that the modulation technique used is available as a priori knowledge of the targeted important signal. The proposed spectral attention-driven intelligent method is consists of two main components, a spectral correlation function (SCF) based spectral visualization scheme and a spectral attention-driven reinforcement learning mechanism that adaptively selects the spectrum range and implements the intelligent signal detection. Simulations illustrate that the proposed method can achieve high accuracy of signal detection while observation of spectrum is limited to few ranges via effectively selecting the spectrum ranges to be observed. Furthermore, the proposed spectral attention-driven machine learning method can lead to an efficient adaptive intelligent spectrum sensor designs in cognitive radio (CR) receivers.Comment: 6 pages, 11 figure

Proactive Empirical Assessment of New Language Feature Adoption via Automated Refactoring: The Case of Java 8 Default Methods

Programming languages and platforms improve over time, sometimes resulting in new language features that offer many benefits. However, despite these benefits, developers may not always be willing to adopt them in their projects for various reasons. In this paper, we describe an empirical study where we assess the adoption of a particular new language feature. Studying how developers use (or do not use) new language features is important in programming language research and engineering because it gives designers insight into the usability of the language to create meaning programs in that language. This knowledge, in turn, can drive future innovations in the area. Here, we explore Java 8 default methods, which allow interfaces to contain (instance) method implementations. Default methods can ease interface evolution, make certain ubiquitous design patterns redundant, and improve both modularity and maintainability. A focus of this work is to discover, through a scientific approach and a novel technique, situations where developers found these constructs useful and where they did not, and the reasons for each. Although several studies center around assessing new language features, to the best of our knowledge, this kind of construct has not been previously considered. Despite their benefits, we found that developers did not adopt default methods in all situations. Our study consisted of submitting pull requests introducing the language feature to 19 real-world, open source Java projects without altering original program semantics. This novel assessment technique is proactive in that the adoption was driven by an automatic refactoring approach rather than waiting for developers to discover and integrate the feature themselves. In this way, we set forth best practices and patterns of using the language feature effectively earlier rather than later and are able to possibly guide (near) future language evolution. We foresee this technique to be useful in assessing other new language features, design patterns, and other programming idioms

A rule-based system for real-time analysis of control systems

An approach to automate the real-time analysis of flight critical health monitoring and system status is being developed and evaluated at the NASA Dryden Flight Research Facility. A software package was developed in-house and installed as part of the extended aircraft interrogation and display system. This design features a knowledge-base structure in the form of rules to formulate interpretation and decision logic of real-time data. This technique has been applied for ground verification and validation testing and flight testing monitoring where quick, real-time, safety-of-flight decisions can be very critical. In many cases post processing and manual analysis of flight system data are not required. The processing is described of real-time data for analysis along with the output format which features a message stack display. The development, construction, and testing of the rule-driven knowledge base, along with an application using the X-31A flight test program, are presented

Premarital Sex Among Adolescent Street Children in Pekanbaru

Premarital sex is any behavior that is driven by sexual desire with the opposite sex before marriage. Some premarital sex activities include feeling, kissing, necking, petting, and intercourse. Premarital sex in adolescents has a negative impact such as unwanted pregnancy, unsafe abortion, resulting in increased maternal, neonatal deaths and perinatal, increasing the incidence of HIV / AIDS, dropping out of school. To Know Relations factors knowledge, girlfriend status, exposure to pornography, family harmony, the negative influence of peers and parental supervision with premarital sex on street adolescent girls. Quantitative analytical observational method with cross sectional design. Samples of 100 teenage children street children in Pekanbaru City. Snow ball sampling technique, Instrument is a questionnaire. Univariate data analysis, multivariate bivariate with logistic regression test. showed 65% (65 people) prenup sex, 78% dating, 74% pornography exposure, peer influence 70%, lack of knowledge of youth 61%, family not harmonious 80%, and low parental supervision 57 %. The related variables (p value <0.05) with premarital sex behavior are boyfriend status, pornographic exposure and peer influence. Status girlfriend most risky 39 times premarital sex. There is relationship and influence of 3 factors to premarital sex on adolescent child of Street of Pekanbaru Town. Suggestions for the formation of containers such as peer counselor and BKR (Youth Family Development) as a precautionary measure to increase the number of premarital sex incidents in the juveniles Street Children Pekanbaru