Advanced communications payload for mobile applications

An advanced satellite payload is proposed for single hop linking of mobile terminals of all classes as well as Very Small Aperture Terminal's (VSAT's). It relies on an intensive use of communications on-board processing and beam hopping for efficient link design to maximize capacity and a large satellite antenna aperture and high satellite transmitter power to minimize the cost of the ground terminals. Intersatellite links are used to improve the link quality and for high capacity relay. Power budgets are presented for links between the satellite and mobile, VSAT, and hub terminals. Defeating the effects of shadowing and fading requires the use of differentially coherent demodulation, concatenated forward error correction coding, and interleaving, all on a single link basis

Teaching case: Towards bridging disciplinary divides in IT education

Response distortion attributable to a variety of human motivations has long been a recognized problem for behavioral research relying on self reports by individuals. Researchers studying unethical IS behaviors usually need to solicit self reports because of the secrecy of such behaviors. Unfortunately, the unethical nature of those behaviors often subject self reports to various response distortions such as socially desirable responding. This paper discusses the method of psychometric adjustment for response distortion and empirically examines response distortion due to socially desirable responding in a software piracy research. The boundary conditions of psychometric adjustment are then discussed in depth and the use of randomized response technique, an alternative to mitigate response distortion, in IS ethics research is highlighted

Positron-inert gas differential elastic scattering

Measurements are being made in a crossed beam experiment of the relative elastic differential cross section (DCS) for 5 to 300 eV positrons scattering from inert gas atoms (He, Ne, Ar, Kr, and Xe) in the angular range from 30 to 134 deg. Results obtained at energies around the positronium (Ps) formation threshold provide evidence that Ps formation and possibly other inelastic channels have an effect on the elastic scattering channel

SCNet: Learning Semantic Correspondence

This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category. Previous approaches focus on either combining a spatial regularizer with hand-crafted features, or learning a correspondence model for appearance only. We propose instead a convolutional neural network architecture, called SCNet, for learning a geometrically plausible model for semantic correspondence. SCNet uses region proposals as matching primitives, and explicitly incorporates geometric consistency in its loss function. It is trained on image pairs obtained from the PASCAL VOC 2007 keypoint dataset, and a comparative evaluation on several standard benchmarks demonstrates that the proposed approach substantially outperforms both recent deep learning architectures and previous methods based on hand-crafted features.Comment: ICCV 201

The Radon Monitoring System in Daya Bay Reactor Neutrino Experiment

We developed a highly sensitive, reliable and portable automatic system (H$^{3}$) to monitor the radon concentration of the underground experimental halls of the Daya Bay Reactor Neutrino Experiment. H$^{3}$ is able to measure radon concentration with a statistical error less than 10\% in a 1-hour measurement of dehumidified air (R.H. 5\% at 25$^{\circ}$C) with radon concentration as low as 50 Bq/m$^{3}$. This is achieved by using a large radon progeny collection chamber, semiconductor $\alpha$-particle detector with high energy resolution, improved electronics and software. The integrated radon monitoring system is highly customizable to operate in different run modes at scheduled times and can be controlled remotely to sample radon in ambient air or in water from the water pools where the antineutrino detectors are being housed. The radon monitoring system has been running in the three experimental halls of the Daya Bay Reactor Neutrino Experiment since November 2013

Scattering of positrons and electrons by alkali atoms

Absolute total scattering cross sections (Q sub T's) were measured for positrons and electrons colliding with sodium, potassium, and rubidium in the 1 to 102 eV range, using the same apparatus and experimental approach (a beam transmission technique) for both projectiles. The present results for positron-sodium and -rubidium collisions represent the first Q sub T measurements reported for these collision systems. Features which distinguish the present comparisons between positron- and electron-alkali atom Q sub T's from those for other atoms and molecules (room-temperature gases) which have been used as targets for positrons and electrons are the proximity of the corresponding positron- and electron-alkali atom Q sub T's over the entire energy range of overlap, with an indication of a merging or near-merging of the corresponding positron and electron Q sub T's near (and above) the relatively low energy of about 40 eV, and a general tendency for the positron-alkali atom Q sub T's to be higher than the corresponding electron values as the projectile energy is decreased below about 40 eV

Using dual neural network architecture to detect the risk of dementia with community health data: Algorithm development and validation study

Background: Recent studies have revealed lifestyle behavioral risk factors that can be modified to reduce the risk of dementia. As modification of lifestyle takes time, early identification of people with high dementia risk is important for timely intervention and support. As cognitive impairment is a diagnostic criterion of dementia, cognitive assessment tools are used in primary care to screen for clinically unevaluated cases. Among them, Mini-Mental State Examination (MMSE) is a very common instrument. However, MMSE is a questionnaire that is administered when symptoms of memory decline have occurred. Early administration at the asymptomatic stage and repeated measurements would lead to a practice effect that degrades the effectiveness of MMSE when it is used at later stages. Objective: The aim of this study was to exploit machine learning techniques to assist health care professionals in detecting high-risk individuals by predicting the results of MMSE using elderly health data collected from community-based primary care services. Methods: A health data set of 2299 samples was adopted in the study. The input data were divided into two groups of different characteristics (ie, client profile data and health assessment data). The predictive output was the result of two-class classification of the normal and high-risk cases that were defined based on MMSE. A dual neural network (DNN) model was proposed to obtain the latent representations of the two groups of input data separately, which were then concatenated for the two-class classification. Mean and k-nearest neighbor were used separately to tackle missing data, whereas a cost-sensitive learning (CSL) algorithm was proposed to deal with class imbalance. The performance of the DNN was evaluated by comparing it with that of conventional machine learning methods. Results: A total of 16 predictive models were built using the elderly health data set. Among them, the proposed DNN with CSL outperformed in the detection of high-risk cases. The area under the receiver operating characteristic curve, average precision, sensitivity, and specificity reached 0.84, 0.88, 0.73, and 0.80, respectively. Conclusions: The proposed method has the potential to serve as a tool to screen for elderly people with cognitive impairment and predict high-risk cases of dementia at the asymptomatic stage, providing health care professionals with early signals that can prompt suggestions for a follow-up or a detailed diagnosis

Too much, too little, or just right? Ways explanations impact end users' mental models

Research is emerging on how end users can correct mistakes their intelligent agents make, but before users can correctly "debug" an intelligent agent, they need some degree of understanding of how it works. In this paper we consider ways intelligent agents should explain themselves to end users, especially focusing on how the soundness and completeness of the explanations impacts the fidelity of end users' mental models. Our findings suggest that completeness is more important than soundness: increasing completeness via certain information types helped participants' mental models and, surprisingly, their perception of the cost/benefit tradeoff of attending to the explanations. We also found that oversimplification, as per many commercial agents, can be a problem: when soundness was very low, participants experienced more mental demand and lost trust in the explanations, thereby reducing the likelihood that users will pay attention to such explanations at all