This work presents a demonstration of the applicability and efficacy of an experimental system capable of noninvasively and nondestructively scanning the transient surface temperature of pulsed microelectronic devices with submicron spatial and sub-microsecond temporal resolutions. The article describes the features of the experimental setup, provides details of the calibration process used to map the changes in the measured surface reflectivity to absolute temperature values, and explains the data acquisition procedure used to measure the transient temperature over a given active region. This thermoreflectance thermometry system is shown to be particularly suited for directly measuring the surface temperature field of devices undergoing the fast transients that are typical of next generation microelectronic devices. To illustrate the experimental approach, both quasisteady and transient temperature measurement results are presented for standard MOSFET devices.
Deception detection has been receivingan increasing amount of attention from the computational linguistics, speech, and multimodal processing communities. One of the major challenges encountered in this task is the availability of data, and most of the research work to date has been conducted on acted or artificially collected data. The generated deception models are thus lacking real-world evidence. In this paper, we explore the use of multimodal real-life data for the task of deception detection. We develop a new deception dataset consisting of videos from reallife scenarios, and build deception tools relying on verbal and nonverbal features. We achieve classification accuracies in the range of 77-82% when using a model that extracts and fuses features from the linguistic and visual modalities. We show that these results outperform the human capability of identifying deceit.
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