Empirical evaluation of computational fear contagion models in crowd dispersions

In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions being influenced by surrounding people’s emotions. While the overall effect is agreed upon, the underlying mechanism of the spread of emotions has seen little quantification and application to computational agents despite extensive evidence of its impacts in everyday life. In this paper, we examine computational models of emotional contagion by implementing two models (Bosse et al., European council on modeling and simulation, pp. 212–218, 2009) and Durupinar, From audiences to mobs: Crowd simulation with psychological factors, PhD dissertation, Bilkent University, 2010) that draw from two separate lines of contagion research: thermodynamics-based and epidemiological-based. We first perform sensitivity tests on each model in an evacuation simulation, ESCAPES, showing both models to be reasonably robust to parameter variations with certain exceptions. We then compare their ability to reproduce a real crowd panic scene in simulation, showing that the thermodynamics-style model (Bosse et al., European council on modeling and simulation, pp. 212–218, 2009) produces superior results due to the ill-suited contagion mechanism at the core of epidemiological models. We also identify that a graduated effect of fear and proximity-based contagion effects are key to producing the superior results. We then reproduce the methodology on a second video, showing that the same results hold, implying generality of the conclusions reached in the first scene.     

Characterization of Sky‐region Morphological‐temporal Airborne Collision Detection

Automated airborne collision‐detection systems are a key enabling technology for facilitating the integration of unmanned aerial vehicles (UAVs) into the national airspace. These safety‐critical systems must be sensitive enough to provide timely warnings of genuine airborne collision threats, but not so sensitive as to cause excessive false alarms. Hence, an accurate characterization of detection and false‐alarm sensitivity is essential for understanding performance tradeoffs, and system designers can exploit this characterization to help achieve a desired balance in system performance. In this paper, we experimentally evaluate a sky‐region, image‐based, aircraft collision‐detection system that is based on morphological and temporal processing techniques. (Note that the examined detection approaches are not suitable for the detection of potential collision threats against a ground clutter background.) A novel collection methodology for collecting realistic airborne collision‐course target footage in both head‐on and tail‐chase engagement geometries is described. Under (hazy) blue sky conditions, our proposed system achieved detection ranges greater than 1540 m in three flight test cases with no false‐alarm events in 14.14 h of nontarget data (under cloudy conditions, the system achieved detection ranges greater than 1170 m in four flight test cases with no false‐alarm events in 6.63 h of nontarget data). Importantly, this paper is the first documented presentation of detection range versus false‐alarm curves generated from airborne target and nontarget image data. © 2012 Wiley Periodicals, Inc.     

Discipline-based instruction to promote interdisciplinary design of wearable and pervasive computing products

This paper reports on a design experience for undergraduates in computer engineering, industrial design, and marketing that focuses on pervasive computing devices. Across a broad range of targeted application areas and user groups, many of the student designs have been wearable computers. Consequently, our course will be of interest to the wearable computing community, particularly in terms of our aim of bridging the gap between design and engineering. For the two most recent offerings of the course, we have utilized external observers and surveyed the students in order to validate the impact of aspects of our process and changes to it. This paper is based upon 5 years of experience and 2 years of analysis of our course, and it presents an overview of our process with both qualitative and quantitative results from these two most recent offerings.     

Comparison of satellite-derived and in-situ observations of ice and snow surface temperatures over Greenland

The most practical way to get spatially broad and continuous measurements of the surface temperature in the data-sparse cryosphere is by satellite remote sensing. The uncertainties in satellite-derived LSTs must be understood to develop internally-consistent decade-scale land surface temperature (LST) records needed for climate studies. In this work we assess satellite-derived “clear-sky” LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and LSTs derived from the Enhanced Thematic Mapper Plus (ETM+) over snow and ice on Greenland. When possible, we compare satellite-derived LSTs with in-situ air temperature observations from Greenland Climate Network (GC-Net) automatic weather stations (AWS). We find that MODIS, ASTER and ETM+ provide reliable and consistent LSTs under clear-sky conditions and relatively-flat terrain over snow and ice targets over a range of temperatures from ? 40 to 0 °C. The satellite-derived LSTs agree within a relative RMS uncertainty of ~ 0.5 °C. The good agreement among the LSTs derived from the various satellite instruments is especially notable since different spectral channels and different retrieval algorithms are used to calculate LST from the raw satellite data. The AWS record in-situ data at a “point” while the satellite instruments record data over an area varying in size from: 57 × 57 m (ETM+), 90 × 90 m (ASTER), or to 1 × 1 km (MODIS). Surface topography and other factors contribute to variability of LST within a pixel, thus the AWS measurements may not be representative of the LST of the pixel. Without more information on the local spatial patterns of LST, the AWS LST cannot be considered valid ground truth for the satellite measurements, with RMS uncertainty ~ 2 °C. Despite the relatively large AWS-derived uncertainty, we find LST data are characterized by high accuracy but have uncertain absolute precision.     

Linear actuation in coextruded dielectric elastomer tubes

Cylindrical fibre actuators have been constructed by a coextrusion method using a thermoplastic polyurethane wall and a conductive grease filler. These actuators may be operated as single fibres or bundled together as actuating ropes. Key results include the validation of Carpi’s wall pressure model [F. Carpi, D.D. Rossi, Dielectric elastomer cylindrical actuators: electromechanical modelling and experimental evaluation, Mater. Sci. Eng. C-Biomimetic Supramol. Syst. 24 (2004) 555–562] and the proof-of-concept demonstration of a technique that can be used for producing inexpensive dielectric elastomer actuators on an industrial scale.     

Imputation techniques for multivariate missingness in software measurement data

The problem of missing values in software measurement data used in empirical analysis has led to the proposal of numerous potential solutions. Imputation procedures, for example, have been proposed to ‘fill-in’ the missing values with plausible alternatives. We present a comprehensive study of imputation techniques using real-world software measurement datasets. Two different datasets with dramatically different properties were utilized in this study, with the injection of missing values according to three different missingness mechanisms (MCAR, MAR, and NI). We consider the occurrence of missing values in multiple attributes, and compare three procedures, Bayesian multiple imputation, k Nearest Neighbor imputation, and Mean imputation. We also examine the relationship between noise in the dataset and the performance of the imputation techniques, which has not been addressed previously. Our comprehensive experiments demonstrate conclusively that Bayesian multiple imputation is an extremely effective imputation technique.

Highly Efficient Quasi 2D Blue Perovskite Electroluminescence Leveraging a Dual Ligand Composition

Perovskite light-emitting diodes (PeLEDs) are advancing because of their superior external quantum efficiencies (EQEs) and color purity. Still, additional work is needed for blue PeLEDs to achieve the same benchmarks as the other visible colors. This study demonstrates an extremely efficient blue PeLED with a 488 nm peak emission, a maximum luminance of 8600 cd m⁻², and a maximum EQE of 12.2% by incorporating the double-sided ethane-1,2-diammonium bromide (EDBr₂) ligand salt along with the long-chain ligand methylphenylammonium chloride (MeCl). The EDBr₂ successfully improves the interaction between 2D perovskite layers by reducing the weak van der Waals interaction and creating a Dion–Jacobson (DJ) structure. Whereas the pristine sample (without EDBr₂) is inhibited by small stacking number (n) 2D phases with nonradiative recombination regions that diminish the PeLED performance, adding EDBr₂ successfully enables better energy transfer from small n phases to larger n phases. As evidenced by photoluminescence (PL), scanning electron microscopy (SEM), and atomic force microscopy (AFM) characterization, EDBr₂ improves the morphology by reduction of pinholes and passivation of defects, subsequently improving the efficiencies and operational lifetimes of quasi-2D blue PeLEDs.