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Policing in many parts of the world—the United States in particular—has embraced an archetypal model: a conception of the police based on the tenets of individuated archetypes, such as the heroic police "warrior" or "guardian." Such policing has in part motivated moves to (1) a reallocative model: reallocating societal resources such that the police are no longer needed in society (defunding and abolishing) because reform strategies cannot fix the way societal problems become manifest in (archetypal) policing; and (2) an algorithmic model: subsuming policing into technocratic judgements encoded in algorithms through strategies such as predictive policing (mitigating archetypal bias). This paper begins by considering the normative basis of the relationship between political community and policing. It then examines the justification of reallocative and algorithmic models in light of the relationship between political community and police. Given commitments to the depth and distribution of security—and proscriptions against dehumanizing strategies—the paper concludes that a nonideal-theory priority rule promoting respect for personhood (manifest in community and dignity-promoting policing strategies) is a necessary condition for the justification of the above models. [ABSTRACT FROM AUTHOR]

In 2019 the total accumulated digital data reached 4.4 trillion gigabytes; Facebook alone produced 4 million gigabytes, which includes 350 million photos and 100 million hours of video watch time, 65 billion messages via WhatsApp, and 95 million photos or videos on Instagram.1 With the vast volumes of data accessible to users, the necessity for filtering agents, which separate relevant data from noise, becomes clear for both the user and the businesses aiming to target specific audiences. The lack of regulation and misalignment of values between businesses and users, however, has led to poor design and implementation of filtering agents, which have been shown to be able to manipulate, extremize, and restrict users, even in situations and contexts beyond access to information and media. This paper aims to map the current state of interaction and dynamics between stakeholder groups and the consequences that arise from the current state and its perpetuation. [ABSTRACT FROM AUTHOR]

Collision warning/collision avoidance (CW/CA) systems must be designed to work seamlessly with a human driver, providing warning or control actions when the driver's response (or lack of) is deemed inappropriate. The effectiveness of CW/CA systems working with a human driver needs to be evaluated thoroughly because of legal/liability and other (e.g. traffic flow) concerns. CW/CA systems tuned only under open-loop manoeuvres were frequently found to work unsatisfactorily with human-in-the-loop. However, tuning CW/CA systems with human drivers co-existing is slow and non-repeatable. Driver models, if constructed and used properly, can capture human/control interactions and accelerate the CW/CA development process. Design and evaluation methods for CW/CA algorithms can be categorised into three approaches, scenario-based, performance-based and human-centred. The strength and weakness of these approaches were discussed in this paper and a humanised errable driver model was introduced to improve the developing process. The errable driver model used in this paper is a model that emulates human driver's functions and can generate both nominal (error-free) and devious (with error) behaviours. The car-following data used for developing and validating the model were obtained from a large-scale naturalistic driving database. Three error-inducing behaviours were introduced: human perceptual limitation, time delay and distraction. By including these error-inducing behaviours, rear-end collisions with a lead vehicle were found to occur at a probability similar to traffic accident statistics in the USA. This driver model is then used to evaluate the performance of several existing CW/CA algorithms. Finally, a new CW/CA algorithm was developed based on this errable driver model. [ABSTRACT FROM AUTHOR]

This paper evaluated the feasibility of using the horizontal radial plume mapping (HRPM) technique to locate multiple emission sources via computational simulation. Seventy-two test maps, each having two Gaussian distributions, were generated in a two-dimensional domain. The HRPM technique with the non-negative least square (NNLS) algorithm was then applied to reconstruct the plumes, assuming a nine-beam scanning beam geometry. The NNLS algorithm successfully reconstructed the source locations of 68 of the 72 test maps. However, when one of the plumes was near the origin, the NNLS did not always identify the peak locations correctly. Furthermore, when the two plumes were spaced closely, the NNLS tended to reconstruct a wide plume covering both plumes instead of separating them due to the resolution limitation of the current nine-beam geometry. In the sensitivity analysis, five sets of random error (1%, 5%, 10%, 20%, and 30%) were added in the path-integrated concentration (PIC) from the 72 test maps, and thus, an additional 360 reconstructions were implemented. Robust results were obtained when the noise added was less than 20%. The results generally support the implementation of the NNLS algorithm in the HRPM technique as described in the U.S. Environmental Agency (EPA) Other Test Method 10 (OTM-10). [ABSTRACT FROM AUTHOR]

Within the homeland security and emergency response communities, there is a need for a low-profile system to detect and locate radioactive sources. RadTrac has been developed at Argonne National Laboratory as an integrated system for the detection, localization, identification, and tracking of radioactive sources in real time. The system is based on a network of radiation detectors and advanced signal-processing algorithms. Features include video surveillance, automated tracking, easy setup, and logging of all data and images. This paper describes the advanced algorithms that were developed and implemented for source detection, localization, and tracking in real time. In the physio-spatial integration approach to source localization, counts from multiple detectors are processed according to the underlying physics linking these counts to obtain the probability that a source is present at any point in space. This information is depicted in a probability density function map. This type of depiction allows the results to be presented in a simple, easy-to-understand manner. It also allows for many different complicated factors to be accounted for in a single image as each factor is computed as a probability density in space. These factors include spatial limitations, variable shielding, directional detectors, moving detectors, and different detector sizes and orientations. The utility and versatility of this approach is described in further detail. Advanced signal-processing algorithms have also been incorporated to improve real-time tracking and to increase signal-to-noise ratios including temporal linking and energy binning. Measurements aimed at demonstrating the sensitivity improvements through the use of advanced signal-processing techniques were performed and are presented. Results of tracking weak sources (<100 μCi 137Cs) using four fixed-position detectors are presented. [ABSTRACT FROM AUTHOR]

This paper focuses on interest rate models with regime switching and extends previous nonlinear threshold models by relaxing the assumption of a fixed number of regimes. Instead we suggest automatic model determination through Bayesian inference via the reversible jump Markov Chain Monte Carlo (MCMC) algorithm. Moreover, we allow the thresholds in the volatility to be driven not only by the interest rate but also by other economic factors. We illustrate our methodology by applying it to interest rates and other economic factors of the American economy. [ABSTRACT FROM AUTHOR]

Recent economic growth and in-migration have led to an interesting patchwork of rapidly growing counties in the United States. This paper identifies 299 U.S. counties that were growing at least 70% from 1970 to 1990, a rate greater than one standard deviation above the mean of all counties. Using a k-means cluster algorithm, seven distinct types of rapidly growing counties, with meaningful spatial patterns, were found. Four of these clusters were primarily urban: smaller fast-growing cities in the south, large sunbelt cities, as well as both white and blue-collar suburban counties ringing many of the more dynamic metropolitan regional centers of the country. Also, there were two predominately rural clusters: retirement counties and several counties in the Intermontane West characterized by environmental amenities and isolated growth. Lastly, we observed a distinct cluster of several Hispanic counties in the southwest primarily along the U.S.–Mexico border. We believe these seven clusters present a remarkably clear portrait of the significant forces underlying the rapid growth of counties in the United States. [Copyright &y& Elsevier]

This paper presents a new algorithm to determine quantitatively column water vapour content ( W ) directly from ATSR2 (Along-Track Scanner Radiometer) Split-Window radiance measurements. First, the Split-Window Covariance-Variance Ratio (SWCVR) method is reviewed. The assumptions made to derive this method are highlighted and its applicability is discussed. Then, an operational use of this method is developed and applied to several ATSR2 datasets. The water vapour contents retrieved using ATSR2 data from SGP'97 (USA), Barrax (Spain) and Cabauw (The Netherlands) are in good agreement with those measured by the quasi-simultaneous radiosonde. The mean and the standard deviation of their difference are 0.04 g cm -2 and 0.22 g cm -2 , respectively. It is shown that water vapour content derived from ATSR2 data using the proposed algorithm is accurate enough in most cases for surface temperature determination with a split-window technique using ATSR2 data and for atmospheric corrections in visible and near-infrared channels of ATSR2. [ABSTRACT FROM AUTHOR]