This paper considers possible future legislation that requires the exclusive use of autonomous cars. The author develops and defends a ‘Liberal Argument Against Mandated Autonomous Cars’, which argues that such a law would be incompatible with classical liberalism, provided that the following condition holds: In the event where the car must ‘choose’ between running over a young person or an old person, or both, autonomous cars are programmed to respond by running over old people in order to save young (...) people. Such a law requiring the use of these cars, provided that these assumptions hold, would violate the important value in classical liberalism that all individuals ought to be treated equally before the law. The paper concludes by arguing that alternative ways of dealing with this problem come with their own set of unpalatable problems. (shrink)

This paper considers possible future legislation that requires the exclusive use of autonomous cars. The author develops and defends a ‘Liberal Argument Against Mandated Autonomous Cars’, which argues that such a law would be incompatible with classical liberalism, provided that the following condition holds: In the event where the car must ‘choose’ between running over a young person or an old person, or both, autonomous cars are programmed to respond by running over old people in order to save young (...) people. Such a law requiring the use of these cars, provided that these assumptions hold, would violate the important value in classical liberalism that all individuals ought to be treated equally before the law. The paper concludes by arguing that alternative ways of dealing with this problem come with their own set of unpalatable problems. (shrink)

The current study replicated and extended the results from a study conducted by Narayan, Mak, and Bialystok (2017) that found effects of top‐down linguistic information on a speaker discrimination task by examining four conditions: rhymes (day‐bay), compounds (day‐dream), reverse compounds (dream‐day), and unrelated words (day‐bee). The original study found that participants were more likely to judge two words to be spoken by the same speaker if the words cohered lexically (created lexical compounds such as day‐dream) or were phonologically related (...) (rhymes, such as day‐bay), but their study contained two limitations: (a) Same‐ and different‐speaker trials were analyzed separately, which obscures effects of response bias, and (b) cross‐gender pairs were used in the different‐speaker trials, potentially inflating performance. The current study addresses these limitations by including only within‐gender trials and by examining sensitivity and bias using signal detection theory. Our results not only provide support of the original study but also provide clear evidence that listeners are biased to judge two words as being produced by the same person when they share either phonological information (rhymes) or lexical‐semantic coherence (compounds). Thus, the current study provides an important modified replication of previous research. (shrink)

Blood is a vital body fluid and can be instrumental in identifying various pathological conditions. Nowadays, a lot of people are suffering from COVID-19 and every country has its own limited testing capacity. Consequently, a system is required to help doctors analyze a patient’s blood structure including COVID-19. Therefore, in this paper, we extracted and selected blood features by proposing a new feature extraction and selection method named stepwise linear discriminant analysis. SWLDA emphasizes on picking confined features from blood (...) structure images and discerning its class based on reversion value such as partial F value. SWLDA begins with picking an equivalence comprising the sole finest X variable and then puts in effort to add more Xs individually, providing the situations are adequate. The process of adding and picking is based on F value to determine which variable would be entered. Then, the picked or the default F-to-enter value is compared with the uppermost partial F value. After this step, the forward addition or backward removal begins and whether the partial test values for all the predictor variables already in the line are estimated is known. Then, the comparison is made between the lowermost partial test value and preselected or defaulting consequence levels such as F0. Finally, the system is trained by employing support vector machine to label the blood images. The performance of the proposed approach is assessed by employing 8 different datasets of blood structures. It is assured that the proposed method has achieved significant results under different blood structure images including COVID-19. (shrink)