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1. Fraud in disaster: Rethinking the phases

   https://doi.org/https://doi.org/10.1016/j.ijdrr.2019.101232  

   B.E. Aguirre, David Lane (July 2019, International journal of disaster risk reduction)

   This paper reexamines the link between disasters and crime to argue that the relationship between the two becomes more evident once the diachronic nature of the disaster is specified. The first section appraises the literature on crime and disaster and calls for more attention to this topic. The subsequent sections of the paper present multiple evidence for the occurrence of crimes and fraud in disasters and the results of preliminary empirical research using U.S. Department of Justice information on the reported white collar and other types of crimes occurring in the aftermath of the Katrina Hurricane and the British Petroleum Gulf Coast oil spill. The findings indicate that crimes and frauds occur during the earliest phases of disasters and that the probability for the occurrence of costlier crimes, akin to white-collar crime, increases, as time elapses. The conclusion calls for a reconceptualization of the phases of disasters. 

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2. Accountability in the Blue-Collar Data-Driven Workplace

   https://doi.org/10.1145/3173574.3173906  

   Kristiansen, Kristian Helbo; Valeur-Meller, Mathias A.; Dombrowski, Lynn; Holten Moller, Naja L.. (April 2018, CHI '18 Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems)

   This paper examines how mobile technology impacts employee accountability in the blue-collar data-driven workplace. We conducted an observation-based qualitative study of how electricians in an electrical company interact with data related to their work accountability, which comprises the information employees feel is reasonable to share and document about their work. The electricians we studied capture data both manually, recording the hours spent on a particular task, and automatically, as their mobile devices regularly track data such as location. First, our results demonstrate how work accountability manifests for employees' manual labor work that has become data-driven. We show how employees work through moments of transparency, privacy, and accountability using data focused on location, identification and time. Second, we demonstrate how this data production is interdependent with employees' beliefs about what is a reasonable level of detail and transparency to provide about their work. Lastly, we articulate specific design implications related to work accountability. 

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3. Astro2020 Science White Paper: Are Supernovae the Dust Producer in the Early Universe?

   Rho, Jeonghee; Milisavljevic, Danny; Sarangi, Arkaprabha; Margutti, Raffaella; Chornock, Ryan; Rest, Armin; Graham, Melissa; Wheeler, J. Craig; DePoy, Darren; Wang, Lifan; et al (April 2019, arXiv e-prints)

   Whether supernovae are a significant source of dust has been a long-standing debate. The large quantities of dust observed in high-redshift galaxies raise a fundamental question as to the origin of dust in the Universe since stars cannot have evolved to the AGB dust-producing phase in high-redshift galaxies. In contrast, supernovae occur within several millions of years after the onset of star formation. This white paper focuses on dust formation in supernova ejecta with US-Extremely Large Telescope (ELT) perspective during the era of JWST and LSST. 

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4. A left-lateralized white matter tract associated with communication in domestic dogs

   https://doi.org/10.1016/2024.09.021  

   Levin, Isabel; Sinha, Mira; Barton, Sophie; Hecht, Erin (November 2024, Current Biology)

   The ability to communicate with conspecifics is an adaptive behavior important for survival and reproduction, particularly in lineages that evolved enlarged brains and complex social behavior. In humans, language is supported by a robust, left-lateralized white matter fiber tract called the arcuate fasciculus, which links Broca’s and Wernicke’s areas, the core neocortical language regions located in the frontal and temporal lobes, respectively1. This tract is also present in chimpanzees, less substantial than in humans and either weakly leftwardly-asymmetric or not asymmetric2. Other mammalian lineages have evolved large brains, complex behavior and social communication in parallel with primates, notably including carnivores. In dogs (Canis familiaris), domestication has almost certainly involved additional selective pressures and environmental factors that have shaped the evolution and development of neural circuits for communication. We report that the dog brain possesses a large, left-lateralized white matter tract that links cortical centers for productive and receptive communication, and that this tract is positively associated with individual variation in receptive vocabulary size. 

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5. Preliminary Analysis of Collar Sensors for Guide Dog Training Using Convolutional Long Short-Term Memory, Kernel Principal Component Analysis and Multi-Sensor Data Fusion

   https://doi.org/10.3390/ani14233403  

   Martin, Devon; Roberts, David L; Bozkurt, Alper (December 2024, Animals)

   Guide dogs play a crucial role in enhancing independence and mobility for people with visual impairment, offering invaluable assistance in navigating daily tasks and environments. However, the extensive training required for these dogs is costly, resulting in a limited availability that does not meet the high demand for such skilled working animals. Towards optimizing the training process and to better understand the challenges these guide dogs may be experiencing in the field, we have created a multi-sensor smart collar system. In this study, we developed and compared two supervised machine learning methods to analyze the data acquired from these sensors. We found that the Convolutional Long Short-Term Memory (Conv-LSTM) network worked much more efficiently on subsampled data and Kernel Principal Component Analysis (KPCA) on interpolated data. Each attained approximately 40% accuracy on a 10-state system. Not needing training, KPCA is a much faster method, but not as efficient with larger datasets. Among various sensors on the collar system, we observed that the inertial measurement units account for the vast majority of predictability, and that the addition of environmental acoustic sensing data slightly improved performance in most datasets. We also created a lexicon of data patterns using an unsupervised autoencoder. We present several regions of relatively higher density in the latent variable space that correspond to more common patterns and our attempt to visualize these patterns. In this preliminary effort, we found that several test states could be combined into larger superstates to simplify the testing procedures. Additionally, environmental sensor data did not carry much weight, as air conditioning units maintained the testing room at standard conditions. 

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