Understanding Neural Pathways in Zebrafish through Deep Learning and High Resolution Electron Microscope Data

The tracing of neural pathways through large volumes of image data is an incredibly tedious and time-consuming process that significantly encumbers progress in neuroscience. We are exploring deep learning's potential to automate segmentation of high-resolution scanning electron microscope (SEM) image data to remove that barrier. We have started with neural pathway tracing through 5.1GB of whole-brain serial-section slices from larval zebrafish collected by the Center for Brain Science at Harvard University. This kind of manual image segmentation requires years of careful work to properly trace the neural pathways in an organism as small as a zebrafish larva (approximately 5mm in total body length). In automating this process, we would vastly improve productivity, leading to faster data analysis and breakthroughs in understanding the complexity of the brain. We will build upon prior attempts to employ deep learning for automatic image segmentation extending methods for unconventional deep learning data.Comment: 8 pages, 5 figures (1a to 5c), PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

Sanctioned Unemployment: The Impact of Occupational Licensing Restrictions on ExOffenders

This note by Annie Zhang explores how occupational licensing restrictions on ex-offenders create a significant barrier to employment that undermines efforts to rehabilitate offenders and reduce recidivism. Zhang notes that these restrictions are particularly burdensome when they apply to low-income occupations like barbering and cosmetology and proposes that licensing boards be required to consider rehabilitation factors in assessing licensing applications, a direct connection between the conviction and the licensed occupation, and a presumption of licensure for ex-offenders that have completed their vocational training at a correctional facility

Effective Paleontological Framing of Climate Change Evidence to Influence Audience Perceptions

The way that media, scientists and others talk about climate change in the messages that they disseminate has an impact on how the public thinks about the topic. This phenomenon is called “framing.” To date, research on framing tends to focus on frames related to personal relevance, future effects (e.g. economic benefits or public health risks), uncertainty and attitudes. However, these frames largely ignore the scientific data inherent within climate change messages. While such data may be present in experimental message designs, generally it is not manipulated for study. In this experimental study, we examine the effects of two types of climate change data on research participants’ perceptions of climate change: computer-derived climate modeling data and data derived from paleoclimate artifacts and evidence. We also investigate the impact of embedding the two types of data within messages that frame climate change as are either hopeful or desperate. To test these messages, we recruited a population of 417 US participants to participate in a pre- post-test online experimental survey design in which they answered several questions related to intentions, emotions and behaviors towards climate change, then were exposed to one of four experimental conditions (paleoclimate with hopeful language/paleoclimate with desperate language/computer-derived with hopeful language/computer-derived with desperate language), and then answered the same series of questions related to intentions, emotions and behaviors towards climate change. Our results suggest that for our manipulations, a “paleo” frame is more effective at connecting with people on an emotional level. However, our data does not provide definitive support for the practical effects of framing around data type on engagement, intention to seek more information or intention to take action.Chemistr

Vortex shedding and hovering of a rigid body in an oscillating flow

The fluid dynamics video shows rigid, spatially asymmetric bodies interacting with oscillating background flows. A free rigid object, here a hollow "pyramid," can hover quite stably against gravity in the oscillating airflow with a zero mean, when its peak speed is sufficiently high. We further show in shadowgraph imaging how this asymmetric body sheds vortices in such an unsteady flow, thus enabling the body to "ratchet" itself through the background flow

An Analysis of a Linear Algebra Based Group Key Exchange Protocol

Group key exchange protocols are used to establish session keys, which can then be used as encryption keys to set up secure channels of communication, between more than two parties simultaneously. Many different group key exchange protocols exist and require security proofs in order to determine the strength of the protocol and answer the following questions: does the protocol provide authentication, and if so, to what degree? Does the protocol provide key secrecy? In this thesis we examine a particular group key exchange protocol that we call the \textit{vector space projection protocol} as first described in “A Group Key Establishment Scheme” by Guzey, Kurt, and Ozdemir, and show using a particular type of security proof — the game based security model — that the protocol as described does not achieve key secrecy. We show that there are at least four adversaries with non-negligible probabilities of winning the key secrecy security game, which indicates that this key exchange protocol is not one that should be implemented