Adaption and application of morphological pseudoconvolutions to scanning tunneling and atomic force microscopy

A recently developed class of digital filters known as morphological pseudoconvolutions are adapted and applied to Scanning Tunneling Microscopy (STM) and Atomic Force Microscopy (AFM) images. These filters are shown to outperform, both visually and in the mean square error sense, previously introduced Wiener filtering techniques. The filters are compared on typical STM/AFM images, using both modeled and actual data. The technique is general, and is shown to perform very well on many types of STM and AFM images

Shift in the Suburbs: An Analysis of Changing Vote Patterns in American Suburbs, 2000-2018

In the 2016 Presidential Election, the movement of well-off, highly-educated suburbs towards the Democratic Party was one of the most significant yet undercovered stories. In this paper, I analyze the political changes in suburbs around five major cities (Boston, Charleston (SC), Cleveland, Los Angeles, and Minneapolis) both in 2016 and in elections dating back to 2000. I find that between 2000 and 2014, municipalities close to core cities with high percentages of college graduates became slightly but significantly more Democratic, with much of this movement taking place around 2004 and 2006. Comparing 2016 to previous elections, I find that proximity to urban areas was indeed more significant than it was in the past, but much of the change in suburban areas can simply be traced to college educated voters becoming dramatically more Democratic. Analysis of opinion data from the American National Election Studies (ANES) and the Cooperative Congressional Election Study (CCES) suggests that this movement is likely due to a combination of liberalizing attitudes on social issues like abortion and a perception of the Republican Party moving to the right on racial issues. While early analysis of data from the 2018 Midterm Elections points to the durability of the suburban shift, it remains too early to determine whether 2016 really marked the beginning of a long-term political realignment in the suburbs

Theory and it ab initio calculation of radiative lifetime of excitons in semiconducting carbon nanotubes

We present theoretical analysis and first-principles calculation of the radiative lifetime of excitons in semiconducting carbon nanotubes. An intrinsic lifetime of the order of 10 ps is computed for the lowest optically active bright excitons. The intrinsic lifetime is however a rapid increasing function of the exciton momentum. Moreover, the electronic structure of the nanotubes dictates the existence of dark excitons nearby in energy to each bright exciton. Both effects strongly influence measured lifetime. Assuming a thermal occupation of bright and dark exciton bands, we find an effective lifetime of the order of 10 ns at room temperature, in good accord with recent experiments.Comment: 12 pages, 3 figure

The Hero’s Journey as a Novel, Narrative, and Improvisational Group Intervention on Quality of Life for People with Parkinson’s Disease

Parkinson’s disease (PD) is one of the fastest-growing neurological diseases in the world. Pharmaceutical and surgical interventions continue to advance to better address motor symptoms. However, disability from non-motor symptoms, such as depression, anxiety, and stress persist. Expressive therapies, including art, music, and dance are being explored and implemented more frequently to address this growing need. We developed this study to examine the feasibility of a novel program using online narrative therapy, with constructs from Joseph Campbell’s Hero’s Journey, and improvisation for people with PD (PWPD). Participants from across the U.S. and U.K. met online via Zoom once a week for one hour over the course of 27 consecutive sessions. A PhD student with a professional background in filmmaking and improvisation developed the sessions and led the program. The primary aim was to assess feasibility, the threshold for which was set at 70% of participants attending a minimum of 75% of the classes. Sixteen of 21 participants (76%) completed the study, with all 16 attending at least 85% of the classes. All participants who completed the study stated they enjoyed the class and would like to continue in the program and see it offered to others with PD. The secondary aim was to explore the participants’ experience within the program using a phenomenological approach. Four major themes emerged: 1) There was a high level of interest in the intervention itself as it was deemed unique; 2) Participants struggled to see their own heroic qualities; 3) They reported high levels of connection within the group and 4) The emphasis on having PD faded, replaced by journeys of self-discovery beyond having PD

Borrowed alleles and convergence in serpentine adaptation

ACKNOWLEDGMENTS. We thank members of the L.Y. and K.B. laboratories for helpful discussions. This work was supported through the European Research Council Grant StG CA629F04E (to L.Y.); a Harvard University Milton Fund Award (to K.B.); Ruth L. Kirschstein National Research Service Award 1 F32 GM096699 from the NIH (to L.Y.); National Science Foundation Grant IOS-1146465 (to K.B.); NIH National Institute of General Medical Sciences Grant 2R01GM078536 (to D.E.S.); and Biotechnology and Biological Sciences Research Council Grant BB/L000113/1 (to D.E.S.)Peer reviewedPublisher PD

Machine Translation for Accessible Multi-Language Text Analysis

English is the international standard of social research, but scholars are increasingly conscious of their responsibility to meet the need for scholarly insight into communication processes globally. This tension is as true in computational methods as any other area, with revolutionary advances in the tools for English language texts leaving most other languages far behind. In this paper, we aim to leverage those very advances to demonstrate that multi-language analysis is currently accessible to all computational scholars. We show that English-trained measures computed after translation to English have adequate-to-excellent accuracy compared to source-language measures computed on original texts. We show this for three major analytics -- sentiment analysis, topic analysis, and word embeddings -- over 16 languages, including Spanish, Chinese, Hindi, and Arabic. We validate this claim by comparing predictions on original language tweets and their backtranslations: double translations from their source language to English and back to the source language. Overall, our results suggest that Google Translate, a simple and widely accessible tool, is effective in preserving semantic content across languages and methods. Modern machine translation can thus help computational scholars make more inclusive and general claims about human communication.Comment: 5000 words, 6 figure