Handshake: The University\u27s New Internship and Job Tool

An overview of Handshake, the new internship and job posting system being implemented by the Career Center. Handshake is available to all students and faculty. This session includes how to use the system to help students find internships and jobs as well as how to automate your Department\u27s Internship Application Process

DME Handout: Support Vector Machines School of Informatics, University of

Support Vector Machines (SVMs) are a relatively new concept in supervised learning, but since the publication of [3] in 1995 they have been applied to a wide variety of problems. In many ways the application of SVMs to almost any learning problem mirrors the enthusiasm (and fashionability) that was observed for neural networks in the second half of the 1980’s. The ingredients of the SVM had, in fact, been around for a decade or so, but they were not put together until the early 90’s. The two key ideas of support vector machines are (i) The maximum margin solution for a linear classifier. (ii) The “kernel trick”; a method of expanding up from a linear classifier to a non-linear one in an efficient manner. Below we discuss these key ideas in turn, and then go on to consider support vector regression and some example applications of SVMs. Further reading on the topic can be found in [2], [7] and [4]. For those keen to keep up with the latest results, the web sit

A cluster or filament of galaxies at redshift z = 2.5?

We report the discovery of 56 new Lyα-emitting candidates (LECs) at redshift z ≈ 2.5 in a field of 8′ × 14′ around two previously known weak radio QSOs and a cosmic microwave background decrement (CMBD) that is plausibly due to the Sunyaev-Zeldovich effect. Broadband and medium-band imaging at the redshifted Lyα wavelength have allowed us to identify the LECs at the redshift of the QSOs. Three of the brightest LECs have been confirmed spectroscopically, with redshifts between z = 2.501 and z = 2.557; one of them is another QSO. Excluding the third QSO, the four spectroscopically confirmed objects form a 3′ filament with a rest-frame velocity dispersion of 1000 km s-1 lying adjacent to the CMBD, and there is a significant concentration of LECs at the northwest end of the filament around the brightest QSO. If confirmed, a velocity dispersion ~1000 km s-1 on a proper scale of ~1 Mpc at redshift z = 2.5 would, in and of itself, constrain the cosmological model to low Ω.Part of this work was supported by NASA grants AR-07551.01-96A (to AY), and GO-5985.01- 94A, GO-6610.01-95A, and GO.2684.03-94A (to RAW) from STScI, which is operated by AURA, Inc., under NASA contract NAS5-26555.Peer Reviewe

TractoR: Magnetic Resonance Imaging and Tractography with R

Statistical techniques play a major role in contemporary methods for analyzing magnetic resonance imaging (MRI) data. In addition to the central role that classical statistical methods play in research using MRI, statistical modeling and machine learning techniques are key to many modern data analysis pipelines. Applications for these techniques cover a broad spectrum of research, including many preclinical and clinical studies, and in some cases these methods are working their way into widespread routine use.In this manuscript we describe a software tool called TractoR (for “Tractography with R”), a collection of packages for the R language and environment, along with additional infrastructure for straightforwardly performing common image processing tasks. TractoR provides general purpose functions for reading, writing and manipulating MR images, as well as more specific code for fitting signal models to diffusion MRI data and performing tractography, a technique for visualizing neural connectivity

Barriers to and Facilitators of Using Remote Measurement Technology in the Long-Term Monitoring of Individuals With ADHD: Interview Study

BACKGROUND: Remote measurement technology (RMT) has the potential to address current research and clinical challenges of attention-deficit/hyperactivity disorder (ADHD) symptoms and its co-occurring mental health problems. Despite research using RMT already being successfully applied to other populations, adherence and attrition are potential obstacles when applying RMT to a disorder such as ADHD. Hypothetical views and attitudes toward using RMT in a population with ADHD have previously been explored; however, to our knowledge, there is no previous research that has used qualitative methods to understand the barriers to and facilitators of using RMT in individuals with ADHD following participation in a remote monitoring period. OBJECTIVE: We aimed to evaluate the barriers to and facilitators of using RMT in individuals with ADHD compared with a group of people who did not have a diagnosis of ADHD. We also aimed to explore participants' views on using RMT for 1 or 2 years in future studies. METHODS: In total, 20 individuals with ADHD and 20 individuals without ADHD were followed up for 10 weeks using RMT that involved active (questionnaires and cognitive tasks) and passive (smartphone sensors and wearable devices) monitoring; 10 adolescents and adults with ADHD and 12 individuals in a comparison group completed semistructured qualitative interviews at the end of the study period. The interviews focused on potential barriers to and facilitators of using RMT in adults with ADHD. A framework methodology was used to explore the data qualitatively. RESULTS: Barriers to and facilitators of using RMT were categorized as health-related, user-related, and technology-related factors across both participant groups. When comparing themes that emerged across the participant groups, both individuals with and without ADHD experienced similar barriers and facilitators in using RMT. The participants agreed that RMT can provide useful objective data. However, slight differences between the participant groups were identified as barriers to RMT across all major themes. Individuals with ADHD described the impact that their ADHD symptoms had on participating (health-related theme), commented on the perceived cost of completing the cognitive tasks (user-related theme), and described more technical challenges (technology-related theme) than individuals without ADHD. Hypothetical views on future studies using RMT in individuals with ADHD for 1 or 2 years were positive. CONCLUSIONS: Individuals with ADHD agreed that RMT, which uses repeated measurements with ongoing active and passive monitoring, can provide useful objective data. Although themes overlapped with previous research on barriers to and facilitators of engagement with RMT (eg, depression and epilepsy) and with a comparison group, there are unique considerations for people with ADHD, for example, understanding the impact that ADHD symptoms may have on engaging with RMT. Researchers need to continue working with people with ADHD to develop future RMT studies for longer periods

The Texas Community-Engagement Research Alliance Against COVID-19 in Disproportionately Affected Communities (TX CEAL) Consortium

The coronavirus disease 2019 (COVID-19) pandemic requires urgent implementation of effective community-engaged strategies to enhance education, awareness, and inclusion of underserved communities in prevention, mitigation, and treatment efforts. The Texas Community-Engagement Alliance Consortium was established with support from the United States’ National Institutes of Health (NIH) to conduct community-engaged projects in selected geographic locations with a high proportion of medically underserved minority groups with a disproportionate burden of COVID-19 disease and hospitalizations. The purpose of this paper is to describe the development of the Consortium. The Consortium organized seven projects with focused activities to address COVID-19 clinical and vaccine trials in highly affected counties, as well as critical statewide efforts. Five Texas counties (Bexar, Dallas, Harris, Hidalgo, and Tarrant) were chosen by NIH because of high concentrations of underserved minority communities, existing community infrastructure, ongoing efforts against COVID-19, and disproportionate burden of COVID-19. Policies and practices can contribute to disparities in COVID-19 risk, morbidity, and mortality. Community engagement is an essential element for effective public health strategies in medically underserved minority areas. Working with partners, the Consortium will use community engagement strategies to address COVID-19 disparities