Strengthening HIV Knowledge and Awareness among Undergraduate Students at Historically Black Colleges and Universities

Objective: We describe baseline HIV knowledge among students at historically black colleges and universities (HBCUs) to inform and strengthen HIV education efforts at HBCUs. Methods: We surveyed 1,230 African American HBCU students from 24 HBCUs; 1,051 responses (85.4 %) were analyzable. Results: Although general HIV knowledge was high among respondents (95% of students correctly responded that having sex without a condom constituted unsafe sex), knowledge deficits were noted (only 25% of students reported that multiple sex partners is a form of unsafe sex, while 25% of students reported that withdrawal of the penis before ejaculation reduced HIV risk). Conclusions: Misperceptions about HIV have implications for unintended sexual transmission of HIV. As African American young adults are disproportionately affected by HIV, strengthening HIV prevention efforts at HBCUs may include correcting misperceptions to reduce sexual risk and decrease HIV-related health disparities among young people

Weighted p-bits for FPGA implementation of probabilistic circuits

Probabilistic spin logic (PSL) is a recently proposed computing paradigm based on unstable stochastic units called probabilistic bits (p-bits) that can be correlated to form probabilistic circuits (p-circuits). These p-circuits can be used to solve problems of optimization, inference and also to implement precise Boolean functions in an "inverted" mode, where a given Boolean circuit can operate in reverse to find the input combinations that are consistent with a given output. In this paper we present a scalable FPGA implementation of such invertible p-circuits. We implement a "weighted" p-bit that combines stochastic units with localized memory structures. We also present a generalized tile of weighted p-bits to which a large class of problems beyond invertible Boolean logic can be mapped, and how invertibility can be applied to interesting problems such as the NP-complete Subset Sum Problem by solving a small instance of this problem in hardware

Exploring the use of learning communities of practice within a degree apprenticeship through university and partnership provision while incorporating the use of inclusive principles and practice

Learning communities and communities of practice (CoPs) are important aspects of the degree apprentice (DA) experience within higher education. DA programming differs to mainstream higher education programmes as the apprentices are ‘employees’ that spend most of their week working within an organisational setting. DAs in the United Kingdom are formally set 20% ‘off the job’ learning hours that include tuition as well as designated studies directly related to a job roles’ knowledge, skills, behaviours and values. This presentation looks at how concepts of learning within communities and inclusive practice have been nurtured within a DA programme to develop sustainable curricular and extra-curricular elements. As a part of ongoing research being undertaken using the BSc (Hons) Professional Practice in Business to Business Sales DA, this presentation focuses on how academic providers and partners work together to deliver inclusive tuition while considering the importance of learning communities of practice that must consider participation of employers and professional organisations. Inclusive practice includes requirements outlined in the new university strategic plan and in the Apprenticeship Standards. Emerging findings from recent apprentice/student questionnaires have indicated that apprentices, especially Generation Y and Z (McCrindle, 2014), are interested in how the providers might incorporate their insights about inclusive practice into their studies and professional practice. The presentation includes reflections from the current Programme Leaders from Consalia Ltd. and Marketing Branding and Tourism and the past Programme Leader (Education) to consider practical recommendations that could be adopted within the learning communities of practice from a Sales area of practice perspective and deliberates on what more needs to be done to create a dialogue that promotes inclusion and diversity (CIPD, 2022) within the university contex

Trajectory computational techniques emphasizing existence, uniqueness, and construction of solutions to boundary problems for ordinary differential equations Final report

Trajectory computational techniques emphasizing existence, uniqueness, and construction of solutions to boundary problems for ordinary differential equation

Visualising gas heating from an RF plasma loudspeaker

In an electro-acoustic transduction mechanism, an ac modulation (here in the audio frequency range) of the electric field in an atmospheric pressure air plasma gives rise to a rapid increase in the gas temperature and dimensions of the gas volume. As in natural lightning, the rapid expansion in the ionised column though the air produces external pressure variations at the modulation frequency.  \ud Spatial and temporal measurement of the gas temperature can identify the nature of the thermal expansion and provide a direct approach to understanding its relationship to the sound pressure wave that is generated. However, the established method through spectroscopic measurement of rotational line emission from nitrogen molecules is limited to the main current channel where relaxation and subsequent optical emission of the excited nitrogen molecules occurs. The wider picture is revealed through the use of the Schlieren method where the refractive index gradients caused by gas heating in the plasma are imaged

Predicting Future Instance Segmentation by Forecasting Convolutional Features

Anticipating future events is an important prerequisite towards intelligent behavior. Video forecasting has been studied as a proxy task towards this goal. Recent work has shown that to predict semantic segmentation of future frames, forecasting at the semantic level is more effective than forecasting RGB frames and then segmenting these. In this paper we consider the more challenging problem of future instance segmentation, which additionally segments out individual objects. To deal with a varying number of output labels per image, we develop a predictive model in the space of fixed-sized convolutional features of the Mask R-CNN instance segmentation model. We apply the "detection head'" of Mask R-CNN on the predicted features to produce the instance segmentation of future frames. Experiments show that this approach significantly improves over strong baselines based on optical flow and repurposed instance segmentation architectures