The provision of distance education within the HE sector - some areas for concern

This paper presents a summary of the findings of a recent survey of the way in which UK higher education institutions (HEIs) are offering distance education (DE) courses, the types of courses being offered, and their modes of delivery. From analysis of the findings of this survey, it is apparent that the emphasis of HEIs is very much on the exploitation of available teaching technology in the delivery of DE courses. However, teaching at a distance is quite different from face-toface teaching, and the evidence suggests that many HEIs fail to implement any meaningful academic staff training for the new role of DE tutor. The authors consider the difficulties this presents to academic staff who are required to move from face-to-face teaching to online facilitating. The paper concludes with an examination of the current provision of staff development and training within UK HEIs and suggests the type of academic staff training required if DE courses are to become truly core activities

Clinical Factors Associated with Low Valsalva Leak Point Pressure Among Women with Stress Urinary Incontinence

Purpose The purpose of this study is to determine the predictive factors that are associated with stress urinary incontinence (SUI) due to intrinsic sphincter deficiency (ISD) in women. Methods Between January 2008 and December 2009, 185 women with urodynamically proven SUI were included in this study and retrospectively reviewed the medical record. Preoperative SUI symptoms were classified by Stamey grade. Valsalva leak point pressure (VLPP) determination series was repeated two times in each subject after finishing one series of VLPP measurement. The patients were classified into three groups according to VLPP; 1) ISD: VLPP≤60 cm H2O, 2) equivocal: 60<VLPP≤90 cm H2O, 3) anatomical incontinence (AI): VLPP>90 cm H2O. Chi-square test and multivariate (logistic regression test) analyses were performed to determine the factors associated with ISD. Results The mean patient age was 54.2 years (range, 44.5 to 68.4 years). Seventy-one women (38.3%) were in the ISD group and 70 (37.8%) in the AI group. The results of univariate and multivariate analyses found that women with ISD had a higher symptom grade than women with AI (P=0.001 and 0.0001, respectively). The number of patients in the ISD and AI group in accordance with the symptom grade were 7 (10%) and 44 (62%) in grade I, 50 (54%) and 23 (25%) in grade II, and 14 (63%) and 3 (14%) in grade III respectively. There was no correlation between VLPP and other clinical factors. Conclusions High symptom grade was the only independent clinical factor that predicted the presence of ISD. This should be considered when counseling the patients with SUI

Defocus Video Matting

Video matting is the process of pulling a high-quality alpha matte and foreground from a video sequence. Current techniques require either a known background (e.g., a blue screen) or extensive user interaction (e.g., to specify known foreground and background elements). The matting problem is generally under-constrained, since not enough information has been collected at capture time. We propose a novel, fully autonomous method for pulling a matte using multiple synchronized video streams that share a point of view but differ in their plane of focus. The solution is obtained by directly minimizing the error in filter-based image formation equations, which are over-constrained by our rich data stream. Our system solves the fully dynamic video matting problem without user assistance: both the foreground and background may be high frequency and have dynamic content, the foreground may resemble the background, and the scene is lit by natural (as opposed to polarized or collimated) illumination.Engineering and Applied Science

Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models

Recent work in speech-to-speech translation (S2ST) has focused primarily on offline settings, where the full input utterance is available before any output is given. This, however, is not reasonable in many real-world scenarios. In latency-sensitive applications, rather than waiting for the full utterance, translations should be spoken as soon as the information in the input is present. In this work, we introduce a system for simultaneous S2ST targeting real-world use cases. Our system supports translation from 57 languages to English with tunable parameters for dynamically adjusting the latency of the output -- including four policies for determining when to speak an output sequence. We show that these policies achieve offline-level accuracy with minimal increases in latency over a Greedy (wait-$k$) baseline. We open-source our evaluation code and interactive test script to aid future SimulS2ST research and application development.Comment: To appear at INTERSPEECH 202

Optical Splitting Trees for High-Precision Monocular Imaging

This paper introduces a framework to design optical splitting tree to perform computational photography tasks that require many sensors with a common optical axis. As the number of sensors increases, designing a good optical system is often difficult because the components have nonideal characteristics.The authors describe an optimization tool takes this into account and finds "good" designs as specified by several weighted coefficients. Assisted by this optimizer, they demonstrate high-dynamic range, focusing, matting, high-speed, and hybrid imaging implemented on a single, reconfigurable camera containing eight sensors.Engineering and Applied Science

Resting state fMRI based multilayer network configuration in patients with schizophrenia.

Novel methods for measuring large-scale dynamic brain organisation are needed to provide new biomarkers of schizophrenia. Using a method for modelling dynamic modular organisation (Mucha et al., 2010), evidence suggests higher 'flexibility' (switching between multilayer network communities) to be a feature of schizophrenia (Braun et al., 2016). The current study compared flexibility between 55 patients with schizophrenia and 72 controls (the COBRE Dataset). In addition, novel methods of 'between resting state network synchronisation' (BRSNS) and the probability of transition from one community to another were used to further describe group differences in dynamic community structure. There was significantly higher schizophrenia group flexibility scores in cerebellar (F (1124) = 9.33, p (FDR) = 0.017), subcortical (F (1124) = 13.14, p (FDR) = 0.005), and fronto-parietal task control (F (1124) = 7.19, p (FDR) = 0.033) resting state networks (RSNs), as well as in the left thalamus (MNI XYZ: -2, -13, 12; F(1, 124) = 17.1, p (FDR) < 0.001) and the right crus I (MNI XYZ: 35, -67, -34; F (1, 124) = 19.65, p (FDR) < 0.001). Flexibility in the left thalamus reflected transitions between communities covering default mode and sensory-somatomotor RSNs. BRSNS scores suggested altered dynamic inter-RSN modular configuration in schizophrenia. This study suggests less stable community structure in a schizophrenia group at an RSN and node level and provides novel methods of exploring dynamic community structure. Mediation of group differences by mean time window correlation did however suggest flexibility to be no better as a schizophrenia biomarker than simpler measures and a range of methodological choices affected results

AdaptNet: Policy Adaptation for Physics-Based Character Control

Motivated by humans' ability to adapt skills in the learning of new ones, this paper presents AdaptNet, an approach for modifying the latent space of existing policies to allow new behaviors to be quickly learned from like tasks in comparison to learning from scratch. Building on top of a given reinforcement learning controller, AdaptNet uses a two-tier hierarchy that augments the original state embedding to support modest changes in a behavior and further modifies the policy network layers to make more substantive changes. The technique is shown to be effective for adapting existing physics-based controllers to a wide range of new styles for locomotion, new task targets, changes in character morphology and extensive changes in environment. Furthermore, it exhibits significant increase in learning efficiency, as indicated by greatly reduced training times when compared to training from scratch or using other approaches that modify existing policies. Code is available at https://motion-lab.github.io/AdaptNet.Comment: SIGGRAPH Asia 2023. Video: https://youtu.be/WxmJSCNFb28. Website: https://motion-lab.github.io/AdaptNet, https://pei-xu.github.io/AdaptNe