Charged Schrodinger Black Holes

We construct charged and rotating asymptotically Schrodinger black hole solutions of IIB supergravity. We begin by obtaining a closed-form expression for the null Melvin twist of a broad class of type IIB backgrounds, including solutions of minimal five-dimensional gauged supergravity, and identify the resulting five-dimensional effective action. We use these results to demonstrate that the near-horizon physics and thermodynamics of asymptotically Schrodinger black holes obtained in this way are essentially inherited from their AdS progenitors, and verify that they admit zero-temperature extremal limits with AdS_2 near-horizon geometries. Notably, the AdS_2 radius is parametrically larger than that of the asymptotic Schrodinger space.Comment: 22 pages, LaTe

Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources

Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an observation (such as bleeding). A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs. The objective of this work is to estimate the current state of the surgical task based on the actions performed or events occurred as the task progresses. We propose Fusion-KVE, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events. Additionally, we examine the strengths and weaknesses of different state estimation models in segmenting states with different representative features or levels of granularity. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging, created using the da Vinci® Xi surgical system. Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models in both JIGSAWS suturing dataset and our RIOUS dataset

Sex Offender Treatment Project: Literature Review

A comprehensive literature review on recidivism by and the treatment of sex offenders.Alaska Department of CorrectionsAcknowledgements / Introduction / Recidivism / Treatment — Voluntary Vs. Involuntary, Treated Vs. Untreated / Treatment — Types, Levels, Evolution, Relapse Prevention and Cost/Benefit Analysis / Treatment and Recidivism as it Relates to Various Types of Sexual Offenders / Other Factors Possibly Involved in Reoffense Potential / Conclusion / Bibliograph

Programação de recursos: um estudo de caso em empresa construtora de médio porte em Pernambuco

Established from the production planning and control, the resource scheduling, when ineffective, generate problems related to the expansion of the execution time, the waste of materials, low productivity and the increase of costs. In order to diagnose the resource scheduling process, this work carried out a case study in a construction company located in the city of Recife-PE. Conducting interviews and applying a questionnaire to those involved in the processes of production planning and control and resources scheduling allowed the verification of compliance with aspects identified as relevant by the bibliography. Within the scope of the production planning and control process, the results showed the non-involvement of subcontractors in the operational planning of the work and the adoption of reference indexes of material consumption and productivity, despite the availability of own indexes. The material resource scheduling showed flaws in the deliveries scheduling on site, the outdating of stock data and the prioritization of scheduling public works over private works. Considering these and other aspects revealed by the study, suggestions for improvement were presented in order to reduce the impact on the deadlines, quality and cost of the works.Realizada a partir do planejamento e controle da produção, a programação de recursos, quando ineficaz, pode gerar problemas relacionados à dilatação de prazo de execução da obra, ao desperdício de materiais, à baixa produtividade e ao aumento de custos do empreendimento. Com o objetivo de diagnosticar o processo de programação de recursos, este trabalho realizou estudo de caso em uma empresa construtora localizada na cidade do RecifePE. A condução de entrevistas e a aplicação de questionário junto aos envolvidos nos processos de planejamento e controle da produção e de programação de recursos permitiu a checagem do atendimento de aspectos identificados como relevantes pela bibliografia. No âmbito do processo de planejamento e controle da produção, os resultados apontaram o não envolvimento dos subcontratados no planejamento operacional da obra e adoção de índices de consumo de materiais e de produtividade de referência a despeito da disponibilidade de índices próprios. A programação de recursos materiais apresentou falhas na programação das entregas em obra, a desatualização dos dados de estoque e a priorização da programação das obras públicas frente às obras privadas. Considerando esses e outros aspectos revelados pelo estudo, foram apresentadas sugestões de melhoria a fim de reduzir o impacto sobre as metas de prazo, qualidade e custo das obras

Mask then classify: multi-instance segmentation for surgical instruments.

PURPOSE The detection and segmentation of surgical instruments has been a vital step for many applications in minimally invasive surgical robotics. Previously, the problem was tackled from a semantic segmentation perspective, yet these methods fail to provide good segmentation maps of instrument types and do not contain any information on the instance affiliation of each pixel. We propose to overcome this limitation by using a novel instance segmentation method which first masks instruments and then classifies them into their respective type. METHODS We introduce a novel method for instance segmentation where a pixel-wise mask of each instance is found prior to classification. An encoder-decoder network is used to extract instrument instances, which are then separately classified using the features of the previous stages. Furthermore, we present a method to incorporate instrument priors from surgical robots. RESULTS Experiments are performed on the robotic instrument segmentation dataset of the 2017 endoscopic vision challenge. We perform a fourfold cross-validation and show an improvement of over 18% to the previous state-of-the-art. Furthermore, we perform an ablation study which highlights the importance of certain design choices and observe an increase of 10% over semantic segmentation methods. CONCLUSIONS We have presented a novel instance segmentation method for surgical instruments which outperforms previous semantic segmentation-based methods. Our method further provides a more informative output of instance level information, while retaining a precise segmentation mask. Finally, we have shown that robotic instrument priors can be used to further increase the performance

daVinciNet: Joint Prediction of Motion and Surgical State in Robot-Assisted Surgery

This paper presents a technique to concurrently and jointly predict the future trajectories of surgical instruments and the future state(s) of surgical subtasks in robot-assisted surgeries (RAS) using multiple input sources. Such predictions are a necessary first step towards shared control and supervised autonomy of surgical subtasks. Minute-long surgical subtasks, such as suturing or ultrasound scanning, often have distinguishable tool kinematics and visual features, and can be described as a series of fine-grained states with transition schematics. We propose daVinciNet - an end-to-end dual-task model for robot motion and surgical state predictions. daVinciNet performs concurrent end-effector trajectory and surgical state predictions using features extracted from multiple data streams, including robot kinematics, endoscopic vision, and system events. We evaluate our proposed model on an extended Robotic Intra-Operative Ultrasound (RIOUS+) imaging dataset collected on a da Vinci Xi surgical system and the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). Our model achieves up to 93.85% short-term (0.5s) and 82.11% long-term (2s) state prediction accuracy, as well as 1.07mm short-term and 5.62mm long-term trajectory prediction error

daVinciNet: Joint Prediction of Motion and Surgical State in Robot-Assisted Surgery

This paper presents a technique to concurrently and jointly predict the future trajectories of surgical instruments and the future state(s) of surgical subtasks in robot-assisted surgeries (RAS) using multiple input sources. Such predictions are a necessary first step towards shared control and supervised autonomy of surgical subtasks. Minute-long surgical subtasks, such as suturing or ultrasound scanning, often have distinguishable tool kinematics and visual features, and can be described as a series of fine-grained states with transition schematics. We propose daVinciNet - an end-to-end dual-task model for robot motion and surgical state predictions. daVinciNet performs concurrent end-effector trajectory and surgical state predictions using features extracted from multiple data streams, including robot kinematics, endoscopic vision, and system events. We evaluate our proposed model on an extended Robotic Intra-Operative Ultrasound (RIOUS+) imaging dataset collected on a da Vinci Xi surgical system and the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). Our model achieves up to 93.85% short-term (0.5s) and 82.11% long-term (2s) state prediction accuracy, as well as 1.07mm short-term and 5.62mm long-term trajectory prediction error.Comment: Accepted to IROS 202

Self-reported quality of care for older adults from 2004 to 2011: a cohort study

Background: little is known about changes in the quality of medical care for older adults over time. Objective: to assess changes in technical quality of care over 6 years, and associations with participants' characteristics. Design: a national cohort survey covering RAND Corporation-derived quality indicators (QIs) in face-to-face structured interviews in participants' households. Participants: a total of 5,114 people aged 50 or more in four waves of the English Longitudinal Study of Ageing. Methods: the percentage achievement of 24 QIs in 10 general medical and geriatric clinical conditions was calculated for each time point, and associations with participants' characteristics were estimated using logistic regression. Results: participants were eligible for 21,220 QIs. QI achievement for geriatric conditions (cataract, falls, osteoarthritis and osteoporosis) was 41% [95% confidence interval (CI): 38–44] in 2004–05 and 38% (36–39) in 2010–11. Achievement for general medical conditions (depression, diabetes mellitus, hypertension, ischaemic heart disease, pain and cerebrovascular disease) improved from 75% (73–77) in 2004–05 to 80% (79–82) in 2010–11. Achievement ranged from 89% for cerebrovascular disease to 34% for osteoarthritis. Overall achievement was lower for participants who were men, wealthier, infrequent alcohol drinkers, not obese and living alone. Conclusion: substantial system-level shortfalls in quality of care for geriatric conditions persisted over 6 years, with relatively small and inconsistent variations in quality by participants' characteristics. The relative lack of variation by participants' characteristics suggests that quality improvement interventions may be more effective when directed at healthcare delivery systems rather than individuals