Vision-Based Force Estimation for Minimally Invasive Telesurgery Through Contact Detection and Local Stiffness Models

In minimally invasive telesurgery, obtaining accurate force information is difficult due to the complexities of in-vivo end effector force sensing. This constrains development and implementation of haptic feedback and force-based automated performance metrics, respectively. Vision-based force sensing approaches using deep learning are a promising alternative to intrinsic end effector force sensing. However, they have limited ability to generalize to novel scenarios, and require learning on high-quality force sensor training data that can be difficult to obtain. To address these challenges, this paper presents a novel vision-based contact-conditional approach for force estimation in telesurgical environments. Our method leverages supervised learning with human labels and end effector position data to train deep neural networks. Predictions from these trained models are optionally combined with robot joint torque information to estimate forces indirectly from visual data. We benchmark our method against ground truth force sensor data and demonstrate generality by fine-tuning to novel surgical scenarios in a data-efficient manner. Our methods demonstrated greater than 90% accuracy on contact detection and less than 10% force prediction error. These results suggest potential usefulness of contact-conditional force estimation for sensory substitution haptic feedback and tissue handling skill evaluation in clinical settings.Comment: Preprint of an article accepted in Journal of Medical Robotics Research \copyright 2024 copyright World Scientific Publishing Compan

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task

Do It for the Gram : A Social Phenomenological Study on Selected Female Instagram Beauty Influencers in Metro Manila

Instagram beauty influencers are often considered trendsetters when conforming to beauty ideals. To live up to the label given to them, they perform beauty practices, improve their appearance, and are cautious of the contents they put out. Despite their life mostly revolving online, it is difficult to decipher an influencers\u27 whole life since their followers only get to see a portion of it, which means that the public eye can easily label them as perfect. This research applies the social phenomenology by Schutz and Luckmann (1673), as the theoretical-methodological approach, along with a qualitative-descriptive research design. A one-on-one virtual interview with ten participants was conducted where questions about the beauty standards and their influencer life were asked. The results showed how damaging Western beauty standards\u27 dominance is not just for ordinary people but also for them. These beauty icons may look as if they have an easy life because of their well-executed posts, yet the results proved otherwise. Consequently, inclusivity is starting to be observed in the beauty community. It was described as overwhelming due to personal circumstances that their influencer life may bring regarding their private events offline

Psychosocial Assessment of Candidates for Transplantation (PACT) Score Identifies High Risk Patients in Pediatric Renal Transplantation

Background: Currently, there is no standardized approach for determining psychosocial readiness in pediatric transplantation. We examined the utility of the Psychosocial Assessment of Candidates for Transplantation (PACT) to identify pediatric kidney transplant recipients at risk for adverse clinical outcomes.Methods: Kidney transplant patients <21-years-old transplanted at Duke University Medical Center between 2005 and 2015 underwent psychosocial assessment by a social worker with either PACT or unstructured interview, which were used to determine transplant candidacy. PACT assessed candidates on a scale of 0 (poor candidate) to 4 (excellent candidate) in areas of social support, psychological health, lifestyle factors, and understanding. Demographics and clinical outcomes were analyzed by presence or absence of PACT and further characterized by high (≥3) and low (≤2) scores.Results: Of 54 pediatric patients, 25 (46.3%) patients underwent pre-transplant evaluation utilizing PACT, while 29 (53.7%) were not evaluated with PACT. Patients assessed with PACT had a significantly lower percentage of acute rejection (16.0 vs. 55.2%, p = 0.007). After adjusting for HLA mismatch, a pre-transplant PACT score was persistently associated with lower odds of acute rejection (Odds Ratio 0.119, 95% Confidence Interval 0.027–0.52, p = 0.005). In PACT subsection analysis, the lack of family availability (OR 0.08, 95% CI 0.01–0.97, p = 0.047) and risk for psychopathology (OR 0.34, 95% CI 0.13–0.87, p = 0.025) were associated with a low PACT score and post-transplant non-adherence.Conclusions: Our study highlights the importance of standardized psychosocial assessments and the potential use of PACT in risk stratifying pre-transplant candidates

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (eg. whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias xspace, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias xspace on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias xspace are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task

The Early Clinical Features of Dengue in Adults: Challenges for Early Clinical Diagnosis

Dengue infection in adults has become increasingly common throughout the world. As most of the clinical features of dengue have been described in children, we undertook a prospective study to determine the early symptoms and signs of dengue in adults. We show here that, overall, dengue cases presented with high rates of symptoms listed in the WHO 1997 or 2009 classification schemes for probable dengue fever thus resulting in high sensitivities of these schemes when applied for early diagnosis. However, symptoms such as myalgia, arthralgia, retro-orbital pain and mucosal bleeding were less frequently reported in older adults. This trend resulted in reduced sensitivity of the WHO classification schemes in older adults even though they showed increased risks of hospitalization and severe dengue. Instead, we suggest that older adults who present with fever and leukopenia should be tested for dengue, even in the absence of other symptoms. This could be useful for early clinical diagnosis in older adults so that they can be monitored and treated for severe dengue, which is especially important when an antiviral drug becomes available

Exploring Destination Loyalty: Application of Social Media Analytics in a Nature-based Tourism Setting

User-generated content across social media platforms is playing an increasingly important role in the tourism context. Understanding tourists’ experiences and opinions about tourism destinations has led to numerous opportunities to provide tourism providers and decision-makers with greater insight. Identifying sentiments, detecting topics of interest, and exploring loyalty behaviors from user-generated content can provide valuable direction for managerial decisions. Few if any studies on social media analytics have demonstrated the support for strategic decision-making. This paper presents a novel and inclusive approach that uses different analytical techniques such as sentiment analysis and topic modeling to extract sentiments and topics of interest from tourists’ conversational data on TripAdvisor from 2002 to 2019, and also explore destination loyalty statements using a keyword clustering approach. Previous destination loyalty literature was used to develop a keyword list that was applied to search for expression of loyalty in online reviews. The robustness of loyalty clusters and optimal number of clusters was also assessed prior to final analysis. Four leading loyalty-focused categories of destination offerings were observed: glaciers, waterfalls, lakes and islands, and hiking and trails. Prioritization of visitor experience enhancements relating to these loyalty inducing destination components are discussed

Integrative Genetic Manipulation of Plasmodium cynomolgi Reveals Multidrug Resistance-1 Y976F Associated With Increased In Vitro Susceptibility to Mefloquine

The lack of a long-term in vitro culture method has severely restricted the study of Plasmodium vivax, in part because it limits genetic manipulation and reverse genetics. We used the recently optimized Plasmodium cynomolgi Berok in vitro culture model to investigate the putative P. vivax drug resistance marker MDR1 Y976F. Introduction of this mutation using clustered regularly interspaced short palindromic repeats-CRISPR-associated protein 9 (CRISPR-Cas9) increased sensitivity to mefloquine, but had no significant effect on sensitivity to chloroquine, amodiaquine, piperaquine, and artesunate. To our knowledge, this is the first reported use of CRISPR-Cas9 in P. cynomolgi, and the first reported integrative genetic manipulation of this species

Faculty Publications for Academic Year 2018-19

Faculty publications of School of Architecture for the academic year 2018- 201

Astrophysics with the Laser Interferometer Space Antenna

Laser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy as it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and other space-based instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed: ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or intermediate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help make progress in the different areas. New research avenues that LISA itself, or its joint exploitation with studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe