Large language models reduce public knowledge sharing on online Q&A platforms.

Large language models (LLMs) are a potential substitute for human-generated data and knowledge resources. This substitution, however, can present a significant problem for the training data needed to develop future models if it leads to a reduction of human-generated content. In this work, we document a reduction in activity on Stack Overflow coinciding with the release of ChatGPT, a popular LLM.

Deep Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images using Multi-scale and Higher-order Processing.

Autonomous Ultrasound Image Quality Assessment (US-IQA) is a promising tool to aid the interpretation by practicing sonographers and to enable the future robotization of ultrasound procedures. However, autonomous US-IQA has several challenges. Ultrasound images contain many spurious artifacts, such as noise due to handheld probe positioning, errors in the selection of probe parameters and patient respiration during the procedure.

JSSE: Joint Sequential Semantic Encoder for Zero-Shot Event Recognition.

Zero-shot learning (ZSL) is a paradigm in transfer learning that aims to recognize unknown categories by having a mere description of them. The problem of ZSL has been thoroughly studied in the domain of static object recognition, however, ZSL for dynamic events (ZSER) such as activities and gestures has hardly been investigated. In this context, this paper addresses ZSER by relying on semantic attributes of events to transfer the learned knowledge from seen classes to unseen ones.

Autonomous Multi-modality Burn Wound Characterization using Artificial Intelligence.

Introduction: Between 5% and 20% of all combat-related casualties are attributed to burn wounds. A decrease in the mortality rate of burns by about 36% can be achieved with early treatment, but this is contingent upon accurate characterization of the burn. Precise burn injury classification is recognized as a crucial aspect of the medical artificial intelligence (AI) field.

Adaptive Human-Robotic Interaction for Robotic-assisted Surgical Settings.

Introduction: Increased complexity in robotic-assisted surgical system interfaces introduces problems with human-robot collaboration that result in excessive mental workload (MWL), adversely impacting a surgeon's task performance and increasing error probability. Real-time monitoring of the operator's MWL will aid in identifying when and how interventions can be best provided to moderate MWL. In this study, an MWL-based adaptive automation system is constructed and evaluated for its effectiveness during robotic-assisted surgery.

Trauma THOMPSON: Clinical Decision Support for the Frontline Medic.

Introduction: U.S. Military healthcare providers increasingly perform prolonged casualty care because of operations in settings with prolonged evacuation times.

ASAP-CORPS: A Semi-Autonomous Platform for COntact-Rich Precision Surgery.

Introduction: Remote military operations require rapid response times for effective relief and critical care. Yet, the military theater is under austere conditions, so communication links are unreliable and subject to physical and virtual attacks and degradation at unpredictable times. Immediate medical care at these austere locations requires semi-autonomous teleoperated systems, which enable the completion of medical procedures even under interrupted networks while isolating the medics from the dangers of the battlefield.

Considering human cognitive architecture in stressful medical prehospital interventions might benefit care providers.

People suffering from critical injuries/illness face marked challenges before transportation to definitive care. Solutions to diagnose and intervene in the prehospital setting are required to improve outcomes. Despite advances in artificial intelligence and robotics, near-term practical interventions for catastrophic injuries/illness will require humans to perform unfamiliar, uncomfortable and risky interventions.

A large-scale empirical investigation of specialization in criminal career.

We use a comprehensive longitudinal dataset on criminal acts over 6 years in a European country to study specialization in criminal careers. We present a method to cluster crime categories by their relative co-occurrence within criminal careers, deriving a natural, data-based taxonomy of criminal specialization. Defining specialists as active criminals who stay within one category of offending behavior, we study their socio-demographic attributes, geographic range, and positions in their collaboration networks relative to their generalist counterparts.

Digital traces of brain drain: developers during the Russian invasion of Ukraine.

Unlabelled: The Russian invasion of Ukraine has caused large scale destruction, significant loss of life, and the displacement of millions of people. Besides those fleeing direct conflict in Ukraine, many individuals in Russia are also thought to have moved to third countries. In particular the exodus of skilled human capital, sometimes called brain drain, out of Russia may have a significant effect on the course of the war and the Russian economy in the long run.

Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills.

Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties.

Evolving collaboration, dependencies, and use in the Rust Open Source Software ecosystem.

Open Source Software (OSS) is widely spread in industry, research, and government. OSS represents an effective development model because it harnesses the decentralized efforts of many developers in a way that scales. As OSS developers work independently on interdependent modules, they create a larger cohesive whole in the form of an ecosystem, leaving traces of their contributions and collaborations.

An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing-Demonstration of a Semi-autonomous Suction Tool.

Objective: This study developed and evaluated a mental workload-based adaptive automation (MWL-AA) that monitors surgeon cognitive load and assist during cognitively demanding tasks and assists surgeons in robotic-assisted surgery (RAS).

Background: The introduction of RAS makes operators overwhelmed. The need for precise, continuous assessment of human mental workload (MWL) states is important to identify when the interventions should be delivered to moderate operators' MWL.

A randomized, controlled, blinded evaluation of augmenting point-of-care ultrasound and remote telementored ultrasound in inexperienced operators.

Background: Handheld ultrasound devices present an opportunity for prehospital sonographic assessment of trauma, even in the hands of novice operators commonly found in military, maritime, or other austere environments. However, the reliability of such point-of-care ultrasound (POCUS) examinations by novices is rightly questioned. A common strategy being examined to mitigate this reliability gap is remote mentoring by an expert.

JSE: Joint Semantic Encoder for zero-shot gesture learning.

Zero-shot learning (ZSL) is a transfer learning paradigm that aims to recognize unseen categories just by having a high-level description of them. While deep learning has greatly pushed the limits of ZSL for object classification, ZSL for gesture recognition (ZSGL) remains largely unexplored. Previous attempts to address ZSGL were focused on the creation of gesture attributes and algorithmic improvements, and there is little or no research concerned with feature selection for ZSGL.

A randomized controlled pilot trial of video-modelling versus telementoring for improved hemorrhage control wound packing.

Introduction: Exsanguination is the most preventable cause of death. Paradigms such as STOP THE BLEED recognize increased responsibility among the less experienced with Wound Packing (WP) being a critical skill. As even trained providers may perform poorly, we compared Video-modelling (VM), a form of behavioural modelling involving video demonstration prior to intervention against remote telementoring (RTM) involving remote real-time expert-guidance.

Robotically applied hemostatic clamping for care-under-fire: harnessing bomb robots for hemorrhage control.

Background: Early hemorrhage control after interpersonal violence is the most urgent requirement to preserve life and is now recognized as a responsibility of law enforcement. Although earlier entry of first responders is advocated, many shooting scenes remain unsafe for humans, necessitating first responses conducted by robots. Thus, robotic hemorrhage control warrants study as a care-under-fire treatment option.

A neurotechnological aid for semi-autonomous suction in robotic-assisted surgery.

Adoption of robotic-assisted surgery has steadily increased as it improves the surgeon's dexterity and visualization. Despite these advantages, the success of a robotic procedure is highly dependent on the availability of a proficient surgical assistant that can collaborate with the surgeon. With the introduction of novel medical devices, the surgeon has taken over some of the surgical assistant's tasks to increase their independence.

The Effects of Gender Signals and Performance in Online Product Reviews.

This work quantifies the effects of signaling gender through gender specific user names, on the success of reviews written on the popular amazon.com shopping platform. Highly rated reviews play an important role in e-commerce since they are prominently displayed next to products.

Procedural Telementoring in Rural, Underdeveloped, and Austere Settings: Origins, Present Challenges, and Future Perspectives.

Telemedicine is perhaps the most rapidly growing area in health care. Approximately 15 million Americans receive medical assistance remotely every year. Yet rural communities face significant challenges in securing subspecialist care.

Inequality is rising where social network segregation interacts with urban topology.

Social networks amplify inequalities by fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation, which is reflected in fragmented social network structure. Geographical impediments such as distance and physical or administrative boundaries also reinforce social segregation.

From the Dexterous Surgical Skill to the Battlefield-A Robotics Exploratory Study.

Introduction: Short response time is critical for future military medical operations in austere settings or remote areas. Such effective patient care at the point of injury can greatly benefit from the integration of semi-autonomous robotic systems. To achieve autonomy, robots would require massive libraries of maneuvers collected with the goal of training machine learning algorithms.