Search for: All records

Linear representation learning is widely studied due to its conceptual simplicity and empirical utility in tasks such as compression, classification, and feature extraction. Given a set of points $$[\x_1, \x_2, \ldots, \x_n] = \X \in \R^{d \times n}$$ and a vector $$\y \in \R^d$$, the goal is to find coefficients $$\w \in \R^n$$ so that $$\X \w \approx \y$$, subject to some desired structure on $$\w$$. In this work we seek $$\w$$ that forms a local reconstruction of $$\y$$ by solving a regularized least squares regression problem. We obtain local solutions through a locality function that promotes the use of columns of $$\X$$ that are close to $$\y$$ when used as a regularization term. We prove that, for all levels of regularization and under a mild condition that the columns of $$\X$$ have a unique Delaunay triangulation, the optimal coefficients' number of non-zero entries is upper bounded by $d+1$, thereby providing local sparse solutions when $$d \ll n$$. Under the same condition we also show that for any $$\y$$ contained in the convex hull of $$\X$$ there exists a regime of regularization parameter such that the optimal coefficients are supported on the vertices of the Delaunay simplex containing $$\y$$. This provides an interpretation of the sparsity as having structure obtained implicitly from the Delaunay triangulation of $$\X$$. We demonstrate that our locality regularized problem can be solved in comparable time to other methods that identify the containing Delaunay simplex. 

Background Team leadership during medical emergencies like cardiac arrest resuscitation is cognitively demanding, especially for trainees. These cognitive processes remain poorly characterized due to measurement challenges. Using virtual reality simulation, this study aimed to elucidate and compare communication and cognitive processes-such as decision-making, cognitive load, perceived pitfalls, and strategies-between expert and novice code team leaders to inform strategies for accelerating proficiency development. Methods A simulation-based mixed methods approach was utilized within a single large academic medical center, involving twelve standardized virtual reality cardiac arrest simulations. These 10- to 15-minutes simulation sessions were performed by seven experts and five novices. Following the simulations, a cognitive task analysis was conducted using a cued-recall protocol to identify the challenges, decision-making processes, and cognitive load experienced across the seven stages of each simulation. Results The analysis revealed 250 unique cognitive processes. In terms of reasoning patterns, experts used inductive reasoning, while novices tended to use deductive reasoning, considering treatments before assessments. Experts also demonstrated earlier consideration of potential reversible causes of cardiac arrest. Regarding team communication, experts reported more critical communications, with no shared subthemes between groups. Experts identified more teamwork pitfalls, and suggested more strategies compared to novices. For cognitive load, experts reported lower median cognitive load (53) compared to novices (80) across all stages, with the exception of the initial presentation phase. Conclusions The identified patterns of expert performance — superior teamwork skills, inductive clinical reasoning, and distributed cognitive strategiesn — can inform training programs aimed at accelerating expertise development. 

On 10 May 2024, a series of coronal mass ejections were detected at Earth followed by one of the most powerful geomagnetic storms since November 2003. Leveraging a multi–technique approach, this paper provides an account of the ground geomagnetic response during the 10–11 May 2024 extreme geomagnetic storm. More specifically, we show that at the mid-latitudes in the American sector, the storm produced extreme ground geomagnetic field perturbations between 01:50 UT and 02:30 UT on 11 May. Then using the Spherical Elementary Current System method, it is shown that the perturbations were associated with an intense westward propagating auroral westward electrojet current. Finally, with the aid of auroral all-sky images from the Missouri Skies Observatory, we demonstrate that an intense isolated substorm event with onset located between the Great Lakes region and the East Coast United States was the main source of the extreme westward electrojet current and the geomagnetic field perturbations at these typical mid-latitude locations. This study emphasizes the increased risk associated with expansion of the auroral oval into the mid-latitudes during extreme geomagnetic activity. 

This study introduces AutoCLC, an AI-powered system designed to assess and provide feedback on closed-loop communication (CLC) in professional learning environments. CLC, where a sender’s Call-Out statement is acknowledged by the receiver’s Check-Back statement, is a critical safety protocol in high-reliability domains, including emergency medicine resuscitation teams. Existing methods for evaluating CLC lack quantifiable metrics and depend heavily on human observation. AutoCLC addresses these limitations by leveraging natural language processing and large language models to analyze audio recordings from Advanced Cardiovascular Life Support (ACLS) simulation training. The system identifies CLC instances, measures their frequency and rate per minute, and categorizes communications as effective, incomplete, or missed. Technical evaluations demonstrate AutoCLC achieves 78.9% precision for identifying Call-Outs and 74.3% for Check-Backs, with a performance gap of only 5% compared to human annotations. A user study involving 11 cardiac arrest instructors across three training sites supported the need for automated CLC assessment. Instructors found AutoCLC reports valuable for quantifying CLC frequency and quality, as well as for providing actionable, example-based feedback. Participants rated AutoCLC highly, with a System Usability Scale score of 76.4%, reflecting above-average usability. This work represents a significant step toward developing scalable, data-driven feedback systems that enhance individual skills and team performance in high-reliability settings. 

We report the two-dimensional (2D) bimetallic selenophosphate, LiGaP2Se6, prepared through direct combination reactions and P2Se5 flux methods. The material is a member of the broad class of van der Waals 2D materials of the type M2P2Q6 (M = metals). The structure was determined using single-crystal X-ray diffraction and refined in the chiral space group P3̅1c, with lattice parameters a = b = 6.2993(9) Å, c = 13.308(3) Å, α = β = 90°, γ = 120°. Differential thermal analysis indicated a congruent melting point at ∼458 °C. Optoelectronic properties were assessed using ultraviolet–visible (UV–vis) spectroscopy, showing a band gap of 2.01 eV, and photoemission yield spectroscopy in air (PYSA), which determined a work function of 5.44 eV. Notably, stability studies on LiGaP2Se6 revealed remarkable resilience despite its Li content, showing no structural changes after 2 weeks in ambient air or after soaking in a water/ethanol bath. 

ABSTRACT Terrestrial animal gaits often use spring-like mechanics to enhance movement economy through elastic energy cycling. Hopping is a relatively simple, constrained task, yet retains essential features of bouncing gaits, requiring cyclic regulation of limb stiffness and generation of high muscle forces to support body weight and enable elastic energy cycling. We investigated how humans adjust hopping frequency and leg stiffness before, during and after experiencing added load. Eighteen participants hopped bipedally for 90 s per trial, with hop frequency and height unconstrained, while kinematic, ground reaction force and ankle muscle electromyographic (EMG) data were collected. We analysed mechanics across four conditions: initial body weight (BWi), two added mass trials (+10% and +20% BW) and final body weight (BWf). With added mass, participants increased leg stiffness and maintained a consistent hopping frequency (∼2.15 Hz); yet, when returning to BWf, the elevated leg stiffness was maintained and hopping frequency increased (to ∼2.36 Hz) and reduced centre of mass (CoM) work per hop. BWf adaptations were driven by greater ankle stiffness, leading to less ankle work. Adaptation rates were consistent across trials, with steady-state mechanics reached in ∼30–40 s. Muscle coactivation decreased following BWi. Triceps surae mean EMG was unchanged with added mass and reduced in BWf. Similar patterns of adaptation were observed in bouncing without an aerial phase. Substantial inter-individual variability was observed in preferred hopping mechanics and adaptation strategy. Together, added mass and increased task familiarity led participants to recalibrate their hopping strategy. Based on literature evidence, the adaptations may align with reduced metabolic cost.