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Path Planning for Autonomous Sailing Vessels:Developing Robust and Efficient Survey Strategies
No full textAutonomous sailing vessels offer a promising solution for maritime research, offering low maintenance and sustainable platforms for environmental monitoring and data collection. These vessels utilize wind power, eliminating the need for conventional fuel and enabling long-duration operations with minimal environmental impact. Their applications range from oceanographic studies to maritime surveillance, where persistent and autonomous data collection is essential. This thesis explores the challenges and methodologies associated with path planning for autonomous sailing, particularly in the context of survey operations. Unlike traditional motorized vessels, sailing autonomy must account for wind variability, sail dynamics, and limited maneuverability, requiring specialized path-planning techniques to ensure efficient and reliable navigation. The research investigates various sail and hull configurations, the dynamics of windpowered propulsion, and the application of autonomy frameworks such as MOOS-IvP. A key focus is on optimizing continuous coverage path planning (CPP) to maximize efficiency while adapting to environmental constraints. By integrating real-time wind data and vessel performance characteristics, the study refines survey strategies that enhance mission effectiveness. Different survey strategies are implemented and evaluated using both simulation and real-world testing on the Charles River. These trials demonstrate the feasibility of fixed-path decomposition approaches and adaptive moving horizon control methods, evaluating methods with the impact of wind conditions on autonomous sailing performance. The results contribute to the development of robust and efficient survey strategies that improve the autonomy and reliability of wind-powered marine vessels.S.M
TelePulse: Enhancing the Teleoperation Experience through Biomechanical Simulation-Based Electrical Muscle Stimulation in Virtual Reality
No full textCHI ’25, Yokohama, JapanThis paper introduces TelePulse, a system integrating biomechanical simulation with electrical muscle stimulation (EMS) to provide precise haptic feedback for robot teleoperation tasks in virtual reality (VR). TelePulse has two components: a physical simulation part that calculates joint torques based on real-time force data from remote manipulators, and an electrical stimulation part that converts these torques into muscle stimulation. Two experiments were conducted to evaluate the system. The first experiment assessed the accuracy of EMS generated through biomechanical simulations by comparing it with electromyography (EMG) data during force-directed tasks, while the second experiment evaluated the impact of TelePulse on teleoperation performance during sanding and drilling tasks. The results suggest that TelePulse provided more accurate stimulation across all arm muscles, thereby enhancing task performance and user experience in the teleoperation environment. In this paper, we discuss the effect of TelePulse on teleoperation, its limitations, and areas for future improvement
Clinical Text De-identification Using Large Language Models: Insights from Organ Procurement Data
No full textThis thesis presents a novel approach to the de-identification of clinical notes from Organ Procurement Organization (OPO) records, leveraging advanced natural language processing (NLP) methodologies. Specifically, we employ in-context learning using large language models (LLMs) to effectively identify and remove protected health information (PHI), aiming to maintain high data utility post-redaction. Our work systematically evaluates the performance of the LLM-based method against established baseline techniques, including traditional Named Entity Recognition (NER) and rules-based systems. Through a slew of experiments, we assesses the strengths and limitations of each method regarding precision and recall. This work will contribute to a uniquely extensive dataset, comprising millions of de-identified OPO clinical notes, which will facilitate ethical healthcare research and enhance compliance with contemporary data protection standards. Ultimately, this dataset holds significant potential for improving processes and outcomes within the field of organ donation and procurement.M.Eng
Search for vector-like leptons with long-lived particle decays in the CMS muon system in proton-proton collisions at √s = 13 TeV
No full textA first search is presented for vector-like leptons (VLLs) exclusively decaying into a light long-lived pseudoscalar boson and a standard model τ lepton. The pseudoscalar boson is assumed to have a mass below the τ+τ− threshold, so that it decays exclusively into two photons. It is identified using the CMS muon system. The analysis is carried out using a data set of proton-proton collisions at a center-of-mass energy of 13 TeV collected by the CMS experiment in 2016–2018, corresponding to an integrated luminosity of 138 fb−1. Selected events contain at least one pseudoscalar boson decaying electromagnetically in the muon system and at least one hadronically decaying τ lepton. No significant excess of data events is observed compared to the background expectation. Upper limits are set at 95% confidence level on the vector-like lepton production cross section as a function of the VLL mass and the pseudoscalar boson mean proper decay length. The observed and expected exclusion ranges of the VLL mass extend up to 700 and 670 GeV, respectively, depending on the pseudoscalar boson lifetime
“Lab‐Quakes”: Quantifying the Complete Energy Budget of High‐Pressure Laboratory Failure
No full textUnderstanding the interplay of various energy sinks during seismic fault slip is essential for advancing earthquake physics and improving hazard assessment. However, quantifying the energy consumed by major dissipative processes remains a challenge. In this study, we investigate energy partitioning during laboratory earthquakes (“lab-quakes”) by performing general shear stick-slip experiments on synthetic granitic cataclasites at elevated confining pressure. Using ultrasound, microstructural, and novel magnetism-based thermal analyses, we independently quantified the energy allocated to seismic radiation, new surfaces, and heat dissipation. These estimates showed good agreement with far-field measurements of mechanical work during the lab-quake. Our findings revealed that under the experimental conditions the majority of the released energy (68%–98%) is dissipated as heat, while seismic radiation accounts for 1%–8%, and the creation of new surfaces consumes <1%–32%. Microstructural observations indicate pre-failure deformation, which includes comminution and development of the principal slip zone, significantly influences energy partitioning. This effect is further evident in the measured shear stress drops, where events with higher stress drops proportionally emitted more energy as seismic waves. This study is the first to constrain the full energy budget of lab-quakes from an observational standpoint, providing critical insights into the dynamics of fault rupture and energy dissipation processes
Combinatorial development of nebulized mRNA delivery formulations for the lungs
No full textInhaled delivery of mRNA has the potential to treat a wide variety of diseases. However, nebulized mRNA lipid nanoparticles (LNPs) face several unique challenges including stability during nebulization and penetration through both cellular and extracellular barriers. Here we develop a combinatorial approach addressing these barriers. First, we observe that LNP formulations can be stabilized to resist nebulization-induced aggregation by altering the nebulization buffer to increase the LNP charge during nebulization, and by the addition of a branched polymeric excipient. Next, we synthesize a combinatorial library of ionizable, degradable lipids using reductive amination, and evaluate their delivery potential using fully differentiated air–liquid interface cultured primary lung epithelial cells. The final combination of ionizable lipid, charge-stabilized formulation and stability-enhancing excipient yields a significant improvement in lung mRNA delivery over current state-of-the-art LNPs and polymeric nanoparticles
Multimodal Non-Contact Sensing of Neonatal Vital Signs Using Radar and Video
No full textPreterm neonates represent a vulnerable population which traditional contact-based monitoring devices are not optimized for their small size and complicated physiology. Adhesive sensors and wires can cause infections, discomfort, and impair the delivery of clinical care. Therefore, these most fragile patients could significantly benefit from remote health monitoring. This thesis establishes the foundation for a multimodal device designed for noncontact monitoring of neonates in the Neonatal Intensive Care Unit (NICU) that integrates a video camera and a radar. The device is used to estimate vital signs such as respiratory rate (RR), using both unimodal (solely video or radar) and multimodal fusion approaches that combine data from both sensors. Preliminary testing was conducted on neonatal simulator mannequins, followed by a clinical study at Tufts Medical Center NICU which collected data from 16 neonates so far (with the goal of reaching 20). The collected data was processed, labeled, and organized using image processing techniques and manual review, and then analyzed using a Video Vision Transformer (ViViT) architecture, incorporating early, intermediate, and late fusion strategies. Initial analysis was conducted on the mannequin data and the first neonatal subject. The results show that for estimating RR in neonates, the early fusion approach outperformed the unimodal methods. In movement detection, compared to human labeling, the fusion techniques achieved high accuracy and precision. To conclude, this study demonstrates that multimodal analysis has the potential to outperform unimodal approaches by improving accuracy against gold standard monitoring, particularly in challenging real-life conditions, including motion artifacts and poor lighting. This work represents a step toward more robust, non-invasive monitoring solutions for neonatal care, with implications for broader applications in remote health monitoring.S.M
TIME 2025: 1st International Workshop on Transformative Insights in Multi-faceted Evaluation
No full textWWW Companion ’25, April 28-May 2, 2025, Sydney, NSW, AustraliaOur workshop brings together domain experts and research students to share insights, practical guidance, and evaluations on key topics, including social network analysis, graph algorithms, web mining, semantics and knowledge, security, privacy, fairness, and ethics on the web. We invite survey, evaluation, or review papers that critically analyze models and datasets from diverse perspectives. These papers serve as essential resources by (i) providing quick reference guides for researchers and practitioners, (ii) enhancing accessibility for newcomers, and (iii) distilling key insights into actionable knowledge. Complementing these contributions, invited talks from experts and industry leaders will offer practical perspectives, fostering cross-domain collaboration in web technologies. Through thought-provoking discussions and networking opportunities, the workshop bridges research and real-world applications, setting a new standard for interdisciplinary exchange in the field
The Limits of Recovering Planted Subgraphs
No full textGiven an arbitrary subgraph H = Hₙ and p = pₙ ∈ (0, 1), the planted subgraph model is defined as follows. A statistician observes the union of the “signal,” which is a random “planted” copy H* of H, together with random noise in the form of an instance of an Erdős–Rényi graph ´ G(n, p). Their goal is to then recover the planted H* from the observed graph. Our focus in this work is to understand the minimum mean squared error (MMSE), defined in terms of recovering the edges of H*, as a function of p and H, for large n. A recent paper [MNS⁺23] characterizes the graphs for which the limiting (as n grows) MMSE curve undergoes a sharp phase transition from 0 to 1 as p increases, a behavior known as the all-or-nothing phenomenon, up to a mild density assumption on H. However, their techniques fail to describe the MMSE curves for graphs that do not display such a sharp phase transition. In this paper, we provide a formula for the limiting MMSE curve for any graph H = Hₙ, up to the same mild density assumption. This curve is expressed in terms of a variational formula over pairs of subgraphs of H, and is inspired by the celebrated subgraph expectation thresholds from probabilistic combinatorics [KK07]. Furthermore, we give a polynomial-time description of the optimizers of this variational problem. This allows one to efficiently approximately compute the MMSE curve for any dense graph H when n is large. The proof relies on a novel graph decomposition of H as well as a new minimax theorem which may be of independent interest. Our results generalize to the setting of minimax rates of recovering arbitrary monotone boolean properties planted in random noise, where the statistician observes the union of a planted minimal element A ⊆ [N] of a monotone property and a random Ber(p)^⊗N vector. In this setting, we provide a variational formula inspired by the so-called “fractional” expectation threshold [Tal10], again describing the MMSE curve (in this case up to a multiplicative constant) for large n.S.M
Finite Rank Perturbation of Non-Hermitian Random Matrices: Heavy Tail and Sparse Regimes
No full textAbstract In this work we investigate spectral properties of squared random matrices with independent entries that have only two finite moments. We revisit the problem of perturbing a large, i.i.d. random matrix by a finite rank error. We prove that under a merely second moment condition, for a large class of perturbation matrix with bounded rank and bounded operator norm, the outlier eigenvalues of perturbed matrix still converge to that of the perturbation, which was previously known when matrix entries have finite fourth moment. We then show that the same perturbation holds for very sparse random matrices with i.i.d. entries, all the way up to a constant number of nonzero entries per row and column