Inferring free surface disturbance properties from Kelvin wakes using convolutional neural network

Abstract: Kelvin wakes are fluid motions generated by a moving disturbance at a free surface. We present a machine learning-based framework for inferring the properties of such moving disturbances from the Kelvin-wake patterns. We perform phase-resolved simulations to establish a dataset of nearly half a million Kelvin wakes generated by disturbances of varying propagating speed, length scale and geometry. Trained with the augmented data, the neural network achieves accuracies of 99.7% and 92.4% in predicting the velocity and the length scale of the disturbance, respectively, even if a random noise has been added to the training data. The explainability of the neural network is demonstrated by quantifying the contribution of the input data to the prediction, which shows a strong connection with the diverging and transverse waves. The accuracy of the neural network in predicting the disturbance length scale is sensitive to wave nonlinearity

Impact of mismatch repair (MMR) status on recurrence in high intermediate risk endometrial cancer

BackgroundApproximately 25 % of endometrial cancers harbor deficiencies in mismatch repair (dMMR). The clinical impact of this molecular aberration remains undefined in patients with high intermediate risk (HIR) endometrial cancer.MethodsWe conducted a retrospective chart review of women diagnosed with Stage I high-intermediate risk endometrioid endometrial cancer in two hospital systems in Southern California between 2016 and 2018. We collected demographic information, mismatch repair status, pathology reports, and time to recurrence.Results244 patients met inclusion criteria, of which 86 (35 %) were found to be dMMR. The dMMR patient population had higher relative risks of lymphovascular space invasion (relative risk 1.63, 95 % confidence interval 1.26-2.10, p-value 0.0002) but were less likely to have deep myometrial invasion (relative risk 0.81, 95 % confidence interval 0.66-0.99, p-value 0.047) when compared to the pMMR EC cohort. No differences were found in the rate of recurrence or time to recurrence based on MMR status.ConclusionsIn this large, multi-institution, cohort study there were no significant differences identified between Stage I HIR dMMR and pMMR endometrioid endometrial cancer populations with regards to recurrence rates or alternate cancer-related outcomes

Strings in AdS3: one-loop partition function and near-extremal BTZ thermodynamics

We revisit the computation of the string partition function in AdS3 focussing on the appearance of spacetime (super) symmetries. We show how the asymptotic symmetries of the AdS3 spacetime, which generate the boundary (super) Virasoro currents, are captured by the one-loop partition sum. We use this to argue that the recent understanding of near-extremal black hole thermodynamics based on the gravitational path integral continues to hold for finite string length. Along the way we clarify some aspects of the AdS3/CFT2 duality and, in particular, deduce which bulk gauge fields lead to boundary currents. We also explain how one can interpolate between supersymmetric and thermal (Atick-Witten) fermion boundary conditions in the target space by suitably tuning rotational chemical potentials in the string partition function

Development of a community-based peer-support intervention to improve contraceptive agency and diffuse self-injectable contraception in Uganda: Application of the human-centered design approach

BackgroundThe low use of self-injectable contraception, coupled with the recognition that many individuals need support beyond training to use self-care technologies successfully, suggests the need for innovative programming. We describe the participatory human-centered design process we used in two districts of Uganda to develop a community-based peer support intervention to improve women's agency to make and act on contraceptive decisions and help diffuse self-injectable contraception.MethodsThe design team included multi-disciplinary researchers from Uganda and the United States, representatives of local community-based organizations and village health teams, and local women of reproductive age. The research group conducted 21 in-depth interviews, 12 observations, and six focus group discussions to understand women's social support needs, contraceptive-seeking experiences, and communication channels. From these data, the design team derived insights into needs and opportunities to improve contraceptive agency and support self-injection use among interested women, spurring a creative idea-generation process to develop a large set of potential solutions. We collectively prioritized the most promising ideas into an integrated, theoretically informed intervention and subsequently prototyped, tested, and refined the intervention.ResultsDesign insights included: women value information from experienced peers and want support to navigate uneven partner dynamics, conflicting contraceptive information, concerns about contraceptive-related side effects, and unreliable contraception services. The final intervention-called I-CAN (English), Nsobola (Lusoga), An Atwero (Langi)-engages experienced self-injection users as 'mentors' to support other women ('mentees') they recruit in community-based settings. Mentors provide informational, instrumental, appraisal, and emotional support tailored to the individual needs of mentees. This support is designed to improve mentees' knowledge, consciousness of their rights related to contraception, self-efficacy, and perceived control related to contraceptive decision-making, self-injection self-efficacy, contraceptive access, and ability to act on preferences.ConclusionsOur iterative human-centered design process incorporated diverse, lived experiences and scientific expertise and resulted in a peer support intervention with the potential to fill an important gap in contraception service delivery in Uganda. Our approach demonstrates that creating complex interventions to prioritize support for women's agency related to contraception in line with a human rights-based approach and to spread new contraceptive methods is feasible

Reversible kink instability drives ultrafast jumping in nematodes and soft robots

Entomopathogenic nematodes (EPNs) exhibit a bending-elastic instability, or kink, before becoming airborne, a feature previously hypothesized but not substantiated to enhance jumping performance. Here, we provide the evidence that this kink is crucial for improving launch performance. We demonstrate that EPNs actively modulate their aspect ratio, forming a liquid-latched α-shaped loop over a slow timescale [Formula: see text] (1 second), and then rapidly open it [Formula: see text] (10 microseconds), achieving heights of 20 body lengths and generating power of ∼104 watts per kilogram. Using a bioinspired physical model [termed the soft jumping model (SoftJM)], we explored the mechanisms and implications of this kink. EPNs control their takeoff direction by adjusting their head position and center of mass, a mechanism verified through phase maps of jump directions in numerical simulations and SoftJM experiments. Our findings reveal that the reversible kink instability at the point of highest curvature on the ventral side enhances energy storage using the nematode's limited muscular force. We investigated the effect of the aspect ratio on kink instability and jumping performance using SoftJM and quantified EPN cuticle stiffness with atomic force microscopy measurements, comparing these findings with those of Caenorhabditis elegans. This investigation led to a stiffness-modified SoftJM design with a carbon fiber backbone, achieving jumps of ∼25 body lengths. Our study reveals how harnessing kink instabilities, a typical failure mode, enables bidirectional jumping in soft robots on complex substrates like sand, offering an approach for designing limbless robots for controlled jumping, locomotion, and even planetary exploration

Automatic Discovery and Diagnosis of Security and Safety Defects in Autonomous Driving Software

The technology behind Autonomous Driving (AD) is rapidly evolving, with companies like Waymo and Baidu already offering commercial robotaxi services in San Francisco and Wuhan, respectively, and Tesla planning its own service. Given the critical safety implications of AD systems, concerns about their reliability and security are slowing widespread adoption. To address these issues, it is essential to understand the boundary of how well AD vehicles handle unexpected real-world scenarios and how secure they are against potential attacks. Improving software testing and analysis techniques can enhance the safety and security of AD vehicles, accelerating their deployment.My dissertation focuses on enhancing testing and debugging in the AD software development life cycle through innovative automated tools. First, I analyzed the security of the AD software planning component and identified a new type of vulnerability: semantic DoS vulnerabilities, which can be exploited by real-world physical threats and have severe consequences. Second, I developed PlanFuzz, a new modular testing tool designed to efficiently discover zero-day semantic DoS vulnerabilities in the planning component. Unlike existing designs that rely on time-consuming and potentially buggy simulators, our novel approach directly connects fuzzing, the proven successful software testing techniques, with AD software testing for planning components. This significantly enhances the ability to discover new vulnerabilities within a realistic time frame. We evaluate PlanFuzz on 3 planning implementations from practical open-source AD systems, and find that it can effectively discover 9 previously-unknown semantic DoS vulnerabilities without false positives. Finally, I introduced an automated cause analysis tool for the AD software stack. This tool, which follows testing, efficiently and automatically identifies the root causes of discovered issues, enabling timely fixes for bugs and vulnerabilities. >98.5% of the manual efforts can be saved with such automated approach

Coding Theory for Composite DNA-based Data Storage

DNA-based data storage systems have garnered significant attention due to their remarkable properties, including high information density, durability, and replicability. However, conventional DNA storage faces critical challenge which is the high synthesis costs. To address this issues, this thesis investigates the coding theory and algorithmic frameworks for composite DNA-based data storage, where information is represented by probability vectors rather than standard nucleotide sequences. By integrating contributions from three core research areas—error correction, decoding, and secret sharing—this work provides a comprehensive approach to enhancing the reliability, efficiency, and security of composite DNA-based data storage systems.First, this thesis introduces a coding scheme for limited-magnitude probability errors (LMPE) that arise during composite DNA synthesis and sequencing. We develop theoretical bounds and coding constructions for LMPE, focusing on minimizing redundancy and computational complexity. The proposed systematic codes demonstrate asymptotic optimality and practicalapplicability in composite DNA-based data storage systems. Second, we presents a novel ramp secret sharing scheme (ARSSS) designed for composite DNA-based data storage systems. Unlike traditional secret sharing approaches based on finite fields, this scheme operates directly on probability vectors. The proposed method reduces sequencing requirements, enhances data security through asymptotic information-theoretic