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    Becoming an Intentional Physician

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    Becoming an Intentional Physician provides a roadmap for medical professionals to cultivate a fulfilling career rooted in empathy, resiliency, and ethical practice. Through personal stories and practical insights, the book guides readers in developing a strong professional identity and embracing the lifelong journey of self-improvement. It underscores the importance of mentorship, meaningful professional relationships, and strategic career planning for physicians to become skilled and committed to compassionate patient-centered care. Written by Dr. Tom Cox, with a foreword by Dr. Tim Bono, the work draws on the author’s extensive experience in medicine and education to offer a thoughtful guide for aspiring and early-career physicians.https://openscholarship.wustl.edu/books/1068/thumbnail.jp

    Effect of Nb, Ti, and Zr Powder Deposition Order on Directed Energy Deposition Created NbTiVZr Multi-Principal Element Alloy

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    Refractory multi-principal Element Alloys (MPEAs) are alloys that contain high atomic percentages of multiple refractory elements. High-throughput techniques such as directed energy deposition (DED) have been utilized to study a wide range of composition for minimal resources. A Laser Engineered Net Shaping (LENS) system is utilized to study the NbTiVZr MPEA. This project studies how the order of deposition of Nb, Ti, and Zr affects the composition of the resulting deposit. The deposition was performed on a V substrate. Six bands were created to test each ordering of Nb, Ti, and Zr. With 275 W laser melting passes and 200 W laser remelt passes, the resulting deposit was shown to not be of a constant composition through scanning electron microscopy analysis (SEM) of the cross section. For most bands, cross section line scans showed three distinct melt pools with different compositions. When Nb and Zr were layered next to each other, they fully incorporated with each other into one melt pool. The general composition of the bands was much higher in Ti than the targeted equiatomic composition. Bands tended to flake off during polishing of the cross section

    Phone Floatation Device

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    A problem of extreme frustration for boaters, kaykers, and lake-goers is that of taking an expensive new phone, with its water resistant capabilities, and watching it slip from their hand to the bottom of a deep body of water, never to be seen again. This project seeks to solve this problem by designing a new attachment for phones that when dropped, causes the phone to be buoyant enough that it quickly floats back to the surface of the water. To solve this problem, several approaches were analyzed from chemical reactions and compressed air that created or released air into a balloon, to a foam based solution. Each idea focusing specifically on reliability mixed with user comfort. This project ultimately ends with a simple solution that could one day become a product to solve this common challenge

    Thirty Productive Years and a Very Promising Future: The Annual Letter of the Center for Social Development

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    In the 2025 annual letter of the Center for Social Development (CSD), Founding Director Michael Sherrraden discusses the integration of the Washington University Social Policy Institute with CSD, summarizes developments at the center, introduces the new team of experts, shines a light on some of the accomplishments during the past year, and offers a vision for the future

    The Illusion of Inclusion: The False Promise of the New Governance Project for Content Moderation

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    Because private companies now control the most prominent communication platforms, the most pressing question in the field of content moderation is how to ensure that the governance of public discourse responds to public values. The prevailing approach, given that the state cannot regulate speech directly, is that state regulation can be substituted with audited self-regulation, broad stakeholder participation, and negotiated rulemaking. In this model, which this article refers to as the “new governance model for content moderation,” companies include advocates as representatives of the public in their processes to govern online speech. Ideally, they negotiate policy goals and share responsibility for achieving them. The end goal is to have a process in which public values are given effect. This article argues, however, that this governance model is unsound in both theory and practice. In the field of content moderation, the ambition of constructing public values through a collaborative process between companies and stakeholders is conceptually incoherent: those interests that cannot elicit the cooperation from corporate actors and are not consistent with the values of participating advocates are excluded by design. In practice, no present or past demonstrations have shown that the inclusion of advocates in speech governance and the agreements they reach with companies have epistemic credibility to construct the public interest. Though those flaws might seem unsurprising, scholars and activists double down on independence, diversity, and expertise as design strategies that can result in self-regulatory bodies that could adequately set policy goals. This article advocates for pluralism as a framework that more effectively achieves the participatory goals of new governance. It argues that the state has a central role to play in creating a plural and contested public sphere. A robust legal system can complement self-regulation and push it structurally in the direction of public values

    Empirically Exploring the Physical Realizability of Adversarial Examples

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    The development of autonomous vehicles (AVs) has been accelerated by advancements in deep neural networks (DNNs), which power the complex perception systems necessary for safe and efficient real-world navigation. However, as AVs increasingly integrate into public transportation networks, the robustness of their perception systems against potential vulnerabilities is critical. Among these threats, adversarial attacks—particularly through the use of adversarial patches—pose significant risks. These patches are carefully crafted perturbations designed to mislead DNNs, potentially compromising AV safety by causing incorrect object recognition or misclassification. While extensive research has demonstrated high attack success rates for adversarial patches in controlled digital environments, their performance under practical conditions remains underexplored. This gap is noteworthy because real-world environments introduce variability such as changes in lighting, object angles, and material textures, which could influence the practical applicability of these attacks. Furthermore, most existing defense mechanisms have been evaluated primarily in digital domains, leaving their robustness in real-world conditions largely unexplored. To address these gaps, we empirically evaluated the performance of adversarial patches through extensive physical experiments, following a systematic approach across diverse real-world environments. To achieve this, we began by training adversarial patches using methodologies from previous works to establish a strong baseline and validate their effectiveness under idealized digital conditions. Following this, we printed the patches and applied them to real-world objects to assess their performance under varying physical conditions. Experiments were conducted with different types of vehicles, including SUVs and sedans, in diverse settings: outdoor environments during the day, outdoor environments at night, and indoor parking lots. This approach enabled us to evaluate the robustness of adversarial patches under conditions resembling real-world AV environments. Our findings indicate that while adversarial patches achieve high attack success rates in controlled digital settings, their effectiveness is notably reduced in real-world environments due to environmental variability. This highlights the critical challenge of bridging the gap between theoretical vulnerability studies and practical adversarial threats in AV systems. By identifying key factors such as lighting conditions, object angles, and material textures that influence the success of adversarial attacks, we offer empirical insights that can guide efforts to improve AV perception system resilience. Furthermore, our results underscore the pressing need for robust defense mechanisms that account for real-world complexities, as current defenses largely focus on digital domains and may not adequately address real-world vulnerabilities. This study contributes to the growing body of knowledge on adversarial machine learning and lays a foundation for future research to enhance the security and robustness of AV technologies in practical settings

    A Computational Algorithm for Dead Time Correction in Fluorescence Lifetime Imaging Microscopy (FLIM)

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    This project aims to develop and implement a computational algorithm for dead time correction in fluorescence lifetime imaging microscopy (FLIM). In photon counting applications, dead time is the time after one photon is counted that another photon cannot be counted due to the limited bandwidth of the electronics that perform the photon counting. Existing dead time correction methods from Light Detection and Ranging (LiDAR) will be adapted to address the challenges in photon counting applications within FLIM. Specifically, an algorithm that compensates for the loss of photon data during the system\u27s dead time will be developed, leveraging statistical models of photon arrival. The project involves extensive computational work, including algorithm development, simulation, and evaluation using FLIM datasets. The ultimate goal is to enhance the accuracy of FLIM measurements

    Computational Complexity of Soundness Verification for Neural Networks

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    Neural networks are an increasingly ubiquitous tool in systems of varying complexity across a range of domains. While these tools can be used to learn and predict complex functions, their opaque nature limits the scope of their acceptable applications. In particular, a lack of performance guarantees means that they are unsuitable for safety-critical applications such as self-driving cars and scheduling systems. Neural networks trained to solve NP-complete problems, in particular, are unlikely to be able to solve the problem exactly. However, a weaker soundness guarantee may be sufficient for some systems, e.g., that positive instances of the problem may be rejected but no negative instances are accepted. Research into the complexity of securing such guarantees would improve our understanding of the applications to which neural networks can be effectively and economically applied. In this work, we show that the verification of soundness for neural networks trained to solve 3SAT formulae is a ΠP2-complete problem and conjecture that the same hardness holds for neural networks solving other NP-complete problems. Therefore, it is unlikely that even these weaker guarantees on the performance of neural networks solving hard problems can be economically secured. A related result drastically expands the set of problems known to be complete for higher levels of the polynomial hierarchy. These problems are formulaically constructed in terms of problems complete for lower levels of the polynomial hierarchy

    Universal Asset Building: New Social Policy for a New Era

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    This Perspective explores the concept of universal asset building, beginning with Child Development Account policy, as a new paradigm in social policy, suggesting that it will be a positive social-policy strategy for navigating the information age, and especially artificial intelligence (AI). Universal asset building can address social and economic challenges posed by rapid technological advancements. The Perspective is adapted from a keynote address given by Michael Sherraden (with Jin Huang and Li Zou) at the joint convening of the 2024 Annual Academic Conference for the Social Policy Research Committee of the Chinese Sociological Association and the 19th International Symposium on Social Policy. The convening was held in Shanghai at Fudan University, December 7–8, 2024

    Fossil Fuel Wealth for Human Development: CDAs Continue in Kazakhstan with Second Year Deposits

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    In January of 2024, Kazakhstan implemented a national Child Development Account (CDA) policy: The National Fund for Children. Through the policy, assets accrue for every child or youth under age 18. This policy brief discusses developments in the operation of the policy and the allocation of funding as of January 2025

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