An Interview with Elizabeth Andrews

...to me, being a citizen lawyer means being a leader in your life in all aspects, not just in your job 9 to 5, but in your community. And so working on real world projects that made a difference policy-wise was a great way to live out that aspiration to be a citizen lawyer. -- Elizabeth Andrews ------------------------------------ Elizabeth Andrews was interviewed on March 8, 2024, in the William & Mary Media Center’s production studio. She begins by describing how a love and respect for the natural world guided her towards public service work. She discusses her decision to attend law school and working in the Virginia Attorney General’s Office on environmental issues. Andrews then reflects on her time directing the Virginia Coastal Policy Clinic, the significance of the real-world policy experience for law students, collaborating on environmental projects, and advice for students interested in environmental law and policy. She ends the interview reflecting on the current state of environmental policy at all levels of government as well as her legacy more generally at William & Mary Law School.https://scholarship.law.wm.edu/oralhist_all/1009/thumbnail.jp

An Interview with Alemante Gebre-Selassie

So when I came here, I wrote back that experience of Law and Development as an important subject... We have to study the actual realities of a country, their culture, their geography, their history, their economic conditions, their social conditions before you propose some kind of recommendation to them. -- Alemante Gebre-Selassie ------------------------------------ The interview starts with Professor Gebre-Selassie’s background and family. He was educated first as a law student at University of Haile Selassie in the 1960s, followed by a J.D. in UW-Madison. He discusses life prior to teaching at W&M and about his home country. Professor Gebre-Selassie emphasizes that the single most important value for the legal profession is dedication. He also stresses the importance of U.S legal scholars to respect regional differences and traditions of other countries before applying “universal” legal doctrines. Professor Gebre-Selassie stresses several times the importance of a closely-knit community of faculty members, which he believes has been diminished, possibly as a result of the global pandemic, during his years as emeritus faculty.https://scholarship.law.wm.edu/oralhist_all/1001/thumbnail.jp

An Interview with Eileen A. Olds

It\u27s your job to speak up. And then it\u27s also your job if you see things that don\u27t... feel right, to address them in some way. -- Judge Eileen Olds ------------------------------------ Sharp, quick-witted, and carrying a solemn charm, Judge Olds shares a strong personal narrative portraying her life as one in which she incessantly pursues justice. Her interview begins with stories of a strong sense of right and wrong in her early years. She goes on to share her rather traumatic experience of being isolated by fellow classmates during law school. Nonetheless, she and her fellow African American students (there were only four in her class) supported one another, even advocating for increased diversity among students and faculty at the law school. As the interview progresses, Judge Olds describes her highly successful career. She touches on rejoining the law school community after many years feeling unable to return. Judge Olds describes her “Tell it to the Judge” program, about how judges are trained, what they can do to be better. She discusses advice for her younger self, and for the law school now. She finishes the interview on a hopeful note, describing the law school today as a much better institution in terms of diversity and priorities.https://scholarship.law.wm.edu/oralhist_all/1005/thumbnail.jp

An Interview with Stephanie and Michael Chu

...we formed these amazing relationships in law school and throughout our careers... We\u27re so in touch with so many people and I\u27m so thankful for that. It\u27s enriched our lives, both of our lives so much. -- Mike Chu ------------------------------------ Stephanie and Michael Chu were interviewed on November 16, 2023, in William & Mary Media Center’s production studio. Navigating through their illustrious careers and personal journeys was made seamless by their attentiveness and quick-witted responses. Their shared experiences at William & Mary Law School, involvement in student publications, and subsequent impactful contributions to the legal and educational sectors unfolded with vivid anecdotes and insights. The interview was not just informative but also infused with a light-hearted spirit, as jokes flowed effortlessly between the two. They begin by describing their upbringing and how they came to W&M for law school. They discuss the close-knit community and various activities in which they participated. They describe the difficulties associated with finding jobs after graduating and the importance of showing up to support their community. They reflect on the significance of building meaningful relationships and having opportunities for creativity in both your life and your career.https://scholarship.law.wm.edu/oralhist_all/1008/thumbnail.jp

ArcSin: Adaptive ranged cosine Similarity injected noise for Language-Driven Visual Tasks

In this study, we address the challenging task of bridging the modality gap between learning from language and inference for visual tasks, including Visual Question Answering (VQA), Image Captioning (IC) and Visual Entailment (VE). We train models for these tasks in a zero-shot cross-modal transfer setting, a domain where the previous state-of-the-art method relied on the fixed scale noise injection, often compromising the semantic content of the original modality embedding. To combat it, we propose a novel method called Adaptive ranged cosine Similarity injected noise (ArcSin). First, we introduce an innovative adaptive noise scale that effectively generates the textual elements with more variability while preserving the original text feature's integrity. Second, a similarity pool strategy is employed, expanding the domain generalization potential by broadening the overall noise scale. This dual strategy effectively widens the scope of the original domain while safeguarding content integrity. Our empirical results demonstrate that these models closely rival those trained on images in terms of performance. Specifically, our method exhibits substantial improvements over the previous state-of-the-art, achieving gains of 1.9 and 1.1 CIDEr points in S-Cap and M-Cap, respectively. Additionally, we observe increases of 1.5 percentage points (pp), 1.4 pp, and 1.4 pp in accuracy for VQA, VQA-E, and VE, respectively, pushing the boundaries of what is achievable within the constraints of image-trained model benchmarks. The code will be released

Reinforcement learning in ophthalmology: potential applications and challenges to implementation

Reinforcement learning is a subtype of machine learning in which a virtual agent, functioning within a set of predefined rules, aims to maximise a specified outcome or reward. This agent can consider multiple variables and many parallel actions at once to optimise its reward, thereby solving complex, sequential problems. Clinical decision making requires physicians to optimise patient outcomes within a set practice framework and, thus, presents considerable opportunity for the implementation of reinforcement learning-driven solutions. We provide an overview of reinforcement learning, and focus on potential applications within ophthalmology. We also explore the challenges associated with development and implementation of reinforcement learning solutions and discuss possible approaches to address them

Experimental study on unloading creep of sandstone under temperature-seepage coupling conditions

Environmental factors (such as temperature and water) and their coupling effects will change the unloading creep properties of surrounding rock after tunnel excavation, threatening the stability of surrounding rock and the safety maintenance of construction sites. To analyze the effects of temperature, seepage flow, and their coupling on the creep characteristics of sandstone unloading, the triaxial unloading creep test was carried out on the sandstone specimen (φ50 mm×100 mm) by using the self-designed temperature control system and the improved pore water conduction specimen sealing device. The results show that during the creep time of 300 min after unloading the confining stress, the axial compression and radial expansion creep speed increase gradually and then slow down. The strain is mainly radial creep. The decrease in temperature leads to the increase in axial creep deformation and the decrease in radial creep deformation of the dry and saturated specimens. With the increase in seepage water pressure, the axial strain reduces, and the seepage water pressure suppresses the axial creep deformation, while the radial strain increases significantly, and the expansion phenomenon is obvious. Seepage water pressure promotes radial creep deformation. With the increase of seepage water pressure, the axial unloading creep rate decreases and the radial unloading creep rate increases. Under the coupling effect of temperature and seepage, the creep rate curve of unloading is divided into the attenuation stage and the stable stage, and the deformation in the attenuation stage is dominant. The radial creep rate is higher than the axial creep rate, and the fluctuation amplitude of the radial creep rate is small, which reaches stability fast. This study can provide scientific information for the stability evaluation of surrounding rock in deep underground engineering

Discovery of Potential Inhibitors of Aldosterone Synthase from Chinese Herbs Using Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Simulation Studies

Aldosterone synthase (CYP11B2) is a key enzyme for the biosynthesis of aldosterone, which plays a significant role for the regulation of blood pressure. Excess aldosterone can cause the dysregulation of the renin-angiotensin-aldosterone system (RAAS) and lead to hypertension. Therefore, research and development of CYP11B2 inhibitor are regarded as a novel approach for the treatment of hypertension. In this study, the pharmacophore models of CYP11B2 inhibitors were generated and the optimal model was used to identify potential CYP11B2 inhibitors from the Traditional Chinese Medicine Database (TCMD, Version 2009). The hits were further refined by molecular docking and the interactions between compounds and CYP11B2 were analyzed. Compounds with high Fitvalue, high docking score, and expected interactions with key residues were selected as potential CYP11B2 inhibitors. Two most promising compounds, ethyl caffeate and labiatenic acid, with high Fitvalue and docking score were reserved for molecular dynamics (MD) study. All of them have stability of ligand binding which suggested that they might perform the inhibitory effect on CYP11B2. This study provided candidates for novel drug-like CYP11B2 inhibitors by molecular simulation methods for the hypertension treatment.</jats:p

RetiGen:Framework leveraging domain generalization and test-time adaptation for multi-view retinal diagnostics

Domain generalization techniques involve training a model on one set of domains and evaluating its performance on different, unseen domains. In contrast, test-time adaptation optimizes the model specifically for the target domain during inference. Both approaches improve diagnostic accuracy in medical imaging models. However, no research to date has leveraged the advantages of both approaches in an end-to-end fashion. Our paper introduces RetiGen, a test-time optimization framework designed to be integrated with existing domain generalization approaches. With an emphasis on the ophthalmic imaging domain, RetiGen leverages unlabeled multi-view color fundus photographs—a critical optical technology in retinal diagnostics. By utilizing information from multiple viewing angles, our approach significantly enhances the robustness and accuracy of machine learning models when applied across different domains. By integrating class balancing, test-time adaptation, and a multi-view optimization strategy, RetiGen effectively addresses the persistent issue of domain shift, which often hinders the performance of imaging models. Experimental results demonstrate that our method outperforms state-of-the-art techniques in both domain generalization and test-time optimization. Specifically, RetiGen increases the generalizability of the MFIDDR dataset, improving the AUC from 0.751 to 0.872, a 0.121 improvement. Similarly, for the DRTiD dataset, the AUC increased from 0.794 to 0.879, a 0.085 improvement. The code for RetiGen is publicly available at https://github.com/RViMLab/RetiGen.</p