The millennial atmospheric lifetime of anthropogenic CO₂

The notion is pervasive in the climate science community and in the public at large that the climate impacts of fossil fuel CO2 release will only persist for a few centuries. This conclusion has no basis in theory or models of the atmosphere/ocean carbon cycle, which we review here. The largest fraction of the CO2 recovery will take place on time scales of centuries, as CO2 invades the ocean, but a significant fraction of the fossil fuel CO2, ranging in published models in the literature from 20–60%, remains airborne for a thousand years or longer. Ultimate recovery takes place on time scales of hundreds of thousands of years, a geologic longevity typically associated in public perceptions with nuclear waste. The glacial/interglacial climate cycles demonstrate that ice sheets and sea level respond dramatically to millennial-timescale changes in climate forcing. There are also potential positive feedbacks in the carbon cycle, including methane hydrates in the ocean, and peat frozen in permafrost, that are most sensitive to the long tail of the fossil fuel CO2 in the atmosphere

A Portfolio Approach to Massively Parallel Bayesian Optimization

One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. They work by building a surrogate model of the black-box to simultaneously select multiple designs via an infill criterion. Still, despite the increased availability of computing resources that enable large-scale parallelism, the strategies that work for selecting a few tens of parallel designs for evaluations become limiting due to the complexity of selecting more designs. It is even more crucial when the black-box is noisy, necessitating more evaluations as well as repeating experiments. Here we propose a scalable strategy that can keep up with massive batching natively, focused on the exploration/exploitation trade-off and a portfolio allocation. We compare the approach with related methods on noisy functions, for mono and multi-objective optimization tasks. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance

Water-mediated ion transport in an anion exchange membrane

Water is a critical component in polyelectrolyte anion exchange membranes (AEMs). It plays a central role in ion transport in electrochemical systems. Gaining a better understanding of molecular transport and conductivity in AEMs has been challenged by the lack of a general methodology capable of capturing and connecting water dynamics, water structure, and ionic transport over time and length scales ranging from those associated with individual bond vibrations and molecular reorientations to those pertaining to macroscopic AEM performance. In this work, we use two-dimensional infrared spectroscopy and semiclassical simulations to examine how water molecules are arranged into successive solvation shells, and we explain how that structure influences the dynamics of bromide ion transport processes in polynorbornene-based materials. We find that the transition to the faster transport mechanism occurs when the reorientation of water molecules in the second solvation shell is fast, allowing a robust hydrogen bond network to form. Our findings provide molecular-level insights into AEMs with inherent transport of halide ions, and help pave the way towards a comprehensive understanding of hydroxide ion transport in AEMs

NCES Progress in International Reading Literacy Study (PIRLS)

The Progress in International Reading Literacy Study (PIRLS) is an international assessment and research project designed to measure reading achievement at the fourth-grade level, as well as school and teacher practices related to instruction. Fourth-grade students complete a reading assessment and questionnaire that addresses students’ attitudes toward reading and their reading habits. In addition, questionnaires are given to students’ teachers and school principals to gather information about students’ school experiences in developing reading literacy. Since 2001, PIRLS has been administered every 5 years, with the United States participating in all past assessments. PIRLS is sponsored by the International Association for the Evaluation of Educational Achievement (IEA) and conducted in the United States by the National Center for Education Statistics (NCES)

Dynamics and kinetics in structural biology: The example of DNA photolyase

All biochemical reactions directly involve structural changes that may occur over a very wide range of timescales from femtoseconds to seconds. Understanding the mechanism of action thus requires determination of both the static structures of the macromolecule involved and short-lived intermediates between reactant and product. This requires either freeze-trapping of intermediates, for example by cryo-electron microscopy, or direct determination of structures in active systems at near-physiological temperature by time-resolved X-ray crystallography. Storage ring X-ray sources effectively cover the time range down to around 100 ps that reveal tertiary and quaternary structural changes in proteins. The briefer pulses emitted by hard X-ray free electron laser sources extend that range to femtoseconds, which covers critical chemical reactions such as electron transfer, isomerization, breaking of covalent bonds, and ultrafast structural changes in light-sensitive protein chromophores and their protein environment. These reactions are exemplified by the time-resolved X-ray studies by two groups of the FAD-based DNA repair enzyme, DNA photolyase, over the time range from 1 ps to 100 μs

Evaluation of the Pediatric Regional Anesthesia Time-Out Checklist: A Simulation Study

Introduction: The Society for Pediatric Anesthesia Quality and Safety Committee developed the Pediatric Regional Anesthesia Time-Out Checklist, consisting of 14 safety items intended to be reviewed by an anesthesia team prior to a regional anesthetic. Primarily, we hypothesized that use of this Checklist would increase the number of safety items performed compared with no checklist, evaluating the usefulness of this tool. Secondarily, we hypothesized that, after checklist training, subjects would show better clinical judgment by electing to perform a regional anesthetic in scenarios in which no programmed error existed and electing to not perform a regional anesthetic in scenarios in which a programmed error did exist. Methods: Each anesthesia attending/trainee pair participated in 12 different randomized video-recorded medium-fidelity regional anesthesia simulation scenarios, receiving checklist training after half of the scenarios had been completed by each pair. In four of the scenarios, subjects were expected to decline to perform the regional anesthetic because of an error programmed into the scenario. Two errors consisted of a maximum dose of local anesthetic given by the surgeon immediately prior to the planned regional anesthetic and two errors consisted of coagulation issues prior to neuraxial block (1 with a low platelet count and 1 receiving low molecular weight heparin). Scenarios were scored for the number of safety items identified and performed by the subjects. Additionally, the team's choice to perform the regional anesthetic or abort was recorded. Results: One-hundred and thirty-two scenarios were performed by 22 physicians. A greater number of safety items were completed after training on the Pediatric Regional Anesthesia Time-Out Checklist, for each of 11 individual groups and when data from all groups was pooled, p Conclusion: Pediatric Regional Anesthesia Time-Out Checklist training led to an increased number of safety items performed prior to a simulated anesthetic.</p

Legalized Violence, Illegal Defense: The Extreme Violence Against Trans Persons in U.S. Prisons

The legally permitted abuse of transgender individuals in the carceral system is a dangerous epidemic that has hit an extreme peak in the past two decades. This issue is further exacerbated, by the extreme overrepresentation of trans persons in prison compared to that of the general population. Moreover, the legal doctrine and laws that should serve as protective barriers for transgender persons in prison have woefully failed in achieving such ends. This review provides an extensive exploration into such legal doctrine to better understand the procedures of these shortcomings and failures. The legal exploration centers the landmark 1994 SCOTUS ruling on the Farmer v. Brennan prison violence case, in conjunction with the Prison Rape Elimination Act (PREA) passed by Congress in 2003. The findings indicate that a flawed interpretation in judicial rulings and missing enforceability, accountability, and applicability structures within these laws all contribute to the dismal state of these legal structures that should be protecting trans persons in prison from increased violence. This legal review concludes with recommended legal reforms and places emphasis on a short term expansion of provisions for equitable societal support measures for trans persons, and a long term solution requiring the abolition of the carceral system

Disability-Inclusive Accommodations in Nursing Education—Addressing Health Equity Needs

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Towards Robust Alignment of Language Models with Human Preferences

The rapid advancement of large language models (LLMs) offers transformative potential across a wide range of applications, but concurrently raises critical safety concerns, under- scoring the importance of aligning AI systems with human values. This thesis investigates a series of methodologies designed to enhance AI alignment through novel optimization strate- gies that improve performance, robustness, and trustworthiness. By addressing limitations intraditionalreinforcementlearningfromhumanfeedback(RLHF)pipelines, ourworkintro- duces streamlined alternatives and complementary techniques to optimize alignment while maintaining computational efficiency and effectiveness. In Chapter 2, we propose f-DPO (generalized Direct Preference Optimization), which extends the DPO framework by incorporating diverse divergence constraints, such as Jensen- Shannon and forward KL divergences. Through an analytical exploration of the Karush- Kuhn-Tucker conditions, we derive simplified relationships between reward functions and optimal policies under these divergences. Empirical evaluations demonstrate that f-DPO balances alignment performance and generative diversity more effectively than RLHF-based ProximalPolicyOptimization(PPO).Italsoachieveslowerexpectedcalibrationerror(ECE) while providing practical benefits, such as improved divergence efficiency. Chapter 3 extends alignment optimization by addressing the limitations of single- sample preference comparison. We introduce Multi-sample Direct Preference Optimization (mDPO) and Multi-sample Identity Preference Optimization (mIPO), frameworks that op- timize group-wise characteristics to improve distributional properties such as diversity and bias reduction. These approaches significantly enhance generative diversity in LLMs and mitigate demographic biases in diffusion models. Moreover, multi-sample methods exhibit robustness to noisy human-labeled preference data, making them particularly effective for fine-tuning in real-world scenarios where label quality may be imperfect. More importantly, it offers more controllability for improving the alignment of generative models over f-dpo. In Chapter 4, we move beyond the traditional supervised learning in chapters 2 and 3, and address a critical challenge in reward modeling for online RLHF: spurious correlations that can distort alignment objectives. We introduce a causal reward modeling framework that integrates causal inference techniques to mitigate these biases. By enforcing counter- factual invariance, this approach ensures reward predictions remain unaffected by irrelevant or confounding variables. Experiments on synthetic and real-world datasets demonstrate significant improvements in addressing biases such as length preference, sycophancy, and concept biases, ultimately enhancing the fairness and reliability of alignment. Together, these contributions advance the theoretical and practical foundations of AI alignment. Byofferingscalableandrobustmethodologies, thisthesisbridgesthegapbetween current capabilities and the long-term goal of developing safe and trustworthy AI systems. The proposed approaches not only improve alignment workflows but also address critical shortcomings in existing pipelines, paving the way for more reliable, diverse, and human- aligned AI systems

Deterministic multi-phonon entanglement between two mechanical resonators on separate substrates

Mechanical systems have emerged as a compelling platform for applications in quantum information, leveraging advances in the control of phonons, the quanta of mechanical vibrations. Experiments have demonstrated the control and measurement of phonon states in mechanical resonators, and while dual-resonator entanglement has been demonstrated, more complex entangled states remain a challenge. Here, we demonstrate rapid multi-phonon entanglement generation and subsequent tomographic analysis, using a scalable platform comprising two surface acoustic wave resonators on separate substrates, each connected to a superconducting qubit. We synthesize a mechanical Bell state with a fidelity of F = 0.872 ± 0.002 , and a multi-phonon entangled N = 2 N00N state with a fidelity of F = 0.748 ± 0.008 . The compact, modular, and scalable platform we demonstrate will enable further advances in the quantum control of complex mechanical systems