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The coronavirus pandemic has brought great challenges to the fashion sector from supply-chain collapse to the shuttering of bricks-and-mortar retail. Research undertaken for this chapter has brought to light that brands are seeking to reach target customers to engage in a more meaningful and sustained conversation. Customers, in turn, are seeking out brands which offer immersive experiences which authentically match their mission statements (Business of Fashion 2020; Mondalek 2021). This chapter will consider the rise, purpose, and intent of the luxury brand publication through the analysis of current trends in the fashion magazine and fanzine market via contemporary luxury fashion campaign practices. A qualitative visual analysis of two case studies will be carried out: Dior Magazine (Dior 2022) and Gucci Love e-zines (Gucci 2021; 2022) to argue that the fashion brand publication offers luxury fashion houses direct communication with their target consumers as communities and extends brands’ fashion offerings into storytelling media which enable them to be consumed beyond their materiality. Through analysis of the Gucci Love e-zines, a new genre of brand publication will be established: the ‘brandzine’. This chapter utilizes theory, methodologies, and qualitative data collection via theories of visual culture; communication theory; market analysis; and brand management theory. This is an up-to-date contemporary analysis of luxury fashion brand visual communication at a time of great change in the brand-customer relationship. The chapter will be of interest to luxury fashion brand managers, brand creatives, graphic designers in the wider creative industries, and fashion communication/promotion/marketing academics and students at both undergraduate and postgraduate levels of study.

Different spring and early summer North Atlantic sea surface temperature anomalies (SSTAs) have been shown to precede recent European summer heatwaves (EuSHWs). So far, the limited number of observed events associated with several physical mechanisms has prevented a robust identification of SSTAs as precursors. Here, we extend beyond previous studies by combining 100 historical simulations (1850–2005) of the MPI Grand-Ensemble with an explainable neural-network method. We find that the spring tripolar North Atlantic pattern with positive SSTAs in the Subtropical Gyre is a precursor of EuSHWs. In addition, positive SSTAs west of the Iberian Peninsula, and in the North Sea, the Baltic Sea and the Mediterranean Sea relate to distinct early summer soil moisture anomaly patterns and are precursors of western and southeastern EuSHWs, respectively. While the phase of the tripolar North Atlantic pattern indicates whether a EuSHW might emerge, regional SSTAs indicate the spatial characteristics of EuSHWs.

Different spring and early summer North Atlantic sea surface temperature anomalies (SSTAs) have been shown to precede recent European summer heatwaves (EuSHWs). So far, the limited number of observed events associated with several physical mechanisms has prevented a robust identification of SSTAs as precursors. Here, we extend beyond previous studies by combining 100 historical simulations (1850–2005) of the MPI Grand-Ensemble with an explainable neural-network method. We find that the spring tripolar North Atlantic pattern with positive SSTAs in the Subtropical Gyre is a precursor of EuSHWs. In addition, positive SSTAs west of the Iberian Peninsula, and in the North Sea, the Baltic Sea and the Mediterranean Sea relate to distinct early summer soil moisture anomaly patterns and are precursors of western and southeastern EuSHWs, respectively. While the phase of the tripolar North Atlantic pattern indicates whether a EuSHW might emerge, regional SSTAs indicate the spatial characteristics of EuSHWs.

BACKGROUND: There is an immunologic rationale to evaluate immunotherapy in the older glioblastoma population, who have been underrepresented in prior trials. The NUTMEG study evaluated the combination of nivolumab and temozolomide in patients with glioblastoma aged 65 years and older. METHODS: NUTMEG was a multicenter 2:1 randomized phase II trial for patients with newly diagnosed glioblastoma aged 65 years and older. The experimental arm consisted of hypofractionated chemoradiation with temozolomide, then adjuvant nivolumab and temozolomide. The standard arm consisted of hypofractionated chemoradiation with temozolomide, then adjuvant temozolomide. The primary objective was to improve overall survival (OS) in the experimental arm. RESULTS: A total of 103 participants were randomized, with 69 in the experimental arm and 34 in the standard arm. The median (range) age was 73 (65-88) years. After 37 months of follow-up, the median OS was 11.6 months (95% CI, 9.7-13.4) in the experimental arm and 11.8 months (95% CI, 8.3-14.8) in the standard arm. For the experimental arm relative to the standard arm, the OS hazard ratio was 0.85 (95% CI, 0.54-1.33). In the experimental arm, there were three grade 3 immune-related adverse events which resolved, with no unexpected serious adverse events. CONCLUSIONS: Due to insufficient evidence of benefit with nivolumab, the decision was made not to transition to a phase III trial. No new safety signals were identified with nivolumab. This complements the existing series of immunotherapy trials. Research is needed to identify biomarkers and new strategies including combinations.

In this report, we explore the ability of language model agents to acquire resources, create copies of themselves, and adapt to novel challenges they encounter in the wild. We refer to this cluster of capabilities as "autonomous replication and adaptation" or ARA. We believe that systems capable of ARA could have wide-reaching and hard-to-anticipate consequences, and that measuring and forecasting ARA may be useful for informing measures around security, monitoring, and alignment. Additionally, once a system is capable of ARA, placing bounds on a system's capabilities may become significantly more difficult. We construct four simple example agents that combine language models with tools that allow them to take actions in the world. We then evaluate these agents on 12 tasks relevant to ARA. We find that these language model agents can only complete the easiest tasks from this list, although they make some progress on the more challenging tasks. Unfortunately, these evaluations are not adequate to rule out the possibility that near-future agents will be capable of ARA. In particular, we do not think that these evaluations provide good assurance that the ``next generation'' of language models (e.g. 100x effective compute scaleup on existing models) will not yield agents capable of ARA, unless intermediate evaluations are performed during pretraining. Relatedly, we expect that fine-tuning of the existing models could produce substantially more competent agents, even if the fine-tuning is not directly targeted at ARA., Comment: 14 pages

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society., Comment: NeurIPS 2023 Outstanding Datasets and Benchmarks Track Paper

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.

Methods of eXplainable Artificial Intelligence (XAI) are used in geoscientific applications to gain insights into the decision-making strategy of Neural Networks (NNs) highlighting which features in the input contribute the most to a NN prediction. Here, we discuss our lesson learned that the task of attributing a prediction to the input does not have a single solution. Instead, the attribution results and their interpretation depend greatly on the considered baseline (sometimes referred to as reference point) that the XAI method utilizes; a fact that has been overlooked so far in the literature. This baseline can be chosen by the user or it is set by construction in the method s algorithm, often without the user being aware of that choice. We highlight that different baselines can lead to different insights for different science questions and, thus, should be chosen accordingly. To illustrate the impact of the baseline, we use a large ensemble of historical and future climate simulations forced with the SSP3-7.0 scenario and train a fully connected NN to predict the ensemble- and global-mean temperature (i.e., the forced global warming signal) given an annual temperature map from an individual ensemble member. We then use various XAI methods and different baselines to attribute the network predictions to the input. We show that attributions differ substantially when considering different baselines, as they correspond to answering different science questions. We conclude by discussing some important implications and considerations about the use of baselines in XAI research.

Convolutional neural networks (CNNs) have recently attracted great attention in geoscience due to their ability to capture non-linear system behavior and extract predictive spatiotemporal patterns. Given their black-box nature however, and the importance of prediction explainability, methods of explainable artificial intelligence (XAI) are gaining popularity as a means to explain the CNN decision-making strategy. Here, we establish an intercomparison of some of the most popular XAI methods and investigate their fidelity in explaining CNN decisions for geoscientific applications. Our goal is to raise awareness of the theoretical limitations of these methods and gain insight into the relative strengths and weaknesses to help guide best practices. The considered XAI methods are first applied to an idealized attribution benchmark, where the ground truth of explanation of the network is known a priori, to help objectively assess their performance. Secondly, we apply XAI to a climate-related prediction setting, namely to explain a CNN that is trained to predict the number of atmospheric rivers in daily snapshots of climate simulations. Our results highlight several important issues of XAI methods (e.g., gradient shattering, inability to distinguish the sign of attribution, ignorance to zero input) that have previously been overlooked in our field and, if not considered cautiously, may lead to a distorted picture of the CNN decision-making strategy. We envision that our analysis will motivate further investigation into XAI fidelity and will help towards a cautious implementation of XAI in geoscience, which can lead to further exploitation of CNNs and deep learning for prediction problems.

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting., Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-bench

James A Katz,1 Paul M Karpecki,2 Alexandra Dorca,3 Sima Chiva-Razavi,4 Heather Floyd,4 Elizabeth Barnes,4 Mark Wuttke,4 Eric Donnenfeld5 1The Midwest Center for Sight, Des Plaines, IL, USA; 2Kentucky Eye Institute, Lexington, KY, USA; 3Favoris AG, Basel, Switzerland; 4Novartis Pharma AG, Basel, Switzerland; 5Ophthalmic Consultants of Long Island, Garden City, NY, USACorrespondence: Eric DonnenfeldOphthalmic Consultants of Long Island, 711 Stewart Avenue, Suite 160, Garden City, NY, 11530, USAEmail ericdonnenfeld@gmail.comAbstract: Presbyopia is a common age-related vision disorder characterized by a progressive inability to focus on near objects. If uncorrected or under-corrected, presbyopia can significantly impact patients’ quality of life. Presbyopia represents an area of considerable unmet need due to its rising prevalence worldwide as the population ages, the high proportion of under-treated individuals in some parts of the world, and the limitations of currently available corrective methods. Progressive or bifocal spectacles are associated with peripheral blur, a restricted visual field and impaired depth perception, which have been linked to an increased risk of falls in the elderly. Contact lens options can be difficult to maintain due to the development of age-related dry eye symptoms and reduced manual dexterity. Other corrective methods involve surgical interventions that modify the optics of the cornea, replace the crystalline lens, or attempt to restore active accommodation. While patients undergoing surgery report satisfactory outcomes post-operatively, many of them eventually require reading glasses. Non-invasive therapies with novel mechanisms of action are currently being investigated; these include miotic agents and UNR844, a lipoic acid choline ester. In this narrative review, available evidence on presbyopia prevalence, quality of life impact and risk factors are described, with a focus on observational studies in non-clinical settings.

Introduction Grades 2 and 3 gliomas (G2/3 gliomas), when combined, are the second largest group of malignant brain tumours in adults. The outcomes for G2/3 gliomas at progression approach the dismal outcomes for glioblastoma (GBM), yet there is a paucity of trials for Australian patients with relapsed G2/3 gliomas compared with patients with GBM. LUMOS will be a pilot umbrella study for patients with relapsed G2/3 gliomas that aims to match patients to targeted therapies based on molecular screening with contemporaneous tumour tissue. Participants in whom no actionable or no druggable mutation is found, or in whom the matching drug is not available, will form a comparator arm and receive standard of care chemotherapy. The objective of the LUMOS trial is to assess the feasibility of this approach in a multicentre study across five sites in Australia, with a view to establishing a national molecular screening platform for patient treatment guided by the mutational analysis of contemporaneous tissue biopsies Methods and analysis This study will be a multicentre pilot study enrolling patients with recurrent grade 2/3 gliomas that have previously been treated with radiotherapy and chemotherapy at diagnosis or at first relapse. Contemporaneous tumour tissue at the time of first relapse, defined as tissue obtained within 6 months of relapse and without subsequent intervening therapy, will be obtained from patients. Molecular screening will be performed by targeted next-generation sequencing at the reference laboratory (PathWest, Perth, Australia). RNA and DNA will be extracted from representative formalin-fixed paraffin embedded tissue scrolls or microdissected from sections on glass slides tissue sections following a review of the histology by pathologists. Extracted nucleic acid will be quantified by Qubit Fluorometric Quantitation (Thermo Fisher Scientific). Library preparation and targeted capture will be performed using the TruSight Tumor 170 (TST170) kit and sampl

James A Katz,1 Paul M Karpecki,2 Alexandra Dorca,3 Sima Chiva-Razavi,4 Heather Floyd,4 Elizabeth Barnes,4 Mark Wuttke,4 Eric Donnenfeld5 1The Midwest Center for Sight, Des Plaines, IL, USA; 2Kentucky Eye Institute, Lexington, KY, USA; 3Favoris AG, Basel, Switzerland; 4Novartis Pharma AG, Basel, Switzerland; 5Ophthalmic Consultants of Long Island, Garden City, NY, USACorrespondence: Eric DonnenfeldOphthalmic Consultants of Long Island, 711 Stewart Avenue, Suite 160, Garden City, NY, 11530, USAEmail ericdonnenfeld@gmail.comAbstract: Presbyopia is a common age-related vision disorder characterized by a progressive inability to focus on near objects. If uncorrected or under-corrected, presbyopia can significantly impact patients’ quality of life. Presbyopia represents an area of considerable unmet need due to its rising prevalence worldwide as the population ages, the high proportion of under-treated individuals in some parts of the world, and the limitations of currently available corrective methods. Progressive or bifocal spectacles are associated with peripheral blur, a restricted visual field and impaired depth perception, which have been linked to an increased risk of falls in the elderly. Contact lens options can be difficult to maintain due to the development of age-related dry eye symptoms and reduced manual dexterity. Other corrective methods involve surgical interventions that modify the optics of the cornea, replace the crystalline lens, or attempt to restore active accommodation. While patients undergoing surgery report satisfactory outcomes post-operatively, many of them eventually require reading glasses. Non-invasive therapies with novel mechanisms of action are currently being investigated; these include miotic agents and UNR844, a lipoic acid choline ester. In this narrative review, available evidence on presbyopia prevalence, quality of life impact and risk factors are described, with a focus on observational studies in non-clinical settings.

We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics., Comment: corrected typos, added references, added authors, added acknowledgements

The earth system is exceedingly complex and often chaotic in nature, making prediction incredibly challenging: we cannot expect to make perfect predictions all of the time. Instead, we look for specific states of the system that lead to more predictable behavior than others, often termed "forecasts of opportunity." When these opportunities are not present, scientists need prediction systems that are capable of saying "I don't know." We introduce a novel loss function, termed the "NotWrong loss", that allows neural networks to identify forecasts of opportunity for classification problems. The NotWrong loss introduces an abstention class that allows the network to identify the more confident samples and abstain (say "I don't know") on the less confident samples. The abstention loss is designed to abstain on a user-defined fraction of the samples via a PID controller. Unlike many machine learning methods used to reject samples post-training, the NotWrong loss is applied during training to preferentially learn from the more confident samples. We show that the NotWrong loss outperforms other existing loss functions for multiple climate use cases. The implementation of the proposed loss function is straightforward in most network architectures designed for classification as it only requires the addition of an abstention class to the output layer and modification of the loss function., Comment: submitted to the Journal of Advances in Earth System Modeling. arXiv admin note: substantial text overlap with arXiv:2104.08236

The earth system is exceedingly complex and often chaotic in nature, making prediction incredibly challenging: we cannot expect to make perfect predictions all of the time. Instead, we look for specific states of the system that lead to more predictable behavior than others, often termed "forecasts of opportunity". When these opportunities are not present, scientists need prediction systems that are capable of saying "I don't know." We introduce a novel loss function, termed "abstention loss", that allows neural networks to identify forecasts of opportunity for regression problems. The abstention loss works by incorporating uncertainty in the network's prediction to identify the more confident samples and abstain (say "I don't know") on the less confident samples. The abstention loss is designed to determine the optimal abstention fraction, or abstain on a user-defined fraction via a PID controller. Unlike many methods for attaching uncertainty to neural network predictions post-training, the abstention loss is applied during training to preferentially learn from the more confident samples. The abstention loss is built upon a standard computer science method. While the standard approach is itself a simple yet powerful tool for incorporating uncertainty in regression problems, we demonstrate that the abstention loss outperforms this more standard method for the synthetic climate use cases explored here. The implementation of proposed loss function is straightforward in most network architectures designed for regression, as it only requires modification of the output layer and loss function., Comment: submitted to the Journal of Advances of Earth System Modeling

Despite the increasingly successful application of neural networks to many problems in the geosciences, their complex and nonlinear structure makes the interpretation of their predictions difficult, which limits model trust and does not allow scientists to gain physical insights about the problem at hand. Many different methods have been introduced in the emerging field of eXplainable Artificial Intelligence (XAI), which aim at attributing the network s prediction to specific features in the input domain. XAI methods are usually assessed by using benchmark datasets (like MNIST or ImageNet for image classification). However, an objective, theoretically derived ground truth for the attribution is lacking for most of these datasets, making the assessment of XAI in many cases subjective. Also, benchmark datasets specifically designed for problems in geosciences are rare. Here, we provide a framework, based on the use of additively separable functions, to generate attribution benchmark datasets for regression problems for which the ground truth of the attribution is known a priori. We generate a large benchmark dataset and train a fully connected network to learn the underlying function that was used for simulation. We then compare estimated heatmaps from different XAI methods to the ground truth in order to identify examples where specific XAI methods perform well or poorly. We believe that attribution benchmarks as the ones introduced herein are of great importance for further application of neural networks in the geosciences, and for more objective assessment and accurate implementation of XAI methods, which will increase model trust and assist in discovering new science., Comment: This is an updated preprint version of the manuscript. This work has been published (open access) in the journal Environmental Data Science with doi: https://doi.org/10.1017/eds.2022.7. Please cite the published version. The dataset of this work is published at: https://mlhub.earth/data/csu_synthetic_attribution

We outline a perspective of an entirely new research branch in Earth and climate sciences, where deep neural networks and Earth system models are dismantled as individual methodological approaches and reassembled as learning, self-validating, and interpretable Earth system model-network hybrids. Following this path, we coin the term "Neural Earth System Modelling" (NESYM) and highlight the necessity of a transdisciplinary discussion platform, bringing together Earth and climate scientists, big data analysts, and AI experts. We examine the concurrent potential and pitfalls of Neural Earth System Modelling and discuss the open question whether artificial intelligence will not only infuse Earth system modelling, but ultimately render them obsolete., Comment: Perspective paper submitted to Nature Machine Intelligence, 23 pages, 3 figures

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.

Correlations between springtime stratospheric ozone extremes and subsequent surface temperatures have been previously reported for both models and observations at particular locations in the Northern Hemisphere. Here we quantify for the first time the potential use of ozone information for Northern Hemisphere seasonal forecasts, using observations and a nine-member chemistry climate model ensemble. The ensemble composite correlations between March total column ozone (TCO) and April surface temperatures display a similar structure to observations, but with slightly lower correlation magnitudes. This is likely due to the larger number of cases smoothing out sampling error in the pattern, which is visible in the difference between correlations calculated from individual ensemble members. Using a linear regression model with March TCO as the predictor, predictions of the following April surface temperatures in regions that show large correlations are possible up to 4 years following the regression model end date in individual ensemble members, and up to 6 years in observations. We create an empirical forecast model to predict the sign of the observed as well as the modeled surface temperature anomalies using March TCO. Through a leave-three-years-out cross-validation method, we show that March TCO can forecast the sign of the April surface temperature anomalies well in parts of Eurasia that show the lowest model internal variability. ©2019. American Geophysical Union. All Rights Reserved., NSF grant (1419667), NSF grant (1338814), NOAA grant (NA16OAR4310064)

Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason. Neural network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of neural networks can enable the discovery of scientifically meaningful connections within geoscientific data. In particular, we use two methods for neural network interpretation called backwards optimization and layerwise relevance propagation, both of which project the decision pathways of a network back onto the original input dimensions. To the best of our knowledge, LRP has not yet been applied to geoscientific research, and we believe it has great potential in this area. We show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network-related geoscience research., Comment: The second version of this manuscript is currently under review at the Journal of Advances in Modeling Earth Systems (JAMES)

Agents are systems that optimize an objective function in an environment. Together, the goal and the environment induce secondary objectives, incentives. Modeling the agent-environment interaction using causal influence diagrams, we can answer two fundamental questions about an agent's incentives directly from the graph: (1) which nodes can the agent have an incentivize to observe, and (2) which nodes can the agent have an incentivize to control? The answers tell us which information and influence points need extra protection. For example, we may want a classifier for job applications to not use the ethnicity of the candidate, and a reinforcement learning agent not to take direct control of its reward mechanism. Different algorithms and training paradigms can lead to different causal influence diagrams, so our method can be used to identify algorithms with problematic incentives and help in designing algorithms with better incentives., Comment: Mostly superseded by arXiv:2102.01685

Multidecadal variability in the North Atlantic jet stream in general circulation models (GCMs) is compared with that in reanalysis products of the twentieth century. It is found that almost all models exhibit multidecadal jet stream variability that is entirely consistent with the sampling of white noise year-to-year atmospheric fluctuations. In the observed record, the variability displays a pronounced seasonality within the winter months, with greatly enhanced variability toward the late winter. This late winter variability exceeds that found in any GCM and greatly exceeds expectations from the sampling of atmospheric noise, motivating the need for an underlying explanation. The potential roles of both external forcings and internal coupled ocean–atmosphere processes are considered. While the late winter variability is not found to be closely connected with external forcing, it is found to be strongly related to the internally generated component of Atlantic multidecadal variability (AMV) in sea surface temperatures (SSTs). In fact, consideration of the seasonality of the jet stream variability within the winter months reveals that the AMV is far more strongly connected to jet stream variability during March than the early winter months or the winter season as a whole. Reasoning is put forward for why this connection likely represents a driving of the jet stream variability by the SSTs, although the dynamics involved remain to be understood. This analysis reveals a fundamental mismatch between late winter jet stream variability in observations and GCMs and a potential source of long-term predictability of the late winter Atlantic atmospheric circulation.

Author Posting. © American Meteorological Society, 2018. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 30 (2018): 1297-1314, doi:10.1175/JCLI-D-17-0286.1., The variance of a jet’s position in latitude is found to be related to its average speed: when a jet becomes stronger, its variability in latitude decreases. This relationship is shown to hold for observed midlatitude jets around the world and also across a hierarchy of numerical models. North Atlantic jet variability is shown to be modulated on decadal time scales, with decades of a strong, steady jet being interspersed with decades of a weak, variable jet. These modulations are also related to variations in the basinwide occurrence of high-impact blocking events. A picture emerges of complex multidecadal jet variability in which recent decades do not appear unusual. An underlying barotropic mechanism is proposed to explain this behavior, related to the change in refractive properties of a jet as it strengthens, and the subsequent effect on the distribution of Rossby wave breaking., We would like to acknowledge funding from NERC and the Research Council of Norway project jetSTREAM under Grants NE/ L01047X/1 (IMPETUS) and 231716, respectively, for a contribution to the work presented here. EAB is supported in part by the NSF Climate and Large-Scale Dynamics Program under Grant 1545675. Y-OK was supported by the NSF Climate and Large-Scale Dynamics Program under Grant 1355339. KW was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). RL was supported by the Met Office and the National Centre for Atmospheric Science., 2018-07-29

Author Posting. © American Meteorological Society, 2018. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 30 (2018): 1297-1314, doi:10.1175/JCLI-D-17-0286.1., The variance of a jet’s position in latitude is found to be related to its average speed: when a jet becomes stronger, its variability in latitude decreases. This relationship is shown to hold for observed midlatitude jets around the world and also across a hierarchy of numerical models. North Atlantic jet variability is shown to be modulated on decadal time scales, with decades of a strong, steady jet being interspersed with decades of a weak, variable jet. These modulations are also related to variations in the basinwide occurrence of high-impact blocking events. A picture emerges of complex multidecadal jet variability in which recent decades do not appear unusual. An underlying barotropic mechanism is proposed to explain this behavior, related to the change in refractive properties of a jet as it strengthens, and the subsequent effect on the distribution of Rossby wave breaking., We would like to acknowledge funding from NERC and the Research Council of Norway project jetSTREAM under Grants NE/ L01047X/1 (IMPETUS) and 231716, respectively, for a contribution to the work presented here. EAB is supported in part by the NSF Climate and Large-Scale Dynamics Program under Grant 1545675. Y-OK was supported by the NSF Climate and Large-Scale Dynamics Program under Grant 1355339. KW was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). RL was supported by the Met Office and the National Centre for Atmospheric Science., 2018-07-29

Multidecadal variability in the North Atlantic jet stream in general circulation models (GCMs) is compared with that in reanalysis products of the twentieth century. It is found that almost all models exhibit multidecadal jet stream variability that is entirely consistent with the sampling of white noise year-to-year atmospheric fluctuations. In the observed record, the variability displays a pronounced seasonality within the winter months, with greatly enhanced variability toward the late winter. This late winter variability exceeds that found in any GCM and greatly exceeds expectations from the sampling of atmospheric noise, motivating the need for an underlying explanation. The potential roles of both external forcings and internal coupled ocean–atmosphere processes are considered. While the late winter variability is not found to be closely connected with external forcing, it is found to be strongly related to the internally generated component of Atlantic multidecadal variability (AMV) in sea surface temperatures (SSTs). In fact, consideration of the seasonality of the jet stream variability within the winter months reveals that the AMV is far more strongly connected to jet stream variability during March than the early winter months or the winter season as a whole. Reasoning is put forward for why this connection likely represents a driving of the jet stream variability by the SSTs, although the dynamics involved remain to be understood. This analysis reveals a fundamental mismatch between late winter jet stream variability in observations and GCMs and a potential source of long-term predictability of the late winter Atlantic atmospheric circulation.

This study introduces a simple analytic expression for calculating the lead time required for a linear trend to emerge in a Gaussian first-order autoregressive process. The expression is derived from the standard error of the regression and is tested using the NCAR Community Earth System Model Large Ensemble of climate change simulations. It is shown to provide a robust estimate of the point in time when the forced signal of climate change has emerged from the natural variability of the climate system with a predetermined level of statistical confidence. The expression provides a novel analytic tool for estimating the time of emergence of anthropogenic climate change and its associated regional climate impacts from either observed or modeled estimates of natural variability and trends., National Science Foundation (U.S.). Climate and Large-Scale Dynamics Program (Grant AGS-1419667)

Background: A significant portion of radiation treatment (30–40%) is delivered with palliative intent. Given the frequency of palliative care (PC) in radiation oncology, we determined the patterns of research focusing on symptom control and palliative care (SCPC) in two prominent radiation oncology journals from 2005–2014. Methods: Original research manuscripts published from 2005–2014 in the International Journal of Radiation Oncology *Biology* Physics (Red Journal) and the Radiotherapy and Oncology Journal (Green Journal) were reviewed to categorize articles as PC and/or SCPC. Articles were categorized as PC if it pertained to any aspect of treatment of metastatic cancer, and as SCPC if symptom control in the metastatic cancer setting was the goal of the research inquiry and/or any domain of palliative clinical practice guidelines was the goal of research inquiry. Results: From 2005–2014, 4.9% (312/6,386) of original research articles published in the Red Journal and 3.5% (84/2,406) published in the Green Journal pertained to metastatic cancer, and were categorized as PC. In the Red Journal, 1.3% (84/6,386) of original research articles were categorized as SCPC; 1.3% (32/2,406) of articles in the Green Journal were categorized as SCPC. There was no trend observed in the proportion of SCPC articles published over time in the Red Journal (P=0.76), the Green Journal (P=0.48), or both journals in aggregate (P=0.38). Conclusions: Despite the fact that palliative radiotherapy is a critical part of radiation oncology practice, PC and SCPC-focused original research is poorly represented in the Red Journal and the Green Journal.

The volcanic eruption of Mount Pinatubo in June 1991 is the largest terrestrial eruption since the beginning of the satellite era. Here, the monthly evolution of atmospheric temperature, zonal winds, and precipitation following the eruption in 14 CMIP5 models is analyzed and strong and robust stratospheric and tropospheric circulation responses are demonstrated in both hemispheres, with tropospheric anomalies maximizing in November 1991. The simulated Southern Hemisphere circulation response projects strongly onto the positive phase of the southern annular mode (SAM), while the Northern Hemisphere exhibits robust North Atlantic and North Pacific responses that differ significantly from that of the typical northern annular mode (NAM) pattern. In contrast, observations show a negative SAM following the eruption, and internal variability must be considered along with forced responses. Indeed, evidence is presented that the observed El Niño climate state during and after this eruption may oppose the eruption-forced positive SAM response, based on the El Niño–Southern Oscillation (ENSO) state and SAM response across the models. The results demonstrate that Pinatubo-like eruptions should be expected to force circulation anomalies across the globe and highlight that great care must be taken in diagnosing the forced response as it may not fall into typical seasonal averages or be guaranteed to project onto typical climate modes., National Science Foundation (U.S.). Climate and Large-Scale Dynamics Program (Grant 1419818), National Science Foundation (U.S.) (Grant 1419667)

PURPOSE: To assess the 12-month efficacy and safety profile of an individualized regimen of ranibizumab 0.5 mg driven by stabilization criteria in patients with macular edema secondary to central retinal vein occlusion (CRVO).DESIGN: A 24-month, prospective, open-label, single-arm, multicenter study.PARTICIPANTS: Three hundred fifty-seven patients.METHODS: Patients were treated with monthly ranibizumab 0.5-mg injections (minimum of 3 injections) until stable visual acuity (VA) was maintained for 3 consecutive months. Thereafter, ranibizumab 0.5 mg was dosed as needed if monthly monitoring indicated a loss of VA resulting from disease activity.MAIN OUTCOME MEASURES: Mean change from baseline at month 12 in best-corrected VA (BCVA; primary end point) and safety over 12 months. The efficacy of this regimen in subgroups categorized by baseline BCVA score, CRVO duration, or presence of macular ischemia (exploratory analysis).RESULTS: At baseline, the mean BCVA was 53.0 letters and mean CRVO duration was 8.9 months (median, 2.4 months). Ranibizumab 0.5-mg treatment resulted in a statistically significant mean gain in BCVA from baseline at month 12 of 12.3 letters (standard deviation [SD], 16.72 letters; P < 0.0001). The mean number of ranibizumab injections up to month 12 was 8.1 (SD, 2.77). At month 12, mean BCVA gains were similar with or without macular ischemia at baseline (11.6 vs. 12.1 letters); the mean BCVA gain was higher with baseline CRVO duration of less than 3 months (13.4 letters) than with a longer duration (≥3-<9 months, 11.1 letters; ≥9 months, 10.9 letters). Patients with lower baseline BCVA had larger mean BCVA gains at month 12 than those with higher baseline BCVA (≤39/40-59/≥60 and 18.0/12.7/8.9 letters, respectively), although the absolute BCVA at month 12 was higher with higher baseline BCVA. No new ocular or nonocular safety events were observed.CONCLUSIONS: An individualized dosing regimen of rani

A systematic analysis of the winter North Atlantic eddy-driven jet stream latitude and wind speed from 52 model integrations, taken from the coupled model intercomparison project phase 3, is carried out and compared to results obtained from the ERA-40 reanalyses. We consider here a control simulation, twentieth century simulation, and two time periods (2046-2065 and 2081-2100) from a twenty-first century, high-emission A2 forced simulation. The jet wind speed seasonality is found to be similar between the twentieth century simulations and the ERA-40 reanalyses and also between the control and forced simulations although nearly half of the models overestimate the amplitude of the seasonal cycle. A systematic equatorward bias of the models jet latitude seasonality, by up to 7A degrees, is observed, and models additionally overestimate the seasonal cycle of jet latitude about the mean, with the majority of the models showing equatorward and poleward biases during the cold and warm seasons respectively. A main finding of this work is that no GCM under any forcing scenario considered here is able to simulate the trimodal behaviour of the observed jet latitude distribution. The models suffer from serious problems in the structure of jet variability, rather than just quantitiative errors in the statistical moments., AuthorCount:3;Funding Agency:NOAA Climate and Global Change Fellowship through the University Corporation of Atmospheric Research Visiting Science Program

BACKGROUND: Identifying the most radiosensitive patient group would have huge clinical implications. METHODS: A tissue bank containing skin fibroblasts, whole blood, lymphocytes, plasma and lymphoblastoid cell lines from clinically radiation hypersensitive patients was established from patients in Europe and Canada. Over-reacting individuals had CTCAE3.0 severe acute side effects grade 2 or more occurring at very low radiation doses where these side effects are unexpected or grade 3-4 lasting more than 4 weeks after the end of radiotherapy and/or requiring surgical intervention at any time or severe late side effects grade 3-4. RESULTS: Eleven patients have been identified with a mean age of 61.6±8.5 years (range 49-74). Two patients were male, 9 female. One patient had non-small cell lung cancer, 6 breast cancer, 2 head and neck cancer, one lymphoma and one meningioma. The mean follow-up time after radiotherapy was 1658±1048 days (range 84-3752). CONCLUSIONS: The establishment of an international tissue bank of the rare group of patients with extreme hypersensitivity to radiotherapy was proven to be feasible and should enable in-depth molecular studies

Video interview conducted on July 8, 2009 with Elizabeth Barnes, shortly after her 100th Birthday, on the subject of living in Carroll County, Maryland. The interviewer was Ann Horvath.

Veena Desa1, Cyril Danjoux2, Yvette Matyas1, Margaret Fitch3, Amna Husain4, Nina Horvath5, Jeff Myers5, Mark Clemons6, Janet E Hux5,7,8, Elizabeth A Barnes21Department of Administration; 2Department of Radiation Oncology; 3Psychosocial and Behavioral Research Unit, Toronto Sunnybrook Regional Cancer Centre; 4The Temmy Latner Centre for Palliative Care, Mount Sinai Hospital, Toronto, Ontario, Canada; 5Department of Family Medicine, Sunnybrook Hospital, Toronto, Ontario, Canada; 6Department of Medical Oncology, Toronto, Ontario, Canada; 7Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; 8Department of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, CanadaAbstract: The scale and complexity of palliative care increasingly demands that researchers move beyond their own discipline and explore interdisciplinary collaboration. At a Palliative Care Research Retreat held in January 2006 at the Toronto Sunnybrook Regional Cancer Centre, researchers from multiple care settings with the center and from other Toronto hospitals came together with the vision of becoming Canadian leaders in palliative care research. As a result of this retreat, five interdisciplinary groups were formed to pursue research in the areas of pain and symptom management, access to services, translational research, education, and communication. An overview of the retreat and direction of research for each group is provided.Keywords: palliative care, research retreat, pain and symptom management, access to care, translational research, palliative care education, communication

The Southern Highland Handicraft Guild met for its fall membership meeting in Penland, North Carolina on October 5, 6, and 7, 1939. Meeting minutes generally contain committee reports, financial statements, new members, announcements of regional events, and discussion about special issues facing the organization. At this particular meeting the Guild celebrated its tenth anniversary. Marguerite Bidstrup spoke about the craft maps being created by the Education Committee. Louise Pitman talked about the exhibit which members had created for the meeting, and about exhibits going out of state to New Hampshire and Florida. Shop manager, Agnes Loeffler, reported on the Allanstand Shop. Loeffler also reported that an Asheville group was forming in order to operate the concessions in the Great Smoky Mountain National Park. The Guild members discussed how they should respond. Allen Eaton sent a letter about purchasing outstanding items from members which could be displayed at the World's Fair in New York. The federal Wage and Hour Law was discussed. The Southern Highland Handicraft Guild was founded in 1930 as the Southern Mountain Handicraft Guild. The name was changed to the Southern Highland Handicraft Guild in 1933 and again in the 1990s to the Southern Highland Craft Guild.

The Southern Highland Handicraft Guild met for its annual membership meeting in Knoxville, Tennessee on March 10, 1939. Meeting minutes generally contain committee reports, financial statements, new members, announcements of regional events, and discussion about special issues facing the organization. At this particular meeting financial matters were handled first. A Nominating Committee was named. Lucy Morgan reported for the Membership and Standards Committee. Marquerite Bidstrup spoke for the Education Committee and described a map the Guild hopes to publish. Clementine Douglas told about additions to the Goodrich Collection. Various exhibitions werediscussed. The Nominating Committee gave their report. Discussion followed on requests received for patterns and the need for educating the public. Lucy Morgan announced Penland's Craft Institute. Minutes were recorded by Elizabeth G. Barnes. Included with the minutes were a list of people attending the meeting and a semi-annual financial statement. The Southern Highland Handicraft Guild was founded in 1930 as the Southern Mountain Handicraft Guild. The name was changed to the Southern Highland Handicraft Guild in 1933 and again in the 1990s to the Southern Highland Craft Guild.

The Southern Highland Handicraft Guild met for its annual membership meeting in Knoxville, Tennessee on March 8, 1940. Meeting minutes generally contain committee reports, financial statements, new members, announcements of regional events, and discussion about special issues facing the organization. At this particular meeting a Publicity Committee Chairman was appointed with selected committee members from each of the nine states. Olive D. Campbell opened discussion on the "relationship between the Southern Highland Handicraft Guild and the Southern Highlanders." A committee was formed. President O.J. Mattil summarized the Guild's relationship with the National Park Service and Guild sales in national parks. The membership voted to begin setting up a second shop. Clementine Douglas was asked to send out a questionnaire about sales preferences. [The attached questionnaire is more about basic Guild operations.] Minutes were recorded by Elizabeth G. Barnes. The Southern Highland Handicraft Guild was founded in 1930 as the Southern Mountain Handicraft Guild. The name was changed to the Southern Highland Handicraft Guild in 1933 and again in the 1990s to the Southern Highland Craft Guild.