Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using Four Dimensional Variational data assimilation

Efforts to implement variational data assimilation routines with functional ecology models and land surface models have been limited, with sequential and Markov chain Monte Carlo data assimilation methods being prevalent. When data assimilation has been used with models of carbon balance, prior or “background” errors (in the initial state and parameter values) and observation errors have largely been treated as independent and uncorrelated. Correlations between background errors have long been known to be a key aspect of data assimilation in numerical weather prediction. More recently, it has been shown that accounting for correlated observation errors in the assimilation algorithm can considerably improve data assimilation results and forecasts. In this paper we implement a Four-Dimensional Variational data assimilation (4D-Var) scheme with a simple model of forest carbon balance, for joint parameter and state estimation and assimilate daily observations of Net Ecosystem CO2 Exchange (NEE) taken at the Alice Holt forest CO2 flux site in Hampshire, UK. We then investigate the effect of specifying correlations between parameter and state variables in background error statistics and the effect of specifying correlations in time between observation errors. The idea of including these correlations in time is new and has not been previously explored in carbon balance model data assimilation. In data assimilation, background and observation error statistics are often described by the background error covariance matrix and the observation error covariance matrix. We outline novel methods for creating correlated versions of these matrices, using a set of previously postulated dynamical constraints to include correlations in the background error statistics and a Gaussian correlation function to include time correlations in the observation error statistics. The methods used in this paper will allow the inclusion of time correlations between many different observation types in the assimilation algorithm, meaning that previously neglected information can be accounted for. In our experiments we assimilate a single year of NEE observations and then run a forecast for the next 14 years. We compare the results using our new correlated background and observation error covariance matrices and those using diagonal covariance matrices. We find that using the new correlated matrices reduces the root mean square error in the 14 year forecast of daily NEE by 44% decreasing from 4.22 gCm−2 day−1 to 2.38 gCm−2 day−

Clinical realism: a new literary genre and a potential tool for encouraging empathy in medical students

Background: Empathy has been re-discovered as a desirable quality in doctors. A number of approaches using the medical humanities have been advocated to teach empathy to medical students. This paper describes a new approach using the medium of creative writing and a new narrative genre: clinical realism. Methods: Third year students were offered a four week long Student Selected Component (SSC) in Narrative Medicine and Creative Writing. The creative writing element included researching and creating a character with a life-changing physical disorder without making the disorder the focus of the writing. The age, gender, social circumstances and physical disorder of a character were randomly allocated to each student. The students wrote repeated assignments in the first person, writing as their character and including details of living with the disorder in all of their narratives. This article is based on the work produced by the 2013 cohort of students taking the course, and on their reflections on the process of creating their characters. Their output was analysed thematically using a constructivist approach to meaning making. Results: This preliminary analysis suggests that the students created convincing and detailed narratives which included rich information about living with a chronic disorder. Although the writing assignments were generic, they introduced a number of themes relating to illness, including stigma, personal identity and narrative wreckage. Some students reported that they found it difficult to relate to “their” character initially, but their empathy for the character increased as the SSC progressed. Conclusion: Clinical realism combined with repeated writing exercises about the same character is a potential tool for helping to develop empathy in medical students and merits further investigation

Overconfident Investors, Predictable Returns, and Excessive Trading

The last several decades have witnessed a shift away from a fully rational paradigm of financial markets toward one in which investor behavior is influenced by psychological biases. Two principal factors have contributed to this evolution: a body of evidence showing how psychological bias affects the behavior of economic actors; and an accumulation of evidence that is hard to reconcile with fully rational models of security market trading volumes and returns. In particular, asset markets exhibit trading volumes that are high, with individuals and asset managers trading aggressively, even when such trading results in high risk and low net returns. Moreover, asset prices display patterns of predictability that are difficult to reconcile with rational-expectations–based theories of price formation. In this paper, we discuss the role of overconfidence as an explanation for these patterns

Cross-Cancer Genome-Wide Analysis of Lung, Ovary, Breast, Prostate, and Colorectal Cancer Reveals Novel Pleiotropic Associations

Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer, and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. Cancer Res; 76(17); 5103-14. ©2016 AACR

Explainable Shapley-Based Allocation (Student Abstract)

The Shapley value is one of the most important normative division scheme in cooperative game theory, satisfying basic axioms. However, some allocation according to the Shapley value may seem unfair to humans. In this paper, we develop an automatic method that generates intuitive explanations for a Shapley-based payoff allocation, which utilizes the basic axioms. Given a coalitional game, our method decomposes it to sub-games, for which it is easy to generate verbal explanations, and shows that the given game is composed of the sub-games. Since the payoff allocation for each sub-game is perceived as fair, the Shapley-based payoff allocation for the given game should seem fair as well. We run an experiment with 210 human participants and show that when applying our method, humans perceive Shapley-based payoff allocation as significantly more fair than when using a general standard explanation

Improving the Perception of Fairness in Shapley-Based Allocations

The Shapley value is one of the most important normative division schemes in cooperative game theory, satisfying basic axioms. However, some allocation according to the Shapley value may seem unfair to humans. In this paper, we develop an automatic method that generates intuitive explanations for a Shapley-based payoff allocation, which utilizes the basic axioms. Given any coalitional game, our method decomposes it to sub-games, for which it is easy to generate verbal explanations, and shows that the given game is composed of the sub-games. Since the payoff allocation for each sub-game is perceived as fair, the Shapley-based payoff allocation for the given game should seem fair as well. We run an experiment with 630 human participants and show that when applying our method, humans perceive the Shapley-based payoff allocation as more fair than the Shapley-based payoff allocation without any explanation or with explanations generated by other methods

Contrastive Explanations for Recommendation Systems

We develop an automatic method that, given a contrastive query from the user, generates contrastive explanations based on items' features and users' preferences. That is, once receiving a recommendation, the users have the option to ask the system why it did not recommend a specific different item. Our method enables a recommendation system to reply with a meaningful and convincing personalized explanation. For example, the recommendation system may recommend a Samsung S22 phone. The user may ask the system why it did not recommend the Xiaomi 12. Based on the user's preferences, all other users' preferences, and the specific phones in question, our method might infer that a good camera is particularly important to the user, and thus, say that the Samsung S22 includes a better camera. We show that humans are more convinced that the recommended item is better than the contrastive item when using our contrastive explanations