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\"Editions de Minuit, publisher of Marguerite Duras and Alain Robbe-Grillet, rarely publishes a debut novel. Jean Echenoz, current star of this revered French literary house and enthusiastic fan of Julia Deck, confesses that he didn't send his first novel to Minuit because the publisher is \"too demanding...too good for me.\" Yet thirty years later, a first novel published by Minuit has gripped French readers and taken the literary world by storm. Viviane is both an engrossing murder mystery and a gripping exploration of madness, a narrative that tests the shifting boundaries of language and the self. For inspiration, Deck read the work of another Minuit star, Samuel Beckett, because, as she says, \"he positions himself within chaos and gives it coherence.\" This breakthrough novel, nominated for the Prix Femina, the Prix France Inter, and the Prix du Premier Roman, is sure to become a contemporary classic. Linda Coverdale, one of the most celebrated French translators working today, has created a faithful and propulsive English text that has been revised and approved by the author. \"-- Provided by publisher.

Land surface models (LSMs) are essential tools for simulating the coupled climate system, representing the dynamics of water, energy, and carbon fluxes on land and their interaction with the atmosphere. However, parameterizing sub‐grid processes at the scales relevant to climate models (∼${\\sim} $ 10–100 km) remains a considerable challenge. The parameterizations typically have a large number of unknown and often correlated parameters, making calibration and uncertainty quantification difficult. Moreover, many existing LSMs are not readily adaptable to the incorporation of modern machine learning (ML) parameterizations trained with in situ and satellite data. This article presents the first version of ClimaLand, a new LSM designed for overcoming these limitations, including a description of the core equations underlying the model, the results of an extensive set of validation exercises, and an assessment of the computational performance of the model. We show that ClimaLand can leverage graphics processing units for computational efficiency, and that its modular architecture and high‐level programming language, Julia, allows for integration with ML libraries. In the future, this will enable efficient simulation, calibration, and uncertainty quantification with ClimaLand. Plain Language Summary Simulating the Earth's atmosphere, ocean, and land surface is an important method that scientists use for understanding the Earth's climate, including its response to climate change. Due the complexity of the processes involved, approximations are made when representing certain aspects of the land surface, such as vegetation heterogeneity or topographical variation. These approximations can be improved by using data (“calibration”), but doing so has a large computational cost. They can also be improved using machine learning (ML), but this requires models to be easily integrated with ML packages. ClimaLand is a new land surface model which has been designed from the start to incorporate ML parameterizations and to more efficiently calibrate parameterizations with data. This article presents the ClimaLand model, benchmarks its computational performance, and compares model output against data in a variety of regimes. Follow‐on studies will improve the core model using ML parameterizations and by calibrating the model. Key Points ClimaLand, a land surface model, simulates energy, water, and carbon fluxes within and across soil, canopy and snow components The soil model simulates flow and phase changes of water in both saturated and unsaturated zones The model runs natively on graphics processing units and is designed to enable the inclusion of data‐driven parameterizations

Background Psychiatric disorders increase risk of neuropsychiatric disease and poor outcomes, yet little is known about the neuropsychiatric manifestations of COVID-19 in the psychiatric population. The primary objective is to synthesize neuropsychiatric outcomes of COVID-19 in people with preexisting psychiatric disorders. Methods Data were collected during an ongoing review of the impact of pandemics on people with existing psychiatric disorders. All study designs and gray literature were included. Medline, PsychInfo, CINAHL, EMBASE, and MedRx were searched from inception to September 1 2020. Risk of bias was assessed using a published tool that can accommodate all study types. Two independent authors screened the studies and extracted data. Data were narratively synthesized, as there were insufficient data to meta-analyze. Evidence was appraised according to GRADE. Results Four case reports were included, comprising 13 participants from three countries. Many large-sample, relevant papers were omitted for not reporting psychiatric history, despite reporting other comorbidities. Included participants ( n = 13) were hospitalized with COVID-19 and appeared to meet criteria for delirium. Myoclonus, rigidity, and alogia were also reported. The most commonly reported preexisting psychiatric diagnoses were mood disorders, schizophrenia, and alcohol use disorder. Conclusions People with preexisting psychiatric disorders may experience delirium, rigidity, myoclonus, and alogia during COVID-19 infection; although higher quality and longitudinal data are needed to better understand these phenomena. Relevant COVID-19 literature does not always report psychiatric history, despite heightened neuropsychiatric vulnerability within this population. Trial Registration:  PROSPERO (CRD42020179611).

Human-AI collaboration is increasingly relevant in consequential areas where AI recommendations support human discretion. However, human-AI teams' effectiveness, capability, and fairness highly depend on human perceptions of AI. Positive fairness perceptions have been shown to foster trust and acceptance of AI recommendations. Yet, work on confirmation bias highlights that humans selectively adhere to AI recommendations that align with their expectations and beliefs -- despite not being necessarily correct or fair. This raises the question whether confirmation bias also transfers to the alignment of gender bias between human and AI decisions. In our study, we examine how gender bias alignment influences fairness perceptions and reliance. The results of a 2x2 between-subject study highlight the connection between gender bias alignment, fairness perceptions, and reliance, demonstrating that merely constructing a ``formally fair'' AI system is insufficient for optimal human-AI collaboration; ultimately, AI recommendations will likely be overridden if biases do not align.