Evaluating the Temporal and the Spatial Heterogeneity of the European Convergence Process, 1980-1999

In this paper, we suggest a general framework that allows testing simultaneously for temporal heterogeneity, spatial heterogeneity and spatial autocorrelation in b-convergence models. Based on a sample of 145 European regions over the 1980-1999 period, we estimate a Seemingly Unrelated Regression Model with spatial regimes and spatial autocorrelation for two sub-periods: 1980-1989 and 1989-1999. The assumption of temporal independence between the two-periods is rejected and the estimation results point to the presence of spatial error autocorrelation in both sub-periods and spatial instability in the second sub-period, indicating the formation of a convergence club between the peripheral regions of the European Union.b-convergence models, spatial autocorrelation, convergence clubs, temporal instability

Evolving Reinforcement Learning Algorithms

We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our method can both learn from scratch and bootstrap off known existing algorithms, like DQN, enabling interpretable modifications which improve performance. Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm. Bootstrapped from DQN, we highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games. The analysis of the learned algorithm behavior shows resemblance to recently proposed RL algorithms that address overestimation in value-based methods.Comment: ICLR 2021 Oral. See project website at https://sites.google.com/view/evolvingr

An Immersive Virtual Reality Curriculum for Pediatric Hematology Clinicians on Shared Decision-making for Hydroxyurea in Sickle Cell Anemia

Although hydroxyurea (HU) is an effective treatment for sickle cell anemia, uptake remains low. Shared decision-making (SDM) is a recommended strategy for HU initiation to elicit family preferences; however, clinicians lack SDM training. We implemented an immersive virtual reality (VR) curriculum at 8 pediatric institutions to train clinicians on SDM that included counseling virtual patients. Clinicians’ self-reported confidence significantly improved following the VR simulations on all communication skills assessed, including asking open-ended questions, eliciting specific concerns, and confirming understanding (Ps≤0.01 for all). VR may be an effective method for educating clinicians to engage in SDM for HU

Symbolic Discovery of Optimization Algorithms

We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques to explore an infinite and sparse program space. To bridge the large generalization gap between proxy and target tasks, we also introduce program selection and simplification strategies. Our method discovers a simple and effective optimization algorithm, $\textbf{Lion}$ (\textit{Evo\textbf{L}$ved S$\textbf{i}$gn M$\textbf{o}$me$\textbf{n}tum}). It is more memory-efficient than Adam as it only keeps track of the momentum. Different from adaptive optimizers, its update has the same magnitude for each parameter calculated through the sign operation. We compare Lion with widely used optimizers, such as Adam and Adafactor, for training a variety of models on different tasks. On image classification, Lion boosts the accuracy of ViT by up to 2% on ImageNet and saves up to 5x the pre-training compute on JFT. On vision-language contrastive learning, we achieve 88.3% $\textit{zero-shot}$ and 91.1% $\textit{fine-tuning}$ accuracy on ImageNet, surpassing the previous best results by 2% and 0.1%, respectively. On diffusion models, Lion outperforms Adam by achieving a better FID score and reducing the training compute by up to 2.3x. For autoregressive, masked language modeling, and fine-tuning, Lion exhibits a similar or better performance compared to Adam. Our analysis of Lion reveals that its performance gain grows with the training batch size. It also requires a smaller learning rate than Adam due to the larger norm of the update produced by the sign function. Additionally, we examine the limitations of Lion and identify scenarios where its improvements are small or not statistically significant. The implementation of Lion is publicly available.Comment: 30 pages, update the tuning instruction

Neural crest progenitors and stem cells

In the vertebrate embryo, multiple cell types originate from a common structure, the neural crest (NC), which forms at the dorsal tips of the neural epithelium. The NC gives rise to migratory cells that colonise a wide range of embryonic tissues and later differentiate into neurones and glial cells of the peripheral nervous system (PNS), pigment cells (melanocytes) in the skin and endocrine cells in the adrenal and thyroid glands. In the head and the neck, the NC also yields mesenchymal cells that form craniofacial cartilages, bones, dermis, adipose tissue, and vascular smooth muscle cells. The NC is therefore a model system to study cell diversification during embryogenesis and phenotype maintenance in the adult. By analysing the developmental potentials of quail NC cells in clonal cultures, we have shown that the migratory NC is a collection of heterogeneous progenitors, including various types of intermediate precursors and highly multipotent cells, some of which being endowed of self-renewal capacity. We also have identified common progenitors for mesenchymal derivatives and neural/melanocytic cells in the cephalic NC. These results are consistent with a hierarchical model of lineage segregation wherein environmental cytokines control the fate of progenitors and stem cells. One of these cytokines, the endothelin3 peptide, promotes the survival, proliferation, and self-renewal capacity of common progenitors for glial cells and melanocytes. At post-migratory stages, when they have already differentiated, NC-derived cells exhibit phenotypic plasticity. Epidermal pigment cells and Schwann cells from peripheral nerves in single-cell culture are able to reverse into multipotent NC-like progenitors endowed with self-renewal. Therefore, stem cell properties are expressed by a variety of NC progenitors and can be re-acquired by differentiated cells of NC origin, suggesting potential function for repair