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Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experience with highly generic learning algorithms or do they require strong inductive biases? Recent advances in collecting large-scale, longitudinal, developmentally realistic video datasets and generic self-supervised learning (SSL) algorithms are allowing us to begin to tackle this nature vs. nurture question. However, existing work typically focuses on image-based SSL algorithms and visual capabilities that can be learned from static images (e.g. object recognition), thus ignoring temporal aspects of the world. To close this gap, here we train self-supervised video models on longitudinal, egocentric headcam recordings collected from a child over a two year period in their early development (6-31 months). The resulting models are highly effective at facilitating the learning of action concepts from a small number of labeled examples; they have favorable data size scaling properties; and they display emergent video interpolation capabilities. Video models also learn more robust object representations than image-based models trained with the exact same data. These results suggest that important temporal aspects of a child's internal model of the world may be learnable from their visual experience using highly generic learning algorithms and without strong inductive biases. 

Cyclobutanes are prominent structural components in natural products and drug molecules. With the advent of strain‐release‐driven synthesis, ring‐opening reactions of bicyclo[1.1.0]butanes (BCBs) provide an attractive pathway to construct these three‐dimensional structures. However, the stereoselective difunctionalization of the central C−C σ‐bonds remains challenging. Reported herein is a covalent‐based organocatalytic strategy that exploits radical NHC catalysis to achieve diastereoselective acylfluoroalkylation of BCBs under mild conditions. The Breslow enolate acts as a single electron donor and provides an NHC‐bound ketyl radical with appropriate steric hindrance, which effectively distinguishes between the two faces of transient cyclobutyl radicals. This operationally simple method tolerates various fluoroalkyl reagents and common functional groups, providing a straightforward access to polysubstituted cyclobutanes (75 examples, up to >19 : 1 d.r.). The combined experimental and theoretical investigations of this organocatalytic system confirm the formation of the NHC‐derived radical and provide an understanding of how stereoselective radical‐radical coupling occurs.

 

Cyclobutanes are prominent structural components in natural products and drug molecules. With the advent of strain‐release‐driven synthesis, ring‐opening reactions of bicyclo[1.1.0]butanes (BCBs) provide an attractive pathway to construct these three‐dimensional structures. However, the stereoselective difunctionalization of the central C−C σ‐bonds remains challenging. Reported herein is a covalent‐based organocatalytic strategy that exploits radical NHC catalysis to achieve diastereoselective acylfluoroalkylation of BCBs under mild conditions. The Breslow enolate acts as a single electron donor and provides an NHC‐bound ketyl radical with appropriate steric hindrance, which effectively distinguishes between the two faces of transient cyclobutyl radicals. This operationally simple method tolerates various fluoroalkyl reagents and common functional groups, providing a straightforward access to polysubstituted cyclobutanes (75 examples, up to >19 : 1 d.r.). The combined experimental and theoretical investigations of this organocatalytic system confirm the formation of the NHC‐derived radical and provide an understanding of how stereoselective radical‐radical coupling occurs.

 

The therapeutic potential of recombinant cytokines has been limited by the severe side effects of systemic administration. We describe a strategy to reduce the dose-limiting toxicities of monomeric cytokines by designing two components that require colocalization for activity and that can be independently targeted to restrict activity to cells expressing two surface markers. We demonstrate the approach with a previously designed mimetic of cytokines interleukin-2 and interleukin-15—Neoleukin-2/15 (Neo-2/15)—both fortrans-activating immune cells surrounding targeted tumor cells and forcis-activating directly targeted immune cells. Intrans-activation mode, tumor antigen targeting of the two components enhanced antitumor activity and attenuated toxicity compared with systemic treatment in syngeneic mouse melanoma models. Incis-activation mode, immune cell targeting of the two components selectively expanded CD8⁺ T cells in a syngeneic mouse melanoma model and promoted chimeric antigen receptor T cell activation in a lymphoma xenograft model, enhancing antitumor efficacy in both cases.

 

In many real-world classification applications such as fake news detection, the training data can be extremely imbalanced, which brings challenges to existing classifiers as the majority classes dominate the loss functions of classifiers. Oversampling techniques such as SMOTE are effective approaches to tackle the class imbalance problem by producing more synthetic minority samples. Despite their success, the majority of existing oversampling methods only consider local data distributions when generating minority samples, which can result in noisy minority samples that do not fit global data distributions or interleave with majority classes. Hence, in this paper, we study the class imbalance problem by simultaneously exploring local and global data information since: (i) the local data distribution could give detailed information for generating minority samples; and (ii) the global data distribution could provide guidance to avoid generating outliers or samples that interleave with majority classes. Specifically, we propose a novel framework GL-GAN, which leverages the SMOTE method to explore local distribution in a learned latent space and employs GAN to capture the global information, so that synthetic minority samples can be generated under even extremely imbalanced scenarios. Experimental results on diverse real data sets demonstrate the effectiveness of our GL-GAN framework in producing realistic and discriminative minority samples for improving the classification performance of various classifiers on imbalanced training data. Our code is available at https://github.com/wentao-repo/GL-GAN.