Protoplanetary disks are the birthplaces of planets, and resolving their three-dimensional structure is key to understanding disk evolution. The unprecedented resolution of ALMA demands modeling approaches that capture features beyond the reach of traditional methods. We introduce a computational framework that integrates physics-constrained neural fields with differentiable rendering and present RadJAX, a GPU-accelerated, fully differentiable line radiative transfer solver achieving up to 10,000x speedups over conventional ray tracers, enabling previously intractable, high-dimensional neural reconstructions. Applied to ALMA CO observations of HD 163296, this framework recovers the vertical morphology of the CO-rich layer, revealing a pronounced narrowing and flattening of the emission surface beyond 400 au - a feature missed by existing approaches. Our work establish a new paradigm for extracting complex disk structure and advancing our understanding of protoplanetary evolution.

The discovery of crystalline reticular materials remains largely trial-and-error despite their societal importance. We introduce an Algorithmic Iterative Reticular Synthesis (AIRES) cycle, which integrates automated synthesis, image recognition, single crystal X-ray diffraction, and customized algorithmic decision-making—crucially, to maximize distinct crystal discoveries rather than optimizing single targets. Demonstrated on zeolitic imidazo44 late frameworks (ZIFs), AIRES achieved twice the discovery rate of random exploration, crystallizing 10 new linkers into diverse ZIF topologies and expanding the single-linker Zn-ZIF library by one-third. By transforming reticular synthesis from an empirical pro47 cess to a systematic exploration, AIRES provides a scalable and efficient blueprint for accelerating materials discovery.

Conditional video diffusion models (CDM) have shown promising results for video synthesis, potentially enabling the generation of realistic echocardiograms to address the problem of data scarcity. However, current CDMs require a paired segmentation map and echocardiogram dataset. We present a new method called Free-Echo for generating realistic echocardiograms from a single end-diastolic segmentation map without additional training data. Our method is based on the 3D-Unet with Temporal Attention Layers model and is conditioned on the segmentation map using a training-free conditioning method based on SDEdit. We evaluate our model on two public echocardiogram datasets, CAMUS and EchoNet-Dynamic. We show that our model can generate plausible echocardiograms that are spatially aligned with the input segmentation map, achieving performance comparable to training-based CDMs. Our work opens up new possibilities for generating echocardiograms from a single segmentation map, which can be used for data augmentation, domain adaptation, and other applications in medical imaging.

Federated Learning (FL) is a promising paradigm that offers significant advancements in privacy-preserving, decentralized machine learning by enabling collaborative training of models across distributed devices without centralizing data. However, the practical deployment of FL systems faces a significant bottleneck: the communication overhead caused by frequently exchanging large model updates between numerous devices and a central server. This communication inefficiency can hinder training speed, model performance, and the overall feasibility of real-world FL applications. In this survey, we investigate various strategies and advancements made in communication-efficient FL, highlighting their impact and potential to overcome the communication challenges inherent in FL systems. Specifically, we define measures for communication efficiency, analyze sources of communication inefficiency in FL systems, and provide a taxonomy and comprehensive review of state-of-the-art communication-efficient FL methods. Additionally, we discuss promising future research directions for enhancing the communication efficiency of FL systems. By addressing the communication bottleneck, FL can be effectively applied and enable scalable and practical deployment across diverse applications that require privacy-preserving, decentralized machine learning, such as IoT, healthcare, or finance.

Light–matter interaction between quantum emitters and optical cavities plays a vital role in fundamental quantum photonics and the development of optoelectronics. Resonant metasurfaces are proven to be an efficient platform for tailoring the spontaneous emission (SE) of the emitters. In this work, we study the interplay between quasi-2D perovskites and dielectric TiO2 metasurfaces. The metasurface, functioning as an open cavity, enhances electric fields near its plane, thereby influencing the emissions of the perovskite. This is verified through angle-resolved photoluminescence (PL) studies. We also conducted reflectivity measurements and numerical simulations to validate the coupling between the quasi-2D perovskites and photonic modes. Notably, our work introduces a spatial mapping approach to study Purcell enhancement. Using fluorescence lifetime imaging microscopy (FLIM), we directly link the PL and lifetimes of the quasi-2D perovskites in spatial distribution when positioned on the metasurface. This correlation provides unprecedented insights into emitter distribution and emitter–resonator interactions. The methodology opens a new (to the best of our knowledge) approach for studies in quantum optics, optoelectronics, and medical imaging by enabling spatial mapping of both PL intensity and lifetime, differentiating between uncoupled quantum emitters and those coupled with different types of resonators.