Smartphone-based indoor localization has emerged as a cost-effective and accurate solution to localize mobile and IoT devices indoors. However, the challenges of device heterogeneity and temporal variations have hindered its widespread adoption and accuracy. Towards jointly addressing these challenges comprehensively, we propose STELLAR, a novel framework implementing a contrastive learning approach that leverages a Siamese multi-headed attention neural network. STELLAR is the first solution that simultaneously tackles device heterogeneity and temporal variations in indoor localization, without the need for retraining the model (re-calibration-free). Our evaluations across diverse indoor environments show 8-75% improvements in accuracy compared to state-of-the-art techniques, to effectively address the device heterogeneity challenge. Moreover, STELLAR outperforms existing methods by 18-165% over 2 years of temporal variations, showcasing its robustness and adaptability.

In this work, we systematically study the problem of personalized text-to-image generation, where the output image is expected to portray information about specific human subjects. E.g., generating images of oneself appearing at imaginative places, interacting with various items, or engaging in fictional activities. To this end, we focus on text-to-image systems that input a single image of an individual to ground the generation process along with text describing the desired visual context. Our first contribution is to fill the literature gap by curating high-quality, appropriate data for this task. Namely, we introduce a standardized dataset (Stellar) that contains personalized prompts coupled with images of individuals that is an order of magnitude larger than existing relevant datasets and where rich semantic ground-truth annotations are readily available. Having established Stellar to promote cross-systems fine-grained comparisons further, we introduce a rigorous ensemble of specialized metrics that highlight and disentangle fundamental properties such systems should obey. Besides being intuitive, our new metrics correlate significantly more strongly with human judgment than currently used metrics on this task. Last but not least, drawing inspiration from the recent works of ELITE and SDXL, we derive a simple yet efficient, personalized text-to-image baseline that does not require test-time fine-tuning for each subject and which sets quantitatively and in human trials a new SoTA. For more information, please visit our project's website: https://stellar-gen-ai.github.io/.

Accurate cell-type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset and discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6 million spatially resolved single cells with dramatic time savings. STELLAR (spatial cell learning) is a geometric deep learning model that works with spatially resolved single-cell datasets to both assign cell types in unannotated datasets based on a reference dataset and discover new cell types.

In an office park in Hawthorne, a robot built by rocket scientists is making pizza. On a conveyor belt, a nozzle spits out sauce, dispensers shake cheese and toppings on top, then a robotic lift carries the raw pie to one of four 900-degree deck ovens. Cameras and sensors track the progress from step to step, making tiny adjustments along the way. In 45 seconds, a finished pizza pops out. It costs just $7 to order (or as much as $10, depending on toppings).

In many respects, we are reinventing modern programming tools for the A.I. age. Models and expensive resources like talent, data and computing power are currently centralized within large tech corporations. TensorFlow, Tensorflow Hub, AutoML, Algorithmia, and cloud computing are all examples of increasing decentralization of artificial intelligence. Accelerate development (1000 brains are better than 100). Make A.I. safer (more people involved to check and balance development).

An entrepreneur with a background in verifying the provenance of so-called conflict minerals is applying that expertise to keep tabs on one of the world's most widely traded commodities: coffee. Tracking this kitchen staple requires a mélange of emerging technologies such as blockchain, artificial intelligence and the internet of things. The goal of his venture, bext360, is to help coffee buyers automate their dealings with fair-trade farmers, allowing them to more closely track the source and quality of the fair trade beans they're buying while speeding up payments for local growers. For buyers, the service promises deeper transparency, as well as a way of automating the verification process. For harvesters and growers -- largely women -- the service could mean more ready access to investment capital, according to bext360 CEO Daniel Jones.