You Can Finally Buy Snap's New AR Specs--for $2,195 Snap CEO Evan Spiegel lays out the company's vision for its augmented-reality smart glasses, arriving later this year. Snap--maker of the popular social app Snapchat--has a new pair of augmented-reality smart glasses called Specs. Snap CEO Evan Spiegel revealed the new glasses at an event during the Augmented World Expo (AWE) tech conference in Long Beach, California. As Snap frames it, this isn't a prototype or developer device--it's the first actual consumer version of the Specs AR glasses, unlike the previous generation exclusively sold to developers and creators. Snap says it expects the devices to ship this fall in the US, UK, and France.

Snap's slimmed down AR Specs go on sale later this year for $2,195 Snap's slimmed down AR Specs go on sale later this year for $2,195 It's the first time the company will be selling its AR glasses to the public. It's been almost five years since Snap began experimenting with standalone augmented reality glasses. Now, the company is finally ready to start selling them to the public, though they won't be cheap. During a keynote today at Augmented World Expo in Long Beach, California, Snap CEO Evan Spiegel unveiled the company's latest AR Specs that he said represent the beginning of a new era in computing. The $2,195 glasses come with a wider field of view, upgraded battery life and, perhaps most importantly, a slimmed down form factor compared with the oversized, developer-only glasses it showed off in 2024 .

Graph neural networks have recently achieved remarkable success in representing graph-structured data, with rapid progress in both the node embedding and graph pooling methods. Yet, they mostly focus on capturing information from the nodes considering their connectivity, and not much work has been done in representing the edges, which are essential components of a graph. However, for tasks such as graph reconstruction and generation, as well as graph classification tasks for which the edges are important for discrimination, accurately representing edges of a given graph is crucial to the success of the graph representation learning. To this end, we propose a novel edge representation learning framework based on Dual Hypergraph Transformation (DHT), which transforms the edges of a graph into the nodes of a hypergraph. This dual hypergraph construction allows us to apply message-passing techniques for node representations to edges. After obtaining edge representations from the hypergraphs, we then cluster or drop edges to obtain holistic graph-level edge representations. We validate our edge representation learning method with hypergraphs on diverse graph datasets for graph representation and generation performance, on which our method largely outperforms existing graph representation learning methods. Moreover, our edge representation learning and pooling method also largely outperforms state-of-theart graph pooling methods on graph classification, not only because of its accurate edge representation learning, but also due to its lossless compression of the nodes and removal of irrelevant edges for effective message-passing.1

The present study aims to investigate a cluster cleaning algorithm that is both computationally simple and capable of solving the PU classification when the SCAR condition is unsatisfied. A secondary objective of this study is to determine the robustness of the LassoJoint method to perturbations of the SCAR condition. In the first step of our algorithm, we obtain cleaning labels from 2-means clustering. Subsequently, we perform logistic regression on the cleaned data, assigning positive labels from the cleaning algorithm with additional true positive observations. The remaining observations are assigned the negative label. The proposed algorithm is evaluated by comparing 11 real data sets from machine learning repositories and a synthetic set. The findings obtained from this study demonstrate the efficacy of the clustering algorithm in scenarios where the SCAR condition is violated and further underscore the moderate robustness of the LassoJoint algorithm in this context.

Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance sampling (IS). Recently, mixture models with negative coefficients, called subtractive mixture models (SMMs), have been proposed as a potentially more expressive alternative. However, how to effectively use SMMs for VI and IS is still an open question as they do not provide latent variable semantics and therefore cannot use sampling schemes for classical MMs. In this work, we study how to circumvent this issue by designing several expectation estimators for IS and learning schemes for VI with SMMs, and we empirically evaluate them for distribution approximation. Finally, we discuss the additional challenges in estimation stability and learning efficiency that they carry and propose ways to overcome them. Code is available at: https://github.com/april-tools/delta-vi.

Ta s k: Your task is to boil tin.