Open-vocabulary 3D object detection has recently attracted considerable attention due to its broad applications in autonomous driving and robotics, which aims to effectively recognize novel classes in previously unseen domains. However, existing point cloud-based open-vocabulary 3D detection models are limited by their high deployment costs. In this work, we propose a novel open-vocabulary monocular 3D object detection framework, dubbed OVM3D-Det, which trains detectors using only RGB images, making it both cost-effective and scalable to publicly available data. Unlike traditional methods, OVM3D-Det does not require high-precision LiDAR or 3D sensor data for either input or generating 3D bounding boxes. Instead, it employs open-vocabulary 2D models and pseudo-LiDAR to automatically label 3D objects in RGB images, fostering the learning of open-vocabulary monocular 3D detectors. However, training 3D models with labels directly derived from pseudo-LiDAR is inadequate due to imprecise boxes estimated from noisy point clouds and severely occluded objects.

Tinder's incoming CEO wants to rid the app of its hookup app reputation, but the app is testing a pretty superficial preference: height. In recent days, users have started noticing a height "filter" in the app. Another dating app, Hinge, already had a height filter for premium users. Both Tinder and Hinge are owned by Match Group. Apparently, though, height is being tested as a paid preference, not a hard filter.

Last summer, Google added the ability for Gemini in Gmail to summarize individual messages or long email threads. It was an especially useful feature for catching up on an email chain while you're on the go or if you were on a smaller screen, like your phone. The only drawback was that you had to manually start the "Summarize this email" process from the Gemini sidebar. In an announcement yesterday, Google says those summary cards will now appear automatically for Workspace users. Starting this week, mobile users will begin seeing summaries at the top of email messages when Gemini determines it's helpful -- for example, in a long thread, or in messages with several replies.

We study embedding table placement for distributed recommender systems, which aims to partition and place the tables on multiple hardware devices (e.g., GPUs) to balance the computation and communication costs. Although prior work has explored learning-based approaches for the device placement of computational graphs, embedding table placement remains to be a challenging problem because of 1) the operation fusion of embedding tables, and 2) the generalizability requirement on unseen placement tasks with different numbers of tables and/or devices.

In this paper, we propose a sequence-to-set method that can transform any sequence generative model based on maximum likelihood to a set generative model where we can evaluate the utility/probability of any set. An efficient importance sampling algorithm is devised to tackle the computational challenge of learning our sequenceto-set model. We present GRU2Set, which is an instance of our sequence-to-set method and employs the famous GRU model as the sequence generative model. To further obtain permutation invariant representation of sets, we devise the SetNN model which is also an instance of the sequence-to-set model. A direct application of our models is to learn an order/set distribution from a collection of e-commerce orders, which is an essential step in many important operational decisions such as inventory arrangement for fast delivery.

In this paper, we propose a sequence-to-set method that can transform any sequence generative model based on maximum likelihood to a set generative model where we can evaluate the utility/probability of any set. An efficient importance sampling algorithm is devised to tackle the computational challenge of learning our sequenceto-set model. We present GRU2Set, which is an instance of our sequence-to-set method and employs the famous GRU model as the sequence generative model. To further obtain permutation invariant representation of sets, we devise the SetNN model which is also an instance of the sequence-to-set model. A direct application of our models is to learn an order/set distribution from a collection of e-commerce orders, which is an essential step in many important operational decisions such as inventory arrangement for fast delivery.

Founder and Hedgehog CEO John Matze joined'FOX & Friends First' to discuss his optimism surrounding the community notes program, staying competitive globally with AI and the possibility of Oracle buying TikTok. Reviewing your Facebook data allows you to see what personal information Facebook has collected about you, helping you make informed decisions about your privacy settings. You might also need a copy of your data, which serves as a backup of your photos, messages and memories in case you lose access to your account or decide to delete it. Additionally, understanding what data Facebook stores can help you better comprehend how the platform uses your information for advertising and content personalization. Here's how to do it.

Pre-trained foundation models, particularly large language models, have achieved remarkable success and led to massive fine-tuned variants. These models are commonly fine-tuned locally and then uploaded by users to cloud platforms such as HuggingFace for secure storage. However, the huge model number and their billion-level parameters impose heavy storage overhead for cloud with limited resources. Our empirical and theoretical analysis reveals that most fine-tuned models in cloud have a small difference (delta) from their pre-trained models. To this end, we propose a novel lossless compression scheme FM-Delta specifically for storing massive fine-tuned models in cloud.

Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections across various real-world settings. However, existing TAG datasets predominantly feature textual information only at the nodes, with edges typically represented by mere binary or categorical attributes. This lack of rich textual edge annotations significantly limits the exploration of contextual relationships between entities, hindering deeper insights into graph-structured data. To address this gap, we introduce Textual-Edge Graphs Datasets and Benchmark (TEG-DB), a comprehensive and diverse collection of benchmark textual-edge datasets featuring rich textual descriptions on nodes and edges. The TEG-DB datasets are large-scale and encompass a wide range of domains, from citation networks to social networks. In addition, we conduct extensive benchmark experiments on TEG-DB to assess the extent to which current techniques, including pre-trained language models (PLMs), graph neural networks (GNNs), proposed novel entangled GNNs and their combinations, can utilize textual node and edge information. Our goal is to elicit advancements in textual-edge graph research, specifically in developing methodologies that exploit rich textual node and edge descriptions to enhance graph analysis and provide deeper insights into complex real-world networks.

Google Photos has just reached its 10th birthday, and the company is celebrating. To mark the occasion, Google is serving up a host of tips and tricks designed to enhance your photos via your mobile device. But first, here are a few stats to show the reach of Google Photos. More than 1.5 billion people use Google Photos on a monthly basis, according to Google. Each month, people run more than 370 million searches, edit 210 million photos, and share 440 million of them. First up is a new and improved photo editor that employs AI to help you fine-tune your images.