Police probe Andrew Mountbatten-Windsor over'sex offences': Stunning update on investigation of former prince as officers appeal for potential'victim survivors' to come forward Trump celebrates Stephen Colbert's final show with brutal'no talent' swipe as bitter host takes one last jab at CBS on way out door Trump warns of possible military action in Cuba and says'I'd be happy to do it' as Marco Rubio declares the nation a'US national security threat' Dangerous truth about melatonin side effects... the astonishing dose you SHOULD be taking... and a new natural grocery store alternative hailed by doctors CIA Nostradamus warned Trump about Iran... now he's calling the President's doctors. Dirty secret Hollywood's Cool Girls don't want you to know. Mom-of-two abandons home in Pennsylvania to live on board CRUISE SHIP year-round - and her kids have'zero concept' their life isn't normal This quiet announcement from Prince William was missed by most... but this is why royal insiders tell me it spells disaster for Harry and Meghan's future: RICHARD EDEN White man charged after he was filmed screaming at black female neighbor and using the phrase'You people' Shock moment'slurring' Britney Spears is arrested for DUI after failing sobriety test Astonishing secret list of elite Hollywood liberals conspiring to elect Spencer Pratt revealed to KENNEDY by her LA moles. Suspected Somali fraudster filmed leaping off Minnesota balcony and driving away in luxury Genesis sedan after feds announced they were charging him with alleged $3.3m scam Inside Pizza Hut restaurant that's still EXACTLY like it was in the 90s... complete with checkered tablecloths, arcade and famous buffet Stephen Colbert's final Late Show episode leaves fans unimpressed as Ryan Reynolds leads series of surprise celebrity cameos How Meryl Streep's husband really feels about her secret relationship with Martin Short: Their years of agony... his hard red line... and why she won't divorce him Look away now, Carrie Bradshaw! Fears for Ariana Grande: Insiders lift the lid on Ethan Slater's costly sacrifice... her private nightmares... and what's really keeping them apart Inside the UK's first AI-powered fertility clinic using state-of-the-art technologies to help women get pregnant - as one couple says'artificial intelligence allowed us to hold our baby in our arms' If you were asked to think about artificial intelligence ( AI), visions of killer robots, dodgy chatbots, or deepfakes might spring to mind.

Instance segmentation in 3D is a challenging task due to the lack of large-scale annotated datasets. In this paper, we show that this task can be addressed effectively by leveraging instead 2D pre-trained models for instance segmentation. We propose a novel approach to lift 2D segments to 3D and fuse them by means of a neural field representation, which encourages multi-view consistency across frames. The core of our approach is a slow-fast clustering objective function, which is scalable and well-suited for scenes with a large number of objects. Unlike previous approaches, our method does not require an upper bound on the number of objects or object tracking across frames. To demonstrate the scalability of the slow-fast clustering, we create a new semi-realistic dataset called the Messy Rooms dataset, which features scenes with up to 500 objects per scene. Our approach outperforms the state-of-the-art on challenging scenes from the ScanNet, Hypersim, and Replica datasets, as well as on our newly created Messy Rooms dataset, demonstrating the effectiveness and scalability of our slow-fast clustering method.

In this paper, we introduce a novel approach to fine-grained cross-view geolocalization.

The aim of knowledge graph (KG) completion is to extend an incomplete KG with missing triples. Popular approaches based on graph embeddings typically work by first representing the KG in a vector space, and then applying a predefined scoring function to the resulting vectors to complete the KG. These approaches work well in transductive settings, where predicted triples involve only constants seen during training; however, they are not applicable in inductive settings, where the KG on which the model was trained is extended with new constants or merged with other KGs. The use of Graph Neural Networks (GNNs) has recently been proposed as a way to overcome these limitations; however, existing approaches do not fully exploit the capabilities of GNNs and still rely on heuristics and adhoc scoring functions. In this paper, we propose a novel approach, where the KG is fully encoded into a GNN in a transparent way, and where the predicted triples can be read out directly from the last layer of the GNN without the need for additional components or scoring functions. Our experiments show that our model outperforms state-of-the-art approaches on inductive KG completion benchmarks.

Modeling interacting dynamical systems, such as fluid dynamics and intermolecular interactions, is a fundamental research problem for understanding and simulating complex real-world systems. Many of these systems can be naturally represented by dynamic graphs, and graph neural network-based approaches have been proposed and shown promising performance. However, most of these approaches assume the underlying dynamics does not change over time, which is unfortunately untrue. For example, a molecular dynamics can be affected by the environment temperature over the time. In this paper, we take an attempt to provide a probabilistic view for time-varying dynamics and propose a model Context-attended Graph ODE (CARE) for modeling time-varying interacting dynamical systems. In our CARE, we explicitly use a context variable to model time-varying environment and construct an encoder to initialize the context variable from historical trajectories. Furthermore, we employ a neural ODE model to depict the dynamic evolution of the context variable inferred from system states. This context variable is incorporated into a coupled ODE to simultaneously drive the evolution of systems. Comprehensive experiments on four datasets demonstrate the effectiveness of our proposed CARE compared with several state-of-the-art approaches.

We introduce motion graph, a novel approach to the video prediction problem, which predicts future video frames from limited past data.

Standard few-shot learning methods employ geometric similarity metrics such as cosine similarity and negative Euclidean distance to gauge the semantic relatedness between two features.

Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources.

In this paper, we propose a novel approach to regularize the distribution of within-class features toeaselabelpropagation difficulty.