Graph Neural Networks (GNNs) have gained significant attention as a powerful modeling and inference method, especially for homophilic graph-structured data. To empower GNNs in heterophilic graphs, where adjacent nodes exhibit dissimilar labels or features, Signed Message Passing (SMP) has been widely adopted. However, there is a lack of theoretical and empirical analysis regarding the limitations of SMP. In this work, we unveil some potential pitfalls of SMP and their remedies. We first identify two limitations of SMP: undesirable representation update for multi-hop neighbors and vulnerability against oversmoothing issues. To overcome these challenges, we propose a novel message passing function called Multiset to Multiset GNN(M2M-GNN). Our theoretical analyses and extensive experiments demonstrate that M2M-GNN effectively alleviates the aforementioned limitations of SMP, yielding superior performance in comparison

While subword tokenizers such as BPE and WordPiece are typically used to build vocabularies for NLP models, the method of decoding text into a sequence of tokens from these vocabularies is often left unspecified, or ill-suited to the method in which they were constructed. We provide a controlled analysis of seven tokenizer inference methods across four different algorithms and three vocabulary sizes, performed on a novel intrinsic evaluation suite we curated for English, combining measures rooted in morphology, cognition, and information theory. We show that for the most commonly used tokenizers, greedy inference performs surprisingly well; and that SaGe, a recently-introduced contextually-informed tokenizer, outperforms all others on morphological alignment.

The task of Question Answering (QA) has attracted significant research interest for long. Its relevance to language understanding and knowledge retrieval tasks, along with the simple setting makes the task of QA crucial for strong AI systems. Recent success on simple QA tasks has shifted the focus to more complex settings. Among these, Multi-Hop QA (MHQA) is one of the most researched tasks over the recent years. In broad terms, MHQA is the task of answering natural language questions that involve extracting and combining multiple pieces of information and doing multiple steps of reasoning. An example of a multi-hop question would be "The Argentine PGA Championship record holder has won how many tournaments worldwide?". Answering the question would need two pieces of information: "Who is the record holder for Argentine PGA Championship tournaments?" and "How many tournaments did [Answer of Sub Q1] win?". The ability to answer multi-hop questions and perform multi step reasoning can significantly improve the utility of NLP systems. Consequently, the field has seen a surge with high quality datasets, models and evaluation strategies. The notion of 'multiple hops' is somewhat abstract which results in a large variety of tasks that require multi-hop reasoning. This leads to different datasets and models that differ significantly from each other and makes the field challenging to generalize and survey. We aim to provide a general and formal definition of the MHQA task, and organize and summarize existing MHQA frameworks. We also outline some best practices for building MHQA datasets. This book provides a systematic and thorough introduction as well as the structuring of the existing attempts to this highly interesting, yet quite challenging task.

Evaluating observational estimators of causal effects demands information that is rarely available: unconfounded interventions and outcomes from the population of interest, created either by randomization or adjustment. As a result, it is customary to fall back on simulators when creating benchmark tasks. Simulators offer great control but are often too simplistic to make challenging tasks, either because they are hand-designed and lack the nuances of real-world data, or because they are fit to observational data without structural constraints. In this work, we propose a general, repeatable strategy for turning observational data into sequential structural causal models and challenging estimation tasks by following two simple principles: 1) fitting real-world data where possible, and 2) creating complexity by composing simple, hand-designed mechanisms. We implement these ideas in a highly configurable software package and apply it to the well-known Adult income data set to construct the \tt IncomeSCM simulator. From this, we devise multiple estimation tasks and sample data sets to compare established estimators of causal effects. The tasks present a suitable challenge, with effect estimates varying greatly in quality between methods, despite similar performance in the modeling of factual outcomes, highlighting the need for dedicated causal estimators and model selection criteria.

Constructing simulation scenes that are both visually and physically realistic is a problem of practical interest in domains ranging from robotics to computer vision. This problem has become even more relevant as researchers wielding large data-hungry learning methods seek new sources of training data for physical decision-making systems. However, building simulation models is often still done by hand. A graphic designer and a simulation engineer work with predefined assets to construct rich scenes with realistic dynamic and kinematic properties. While this may scale to small numbers of scenes, to achieve the generalization properties that are required for data-driven robotic control, we require a pipeline that is able to synthesize large numbers of realistic scenes, complete with 'natural' kinematic and dynamic structures. To attack this problem, we develop models for inferring structure and generating simulation scenes from natural images, allowing for scalable scene generation from web-scale datasets. To train these image-to-simulation models, we show how controllable text-to-image generative models can be used in generating paired training data that allows for modeling of the inverse problem, mapping from realistic images back to complete scene models. We show how this paradigm allows us to build large datasets of scenes in simulation with semantic and physical realism. We present an integrated end-to-end pipeline that generates simulation scenes complete with articulated kinematic and dynamic structures from real-world images and use these for training robotic control policies. We then robustly deploy in the real world for tasks like articulated object manipulation. In doing so, our work provides both a pipeline for large-scale generation of simulation environments and an integrated system for training robust robotic control policies in the resulting environments.

Positive-unlabeled (PU) learning aims to train a classifier using the data containing only labeled-positive instances and unlabeled instances. However, existing PU learning methods are generally hard to achieve satisfactory performance on trifurcate data, where the positive instances distribute on both sides of the negative instances. To address this issue, firstly we propose a PU classifier with asymmetric loss (PUAL), by introducing a structure of asymmetric loss on positive instances into the objective function of the global and local learning classifier. Then we develop a kernel-based algorithm to enable PUAL to obtain non-linear decision boundary. We show that, through experiments on both simulated and real-world datasets, PUAL can achieve satisfactory classification on trifurcate data.

It'll soon be Apple's turn to talk about its next major operating system updates, giving developers a chance to get their apps ready ahead of a broad rollout this fall. The company's Worldwide Developers Conference is right around the corner. Apple is sure to reveal some of the main features of iOS 18 and iPadOS 18, as well as what's ahead for the likes of watchOS, macOS and visionOS at WWDC 2024. Given the current tech climate, though, it seems likely that Apple is about to follow its rivals by making a big leap into the realm of generative AI. That could be a major focus of the keynote, since those are the only two letters investors seem to give a hoot about hearing these days. The Apple rumor mill never stops churning, so we've heard some bits and pieces about what WWDC will perhaps entail.

When a book titled Terrorism: A Very Short Introduction, written by the British professor and historian Charles Townshend, was found by police near the pro-Palestine student encampment at Columbia University, it was held up by New York Police Department (NYPD) Deputy Commissioner Kaz Daughtry as evidence of some kind of foreign, radicalising influence on student activism. Apparently, for Daughtry, reading a book on terrorism is evidence of radicalisation. Knowing about terrorism makes you at risk of committing terrorism. Finding a book near a student encampment confirms that pro-Palestine solidarity is linked to terrorism. What Daughtry was arguably trying to do was darken Palestine activism on college campuses across the United States with the association of terrorism.

If you use Facebook, WhatsApp or Instagram, you've probably noticed a new character pop up answering search queries or eagerly offering tidbits of information in your feeds, with varying degrees of accuracy. It's Meta AI, and it's here to help, at least according to Meta Platforms' CEO Mark Zuckerberg, who calls it "the most intelligent AI assistant that you can freely use." The chatbot can recommend local restaurants, offer more information on something you see in a Facebook post, search for airline flights or generate images in the blink of an eye. If you're chatting with friends to plan a night out, you can invite it into your group conversation by typing @MetaAI, then ask it to recommend, say, cocktail bars. Meta's AI tool has been integrated into chat boxes and search bars throughout the tech giant's platforms. The assistant appears, for example, at the top of your chat list on Messenger.

I'm a reporter on the WIRED Politics desk, and I'm taking over for Makena this week to talk about politicians rising from the dead in India and the rapper Eminem endorsing opposition parties in South Africa. These things haven't really happened, obviously, but deepfakes created by generative AI have made it seem like they have. Already, we're seeing how politicians, campaigns, and regular people are using generative AI in elections. And this is only the beginning. So today, WIRED is launching a project to track it, all over the world.