See, e.g., Resnick and Varian (1997); Schafer et al. (2007); Koren et al. (2009); Davidson et al. (2010); McAuley and Leskovec (2013); Das et al. (2017). Consider, more specifically, representing the ratings of d 1 users on d 2 products/items by a d 1 d 2 matrix. For all practical purposes, both d 1 and d 2 can be very large yet only a rather small number of the entries can be observed. The idea is that if the interaction between users and products can be approximately captured by a handful of latent user-specific and product-specific characteristics, then it is possible to infer the whole user-item rating matrix from these sparsely observed entries, and hence recommend products to users who may be genuinely interested in them. Since the pioneering works of Cand` es and Tao (2009); Candes and Plan (2010); Candes and Recht (2012), a lot of impressive progress has been made to make these techniques more accurate and scalable, and to better understand the statistical and computational underpinnings of the problem.

The story of the computer hacker Gary McKinnon and his long battle against extradition to the US is to be turned into a feature film. It will tell the story of how a young man hunting for evidence of UFOs found his way into the Pentagon's system and carried out what US authorities described as "the biggest military computer hack of all time" and then faced the possibility of a long sentence in a US high-security prison. The film, The People v Gary McKinnon, will be directed by Paul McGuigan, who made Gangster Number 1 and Lucky Number Slevin. The screenplay is by Peter Harness, who has written scripts for Wallander, Doctor Who, McMafia and Sherlock as well as the film Is Anybody There? It will be produced by Wall to Wall Media and Warner Brothers.

The Pentagon revealed its new B-21 nuclear stealth bomber Friday in Palmdale, California. DEADLY STEALTH: This US bomber is why China suddenly wants to talk about nukes and AI. HIGH-TECH HEALTH: AI could help predict lung cancer risks in non-smokers. FORCE MULTIPLIER: US accelerates race for new military tech against China. PAYCHECK PROBLEMS: AI may be greater threat to wages than jobs, European study finds.

Critics might complain that modern pop music is soulless and artificial - but a new'AI popstar' takes that to a whole new level. Anna Indiana, a self-described AI singer-songwriter, has been ridiculed after releasing her first single. In a video posted to YouTube, Anna performs a pop song to a backing track of piano, guitar, and drums. Introducing itself, the AI explains: 'Everything from the key, tempo, chord progression, melody notes, rhythm, lyrics, and my image and singing, is auto-generated using AI.' However, music fans have not reacted well to the release, calling it'horrifying' and'unnerving'.

In our study, we delve into the realm of evaluating Despite large language models (Arkoudas, 2023; large language models' (LLMs) ability to generate OpenAI, 2022) perform strikingly in plenty of reasoning counterfactually coherent thoughts. Specifically, benchmarks (Cobbe et al., 2021; Hendrycks we proposed an innovative evaluation system et al., 2021a), late studies observe an internal inconsistency that quantitatively measures the evolution of information in their reasoning processes (Saparov and in statement pairs, ensuring that they adhere He, 2023; Arkoudas, 2023). The inconsistency is to a specified logical relationship. Our approach attributed to misunderstanding and misapplication includes designing a specialized task where models of logical relations. However, logical relations in are presented with mismatched argument-premise complex language reasoning are not yet properly pairs bound by a specific logical relation. The objective quantified and evaluated.

State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth. However, an essential aspect often overlooked is the specific camera geometry used during image capture. The influence of different optical systems on the final scene appearance is frequently overlooked. This study introduces a framework that intimately integrates a text-to-image diffusion model with the particular lens geometry used in image rendering. Our method is based on a per-pixel coordinate conditioning method, enabling the control over the rendering geometry. Notably, we demonstrate the manipulation of curvature properties, achieving diverse visual effects, such as fish-eye, panoramic views, and spherical texturing using a single diffusion model.

Classical clustering methods do not provide users with direct control of the clustering results, and the clustering results may not be consistent with the relevant criterion that a user has in mind. In this work, we present a new methodology for performing image clustering based on user-specified text criteria by leveraging modern vision-language models and large language models. We call our method Image Clustering Conditioned on Text Criteria (IC|TC), and it represents a different paradigm of image clustering. IC|TC requires a minimal and practical degree of human intervention and grants the user significant control over the clustering results in return. Our experiments show that IC|TC can effectively cluster images with various criteria, such as human action, physical location, or the person's mood, while significantly outperforming baselines.

Text-guided image editing is widely needed in daily life, ranging from personal use to professional applications such as Photoshop. However, existing methods are either zero-shot or trained on an automatically synthesized dataset, which contains a high volume of noise. Thus, they still require lots of manual tuning to produce desirable outcomes in practice. To address this issue, we introduce MagicBrush (https://osu-nlp-group.github.io/MagicBrush/), the first large-scale, manually annotated dataset for instruction-guided real image editing that covers diverse scenarios: single-turn, multi-turn, mask-provided, and mask-free editing. MagicBrush comprises over 10K manually annotated triplets (source image, instruction, target image), which supports trainining large-scale text-guided image editing models. We fine-tune InstructPix2Pix on MagicBrush and show that the new model can produce much better images according to human evaluation. We further conduct extensive experiments to evaluate current image editing baselines from multiple dimensions including quantitative, qualitative, and human evaluations. The results reveal the challenging nature of our dataset and the gap between current baselines and real-world editing needs.

Diffusion models, which have emerged to become popular text-to-image generation models, can produce high-quality and content-rich images guided by textual prompts. However, there are limitations to semantic understanding and commonsense reasoning in existing models when the input prompts are concise narrative, resulting in low-quality image generation. To improve the capacities for narrative prompts, we propose a simple-yet-effective parameter-efficient fine-tuning approach called the Semantic Understanding and Reasoning adapter (SUR-adapter) for pre-trained diffusion models. To reach this goal, we first collect and annotate a new dataset SURD which consists of more than 57,000 semantically corrected multi-modal samples. Each sample contains a simple narrative prompt, a complex keyword-based prompt, and a high-quality image. Then, we align the semantic representation of narrative prompts to the complex prompts and transfer knowledge of large language models (LLMs) to our SUR-adapter via knowledge distillation so that it can acquire the powerful semantic understanding and reasoning capabilities to build a high-quality textual semantic representation for text-to-image generation. We conduct experiments by integrating multiple LLMs and popular pre-trained diffusion models to show the effectiveness of our approach in enabling diffusion models to understand and reason concise natural language without image quality degradation. Our approach can make text-to-image diffusion models easier to use with better user experience, which demonstrates our approach has the potential for further advancing the development of user-friendly text-to-image generation models by bridging the semantic gap between simple narrative prompts and complex keyword-based prompts. The code is released at https://github.com/Qrange-group/SUR-adapter.

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. At present, the volume of related research on distributed GNN training is exceptionally vast, accompanied by an extraordinarily rapid pace of publication. Moreover, the approaches reported in these studies exhibit significant divergence. This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training. As a result, there is a pressing need for a survey to provide correct recognition, analysis, and comparisons in this field. In this paper, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks, emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.