Image Segmentation is one of the core tasks in Computer Vision and solving it often depends on modeling the image appearance data via the color distributions of each it its constituent regions. Whereas many segmentation algorithms handle the appearance models dependence using alternation or implicit methods, we propose here a new approach to directly estimate them from the image without prior information on the underlying segmentation. Our method uses local high order color statistics from the image as an input to tensor factorization-based estimator for latent variable models. This approach is able to estimate models in multiregion images and automatically output the regions proportions without prior user interaction, overcoming the drawbacks from a prior attempt to this problem. We also demonstrate the performance of our proposed method in many challenging synthetic and real imaging scenarios and show that it leads to an efficient segmentation algorithm.

Nearly a year to the date since their last meeting, U.S. President Joe Biden and Chinese leader Xi Jinping will sit down Wednesday in the San Francisco Bay Area to try and put a floor under ties that have plummeted to fresh lows in recent months. When Biden and Xi meet on the sidelines of the Asia-Pacific Economic Cooperation forum in San Francisco, both will have a laundry list of concerns to discuss. From military-to-military lines of communication, Taiwan, and the South and East China Seas to tough U.S. semiconductor export controls, the manufacture and export of fentanyl, and artificial intelligence threats -- all will be on the table during several hours of discussions. But don't expect the talks -- the pair's seventh interaction since the start of the Biden administration but just the second in-person meeting -- to yield any dramatic breakthroughs.

Large language models (LLMs) enable zero-shot approaches in open-domain question answering (ODQA), yet with limited advancements as the reader is compared to the retriever. This study aims at the feasibility of a zero-shot reader that addresses the challenges of computational cost and the need for labeled data. We find that LLMs are distracted due to irrelevant documents in the retrieved set and the overconfidence of the generated answers when they are exploited as zero-shot readers. To tackle these problems, we mitigate the impact of such documents via Distraction-aware Answer Selection (DAS) with a negation-based instruction and score adjustment for proper answer selection. Experimental results show that our approach successfully handles distraction across diverse scenarios, enhancing the performance of zero-shot readers. Furthermore, unlike supervised readers struggling with unseen data, zero-shot readers demonstrate outstanding transferability without any training.

When US President Joe Biden meets with his Chinese counterpart Xi Jinping in the San Francisco Bay Area this week, the pair will have a long list of matters to discuss, including the Israel-Hamas war and Russia's ongoing invasion of Ukraine. Behind the scenes at the APEC summit, however, US officials hope to strike up a dialogue with China about placing guardrails around military use of artificial intelligence, with the ultimate goal of lessening the potential risks that rapid adoption--and reckless use--of the technology might bring. "We have a collective interest in reducing the potential risks from the deployment of unreliable AI applications," because of risks of unintended escalation, says a senior State Department official familiar with recent efforts to broach the issue, who spoke on condition of anonymity. "We very much hope to have a further conversation with China on this issue." Biden's meeting with Xi this week may provide momentum for more military dialogue.

ChatGPT has gained a huge popularity since its introduction. Its positive aspects have been reported through many media platforms, and some analyses even showed that ChatGPT achieved a decent grade in professional exams, adding extra support to the claim that AI can now assist and even replace humans in industrial fields. Others, however, doubt its reliability and trustworthiness. This paper investigates the trustworthiness of ChatGPT and GPT-4 regarding logically consistent behaviour, focusing specifically on semantic consistency and the properties of negation, symmetric, and transitive consistency. Our findings suggest that while both models appear to show an enhanced language understanding and reasoning ability, they still frequently fall short of generating logically consistent predictions. We also ascertain via experiments that prompt designing, few-shot learning and employing larger large language models (LLMs) are unlikely to be the ultimate solution to resolve the inconsistency issue of LLMs.

Deep learning has shown impressive results in a variety of time series forecasting tasks, where modeling the conditional distribution of the future given the past is the essence. However, when this conditional distribution is non-stationary, it poses challenges for these models to learn consistently and to predict accurately. In this work, we propose a new method to model non-stationary conditional distributions over time by clearly decoupling stationary conditional distribution modeling from non-stationary dynamics modeling. Our method is based on a Bayesian dynamic model that can adapt to conditional distribution changes and a deep conditional distribution model that handles multivariate time series using a factorized output space. Our experimental results on synthetic and real-world datasets show that our model can adapt to non-stationary time series better than state-of-the-art deep learning solutions.

What would American cities look like 100 years after human beings have left, with the streets devoid of human life - and beginning to be reclaimed by nature? While the chatbot put our future world in text, the AI photo generator Midjourney painted pictures of these abandoned metropolises, showing the concrete jungles transforming into jungles. Kieron Connolly, author of Abandoned Places and Abandoned Civilizations, says that visions of abandoned cities have a unique power. This isn't what city life is supposed to look like. Nature is allowed to reclaim the land,' Connolly said. ChatGPT writes, 'In the year 2123, the once-thriving metropolis of Chicago stands as a haunting testament to the passage of time and the resilience of nature.

A U.S. military unit operating MQ-9 spy drones has completed its withdrawal from the Maritime Self-Defense Force's Kanoya air base in Kagoshima Prefecture for relocation to Okinawa Prefecture, the Japanese government said Sunday. The Defense Ministry's Kyushu Defense Bureau announced the unit's withdrawal from the Japanese base, where eight MQ-9 aircraft were operated for a limited period of one year from November last year. Up to 200 U.S. military personnel related to the operations were stationed there. The unit will be transferred to the U.S. military's Kadena Air Base in Okinawa Prefecture, near Kagoshima. The drones are set to be used to strengthen surveillance of Chinese military ships in the East China Sea.

Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several settings. These architectures take advantage of a graph structure and relational inductive biases to learn a single (global) inductive model to predict any number of the input time series, each associated with a graph node. Despite the gain achieved in computational and data efficiency w.r.t. fitting a set of local models, relying on a single global model can be a limitation whenever some of the time series are generated by a different spatiotemporal stochastic process. The main objective of this paper is to understand the interplay between globality and locality in graph-based spatiotemporal forecasting, while contextually proposing a methodological framework to rationalize the practice of including trainable node embeddings in such architectures. We ascribe to trainable node embeddings the role of amortizing the learning of specialized components. Moreover, embeddings allow for 1) effectively combining the advantages of shared message-passing layers with node-specific parameters and 2) efficiently transferring the learned model to new node sets. Supported by strong empirical evidence, we provide insights and guidelines for specializing graph-based models to the dynamics of each time series and show how this aspect plays a crucial role in obtaining accurate predictions.

Ken Kocienda walks toward me, with a small white square pinned to his shirt. "Play songs written by Prince, but not performed by Prince," he says. The Sinéad O'Connor version of'Nothing Compares 2 U'--a song originally written by Prince--begins to play. A green volume meter, pause button, and next-song button appear on his hand. He twists his wrist clockwise, and the volume rises. Anticlockwise, and the song gets quieter.