To satisfy the broad applications and insatiable hunger for deploying low latency multimedia data classification and data privacy in a cloud-based setting, federated learning (FL) has emerged as an important learning paradigm. For the practical cases involving limited computational power and only unlabeled data in many wireless communications applications, this work investigates FL paradigm in a resource-constrained and label-missing environment. Specifically, we propose a novel framework of UFed-GAN: Unsupervised Federated Generative Adversarial Network, which can capture user-side data distribution without local classification training. We also analyze the convergence and privacy of the proposed UFed-GAN. Our experimental results demonstrate the strong potential of UFed-GAN in addressing limited computational resources and unlabeled data while preserving privacy.

Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to automatize this process. However, most existing AI algorithms rely on many expert annotations and lack a comprehensive evaluation of accuracy and efficiency in real-world multinational settings. To overcome these limitations, we organized the FLARE 2022 Challenge, the largest abdominal organ analysis challenge to date, to benchmark fast, low-resource, accurate, annotation-efficient, and generalized AI algorithms. We constructed an intercontinental and multinational dataset from more than 50 medical groups, including Computed Tomography (CT) scans with different races, diseases, phases, and manufacturers. We independently validated that a set of AI algorithms achieved a median Dice Similarity Coefficient (DSC) of 90.0% by using 50 labeled scans and 2000 unlabeled scans, which can significantly reduce annotation requirements. They also enabled automatic extraction of key organ biology features, which was labor-intensive with traditional manual measurements. This opens the potential to use unlabeled data to boost performance and alleviate annotation shortages for modern AI models. Abdominal organs are high cancer incidence areas, such as liver cancer, kidney cancer, pancreas cancer, and gastric cancer [1]. Computed Tomography (CT) scanning has been a major imaging technology for the diagnosis and treatment of abdominal cancer because it can yield important prognostic information with fast imaging speed for cancer patients, which has been recommended by many clinical treatment guidelines. In order to quantify abdominal organs, radiologists and clinicians need to manually delineate organ boundaries in each slice of the 3D CT scans [2], [3]. However, manual segmentation is time-consuming and inherently subjective with inter-and intra-expert variability.

In real-world traffic scenarios, agents such as pedestrians and car drivers often observe neighboring agents who exhibit similar behavior as examples and then mimic their actions to some extent in their own behavior. This information can serve as prior knowledge for trajectory prediction, which is unfortunately largely overlooked in current trajectory prediction models. This paper introduces a novel Predecessor-and-Successor (PnS) method that incorporates a predecessor tracing module to model the influence of predecessors (identified from concurrent neighboring agents) on the successor (target agent) within the same scene. The method utilizes the moving patterns of these predecessors to guide the predictor in trajectory prediction. PnS effectively aligns the motion encodings of the successor with multiple potential predecessors in a probabilistic manner, facilitating the decoding process. We demonstrate the effectiveness of PnS by integrating it into a graph-based predictor for pedestrian trajectory prediction on the ETH/UCY datasets, resulting in a new state-of-the-art performance. Furthermore, we replace the HD map-based scene-context module with our PnS method in a transformer-based predictor for vehicle trajectory prediction on the nuScenes dataset, showing that the predictor maintains good prediction performance even without relying on any map information.

As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated. A key concern in this regard is the ability to identify whether a given sample is from the training distribution, or is an "Out-Of-Distribution" (OOD) sample. In addition, adversaries can manipulate OOD samples in ways that lead a classifier to make a confident prediction. In this study, we present a novel approach for certifying the robustness of OOD detection within a $\ell_2$-norm around the input, regardless of network architecture and without the need for specific components or additional training. Further, we improve current techniques for detecting adversarial attacks on OOD samples, while providing high levels of certified and adversarial robustness on in-distribution samples. The average of all OOD detection metrics on CIFAR10/100 shows an increase of $\sim 13 \% / 5\%$ relative to previous approaches.

The 2023 World Scrabble Championship, held last month in Las Vegas, was an instant classic. The best-of-seven finals went the distance, with tense games, obscure words, strategic genius, and a Scrabble-record audience of 900 watching on Twitch. The winner was David Eldar, 33, of Melbourne, Australia, who defeated Harshan Lamabadusuriya, 44, a pediatrician who lives in Southmoor, England, to capture the $10,000 first prize. To reach the finals, they topped a field of 134 players from 29 countries--from Poland to Pakistan, Singapore to Sierra Leone--in a four-day, 32-game tournament. Game 6 of the finals was hailed by Scrabble experts as one of most exciting high-stakes games ever. I discussed it online with the two competitors. Our conversation has been edited and condensed for clarity. Note: The event used the international-English Scrabble dictionary, which includes substantially more words than the lexicon governing competitive play in North America. To avoid confusion, words acceptable only in the international word list are marked with a #. Stefan Fatsis: David, you trail three games to two and are up first in Game 6.

Erica is on YouTube, detailing how much it costs to hire a divorce attorney in the state of Massachusetts. Dr. Dass is selling private medical insurance in the UK. But Jason has been on Facebook spreading disinformation about France's relationship with its former colony, Mali. And Gary has been caught impersonating a CEO as part of an elaborate crypto scam. They're deepfakes, let loose into the wild by Victor Riparbelli, CEO of Synthesia.

Four years ago, a few hundred miles off the coast of West Africa, a crane lifted a bulbous yellow submarine from the research vessel Poseidon and lowered it into the Atlantic. Inside the sub, Karen Osborn, a zoologist at the Smithsonian Institution who was swaddled in warm clothes, tried to ward off nausea. During half an hour of safety checks, Osborn watched water slosh across the submarine's round window, washing-machine style. Then the crew gave the all-clear and the vessel descended. In the waters of Cape Verde, a volcanic archipelago that is famous for its marine life, Osborn felt the seasickness dissipate.

More than 70 countries are due to hold regional or national elections by the end of 2024. It will be a period of huge political significance across the globe, with more than 2 billion people (mostly from the global south) directly affected by the outcome of these elections. The stakes for the integrity of democracy have never been higher. As concerns mount about the influential role of information pollution, disseminated through the vast platforms of US and Chinese corporations, in shaping these elections, a new shadow looms: how artificial intelligence – more specifically, generative AI such as OpenAI's ChatGPT – has increasingly moved into the mainstream of technology. The recent wave of hype around AI has seen a fair share of doom-mongering.

While artificial intelligence (AI) has made many successful applications in various domains, its adoption in healthcare lags a little bit behind other high-stakes settings. Several factors contribute to this slower uptake, including regulatory frameworks, patient privacy concerns, and data heterogeneity. However, one significant challenge that impedes the implementation of AI in healthcare, particularly in orthopedics, is the lack of explainability and interpretability around AI models. Addressing the challenge of explainable AI (XAI) in orthopedics requires developing AI models and algorithms that prioritize transparency and interpretability, allowing clinicians, surgeons, and patients to understand the contributing factors behind any AI-powered predictive or descriptive models. The current contribution outlines several key challenges and opportunities that manifest in XAI in orthopedic practice. This work emphasizes the need for interdisciplinary collaborations between AI practitioners, orthopedic specialists, and regulatory entities to establish standards and guidelines for the adoption of XAI in orthopedics.

This paper details the challenges in applying two computer vision systems, an EfficientDET supervised learning model and the unsupervised RX spectral classifier, to 98.9 GB of drone imagery from the Wu-Murad wilderness search and rescue (WSAR) effort in Japan and identifies 3 directions for future research. There have been at least 19 proposed approaches and 3 datasets aimed at locating missing persons in drone imagery, but only 3 approaches (2 unsupervised and 1 of an unknown structure) are referenced in the literature as having been used in an actual WSAR operation. Of these proposed approaches, the EfficientDET architecture and the unsupervised spectral RX classifier were selected as the most appropriate for this setting. The EfficientDET model was applied to the HERIDAL dataset and despite achieving performance that is statistically equivalent to the state-of-the-art, the model fails to translate to the real world in terms of false positives (e.g., identifying tree limbs and rocks as people), and false negatives (e.g., failing to identify members of the search team). The poor results in practice for algorithms that showed good results on datasets suggest 3 areas of future research: more realistic datasets for wilderness SAR, computer vision models that are capable of seamlessly handling the variety of imagery that can be collected during actual WSAR operations, and better alignment on performance measures.