We consider the problem of dynamic platoon leader selection, user association, channel assignment, and power allocation on a cellular vehicle-to-everything (C-V2X) based highway, where multiple vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links share the frequency resources. There are multiple roadside units (RSUs) on a highway, and vehicles can form platoons, which has been identified as an advanced use case to increase road efficiency. The traditional optimization methods, requiring global channel information at a central controller, are not viable for high-mobility vehicular networks. To deal with this challenge, we propose a distributed multi-agent reinforcement learning (MARL) for resource allocation (RA). Each platoon leader, acting as an agent, can collaborate with other agents for joint sub-band selection and power allocation for its V2V links, and joint user association and power control for its V2I links. Moreover, each platoon can dynamically select the vehicle most suitable to be the platoon leader. We aim to maximize the V2V and V2I packet delivery probability in the desired latency using the deep Q-learning algorithm. Simulation results indicate that our proposed MARL outperforms the centralized hill-climbing algorithm, and platoon leader selection helps to improve both V2V and V2I performance.

It started in the Caribbean Sea. Jaime Ascencio, then a business development engineer working across Latin America, was eager to find sustainable ways to combat the coastal erosion that was eating away at the region's treasured beaches--and threatening the tourism dollars brought in by its seaside resorts. "If there is no sand, there are no guests," he says. But Ascensio, who knew that artificial reefs could make for natural breakwaters, could only find solutions that were neither sustainable nor stable enough to resist the force of the waves. So he went on to get a master's in coastal engineering at the celebrated Delft University of Technology in the Netherlands--and developed one himself.

Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt. Could such models render real images obsolete for training image prediction models? In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet classification. Provided only with the class names that have been used to build the dataset, we explore the ability of Stable Diffusion to generate synthetic clones of ImageNet and measure how useful these are for training classification models from scratch. We show that with minimal and class-agnostic prompt engineering, ImageNet clones are able to close a large part of the gap between models produced by synthetic images and models trained with real images, for the several standard classification benchmarks that we consider in this study. More importantly, we show that models trained on synthetic images exhibit strong generalization properties and perform on par with models trained on real data for transfer. Project page: https://europe.naverlabs.com/imagenet-sd/

Nikki Haley, presidential candidate and former U.S. ambassador to the U.N., weighs in after President Biden authorized an air strike in response to an Iranian drone that killed an American. Republican presidential candidate Nikki Haley called on the Biden administration to get tough on a slew of foreign adversaries or risk war after an American citizen was killed in an Iranian drone strike in Syria. "It shows what happens when there's American weakness," Haley said Friday of the attack on "America's Newsroom." "Whether it's in Afghanistan, whether you see it in Ukraine, whether you see it on the southern border, you're going to continue to see more of these things happen." "There is no deterrence," she continued.

Automatic language identification is a challenging problem. Discriminating between closely related languages is especially difficult. This paper presents a machine learning approach for automatic language identification for the Nordic languages, which often suffer miscategorisation by existing state-of-the-art tools. Concretely we will focus on discrimination between six Nordic languages: Danish, Swedish, Norwegian (Nynorsk), Norwegian (Bokm{\aa}l), Faroese and Icelandic.

Check out what's clicking on Foxnews.com. Russia stepped up its missile and drone attacks against Ukraine on Wednesday, killing students and other civilians, in a violent follow-up to dueling high-level diplomatic missions aimed at bringing peace after 13 months of war. "Russia is shelling the city with bestial savagery," President Volodymyr Zelenskyy wrote in a Telegram post accompanying video showing what he said was a Russian missile striking a nine-story apartment building on a busy road in the southeastern city of Zaporizhzhia. "Residential areas where ordinary people and children live are being fired at." At least one person was killed in the attack shown in the Zaporizhzhia video, apparently recorded by closed circuit TV cameras.

Former U.S. Amb. to NATO Kurt Volker says the Russian fighter jet collision was'intentional' and requires a'firm response' from the U.S. The Biden administration blasted Russia for honoring two pilots for downing a U.S. drone in international airspace while saying the aviator who crashed into the drone was "at best, just an idiot." Last week, the Kremlin issued state awards to the fighter jet pilots responsible for downing the U.S. MQ-9 Reaper drone over the Black Sea earlier the week prior. In an official statement, the Ministry of Defense commended the pilots for preventing the drone from "violating the boundaries of the temporary airspace regime established for the special military operation." U.S. European Command said a Russian Su-27 fighter jet colliding with a U.S. MQ-9 Reaper drone over the Black Sea. A screenshot shows a jet dumping fuel.

This paper proposes a methodology for discovering meaningful properties in data by exploring the latent space of unsupervised deep generative models. We combine manipulation of individual latent variables to extreme values outside the training range with methods inspired by causal inference into an approach we call causal disentanglement with extreme values (CDEV) and show that this approach yields insights for model interpretability. Using this technique, we can infer what properties of unknown data the model encodes as meaningful. We apply the methodology to test what is meaningful in the communication system of sperm whales, one of the most intriguing and understudied animal communication systems. We train a network that has been shown to learn meaningful representations of speech and test whether we can leverage such unsupervised learning to decipher the properties of another vocal communication system for which we have no ground truth. The proposed technique suggests that sperm whales encode information using the number of clicks in a sequence, the regularity of their timing, and audio properties such as the spectral mean and the acoustic regularity of the sequences. Some of these findings are consistent with existing hypotheses, while others are proposed for the first time. We also argue that our models uncover rules that govern the structure of communication units in the sperm whale communication system and apply them while generating innovative data not shown during training. This paper suggests that an interpretation of the outputs of deep neural networks with causal methodology can be a viable strategy for approaching data about which little is known and presents another case of how deep learning can limit the hypothesis space. Finally, the proposed approach combining latent space manipulation and causal inference can be extended to other architectures and arbitrary datasets.

Incorporating the dynamics knowledge into the model is critical for achieving accurate trajectory prediction while considering the spatial and temporal characteristics of the vessel. However, existing methods rarely consider the underlying dynamics knowledge and directly use machine learning algorithms to predict the trajectories. Intuitively, the vessel's motions are following the laws of dynamics, e.g., the speed of a vessel decreases when turning a corner. Yet, it is challenging to combine dynamic knowledge and neural networks due to their inherent heterogeneity. Against this background, we propose MSTFormer, a motion inspired vessel trajectory prediction method based on Transformer. The contribution of this work is threefold. First, we design a data augmentation method to describe the spatial features and motion features of the trajectory. Second, we propose a Multi-headed Dynamic-aware Self-attention mechanism to focus on trajectory points with frequent motion transformations. Finally, we construct a knowledge-inspired loss function to further boost the performance of the model. Experimental results on real-world datasets show that our strategy not only effectively improves long-term predictive capability but also outperforms backbones on cornering data.The ablation analysis further confirms the efficacy of the proposed method. To the best of our knowledge, MSTFormer is the first neural network model for trajectory prediction fused with vessel motion dynamics, providing a worthwhile direction for future research.The source code is available at https://github.com/simple316/MSTFormer.

Former U.S. Amb. to NATO Kurt Volker says the Russian fighter jet collision was'intentional' and requires a'firm response' from the U.S. The United States has resumed its normal flights through international waters over the Black Sea following the crash of a drone due to Russian interference. U.S. officials said Friday that a RQ-4 Global Hawk flew through the region -- the first U.S. aircraft to do so since the skirmish, according to Reuters. An RQ-4 Global Hawk takes off from Andersen Air Force Base, Guam (U.S. Air Force photo/Senior Airman Nichelle Anderson) Military officials assured the public that the Russian harassment of the US drone on Tuesday would not affect regular operations in the region. Defense Secretary Lloyd Austin summarized the incident Wednesday in a press conference, saying, "Two Russian jets dumped fuel on an unmanned U.S. MQ-9 aircraft conducting routine operations in international airspace. And one Russian jet intercepted and hit our MQ-9 aircraft, resulting in a crash."