Playful bear cubs and a swirling superpod of dolphins compete for People's Choice honors. Josef has wanted to photograph lynxes for a long time. He was delighted when the opportunity arose to spend two weeks observing them from a hide at Torre de Juan Abad, Ciudad Real, Spain. It's common for young lynxes to play with their prey before killing it. This one repeatedly threw the rodent high in the air and caught it again.

We present a hybrid neural network model for inferring the position of mobile robots using Channel State Information (CSI) data from a Massive MIMO system. By leveraging an existing CSI dataset, our approach integrates a Convolutional Neural Network (CNN) with a Multilayer Perceptron (MLP) to form a Hybrid Neural Network (HyNN) that estimates 2D robot positions. CSI readings are converted into synthetic images using the TINTO tool. The localisation solution is integrated with a robotics simulator, and the Robot Operating System (ROS), which facilitates its evaluation through heterogeneous test cases, and the adoption of state estimators like Kalman filters. Our contributions illustrate the potential of our HyNN model in achieving precise indoor localisation and navigation for mobile robots in complex environments. The study follows, and proposes, a generalisable procedure applicable beyond the specific use case studied, making it adaptable to different scenarios and datasets.

We used a single RTX 2080 Ti GPU. While T5-small underperforms its larger version, T5-11B, the latter has 11 billion parameters. Gabon and South Africa, are ranked 119th and 121st, respectively . ANSWER: 119th HUMAN: What is Gabon ' s ranking? ColdGAN: What is Gabon ' s rank on the HDI?

NATO Secretary General Mark Rutte gives insight on the talks between President Donald Trump, Volodymyr Zelenskyy and European leaders, security guarantees for Ukraine and more on'The Ingraham Angle.' Two German warplanes were scrambled overnight from Romania after Russia launched a large-scale missile and drone attack in Ukraine less than a mile from the NATO borderline. Romania's Ministry of Defense said on Wednesday that two German Eurofighter Typhoon aircraft, stationed at Romania's Mihail Kogălniceanu Air Base as part of NATO's Enhanced Air Policing mission, were deployed "to monitor the air situation," but noted that this time no Russian aircraft or projectiles crossed the NATO border. Despite last week's talks between Russian President Vladimir Putin and President Donald Trump, Moscow has continued its aerial bombardment of Ukraine, including in an overnight attack that targeted oil and port facilities in the Odesa region on and near the Danube River, which separates the Ukrainian border with the allied NATO nation of Romania. The Eurofighter EF-2000 Typhoon of the German Air Force takes off from Los Llanos military air base during the Tactical Leadership Program in Albacete, Spain, on Nov. 21, 2024. The deployment of NATO jets comes after numerous incidents in recent weeks have increasingly threatened, and even crossed, NATO borders as the U.S. and Europe continue to push for Russia to end its war.

In this paper, we tackle the Bayesian estimation of point process intensity as a function of covariates. We propose a novel augmentation of permanental process called augmented permanental process, a doubly-stochastic point process that uses a Gaussian process on covariate space to describe the Bayesian a pri-ori uncertainty present in the square root of intensity, and derive a fast Bayesian estimation algorithm that scales linearly with data size without relying on either domain discretization or Markov Chain Monte Carlo computation. The proposed algorithm is based on a non-trivial finding that the representer theorem, one of the most desirable mathematical property for machine learning problems, holds for the augmented permanental process, which provides us with many significant computational advantages. We evaluate our algorithm on synthetic and real-world data, and show that it outperforms state-of-the-art methods in terms of predictive accuracy while being substantially faster than a conventional Bayesian method.

This work presents a user-centric recommendation framework, designed as a pipeline with four distinct, connected, and customizable phases. These phases are intended to improve explainability and boost user engagement. We have collected the historical Last.fm track playback records of a single user over approximately 15 years. The collected dataset includes more than 90,000 playbacks and approximately 14,000 unique tracks. From track playback records, we have created a dataset of user temporal contexts (each row is a specific moment when the user listened to certain music descriptors). As music descriptors, we have used community-contributed Last.fm tags and Spotify audio features. They represent the music that, throughout years, the user has been listening to. Next, given the most relevant Last.fm tags of a moment (e.g. the hour of the day), we predict the Spotify audio features that best fit the user preferences in that particular moment. Finally, we use the predicted audio features to find tracks similar to these features. The final aim is to recommend (and discover) tracks that the user may feel like listening to at a particular moment. For our initial study case, we have chosen to predict only a single audio feature target: danceability. The framework, however, allows to include more target variables. The ability to learn the musical habits from a single user can be quite powerful, and this framework could be extended to other users.

This paper presents the Greedy Min-Cut Bayesian Consensus (GMCBC) algorithm for the structural fusion of Bayesian Networks (BNs). The method is designed to preserve essential dependencies while controlling network complexity. It addresses the limitations of traditional fusion approaches, which often lead to excessively complex models that are impractical for inference, reasoning, or real-world applications. As the number and size of input networks increase, this issue becomes even more pronounced. GMCBC integrates principles from flow network theory into BN fusion, adapting the Backward Equivalence Search (BES) phase of the Greedy Equivalence Search (GES) algorithm and applying the Ford-Fulkerson algorithm for minimum cut analysis. This approach removes non-essential edges, ensuring that the fused network retains key dependencies while minimizing unnecessary complexity. Experimental results on synthetic Bayesian Networks demonstrate that GMCBC achieves near-optimal network structures. In federated learning simulations, GMCBC produces a consensus network that improves structural accuracy and dependency preservation compared to the average of the input networks, resulting in a structure that better captures the real underlying (in)dependence relationships. This consensus network also maintains a similar size to the original networks, unlike unrestricted fusion methods, where network size grows exponentially.

Image geolocalization, in which, traditionally, an AI model predicts the precise GPS coordinates of an image is a challenging task with many downstream applications. However, the user cannot utilize the model to further their knowledge other than the GPS coordinate; the model lacks an understanding of the location and the conversational ability to communicate with the user. In recent days, with tremendous progress of large multimodal models (LMMs) proprietary and open-source researchers have attempted to geolocalize images via LMMs. However, the issues remain unaddressed; beyond general tasks, for more specialized downstream tasks, one of which is geolocalization, LMMs struggle. In this work, we propose to solve this problem by introducing a conversational model GAEA that can provide information regarding the location of an image, as required by a user. No large-scale dataset enabling the training of such a model exists. Thus we propose a comprehensive dataset GAEA with 800K images and around 1.6M question answer pairs constructed by leveraging OpenStreetMap (OSM) attributes and geographical context clues. For quantitative evaluation, we propose a diverse benchmark comprising 4K image-text pairs to evaluate conversational capabilities equipped with diverse question types. We consider 11 state-of-the-art open-source and proprietary LMMs and demonstrate that GAEA significantly outperforms the best open-source model, LLaVA-OneVision by 25.69% and the best proprietary model, GPT-4o by 8.28%. Our dataset, model and codes are available