Precise outbreak forecasting of infectious diseases is essential for effective public health responses and epidemic control. The increased availability of machine learning (ML) methods for time-series forecasting presents an enticing avenue to enhance outbreak forecasting. Though the COVID-19 outbreak demonstrated the value of applying ML models to predict epidemic profiles, using ML models to forecast endemic diseases remains underexplored. In this work, we present ForecastNet-XCL (an ensemble model based on XGBoost+CNN+BiLSTM), a deep learning hybrid framework designed to addresses this gap by creating accurate multi-week RSV forecasts up to 100 weeks in advance based on climate and temporal data, without access to real-time surveillance on RSV. The framework combines high-resolution feature learning with long-range temporal dependency capturing mechanisms, bolstered by an autoregressive module trained on climate-controlled lagged relations. Stochastic inference returns probabilistic intervals to inform decision-making. Evaluated across 34 U.S. states, ForecastNet-XCL reliably outperformed statistical baselines, individual neural nets, and conventional ensemble methods in both within- and cross-state scenarios, sustaining accuracy over extended forecast horizons. Training on climatologically diverse datasets enhanced generalization furthermore, particularly in locations having irregular or biennial RSV patterns. ForecastNet-XCL's efficiency, performance, and uncertainty-aware design make it a deployable early-warning tool amid escalating climate pressures and constrained surveillance resources.

Thank you for your in-depth and constructive reviews, they will give us an excellent chance to further improve the paper. We address the reviewers concerns individually below. Minor Concerns: We will address these concerns in the revision. Empirically, then, it makes sense to compare to other mini-batch methods, like IS. 'mean' is the correct one. For O1, you are correct in your understanding.

Despite considerable progress in stereo depth estimation, omnidirectional imaging remains underexplored, mainly due to the lack of appropriate data. We introduce Helvipad, a real-world dataset for omnidirectional stereo depth estimation, consisting of 40K frames from video sequences across diverse environments, including crowded indoor and outdoor scenes with diverse lighting conditions. Collected using two 360{\deg} cameras in a top-bottom setup and a LiDAR sensor, the dataset includes accurate depth and disparity labels by projecting 3D point clouds onto equirectangular images. Additionally, we provide an augmented training set with a significantly increased label density by using depth completion. We benchmark leading stereo depth estimation models for both standard and omnidirectional images. The results show that while recent stereo methods perform decently, a significant challenge persists in accurately estimating depth in omnidirectional imaging. To address this, we introduce necessary adaptations to stereo models, achieving improved performance.

Unsupervised rationale extraction aims to extract text snippets to support model predictions without explicit rationale annotation. Researchers have made many efforts to solve this task. Previous works often encode each aspect independently, which may limit their ability to capture meaningful internal correlations between aspects. While there has been significant work on mitigating spurious correlations, our approach focuses on leveraging the beneficial internal correlations to improve multi-aspect rationale extraction. In this paper, we propose a Multi-Aspect Rationale Extractor (MARE) to explain and predict multiple aspects simultaneously. Concretely, we propose a Multi-Aspect Multi-Head Attention (MAMHA) mechanism based on hard deletion to encode multiple text chunks simultaneously. Furthermore, multiple special tokens are prepended in front of the text with each corresponding to one certain aspect. Finally, multi-task training is deployed to reduce the training overhead. Experimental results on two unsupervised rationale extraction benchmarks show that MARE achieves state-of-the-art performance. Ablation studies further demonstrate the effectiveness of our method. Our codes have been available at https://github.com/CSU-NLP-Group/MARE.

Thousands of emperor penguins waddling around Antarctica have a stalker: A yellow rover tracking their every move. ECHO is a remote-controlled ground robot that silently spies on the emperor penguin colony in Atka Bay. The robot is being monitored by the Single Penguin Observation and Tracking observatory. Both the SPOT observatory, which is also remote-operated through a satellite link, and the ECHO robot capture photographs and videos of animal population in the Arctic. The research is part of the Marine Animal Remote Sensing Lab (MARE), designed to measure the health of the Antarctic marine ecosystem.

Artificial intelligence must be the future for network security, according to Fortinet. With the threat landscape constantly evolving and increasing in complexity, continued digital innovation, technological developments, and the introduction of 5G, coupled with the challenges of accelerated remote working practices and a growing cybersecurity skills gap, have collectively exacerbated the challenges that CISOs face in terms of protecting their companies' digital assets. As CISOs assess their cybersecurity posture, it's essential that they consider how to leverage new and emerging technologies to best protect their infrastructure, the company says. There have been significant developments in the artificial intelligence (AI) space that make it an increasingly strategic investment. However, Fortinet says it can be challenging for CISOs to cut through the hype and understand which AI-based solution is best suited to their organisation.

"Made for Love," which is now streaming on HBO Max, opens on a vast expanse of desert, empty save for a geometric building in the distance. A lid on the ground is unlatched, and out pops a woman in a sequinned dress, gasping for breath, her hair drenched with water and a little blood. The woman is Hazel Green, and she is portrayed by Cristin Milioti, a strongly expressive actor who has become known for deploying her feral intellect to outsmart male villains in science-fiction thrillers. If you have seen Milioti take down a video-game dictator in the "Black Mirror" episode "USS Callister," or hack a time-loop purgatory in the 2020 comedy "Palm Springs," then you might be able to guess the story of "Made for Love," even before Hazel raises her middle finger at the structure on the horizon. The place is clearly the source of some terror--one that is futuristic yet eerily familiar.

Moon caves could provide shelter for astronauts exploring Earth's nearest neighbor, researchers say. A new analysis of data gathered by NASA's twin Gravity Recovery and Interior Laboratory (GRAIL) spacecraft, which mapped the moon's gravitational field in unprecedented detail, turned up a number of new candidates for lava tubes -- cave-like structures that could be large enough to house supplies and astronauts. Space is a harsh environment. Radiation from the sun, galactic cosmic rays and constantly falling micrometeorites all present a threat to human explorers. "A lava tube provides a safe haven from all these hazardous environmental conditions," study team member Rohan Sood, a graduate student at Purdue University in Indiana, told Space.com.