Accurate precipitation estimates at individual locations are crucial for weather forecasting and spatial analysis. This study presents a paradigm shift by leveraging Deep Neural Networks (DNNs) to surpass traditional methods like Kriging for station-specific precipitation approximation. We propose two innovative NN architectures: one utilizing precipitation, elevation, and location, and another incorporating additional meteorological parameters like humidity, temperature, and wind speed. Trained on a vast dataset (1980-2019), these models outperform Kriging across various evaluation metrics (correlation coefficient, root mean square error, bias, and skill score) on a five-year validation set. This compelling evidence demonstrates the transformative power of deep learning for spatial prediction, offering a robust and precise alternative for station-specific precipitation estimation.

The FBI has launched an investigation into mysterious glowing lights that have been spotted over New Jersey for the last few weeks. Eyewitnesses reported unexplained'car-sized' drones over the Trump National Golf Club in Bedminster and the Picatinny Arsenal Military Base in Rockaway, among other locations throughout northern New Jersey. Video footage revealed the drones featured green and red lights on their wings and multiple eyewitness described them as large as a small car. However, the flying objects are larger than drones used by hobbyists, raising questions about their proximity to those specific locations. The Federal Aviation Administration (FAA) was first alerted about the strange activity in Morris County, where the military base is located, on November 18, but sights also surfaced in nearby Menham, Chester and Morristown.

This paper presents a novel multimodal perception system for a real open environment. The proposed system includes an embedded computation platform, cameras, ultrasonic sensors, GPS, and IMU devices. Unlike the traditional frameworks, our system integrates multiple sensors with advanced computer vision algorithms to help users walk outside reliably. The system can efficiently complete various tasks, including navigating to specific locations, passing through obstacle regions, and crossing intersections. Specifically, we also use ultrasonic sensors and depth cameras to enhance obstacle avoidance performance. The path planning module is designed to find the locally optimal route based on various feedback and the user's current state. To evaluate the performance of the proposed system, we design several experiments under different scenarios. The results show that the system can help users walk efficiently and independently in complex situations. Keywords: perception system, computer vision, deep learning, semantic segmentation, object detection.

Home robots performing personalized tasks must adeptly balance user preferences with environmental affordances. We focus on organization tasks within constrained spaces, such as arranging items into a refrigerator, where preferences for placement collide with physical limitations. The robot must infer user preferences based on a small set of demonstrations, which is easier for users to provide than extensively defining all their requirements. While recent works use Large Language Models (LLMs) to learn preferences from user demonstrations, they encounter two fundamental challenges. First, there is inherent ambiguity in interpreting user actions, as multiple preferences can often explain a single observed behavior. Second, not all user preferences are practically feasible due to geometric constraints in the environment. To address these challenges, we introduce APRICOT, a novel approach that merges LLM-based Bayesian active preference learning with constraint-aware task planning. APRICOT refines its generated preferences by actively querying the user and dynamically adapts its plan to respect environmental constraints. We evaluate APRICOT on a dataset of diverse organization tasks and demonstrate its effectiveness in real-world scenarios, showing significant improvements in both preference satisfaction and plan feasibility. The project website is at https://portal-cornell.github.io/apricot/

Search-based agents: These agents use search algorithms to find solutions to problems. For example, a search-based agent might use algorithms like breadth-first search or A* to find the shortest path from one location to another. Planning agents: These agents use planning algorithms to determine the steps necessary to achieve a specific goal. For example, a robot equipped with a planning agent might use a planning algorithm to determine the sequence of actions necessary to pick up an object and place it in a specific location. Reasoning agents: These agents use reasoning and logic to solve problems.

Machine learning models are set to play an increasing role in aiding decision-making processes in both governmental and commercial industries in the years to come. One noteworthy area where this is likely to happen is in the geospatial domain, where information obtained from GPS devices and satellite and aerial imagery is used to make both strategic and business decisions. It is thus important to understand how models in this domain stand up to adversarial attack and how trustworthy their outputs are. In April 2021, F-Secure conducted a threat analysis study of machine learning models in the geospatial domain. We investigated several possible attacks and attack goals and proposed mitigations against them.

Using artificial intelligence for rapid data collection and integration of shrunk the commander's decision cycle from days to minutes in some instances in a recent information experiment by U.S. Northern Command, the head of NORTHCOM said Wednesday. Speaking to reporters at the Pentagon, Gen. Glen VanHerck said the Global Information Dominance Exercise or GIDE, "focused a lot on contested logistics to give us a scenario where maybe a line of communication such as the Panama Canal may be challenged," by a peer competitor such as China or Russia. The experiment wrapped up during the second week of July. The experiment was hosted by NORTHCOM but included 11 combatant commands, which illustrated how they can integrate and act on data from satellites, planes, and other sources. It also tested the command's ability to use new artificial intelligence abilities to monitor and predict potential threats using those data sources.

This article is a summary of the research paper "GAN Dissection: Visualising And Understanding Generative Adversarial Networks". The paper provides an excellent insight into the internal representation of GANs and gives a close answer to the following question. What the hell do GANs learn? We've all seen stunning results produced by GANs, almost indistinguishable from human work in some cases. But how they represent learned knowledge is still a mystery.

We discuss some wonders in the field of image processing with machine learning advancements. Image processing can be defined as the technical analysis of an image by using complex algorithms. Here, image is used as the input, where the useful information returns as the output. According to a report, the image processing industry will reach USD 38.9 billion by 2021. Meanwhile, the Artificial Intelligence industry is also turning to a considerable growth curve.

One of the biggest AI-driven titles of modern times, Alien: Isolation brings the terror of the xenomorph to video games. While the alien, LV-426 and many other key elements of the franchise have previously been explored, Alien: Isolation takes a different route. A horror game steeped in the aesthetic and style of Ridley Scott's classic 1979 movie: in which the player is trapped on the Sevastopol space station with a single, intelligent, near-invincible alien. For this experience to play out as expected, the alien itself has to carry a large amount of the experience: a demanding feat for any AI in an industry in which non-player characters are either heavily scripted or suffer really short life spans. So how did developers Creative Assembly pull it off?