Assume that inside an initial planar area there are smart mobile evaders attempting to avoid detection by a team of sweeping searching agents. All sweepers detect evaders with fan-shaped sensors, modeling the field of view of real cameras. Detection of all evaders is guaranteed with cooperative sweeping strategies, by setting requirements on sweepers' speed, and by carefully designing their trajectories. Assume the smart evaders have an upper limit on their speed which is a-priori known to the sweeping team. An easier task for the team of sweepers is to confine evaders to the domain in which they are initially located. The sweepers accomplish the confinement task if they move sufficiently fast and detect evaders by applying an appropriate search strategy. Any given search strategy results in a minimal sweeper's speed in order to be able to detect all evaders. The minimal speed guarantees the ability of the sweeping team to confine evaders to their original domain, and if the sweepers move faster they are able to detect all evaders that are present in the region. We present results on the total search time for a novel pincer-movement based search protocol that utilizes complementary trajectories along with adaptive sensor geometries for any even number of pursuers.

A massive fire was ignited in the Crimean port city of Sevastopol following a suspected drone attack on a fuel storage tank. The blaze was assigned the highest ranking in terms of how complicated it will be to extinguish, Mikhail Razvozhayev, the Russian-installed governor, wrote on Telegram on Saturday. The fire was still burning but it had been contained and no one was injured. The oil reservoir fire did not cause any casualties and would not hinder fuel supplies in Sevastopol, he said. "The four fuel tanks that were hit, they are practically burnt out already," said Razvozhayev, adding an area of 1,000 square metres (11,000 square feet) had been engulfed in flames.

We propose TR0N, a highly general framework to turn pre-trained unconditional generative models, such as GANs and VAEs, into conditional models. The conditioning can be highly arbitrary, and requires only a pre-trained auxiliary model. For example, we show how to turn unconditional models into class-conditional ones with the help of a classifier, and also into text-to-image models by leveraging CLIP. TR0N learns a lightweight stochastic mapping which "translates" between the space of conditions and the latent space of the generative model, in such a way that the generated latent corresponds to a data sample satisfying the desired condition. The translated latent samples are then further improved upon through Langevin dynamics, enabling us to obtain higher-quality data samples. TR0N requires no training data nor fine-tuning, yet can achieve a zero-shot FID of 10.9 on MS-COCO, outperforming competing alternatives not only on this metric, but also in sampling speed -- all while retaining a much higher level of generality. Our code is available at https://github.com/layer6ai-labs/tr0n.

Sudden onset of precipitation frequently endangers human lives and causes damage and disruption to infrastructure through flooding and landslides, and is often accompanied by other hazardous weather phenomena such as hail, lightning and windstorms. Precipitation is also a fundamental driver of agriculture and hydroelectric power generation. Consequently, short-term precipitation forecasts are important tools that can benefit infrastructure managers, emergency services and the general public if provided in a timely manner. Numerical weather prediction (NWP) models can typically forecast the probability and general intensity of precipitation occurring in a wider area, but they struggle at short spatial and temporal scales [1] because of the long running time and the time needed to assimilate data, i.e. to incorporate observational data used as the initial conditions. This problem is particularly severe with convective precipitation, which is associated with the highest rainfall rates, and originates from cells with a spatial scale on the order of a few tens of kilometers, making the exact location of the precipitation difficult to predict with NWP [2]. Experience over decades has shown that at lead times of minutes to a few hours, statistical and data-driven models that make optimal use of the latest available observations are useful tools for the short term prediction, or nowcasting, of precipitation. Such models have been widely deployed by meteorological agencies. A common way to implement precipitation nowcasting is Lagrangian extrapolation: using motion-detection algorithms to derive motion vectors from consecutive measurements of rainfall by weather radar, then advecting the precipitation field using these vectors to predict its future movement [3, 4].

This paper compares three controllers for quasi-passive exoskeletons. The Utility Maximizing Controller (UMC) uses intent estimation to recognize user motions and decision theory to activate the support mechanism. The intent estimation algorithm requires demonstrations for each motion to be recognized. Depending on what motion is recognized, different control signals are sent to the exoskeleton. The Extended UMC (E-UMC) adds a calibration step and a velocity module to trigger the UMC. As a benchmark, and to compare the behavior of the controllers irrespective of the hardware, a Passive Exoskeleton Controller (PEC) is developed as well. The controllers were implemented on a hip exoskeleton and evaluated in a user study consisting of two phases. First, demonstrations of three motions were recorded: squat, stoop left and stoop right. Afterwards, the controllers were evaluated. The E-UMC combines benefits from the UMC and the PEC, confirming the need for the two extensions. The E-UMC discriminates between the three motions and does not generate false positives for previously unseen motions such as stair walking. The proposed methods can also be applied to support other motions.

Russia's Black Sea Fleet has warded off a drone attack on the Crimean port of Sevastopol, the Moscow-installed governor of the city says. "An attempted attack on Sevastopol was repelled from 3:30am [00:30 GMT]," Mikhail Razvojayev said on Telegram on Monday. "A surface drone [naval] was destroyed by the anti-sabotage forces, the second one exploded on its own," he said, adding that no damage was reported. Passenger ferry service were suspended in the port city, Russia's Interfax news agency reported, citing Sevastopol transport authorities. No reason was given, but the agency said traffic had been suspended in the past due to drone attacks or storms.

This year is the 10th anniversary of Lucas Pope's wrenching game about being a border officer in the functional communist country of Arstotzka, deciding when to turn desperate people away and when to risk your job but save your conscience by letting them slip by. It remains an impactful and illuminating exploration of how documentation can save or cost lives, and the moral and human cost of border enforcement. Your wife, Nour, has finally decided to risk getting out of Syria as the bombs keep falling – but you must stay behind, to look after your mother and grandfather. In this game, you are not the person fleeing but the person trying to survive back home, waiting for text message updates or an occasional photo and doing your best to give good advice from afar. An affecting work of interactive fiction told through the medium of the smartphone.

This is an edition of The Atlantic Daily, a newsletter that guides you through the biggest stories of the day, helps you discover new ideas, and recommends the best in culture. The tech world's most attention-grabbing man had a very busy week. Elon Musk launched a rocket, dealt with bad news at Tesla, stoked fear that AI could end humankind, and rolled out another controversial change on Twitter. Through it all, Musk exemplifies the danger of what happens when technology and ego collide. Earlier today, a SpaceX rocket exploded in the skies over the Gulf of Mexico, detonating itself after the booster failed to separate from the upper portion of the vehicle after launch.

The current approach to exploring and monitoring complex underwater ecosystems, such as coral reefs, is to conduct surveys using diver-held or static cameras, or deploying sensor buoys. These approaches often fail to capture the full variation and complexity of interactions between different reef organisms and their habitat. The CUREE platform presented in this paper provides a unique set of capabilities in the form of robot behaviors and perception algorithms to enable scientists to explore different aspects of an ecosystem. Examples of these capabilities include low-altitude visual surveys, soundscape surveys, habitat characterization, and animal following. We demonstrate these capabilities by describing two field deployments on coral reefs in the US Virgin Islands. In the first deployment, we show that CUREE can identify the preferred habitat type of snapping shrimp in a reef through a combination of a visual survey, habitat characterization, and a soundscape survey. In the second deployment, we demonstrate CUREE's ability to follow arbitrary animals by separately following a barracuda and stingray for several minutes each in midwater and benthic environments, respectively.

This paper provides a collision avoidance perspective to maritime autonomy, in the shift towards Maritime Autonomous Surface Ships (MASS). In particular, the paper presents the developments related to the Greenhopper, Denmark's first autonomous harbour bus. The collision and grounding avoidance scheme, called the Short Horizon Planner (SHP), is described and discussed in detail. Furthermore, the required autonomy stack for facilitating safe and rule-compliant collision avoidance is presented. The inherent difficulties related to adhering to the COLREGs are outlined, highlighting some of the operational constraints and challenges within the space of autonomous ferries and harbour buses. Finally, collision and grounding avoidance is demonstrated using a simulation of the whole Greenhopper autonomy stack.