Drones, missiles and cross-border artillery took centre stage during the 62nd week of Russia's war in Ukraine, as the 63rd began with a dramatic allegation from Russia – that Ukraine made an attempt on President Vladimir Putin's life. Ukraine may have targeted Russian fuel depots – a possible preamble to its expected counteroffensive. Russia, meanwhile, sharply intensified strikes against Ukrainian civilians, claiming dozens of lives. Ukraine was likely responsible for explosions in Kozacha Bay, near Sevastopol on the Crimean Peninsula, where the Russian Black Sea Fleet has a base, on April 29. Footage showed a massive black mushroom cloud rising from a fuel tank park.

Russia has blamed Ukraine for setting ablaze one of its oil refineries, while Kyiv has accused Moscow of launching dozens of overnight strikes by unmanned aerial vehicles for the second day running. The targeting of the fuel facility on Thursday occurred at the Ilsky refinery near the Black Sea port of Novorossiysk in the Krasnodar region, Russia's TASS news agency reported citing local emergency services. A fuel reservoir was on fire, it said, but gave no further details. A day earlier, a fuel depot further to the west caught fire near a bridge linking Russia's mainland with the occupied Crimean Peninsula. "A second turbulent night for our emergency services," Krasnodar Governor Veniamin Kondratyev wrote on Telegram, confirming tanks with oil products were set ablaze.

Analysis of Electrochemical Impedance Spectroscopy (EIS) data for electrochemical systems often consists of defining an Equivalent Circuit Model (ECM) using expert knowledge and then optimizing the model parameters to deconvolute various resistance, capacitive, inductive, or diffusion responses. For small data sets, this procedure can be conducted manually; however, it is not feasible to manually define a proper ECM for extensive data sets with a wide range of EIS responses. Automatic identification of an ECM would substantially accelerate the analysis of large sets of EIS data. We showcase machine learning methods to classify the ECMs of 9,300 impedance spectra provided by QuantumScape for the BatteryDEV hackathon. The best-performing approach is a gradient-boosted tree model utilizing a library to automatically generate features, followed by a random forest model using the raw spectral data. A convolutional neural network using boolean images of Nyquist representations is presented as an alternative, although it achieves a lower accuracy. We publish the data and open source the associated code. The approaches described in this article can serve as benchmarks for further studies. A key remaining challenge is the identifiability of the labels, underlined by the model performances and the comparison of misclassified spectra.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Ukrainian President Volodymyr Zelenskyy traveled to Helsinki for talks with the prime ministers of four Nordic countries Wednesday as part of his effort to secure greater firepower for his country's armed forces as they figure out how to dislodge Russian troops from occupied areas of Ukraine. The Nordic countries -- Finland, Sweden, Norway and Denmark -- have been among Kyiv's strongest backers since Russia's full-scale invasion of Ukraine in February 2022. Before the meeting with Zelenskyy in Finland's capital, Nordic officials appeared ready to provide more aid as the war stretches into its 15th month.

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.