Few-shot sound event detection is the task of detecting sound events, despite having only a few labelled examples of the class of interest. This framework is particularly useful in bioacoustics, where often there is a need to annotate very long recordings but the expert annotator time is limited. This paper presents an overview of the second edition of the few-shot bioacoustic sound event detection task included in the DCASE 2022 challenge. A detailed description of the task objectives, dataset, and baselines is presented, together with the main results obtained and characteristics of the submitted systems. This task received submissions from 15 different teams from which 13 scored higher than the baselines. The highest F-score was of 60% on the evaluation set, which leads to a huge improvement over last year's edition. Highly-performing methods made use of prototypical networks, transductive learning, and addressed the variable length of events from all target classes. Furthermore, by analysing results on each of the subsets we can identify the main difficulties that the systems face, and conclude that few-show bioacoustic sound event detection remains an open challenge.

As engineered systems grow in complexity, there is an increasing need for automatic methods that can detect, diagnose, and even correct transient anomalies that inevitably arise and can be difficult or impossible to diagnose and fix manually. Among the most sensitive and complex systems of our civilization are the detectors that search for incredibly small variations in distance caused by gravitational waves -- phenomena originally predicted by Albert Einstein to emerge and propagate through the universe as the result of collisions between black holes and other massive objects in deep space. The extreme complexity and precision of such detectors causes them to be subject to transient noise issues that can significantly limit their sensitivity and effectiveness. In this work, we present a demonstration of a method that can detect and characterize emergent transient anomalies of such massively complex systems. We illustrate the performance, precision, and adaptability of the automated solution via one of the prevalent issues limiting gravitational-wave discoveries: noise artifacts of terrestrial origin that contaminate gravitational wave observatories' highly sensitive measurements and can obscure or even mimic the faint astrophysical signals for which they are listening. Specifically, we demonstrate how a highly interpretable convolutional classifier can automatically learn to detect transient anomalies from auxiliary detector data without needing to observe the anomalies themselves. We also illustrate several other useful features of the model, including how it performs automatic variable selection to reduce tens of thousands of auxiliary data channels to only a few relevant ones; how it identifies behavioral signatures predictive of anomalies in those channels; and how it can be used to investigate individual anomalies and the channels associated with them.

The oceans are a source of an impressive mixture of complex data that could be used to uncover relationships yet to be discovered. Such data comes from the oceans and their surface, such as Automatic Identification System (AIS) messages used for tracking vessels' trajectories. AIS messages are transmitted over radio or satellite at ideally periodic time intervals but vary irregularly over time. As such, this paper aims to model the AIS message transmission behavior through neural networks for forecasting upcoming AIS messages' content from multiple vessels, particularly in a simultaneous approach despite messages' temporal irregularities as outliers. We present a set of experiments comprising multiple algorithms for forecasting tasks with horizon sizes of varying lengths. Deep learning models (e.g., neural networks) revealed themselves to adequately preserve vessels' spatial awareness regardless of temporal irregularity. We show how convolutional layers, feed-forward networks, and recurrent neural networks can improve such tasks by working together. Experimenting with short, medium, and large-sized sequences of messages, our model achieved 36/37/38% of the Relative Percentage Difference - the lower, the better, whereas we observed 92/45/96% on the Elman's RNN, 51/52/40% on the GRU, and 129/98/61% on the LSTM. These results support our model as a driver for improving the prediction of vessel routes when analyzing multiple vessels of diverging types simultaneously under temporally noise data.

Fifth Generation (5G) networks must support billions of heterogeneous devices while guaranteeing optimal Quality of Service (QoS). Such requirements are impossible to meet with human effort alone, and Machine Learning (ML) represents a core asset in 5G. ML, however, is known to be vulnerable to adversarial examples; moreover, as our paper will show, the 5G context is exposed to a yet another type of adversarial ML attacks that cannot be formalized with existing threat models. Proactive assessment of such risks is also challenging due to the lack of ML-powered 5G equipment available for adversarial ML research. To tackle these problems, we propose a novel adversarial ML threat model that is particularly suited to 5G scenarios, and is agnostic to the precise function solved by ML. In contrast to existing ML threat models, our attacks do not require any compromise of the target 5G system while still being viable due to the QoS guarantees and the open nature of 5G networks. Furthermore, we propose an original framework for realistic ML security assessments based on public data. We proactively evaluate our threat model on 6 applications of ML envisioned in 5G. Our attacks affect both the training and the inference stages, can degrade the performance of state-of-the-art ML systems, and have a lower entry barrier than previous attacks.

Russian President Vladimir Putin has waged "economic terrorism" in Ukraine by stocking its farmlands and Black Sea ports full of explosive mines, an official working to de-mine Kyiv told Fox News. "There's 20 plus years of mining work already in Ukraine and for every day of war there's an additional 30 days of mining work that will be required," Cameron Chill, CEO of drone company Draganfly Inc. (DPRO), explained to Fox News upon his return from Kyiv. Chill has been working with Ukraine's emergency management services to deploy drones to efficiently detect buried mines left behind by Russian forces after they withdrew from areas surrounding Kyiv in late March. Draganfly officials help Ukrainians detect landmines after Russian forces withdrew from Kyiv. However, the drone expert said where Russian troops left the explosive devices says a lot about the war Putin is raging against its former Soviet neighbor.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A crewless robotic boat retracing the 1620 sea voyage of the Mayflower has landed near Plymouth Rock. The sleek Mayflower Autonomous Ship met with an escort boat as it approached the Massachusetts shoreline Thursday, more than 400 years after its namesake's historic journey from England. It was towed into Plymouth Harbor -- per U.S. Coast Guard rules for crewless vessels -- and docked near a replica of the original Mayflower that brought the Pilgrims to America.

On Thursday, history repeated itself on the shores of Plymouth. In 1620, English pilgrims arrived in North America on the Mayflower. Now, 402 years later, another ship with that name found its way to the Massachusetts coastline. The first Mayflower had more than 100 people on board, the modern version had zero. The Mayflower Autonomous Ship, designed by nautical research company Promare and IBM, completed its voyage from England almost entirely without human assistance.

A crewless robotic boat retracing the 1620 sea voyage of the Mayflower has landed near Plymouth Rock. The sleek Mayflower Autonomous Ship met with an escort boat as it approached the Massachusetts shoreline Thursday, more than 400 years after its namesake's historic journey from England. It was towed into Plymouth Harbor -- per U.S. Coast Guard rules for crewless vessels -- and docked near a replica of the original Mayflower that brought the Pilgrims to America. Piloted by artificial intelligence technology, the 50-foot (15-meter) trimaran didn't have a captain, navigator or any humans on board. The solar-powered ship's first attempt to cross the Atlantic in 2021 was beset with technical problems, forcing it back to its home port of Plymouth, England -- the same place the Pilgrim settlers sailed from in 1620.

When observations are truncated, we are limited to an incomplete picture of our dataset. Recent methods deal with truncated density estimation problems by turning to score matching, where the access to the intractable normalising constant is not required. We present a novel extension to truncated score matching for a Riemannian manifold. Applications are presented for the von Mises-Fisher and Kent distributions on a two dimensional sphere in $\R^3$, as well as a real-world application of extreme storm observations in the USA. In simulated data experiments, our score matching estimator is able to approximate the true parameter values with a low estimation error and shows improvements over a maximum likelihood estimator.

This paper derives a perturbation bound on the optimal subspace estimator obtained from a subset of its canonical projections contaminated by noise. This fundamental result has important implications in matrix completion, subspace clustering, and related problems.