This paper addresses the challenges of optimally placing a finite number of sensors to detect Poisson-distributed targets in a bounded domain. We seek to rigorously account for uncertainty in the target arrival model throughout the problem. Sensor locations are selected to maximize the probability that no targets are missed. While this objective function is well-suited to applications where failure to detect targets is highly undesirable, it does not lead to a computationally efficient optimization problem. We propose an approximation of the objective function that is non-negative, submodular, and monotone and for which greedy selection of sensor locations works well. We also characterize the gap between the desired objective function and our approximation. For numerical illustrations, we consider the case of the detection of ship traffic using sensors mounted on the seafloor.

The United States military says it has killed a leader of the ISIL (ISIS) group in eastern Syria in a drone strike. The strike on Friday resulted in the death of Osama al-Muhajer, the US Central Command said in a statement on Sunday. "We have made it clear that we remain committed to the defeat of ISIS throughout the region," US Central Command (CENTCOM) chief General Michael Kurilla said, using another acronym for the ISIL armed group. "ISIS remains a threat, not only to the region but well beyond," he added. According to CENTCOM, no civilians were killed in the operation but coalition forces are "assessing reports of a civilian injury".

On the Antarctic research ship Sir David Attenborough, engineers are gathered around a 4-metre square opening in the hull, known as the moon pool. A white robot floats in the water, its headlights illuminating the sides of the pool. "Now push it forward and drop it to the bottom," says Jamie Neilson, an engineering supervisor at Seatronics, the maker of this remotely operated vehicle (ROV).

Russian fighter jets have again flown dangerously close to several US MQ-9 Reaper drones operating over Syria – the second such incident of harassment in 24 hours – setting off flares and forcing Washington's unmanned aerial vehicles to take evasive manoeuvres, the United States air force said. The protest from the US air forces came as the French military said that two of its fighter jets on patrol over the Iraq-Syria border area were forced to manoeuvre "to control the risk of accident" involving a Russian Sukhoi SU-35 warplane on Thursday. The Russian aircraft had engaged in "non-professional interaction" with two of France's Rafale planes deployed to the region as part of "Operation Chammal", which seeks to contain the ISIL (ISIS) group in Iraq and Syria, the French military said. Two separate incidents on Wednesday and Thursday involving Russian warplanes and US Reaper drones were captured on video, the US said. "The events represent a new level of unprofessional and unsafe action by Russian air forces operating in Syria," the US military said.

Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpretability and the lack of robustness against adversarial examples or out-of-distribution inputs. In this exploratory review, we explore the possibilities and limits of adversarial attacks for explainable machine learning models. First, we extend the notion of adversarial examples to fit in explainable machine learning scenarios, in which the inputs, the output classifications and the explanations of the model's decisions are assessed by humans. Next, we propose a comprehensive framework to study whether (and how) adversarial examples can be generated for explainable models under human assessment, introducing and illustrating novel attack paradigms. In particular, our framework considers a wide range of relevant yet often ignored factors such as the type of problem, the user expertise or the objective of the explanations, in order to identify the attack strategies that should be adopted in each scenario to successfully deceive the model (and the human). The intention of these contributions is to serve as a basis for a more rigorous and realistic study of adversarial examples in the field of explainable machine learning.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The International Seabed Authority -- the United Nations body that regulates the world's ocean floor -- is preparing to resume negotiations that could open the international seabed for mining, including for materials critical for the green energy transition. Years long negotiations are reaching a critical point where the authority will soon need to begin accepting mining permit applications, adding to worries over the potential impacts on sparsely researched marine ecosystems and habitats of the deep sea. Here's a look at what deep sea mining is, why some companies and countries are applying for permits to carry it out and why environmental activists are raising concerns.

Exploring the climate impacts of various anthropogenic emissions scenarios is key to making informed decisions for climate change mitigation and adaptation. State-of-the-art Earth system models can provide detailed insight into these impacts, but have a large associated computational cost on a per-scenario basis. This large computational burden has driven recent interest in developing cheap machine learning models for the task of climate model emulation. In this manuscript, we explore the efficacy of randomly wired neural networks for this task. We describe how they can be constructed and compare them to their standard feedforward counterparts using the ClimateBench dataset. Specifically, we replace the serially connected dense layers in multilayer perceptrons, convolutional neural networks, and convolutional long short-term memory networks with randomly wired dense layers and assess the impact on model performance for models with 1 million and 10 million parameters. We find that models with less complex architectures see the greatest performance improvement with the addition of random wiring (up to 30.4% for multilayer perceptrons). Furthermore, out of 24 different model architecture, parameter count, and prediction task combinations, only one saw a statistically significant performance deficit in randomly wired networks compared to their standard counterparts, with 14 cases showing statistically significant improvement. We also find no significant difference in prediction speed between networks with standard feedforward dense layers and those with randomly wired layers. These findings indicate that randomly wired neural networks may be suitable direct replacements for traditional dense layers in many standard models.

Transfer learning transfers the knowledge acquired by a model from a source task to multiple downstream target tasks with minimal fine-tuning. The success of transfer learning at improving performance, especially with the use of large pre-trained models has made transfer learning an essential tool in the machine learning toolbox. However, the conditions under which the performance is transferable to downstream tasks are not understood very well. In this work, we analyze the transfer of performance for classification tasks, when only the last linear layer of the source model is fine-tuned on the target task. We propose a novel Task Transfer Analysis approach that transforms the source distribution (and classifier) by changing the class prior distribution, label, and feature spaces to produce a new source distribution (and classifier) and allows us to relate the loss of the downstream task (i.e., transferability) to that of the source task. Concretely, our bound explains transferability in terms of the Wasserstein distance between the transformed source and downstream task's distribution, conditional entropy between the label distributions of the two tasks, and weighted loss of the source classifier on the source task. Moreover, we propose an optimization problem for learning the transforms of the source task to minimize the upper bound on transferability. We perform a large-scale empirical study by using state-of-the-art pre-trained models and demonstrate the effectiveness of our bound and optimization at predicting transferability. The results of our experiments demonstrate how factors such as task relatedness, pretraining method, and model architecture affect transferability.

Researchers are increasingly using small, autonomous underwater robots to collect data in the world's oceans. Rescuers spotted debris from the tourist submarine Titan on the ocean floor near the wreck of the Titanic on June 22, 2023, indicating that the vessel suffered a catastrophic failure and the five people aboard were killed. Bringing people to the bottom of the deep ocean is inherently dangerous. At the same time, climate change means collecting data from the world's oceans is more vital than ever. Purdue University mechanical engineer Nina Mahmoudian explains how researchers reduce the risks and costs associated with deep-sea exploration: Send down subs, but keep people on the surface.

Russia has started tactical fighter jet exercises over the Baltic Sea with the goal of testing readiness to perform combat and other special operations, the country's defence ministry has said, a day after Moscow said its jets had scrambled to intercept United Kingdom military planes over the Black Sea. "The main goal of the exercise is to test the readiness of the flight crew to perform combat and special tasks as intended," Russia's defence ministry said on Tuesday. "The crews of the Su-27 [fighter jets] of the Baltic Fleet fired from airborne weapons at cruise missiles and mock enemy aircraft," the ministry announced on the Telegram messaging channel, adding that as well as improving skills, Russian fighter pilots are on "round-the-clock combat duty" guarding the air space of Russia's Kaliningrad exclave. Wedged between Poland and Lithuania on the Baltic coast, Kaliningrad is Moscow's westernmost state and was part of Germany until the end of World War II. Given to the Soviet Union at the Potsdam Conference in 1945, the enclave has roughly 1 million residents – mainly Russians but also a small number of Ukrainians, Poles and Lithuanians.