We do this for all combinations of blocks and tokens. 1 2 Class representations in image tokens across the hierarchy Asterisks indicate a significant difference between both types of tokens. We additionally conducted an analysis comparing the class similarity change rate of class-and context-labeled tokens in self-attention layers. Figure 17: Agreement rate difference between correctly classified vs. misclassified samples. Figure 18: Percentage of instances where the layer's final predictions match any of the top-5 predictions of the most activated memories. AUC is better, while in the positive perturbation experiments (POS) a lower AUC is better.

Video released Thursday by the LAPD shows police flying a drone to get a closer look at a man inside a van who had been shot by officers in Boyle Heights last month. Police said they shot the man after he ignored their commands to drop what appeared to be a rifle. The rifle, it turned out, was a battery-powered airsoft gun, which shoots plastic pellets. As the drone zoomed up to the white utility van, it captured a graphic image of Jeremy Flores, 26, slumped over the steering wheel, the fake gun lying on his lap. A SWAT team then approached and pulled Flores out of the vehicle.

--Federated Learning (FL) is a privacy-preserving machine learning technique that allows decentralized collaborative model training across a set of distributed clients, by avoiding raw data exchange. A fundamental component of FL is the selection of a subset of clients in each round for model training by a central server . Current selection strategies are myopic in nature in that they are based on past or current interactions, often leading to inefficiency issues such as straggling clients. In this paper, we address this serious shortcoming by proposing the RIFLES approach that builds a novel availability forecasting layer to support the client selection process. We make the following contributions: (i) we formalise the sequential selection problem and reduce it to a scheduling problem and show that the problem is NP-complete, (ii) leveraging heartbeat messages from clients, RIFLES build an availability prediction layer to support (long term) selection decisions, (iii) we propose a novel adaptive selection strategy to support efficient learning and resource usage. T o circumvent the inherent exponential complexity, we present RIFLES, a heuristic that leverages clients' historical availability data by using a CNN-LSTM time series forecasting model, allowing the server to predict the optimal participation times of clients, thereby enabling informed selection decisions. By comparing against other FL techniques, we show that RIFLES provide significant improvement by between 10%- 50% on a variety of metrics such as accuracy and test loss. T o the best of our knowledge, it is the first work to investigate FL as a scheduling problem.

Military personnel and security agents often face significant physical risks during conflict and engagement situations, particularly in urban operations. Ensuring the rapid and accurate communication of incidents involving injuries is crucial for the timely execution of rescue operations. This article presents research conducted under the scope of the Brazilian Navy's ``Soldier of the Future'' project, focusing on the development of a Casualty Detection System to identify injuries that could incapacitate a soldier and lead to severe blood loss. The study specifically addresses the detection of soldier falls, which may indicate critical injuries such as hypovolemic hemorrhagic shock. To generate the publicly available dataset, we used smartwatches and smartphones as wearable devices to collect inertial data from soldiers during various activities, including simulated falls. The data were used to train 1D Convolutional Neural Networks (CNN1D) with the objective of accurately classifying falls that could result from life-threatening injuries. We explored different sensor placements (on the wrists and near the center of mass) and various approaches to using inertial variables, including linear and angular accelerations. The neural network models were optimized using Bayesian techniques to enhance their performance. The best-performing model and its results, discussed in this article, contribute to the advancement of automated systems for monitoring soldier safety and improving response times in engagement scenarios.

The US Army is closer to unleashing robots on the battlefield after sending one dubbed'Lone Wolf' to the Middle East. The robot dog features an AR-15/M16-pattern rifle on its back that is attached to an AI-powered rotating mount capable of spotting aerial targets. The armed machine was sent overseas for rehearsal drills at the Red Sands Integrated Experimentation Center in Saudi Arabia. The military shared a photo of Lone Wolf last week, showing a Korean-made Ghost Robotics Vision 60 Quadrupedal-Unmanned Ground Vehicle (Q-UGV) at an undisclosed location. The US Army recently carried out testing of a new war machine in the Middle East.

The Chinese military recently unveiled a new kind of battle buddy for its soldiers: a "robot dog" with a machine gun strapped to its back. In video distributed by the state-run news agency CCTV, People's Liberation Army personnel are shown operating on a testing range alongside a four-legged robot with what appears to be a variant of the standard-issue 5.8 x 42-mm QBZ-95 assault rifle mounted on it as part of China's recent Golden Dragon 24 joint military exercises with Cambodia in the Gulf of Thailand. In one scenario, Chinese soldiers stand on either side of a doorway while the robot dog enters the building ahead of them; in another, the robot fires off a burst of bullets as it advances on a target. "It can serve as a new member in our urban combat operations, replacing our members to conduct reconnaissance and identify enemy [sic] and strike the target during our training," one Chinese soldier shown operating the robot told CCTV. This isn't the first time the Chinese military-industrial complex has shown off an armed robot dog. In October 2022, Chinese defense company Kestrel Defense published a video showing an unmanned aerial vehicle air-dropping a quadrupedal ground vehicle affixed with a 5.8 x 42-mm QBB-97 light machine gun on a roof during an urban warfare experiment.

ZeroMark, a defense startup based in the United States, thinks it has a solution. It wants to turn the rifles of frontline soldiers into "handheld Iron Domes." The idea is simple: Make it easier to shoot a drone out of the sky with a bullet. The problem is that drones are fast and maneuverable, making them hard for even a skilled marksman to hit. ZeroMark's system would add aim assistance to existing rifles, ostensibly helping soldiers put a bullet in just the right place.

The Chinese army has debuted its latest weapon: a gun-toting robotic dog. The mechanical canine, which has an automatic rifle on its back, was front and centre of recent joint military drills with Cambodia, according to footage from the state broadcaster CCTV. The dog was backed up by a similarly-armed quadcopter in the drills, which saw the machines paired with human soldiers in dry runs for urban assaults. "It can serve as a new member in our urban combat operations, replacing our human members to conduct reconnaissance and identify enemy and strike the target," Chen Wei, a Chinese soldier, said in the video. While they may be technologically advanced, the killer robots are hardly sleek pieces of military hardware; both dog and drone appear to be off-the-shelf pieces of consumer technology with a conventional rifle bolted on top.

Photos have emerged showing children in Mexico wielding rifles as they have been recruited by a volunteer police force to combat organized crime, a report says. The children, who are as young as 12, paraded around a sports field before joining the patrol in Ayahualtempa, a village in the southwestern Guerrero state where authorities report being overwhelmed by kidnappings, according to Reuters. "We can't study because of lawlessness," one of the teenagers was quoted by the news agency as telling the Milenio television channel, adding that he had learned how to shoot a gun following a series of lessons. In early January, an alleged cartel drone attack in the Guerrero state left five people dead. Children hold rifles before a ceremony to join the ranks of the community police a few days after an armed group abducted four people from Ayahualtempa, in Mexico's Guerrero state, on January 24.