Atlanta Falcons are dealing with the worst quarterback situation in the NFL and it isn't even close Fat Bear Week bracket is set: meet the heavyweights, underdogs and defending champ in this year's tournament Riley Green drops 19-track album'That's Just Me' and country music fans can't get enough Attorney reveals Lane Kiffin used ChatGPT for legal advice during LSU's failed bid to add pro players Baker Mayfield loses bet to coach Todd Bowles, has'rough couple of days' including at press conference Falcons star Kyle Pitts Sr predicts CFB players will unionize, says pro players returning to school is'crazy' New trailer for'Other Mommy' with Jessica Chastain reveals a sinister entity terrorizing a young girl'God has been so good to me': Ella Langley reacts to incredible nine CMA Awards nominations'How can you fight during a love song?' Riley Green defuses crowd fight at New Mexico show Netflix drops full trailer for Ben Affleck's'Animals' and expectations are already soaring Dan Lanning spars with reporter after Dante Moore misses media following Oregon's loss to Oklahoma State Best hikes to try around Lubbock ahead of Texas Tech's week one matchup with Abilene Christian Elon Musk's mom uses Grok 10x times a day. "Why Does The NCAA Even Exist?" Craig Carton TORCHES Its Pathetic Michigan Hail Mary Statement | The Craig Carton Show OutKick Mike Vrabel snaps at reporter over Drake Maye's struggles after Pats win: 'you're not going to ruin my Sunday' Patriots on the DOORSTEP OF DESPAIR after losing to Seahawks, Should New England be concerned? The Seattle Seahawks beat the New England Patriots 13-10, following Drake Maye's 3 INTs and Sam Darnold's injury. Nick Wright, Chris Broussard, and Kevin Wildes ask if Patriot fans should be concerned, and if the Seahawks were impressive. The New England Patriots got their first win of the season Sunday, and head coach Mike Vrabel wasn't about to let a few perfectly valid questions about his quarterback spoil the celebration.

Mike Vrabel snaps at reporter over Drake Maye's struggles after Pats win: 'you're not going to ruin my Sunday' Fat Bear Week bracket is set: meet the heavyweights, underdogs and defending champ in this year's tournament Riley Green drops 19-track album'That's Just Me' and country music fans can't get enough Attorney reveals Lane Kiffin used ChatGPT for legal advice during LSU's failed bid to add pro players Baker Mayfield loses bet to coach Todd Bowles, has'rough couple of days' including at press conference Falcons star Kyle Pitts Sr predicts CFB players will unionize, says pro players returning to school is'crazy' New trailer for'Other Mommy' with Jessica Chastain reveals a sinister entity terrorizing a young girl'God has been so good to me': Ella Langley reacts to incredible nine CMA Awards nominations'How can you fight during a love song?' Riley Green defuses crowd fight at New Mexico show Netflix drops full trailer for Ben Affleck's'Animals' and expectations are already soaring Dan Lanning spars with reporter after Dante Moore misses media following Oregon's loss to Oklahoma State Best hikes to try around Lubbock ahead of Texas Tech's week one matchup with Abilene Christian Elon Musk's mom uses Grok 10x times a day. "Why Does The NCAA Even Exist?" Craig Carton TORCHES Its Pathetic Michigan Hail Mary Statement | The Craig Carton Show OutKick Atlanta Falcons are dealing with the worst quarterback situation in the NFL and it isn't even close Falcons' QB Cooper Rush and Head Coach Kevin Stefanski address the media following Atlanta's Week 1 loss to the Pittsburgh Steelers. And it isn't very close at all. The Falcons on Sunday started third-stringer Cooper Rush for the second time this season while Michael Penix Jr. and Tua Tagovailoa were designated as inactive due to ongoing health issues. This page may contain affiliate links to legal sports betting partners.

Why the Packers should cover -3.5 against the Jets in NFL Week 2 after their Vikings collapse SMU vs Louisville could be a college football shootout with the over at 58.5 looking strong Clay Holmes and Chase Burns set up a pitchers' duel as Cubs visit Reds at Great American Ball Park Local company helps bring visitors back after NC's Hurricane Helene Piers Morgan shares why he'instantly' left the UK Larry Kudlow: Let's not throw a wrench into the AI movement Mark Levin: AI data centers aren't going to kill you The CCP would use AI to'malevolent' ends: Matthew Continetti Iran was preparing to launch'Armageddon,' Rep Tim Burchett says Iran was preparing to launch'Armageddon,' Rep Tim Burchett says Assistant AG warns'vulnerabilities are severe' amid alleged election fraud crackdown Saudi Arabia's capital of Riyadh on edge after reported explosions This page may contain affiliate links to legal sports betting partners. If you sign up or place a wager, FOX News may be compensated. This content was created by a team that works independently from the Fox newsroom. Pittsburgh Steelers quarterback Aaron Rodgers (8) looks to pass during an NFL football game against the Atlanta Falcons, Sunday, Sept. 13, 2026, in Pittsburgh. Week 2 brings us 15 teams looking to push their record to 2-0, and 15 teams looking to avoid the fate of being 0-2.

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Intercepting fast moving objects, by its very nature, is challenging because of its tight time constraints. This problem becomes further complicated in the presence of sensor noise because noisy sensors provide, at best, incomplete information, which results in a distribution over target states to be intercepted. Since time is of the essence, to hit the target, the planner must begin directing the interceptor, in this case a robot arm, while still receiving information. We introduce an tree-like structure, which is grown using kinodynamic motion primitives in state-time space. This tree-like structure encodes reachability to multiple goals from a single origin, while enabling real-time value updates as the target belief evolves and seamless transitions between goals. We evaluate our framework on an interception task on a 6 DOF industrial arm (ABB IRB-1600) with an onboard stereo camera (ZED 2i). A robust Innovation-based Adaptive Estimation Adaptive Kalman Filter (RIAE-AKF) is used to track the target and perform belief updates.

Department of Applied Mathematics Harbin Institute of T echnology, W eihai Weihai, China gaoxiang.zhao@stu.hit.edu.cn Abstract--Real-Time Auction (RT A) Interception aims to filter out invalid or irrelevant traffic to enhance the integrity and reliability of downstream data. However, two key challenges remain: (i) the need for accurate estimation of traffic quality together with sufficiently high confidence in the model's predictions--typically addressed through uncertainty modeling--and (ii) the efficiency bottlenecks that such uncertainty modeling introduces in real-time applications due to repeated inference. T o address these challenges, we propose DAUM, a joint modeling framework that integrates multi-objective learning with uncertainty modeling, yielding both traffic quality predictions and reliable confidence estimates. Building on DAUM, we further apply knowledge distillation to reduce the computational overhead of uncertainty modeling, while largely preserving predictive accuracy and retaining the benefits of uncertainty estimation. Experiments on the JD advertisement dataset demonstrate that DAUM consistently improves predictive performance, with the distilled model delivering a tenfold increase in inference speed. In online advertising, RT A mechanisms play a central role in determining which traffic are exposed to downstream systems. Since not all incoming traffic contributes equally to campaign performance, an effective interception process is needed to filter out unproductive requests while preserving those that align with predefined objectives. Achieving this goal is particularly challenging because it requires not only the accurate prediction of multiple user-behavior metrics but also dependable estimates of prediction confidence under highly dynamic conditions. A natural way to address these requirements is to combine multi-objective optimization with uncertainty modeling.

We present a reinforcement learning framework for autonomous goalkeeping with humanoid robots in real-world scenarios. While prior work has demonstrated similar capabilities on quadrupedal platforms, humanoid goalkeeping introduces two critical challenges: (1) generating natural, human-like whole-body motions, and (2) covering a wider guarding range with an equivalent response time. Unlike existing approaches that rely on separate teleoperation or fixed motion tracking for whole-body control, our method learns a single end-to-end RL policy, enabling fully autonomous, highly dynamic, and human-like robot-object interactions. To achieve this, we integrate multiple human motion priors conditioned on perceptual inputs into the RL training via an adversarial scheme. We demonstrate the effectiveness of our method through real-world experiments, where the humanoid robot successfully performs agile, autonomous, and naturalistic interceptions of fast-moving balls. In addition to goalkeeping, we demonstrate the generalization of our approach through tasks such as ball escaping and grabbing. Our work presents a practical and scalable solution for enabling highly dynamic interactions between robots and moving objects, advancing the field toward more adaptive and lifelike robotic behaviors.

This paper addresses a critical aerial defense challenge in contested airspace, involving three autonomous aerial vehicles -- a hostile drone (the pursuer), a high-value drone (the evader), and a protective drone (the defender). We present a cooperative guidance framework for the evader-defender team that guarantees interception of the pursuer before it can capture the evader, even under highly dynamic and uncertain engagement conditions. Unlike traditional heuristic, optimal control, or differential game-based methods, we approach the problem within a time-constrained guidance framework, leveraging true proportional navigation based approach that ensures robust and guaranteed solutions to the aerial defense problem. The proposed strategy is computationally lightweight, scalable to a large number of agent configurations, and does not require knowledge of the pursuer's strategy or control laws. From arbitrary initial geometries, our method guarantees that key engagement errors are driven to zero within a fixed time, leading to a successful mission. Extensive simulations across diverse and adversarial scenarios confirm the effectiveness of the proposed strategy and its relevance for real-time autonomous defense in contested airspace environments.

--This paper introduces the concept of trajectory encryption in cooperative simultaneous target interception, wherein heterogeneity in guidance principles across a team of unmanned autonomous systems is leveraged as a strategic design feature. By employing a mix of heterogeneous time-to-go formulations leading to a cooperative guidance strategy, the swarm of vehicles is able to generate diverse trajectory families. This diversity expands the feasible solution space for simultaneous target interception, enhances robustness under disturbances, and enables flexible time-to-go adjustments without predictable detouring. From an adversarial perspective, heterogeneity obscures the collective interception intent by preventing straightforward prediction of swarm dynamics, effectively acting as an encryption layer in the trajectory domain. Simulations demonstrate that the swarm of heterogeneous vehicles is able to intercept a moving target simultaneously from a diverse set of initial engagement configurations. Cooperative intercept missions, once limited to large-scale interceptor systems, are also being realized using agile teams of small drones.

This paper presents an input-constrained nonlinear guidance law to address the problem of intercepting a stationary target in contested environments with multiple defending agents. Contrary to prior approaches that rely on explicit knowledge of defender strategies or utilize conservative safety conditions based on a defender's range, our work characterizes defender threats geometrically through engagement zones that delineate inevitable interception regions. Outside these engagement zones, the interceptor remains invulnerable. The proposed guidance law switches between a repulsive safety maneuver near these zones and a pursuit maneuver outside their influence. To deal with multiple engagement zones, we employ a smooth minimum function (log-sum-exponent approximation) that aggregates threats from all the zones while prioritizing the most critical threats. Input saturation is modeled and embedded in the non-holonomic vehicle dynamics so the controller respects actuator limits while maintaining stability. Numerical simulations with several defenders demonstrate the proposed method's ability to avoid engagement zones and achieve interception across diverse initial conditions.