Assad regime planned US journalist's kidnap for weeks, BBC finds US journalist Austin Tice, who disappeared in Syria in 2012, was kidnapped in a calculated operation which was more than a month in the planning, the BBC can reveal. A member of the group which detained Tice has also claimed the abduction was sanctioned by Syria's leader at the time, President Bashar al-Assad. Tice was seen getting into a car with a driver he knew and trusted. For years, many assumed they had stumbled upon a government checkpoint, and that the abduction of the journalist had been opportunistic. But we have uncovered evidence that, in fact, his disappearance was anything but chance.

Operation Epic Fury targets Iran's nuclear weapon program, ballistic missile sites, and drone capabilities while sanctions choke oil exports and sever economic lifelines.

'This is an apartheid regime' Does Trump have real leverage over Netanyahu? A film documenting Israel's use of AI-powered systems to target Palestinians in Gaza has received a 25-minute standing ovation at its premiere at the Venice Film Festival. The 80-minute film, NAZA, was directed by Israeli filmmakers Yuval Abraham and Rachel Szor, who won an Oscar in 2025 for No Other Land, a documentary on Israeli settler pogroms in the occupied West Bank, made with Palestinian co-directors Basel Adra and Hamdan Ballal. Their voices and faces were digitally altered, and they were filmed at night on Tel Aviv rooftops. "We are letting the soldiers and the officers speak about what they did," Abraham told the AFP news agency.

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Improving energy performance can effectively extend the time a robot can operate and reduce battery load, enabling lighter, more flexible, and more durable robotic systems. Nature has evolved optimal energy-saving locomotion strategies through billions of years of natural selection, providing unparalleled blueprints for robotic optimization. Among diverse modes of aquatic locomotion, intermittent swimming, also called bout-and-glide swimming, is a widespread adaptive behavior in aquatic organisms of a wide range of sizes, including larval zebrafish, red-nose tetra, koi carp, and even whales. This natural bout-and-glide gait features alternating motion phases: short periods of active body and tail undulation for propulsion, followed by passive gliding with a streamlined, straight body posture. It is widely recognized that this intermittent swimming gait is closely associated with optimizing biological energy, making it of great research value to transplant and explore such natural motion mechanisms into robotic control systems.

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Steve Forbes argues that the Trump administration should ratchet up pressure to remove the Iranian regime, would not discard 'hand-to-hand combat' to keep Strait of Hormuz open.

In such settings, the inverse scattering transform (IST) of Ablowitz, Kaup, Newell, and Segur [2] has enjoyed a rich and successful history, and is now the standard theoretical framework for deriving reduced-order evolution equations for soliton dynamics. Although these derivations are traditionally of an analytical - rather than data-driven - nature, recent work has employed the IST formalism as a tool for experimental data analysis, using the technique to analyze soliton content from empirical measurements [8, 15, 24]. Moreover, recent approaches using alternative parameterization techniques have demonstrated that the learning of reduced-order, interpretable equations of motion for solitons is tenable in a data-driven setting [6, 26, 27]. Despite the success of this recent work, however, little effort has been devoted to developing a data-driven modeling approach based on the IST itself, most likely due to the fact that the framework is fundamentally problem-specific. In this paper, we address the question of whether effective soliton dynamics can be inferred directly from observed scattering data (as opposed to being derived or approximated analytically).

Recent work has established a fundamental trilemma between Byzantine robustness, local differential privacy (LDP), and optimization error in distributed learning. We show that this trilemma does not universally extend to generalization error, but instead depends critically on the privacy regime. Specifically, in the high-noise regime (strong privacy), we prove that increasing privacy reduces the generalization error, i.e., there is no tension between robustness and privacy. In the low-noise regime (weaker privacy), however, the tension between robustness and privacy reappears and increasing privacy indeed degrades generalization. Our theory explains this surprising non-monotonic behavior of the generalization error via matching lower and upper bounds on the algorithmic stability of Byzantine-robust distributed learning under LDP constraints. We corroborate and further analyze these theoretical findings with empirical evaluations.

In recent years, it has received increasing attention, particularly through post-hoc detection and training-based methods.