Brain-computer interfaces (BCIs) are bidirectional systems that interact with the brain, allowing neuronal stimulation as well as the acquisition of neural data. Being invasive interfaces extensively used in medical therapy, BCIs can be classified according to their invasiveness level. In this sense and as an example, invasive BCIs focused on neural recording have been used to control prosthetic limbs in impaired patients, while BCIs for neuromodulation have been helpful for treating neurodegenerative conditions, such as Parkinson's disease.9 The second main family of BCIs, in terms of invasiveness, is the non-invasive one. BCIs based on non-invasive principles and, mainly, those focused on neural data acquisition such as electroencephalography (EEG), have gained popularity in recent years, extending their usage from traditional medical scenarios to new domains, such as entertainment or video games. However, despite the benefits of non-invasive BCIs, some works in the literature have identified cybersecurity issues from a neural data acquisition perspective. Martinovic et al.19 demonstrated that an attacker could obtain sensitive personal data from BCI users, taking advantage of their cerebral responses (P300 potentials) when presented with known visual stimuli. Bonaci et al.1 also described a scenario where attackers could maliciously add or modify software modules that cause the BCI to take dangerous action against users. Finally, Takabi et al.24 highlighted that most APIs used to develop BCI applications offered complete access over the information acquired by the BCI, presenting confidentiality problems. Cybersecurity of invasive BCIs is also a challenge that has been identified in the literature and whose application is in its initial stages3,4,8 This situation is complicated by the recent introduction of novel BCI designs based on nanotechnology aiming to surpass the limitations of traditional BCIs.

America's credibility among its adversaries has dwindled under President Biden, with some experts arguing a line can be drawn from the disastrous U.S. withdrawal from Afghanistan to more recent events such as the Chinese spy balloon and the downing of a U.S. drone by Russian forces. "I think the Biden administration's disastrous withdrawal from Afghanistan was a key catalyst for multiple trends that have undermined U.S. influence and deterrence," James Phillips, the senior research fellow for foreign policy at the Heritage Foundation, told Fox News Digital. "U.S. allies were shocked by the naive assumptions behind the withdrawal, the speed with which Washington abandoned longtime allies, and the incompetence of the policymakers that supervised the withdrawal." Phillips argues that it was not just American allies who took note of the administration's hastily executed exit from Afghanistan, but also adversaries such as China and Russia, who no longer fear U.S. deterrence. "U.S. adversaries perceived the withdrawal from Afghanistan as a manifestation of U.S. weakness and a desire to rapidly exit the Middle East," Phillips said.

The emergence of effective natural language processing tools such as ChatGPT means it's time to begin understanding how to harden against AI-enabled cyberattacks. The natural language generation capabilities of large language models (LLMs) are a natural fit for one of cybercrime's most important attack vectors: phishing. Phishing relies on fooling people and the ability to generate effective language and other content at scale is a major tool in the hacker's kit. Fortunately, there are several good ways to mitigate this growing threat. A leader tasked with cybersecurity can get ahead of the game by understanding where we are in the story of machine learning (ML) as a hacking tool.

America's Federal Trade Commission has warned it may crack down on companies that not only use generative AI tools to scam folks, but also those making the software in the first place, even if those applications were not created with that fraud in mind. Now the US government agency is wagging its finger at those using generative machine-learning tools to hoodwink victims into parting with their cash and suchlike as well as the people who made the code to begin with. Commercial software and cloud services, as well as open source tools, can be used to churn out fake images, text, videos, and voices on an industrial scale, which is all perfect for cheating marks. Picture adverts for stuff featuring convincing but faked endorsements by celebrities; that kind of thing is on the FTC's radar. And to be clear, there are no new rules or regulations at play here: it's just the FTC doing its usual thing of reminding people that today's tech fads are still covered by consumer protection laws, in the US at least.

U.S. CENTCOM Commander General Michael Kurilla told senators Thursday that the Pentagon has seen an "increase recently in the unprofessional and unsafe behavior of the Russian air force." The U.S. military carried out several airstrikes in Syria on Thursday in response to a drone strike Iranian forces conducted earlier in the day on a coalition base that killed one American. The Defense Department said Iran's Islamic Revolutionary Guards Corps crashed a UAV into a building near Hasakah in northeast Syria at approximately 1:38 p.m. local time, leaving one U.S. contractor dead. The attack also wounded five U.S. service members and another U.S. contractor. U.S. intelligence assessed the UAV and determined it to be of Iranian origin -- so President Biden authorized the military to retaliate, the Pentagon said.

The United States military has said it carried out multiple air strikes in eastern Syria against Iran-aligned groups who it blamed for a deadly drone attack earlier that killed a contractor, injured another, and wounded five US troops, the Pentagon said. The US attacks late on Thursday night were in retaliation for an attack against a US-led coalition base near Hassakeh in northeast Syria at approximately 01:38pm (10:38 GMT) the same day, the Pentagon said in a statement. US intelligence has assessed that the drone was Iranian in origin and US Defence Secretary Lloyd Austin said the strikes targeted groups affiliated with Iran's Islamic Revolutionary Guards Corps in eastern Syria. "The airstrikes were conducted in response to today's attack as well as a series of recent attacks against Coalition forces in Syria by groups affiliated with the IRGC," Austin said in a statement. Austin said he authorised the retaliatory strikes at the direction of US President Joe Biden.

It's early autumn in central China, and the streets of Ding Qingzi's village are turning into gold. Thousands of husked corncobs lie in orderly rectangles in front of homes, their kernels drying in the sun. The harvest is one of the heartbeats of rural life in Anhui Province, a constant that Ding, 35, has known since childhood. Yet few other rhythms remain. Except for the corn, the streets are almost empty. The sounds of children have faded. And for years, Ding struggled to find a wife. Few young women still live in the village. Fewer still would marry a welder unable to buy a house or pay a bride-price. "My family is not rich," Ding says.

SEOUL – North Korea has tested a new nuclear-capable underwater attack drone that can generate a radioactive tsunami, state media reported on Friday, as it blamed joint military drills by South Korea and the U.S. for raising tensions in the region. During the drill, the new North Korean drone cruised underwater at a depth of 80 to 150 meters for over 59 hours and detonated in waters off its east coast on Thursday, North Korean state news agency KCNA said. Dubbed Haeil, or tsunami, the drone system is intended to make sneak attacks in enemy waters and destroy naval striker groups and major operational ports by making a superscale radioactive wave through an underwater explosion, the KCNA said. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

North Korea has tested a new underwater nuclear-capable attack drone designed to unleash a "radioactive tsunami" that would destroy enemy naval vessels and ports, state media has reported. During a military exercise conducted this week under the guidance of the country's leader Kim Jong Un, North Korea's military deployed and test-fired the new weapons system, the mission of which was to test the ability to set off a "super-scale" destructive blast and wave, the country's state news agency KCNA said on Friday. "This nuclear underwater attack drone can be deployed at any coast and port or towed by a surface ship for operation," KCNA said. The news agency said that during the exercise, the drone was put in the water off South Hamgyong province on Tuesday and cruised underwater for 59 hours and 12 minutes, at a depth of some 80 to 150 metres (260 to 490 feet), before detonating in waters off its east coast on Thursday. KCNA did not elaborate on the drone's nuclear capabilities.

Generalizability of Functional Forms for Interatomic Potential Models Discovered by Symbolic Regression Alberto Hernandez and Tim Mueller ABSTRACT In recent years there has been great progress in the use of machine learning algorithms to develop interatomic potential models. Machine-learned potential models are typically orders of magnitude faster than density functional theory but also orders of magnitude slower than physics-derived models such as the embedded atom method. In our previous work, we used symbolic regression to develop fast, accurate and transferrable interatomic potential models for copper with novel functional forms that resemble those of the embedded atom method. To determine the extent to which the success of these forms was specific to copper, here we explore the generalizability of these models to other facecentered cubic transition metals and analyze their out-of-sample performance on several material properties. We found that these forms work particularly well on elements that are chemically similar to copper. When compared to optimized Sutton-Chen models, which have similar complexity, the functional forms discovered using symbolic regression perform better across all elements considered except gold where they have a similar performance. They perform similarly to a moderately more complex embedded atom form on properties on which they were trained, and they are more accurate on average on other properties. We attribute this improved generalized accuracy to the relative simplicity of the models discovered using symbolic regression. We discuss the implications of these results to the broader application of symbolic regression to the development of new potentials and highlight how models discovered for one element can be used to seed new searches for different elements. I. INTRODUCTION Researchers across several fields apply molecular dynamics and Monte Carlo simulations to advance the scientific understanding, discovery, and design of materials and molecules. Using these methods, the thermodynamic and kinetic properties of a material can be computed with knowledge of the potential energy surface. Ab initio methods such density functional theory [1] (DFT), which has demonstrated good predictive accuracy [2-4] across many chemistries and configurations of atoms, can be used to compute the potential energy surface, but the computational cost and non-linear scaling of these methods severely limits the time scale and number of atoms that can be practically modeled. Surrogate models, such as cluster expansions [5] and interatomic potential models (or force fields) [6-15], are normally orders of magnitude faster than ab initio methods and usually scale linearly with respect to system size. The improved speed and scaling of surrogate models enable atomistic simulations that inform the design of materials at larger time and length scales. Different types of interatomic potentials are commonly used for materials modeling.