Fox News chief national security correspondent Jennifer Griffin reports that the U.S. military has shot down'dozens' of ballistic and cruise missiles as well as attack drones. The Philippine president said Monday his administration has no plan to give the U.S. military access to more Philippine army camps and stressed that the American military presence was sparked by China's aggressive actions in the disputed South China Sea. President Ferdinand Marcos Jr., who took office in 2022, allowed American forces and weapons access to four additional Philippine military bases, bringing to nine the number of camps where U.S. troops can rotate indefinitely under a 2014 agreement. The Biden administration has been strengthening an arc of security alliances in the region to better counter China, a move that dovetails with Philippine efforts to shore up its external defense, especially in the South China Sea. Marcos' decision last year alarmed China because two of the new sites were located just across from Taiwan and southern China.

Video captures the moment and aftermath of what appears to be a drone, allegedly of Ukrainian origin, striking Russian drone production facility. Russian officials claimed that only a worker's dormitory was hit. More civilians died across Ukraine on Sunday as analysts warned that delays in U.S. military assistance would see Kyiv struggle to fight off Russian offensives. One man was killed Sunday after a Russian drone hit the truck he was driving in the Sumy region, the local prosecutor's office said. Elsewhere, a 67-year-old woman was killed after shelling hit an apartment block in the Donetsk region, said Gov. Vadym Filashkin. Officials in the Kharkiv region also said Sunday that they had retrieved the bodies of a 61-year-old woman and a 68-year-old man killed by a Russian strike the previous day.

After several reports of artificial intelligence-generated child pornography surfaced in California, Ventura County Dist. Erik Nasarenko advocated for a change to state law to protect children who are increasingly vulnerable to this misuse of technology. Last December, Nasarenko received his first tip regarding a person who had artificially created photos depicting an underaged girl performing sex acts with an adult man. "When it came to my attention, I said let's file [charges]," Nasarenko told The Times. But, because of current loopholes in California law, he learned that he couldn't press charges in cases where the photos of children are AI-generated.

"Noise" is a fuzzy word--a noisy one, in the statistical sense. Its meanings run the gamut from the negative to the positive, from the overpowering to the mysterious, from anarchy to sublimity. The negative seems to lie at the root: etymologists trace the word to "nuisance" and "nausea." Noise is what drives us mad; it sends the Grinch over the edge at Christmastime. ("Oh, the Noise! Noise!") Noise is the sound of madness itself, the din within our minds. The demented narrator of Poe's "The Tell-Tale Heart" jabbers about noise while he hallucinates his victim's heartbeat: "I found that the noise was not within my ears. . . . The noise steadily increased. . . . Yet noise can be righteous and majestic. The Psalms are full of joyful noise, noise unto the Lord. In the Book of Ezekiel, the voice of God is said to be "like a noise of many waters." In "Paradise Lost," Heaven makes "infernal noise" as it beats back the armies of Hell. At the same time, the word can summon all manner of ...

Millions of people have watched Mike Hughes die. It happened on February 22, 2020, not far from Highway 247 near the Mojave Desert city of Barstow, California. A homemade rocket ship with Hughes strapped in it took off from a launching pad mounted on a truck. A trail of steam billowed behind the rocket as it swerved and then shot upward, a detached parachute unfurling ominously in its wake. In a video recorded by the journalist Justin Chapman, Hughes disappears into the sky, a dark pinpoint in a vast, uncaring blueness.

The ruling Liberal Democratic Party on Monday unveiled its first poster featuring a catchphrase created using generative artificial intelligence. The slogan, written in red on a white background, pledges to the public a real feeling of economic revitalization, amid Prime Minister and LDP President Fumio Kishida's drive to raise wages to fuel economic growth. Generative AI tools, including ChatGPT, studied Kishida's remarks and party policy documents over the past three years to draw up drafts, according to people familiar with the matter. The AI-crafted slogan was chosen after LDP executives screened more than 500 candidate phrases, including ones proposed by copywriters. "This doesn't mean at all that an election will be called soon," Takuya Hirai, chair of the LDP's Public Relations Headquarters, told a news conference, referring to speculation that Kishida will call a snap general election as early as June.

It is assumed that readers are already familiar with Language Models of various flavors such as: Transformer-based Language Models (currently the most promising and studied LMs) [78]; Multimodal Foundation Models such as Blip-2 [48] and CLIP [61]; Auto-regressive Language Models [15, 51]; Recurrent Neural Network Language Models [75]; State space language models [40]; Hybrid Models [24] as well as the current and proposed use cases and the various technologies underlying them [1, 65, 70]. There is growing interest in LM performance and benchmarks [13, 16, 18,46, 47, 64,72, 74, 80] with recent acknowledgement that this is a hard problem [53]. Many suggestions are proposed in the commercial literature [17] and a large number of benchmark-based methods have surfaced (Big Bench [67], GLUE Benchmark, SuperGLUE Benchmark, OpenAI Moderation API, MMLU, EleutherAI LM Eval, OpenAI Evals Adversarial NLI, LIT, ParlAI, CoQA, LAMBADA, HellaSwag, LogiQA, MultiNLI, SQUAD to name a few). A review of existing approaches demonstrates that they are not quantitative or rigorous enough to past muster with respect to accepted testing requirements [3, 55]. In particular, existing use of benchmarks do not investigate the extent to which a benchmark can predict or quantify certain properties on future prompts (that is, statistical soundness of any conclusions) and do not identify factors affecting performance dependence as would be possible with more rigorous experimental design and test execution. LMs can be black box, gray box or white box according to the visibility into the architecture and training data used to create an LM (see Table 1). Remote Black Box LMs typically throttle the number of prompts so sustained access for testing could be difficult unless priority access to an API is given. For example, ChatGPT limits users to a small number of free prompts but allows unlimited prompts on its subscription option. Additionally, reproducability may not be guaranteed because of randomness in the response generation and/or continuous adaptation of the LM platform.

In an era of increased climatic disasters, there is an urgent need to develop reliable frameworks and tools for evaluating and improving community resilience to climatic hazards at multiple geographical and temporal scales. Defining and quantifying resilience in the social domain is relatively subjective due to the intricate interplay of socioeconomic factors with disaster resilience. Meanwhile, there is a lack of computationally rigorous, user-friendly tools that can support customized resilience assessment considering local conditions. This study aims to address these gaps through the power of CyberGIS with three objectives: 1) To develop an empirically validated disaster resilience model - Customized Resilience Inference Measurement designed for multi-scale community resilience assessment and influential socioeconomic factors identification, 2) To implement a Platform for Resilience Inference Measurement and Enhancement module in the CyberGISX platform backed by high-performance computing, 3) To demonstrate the utility of PRIME through a representative study. CRIM generates vulnerability, adaptability, and overall resilience scores derived from empirical hazard parameters. Computationally intensive Machine Learning methods are employed to explain the intricate relationships between these scores and socioeconomic driving factors. PRIME provides a web-based notebook interface guiding users to select study areas, configure parameters, calculate and geo-visualize resilience scores, and interpret socioeconomic factors shaping resilience capacities. A representative study showcases the efficiency of the platform while explaining how the visual results obtained may be interpreted. The essence of this work lies in its comprehensive architecture that encapsulates the requisite data, analytical and geo-visualization functions, and ML models for resilience assessment.

DNA-encoded libraries (DELs) are a powerful approach for rapidly screening large numbers of diverse compounds. One of the key challenges in using DELs is library design, which involves choosing the building blocks that will be combinatorially combined to produce the final library. In this paper we consider the task of protein-protein interaction (PPI) biased DEL design. To this end, we evaluate several machine learning algorithms on the PPI modulation task and use them as a reward for the proposed GFlowNet-based generative approach. We additionally investigate the possibility of using structural information about building blocks to design a hierarchical action space for the GFlowNet. The observed results indicate that GFlowNets are a promising approach for generating diverse combinatorial library candidates.

Cyber attacks continue to pose significant threats to individuals and organizations, stealing sensitive data such as personally identifiable information, financial information, and login credentials. Hence, detecting malicious websites before they cause any harm is critical to preventing fraud and monetary loss. To address the increasing number of phishing attacks, protective mechanisms must be highly responsive, adaptive, and scalable. Fortunately, advances in the field of machine learning, coupled with access to vast amounts of data, have led to the adoption of various deep learning models for timely detection of these cyber crimes. This study focuses on the detection of phishing websites using deep learning models such as Multi-Head Attention, Temporal Convolutional Network (TCN), BI-LSTM, and LSTM where URLs of the phishing websites are treated as a sequence. The results demonstrate that Multi-Head Attention and BI-LSTM model outperform some other deep learning-based algorithms such as TCN and LSTM in producing better precision, recall, and F1-scores.