Generating unsafe sub-requirements from a partitioned input space to support verification-guided test cases for formal verification of black-box models is a challenging problem for researchers. The size of the search space makes exhaustive search computationally impractical. This paper investigates a meta-heuristic approach to search for unsafe candidate sub-requirements in partitioned input space. We present a Negative Selection Algorithm (NSA) for identifying the candidates' unsafe regions within given safety properties. The Meta-heuristic capability of the NSA algorithm made it possible to estimate vast unsafe regions while validating a subset of these regions. We utilize a parallel execution of partitioned input space to produce safe areas. The NSA based on the prior knowledge of the safe regions is used to identify candidate unsafe region areas and the Marabou framework is then used to validate the NSA results. Our preliminary experimentation and evaluation show that the procedure finds candidate unsafe sub-requirements when validated with the Marabou framework with high precision.

Computed Tomography (CT) is an imaging technique where information about an object are collected at different angles (called projections or scans). Then the cross-sectional image showing the internal structure of the slice is produced by solving an inverse problem. Limited by certain factors such as radiation dosage, projection angles, the produced images can be noisy or contain artifacts. Inspired by the success of transformer for natural language processing, the core idea of this preliminary study is to consider a projection of tomography as a word token, and the whole scan of the cross-section (A.K.A. sinogram) as a sentence in the context of natural language processing. Then we explore the idea of foundation model by training a masked sinogram model (MSM) and fine-tune MSM for various downstream applications including CT reconstruction under data collections restriction (e.g., photon-budget) and a data-driven solution to approximate solutions of the inverse problem for CT reconstruction. Models and data used in this study are available at https://github.com/lzhengchun/TomoTx.

Chronic kidney disease (CKD) represents a slowly progressive disorder that can eventually require renal replacement therapy (RRT) including dialysis or renal transplantation. Early identification of patients who will require RRT (as much as 1 year in advance) improves patient outcomes, for example by allowing higher-quality vascular access for dialysis. Therefore, early recognition of the need for RRT by care teams is key to successfully managing the disease. Unfortunately, there is currently no commonly used predictive tool for RRT initiation. In this work, we present a machine learning model that dynamically identifies CKD patients at risk of requiring RRT up to one year in advance using only claims data. To evaluate the model, we studied approximately 3 million Medicare beneficiaries for which we made over 8 million predictions. We showed that the model can identify at risk patients with over 90% sensitivity and specificity. Although additional work is required before this approach is ready for clinical use, this study provides a basis for a screening tool to identify patients at risk within a time window that enables early proactive interventions intended to improve RRT outcomes.

There has been considerable interest in the regulation of artificial intelligence (AI), recently. It is increasingly recognized that so-called high-risk applications of AI, such as in Human Resources, Retail Banking, or within public schools, be it admissions or assessment, cannot be served by black-box AI systems with no human control. It is not clear [10], however, how to phrase even the desiderata for the regulation of AI. Here, we suggest that the desiderata could be the same as in the Civil Rights Act of 1964 and much of the subsequent civil-right legislation world-wide: equal treatment and equal impact. At the same time, we point out that these desiderata could be in conflict [34]. Let us illustrate the conflict on an example of a system that performs credit-risk estimate in a consumer-credit company.

While the news shocked the chip sector by the time markets closed Thursday, sending the Philadelphia semiconductor index down 1.9% and Nvidia and AMD down 7.6% and 3% respectively, the letters from the U.S. officials appeared to target a narrow but critical part of China's computing industry. The regulations appear to focus on chips called GPUs with the most powerful computing capabilities, a critical but niche market with only two meaningful players, Nvidia and AMD. Their only potential rival - Intel Corp - is trying to break into the market but has not released competitive products. Originally designed for video games, the usage of GPUs, or graphic processing units, have been expanded to a wider array of applications that include handling artificial intelligence work like image recognition, categorizing cat photos or scouring digital satellite imagery for military equipment. Because all the chip suppliers are American, the U.S. controls access to the technology.

In that case, the Navy said it spotted an Iranian Islamic Revolutionary Guard Corps naval support ship towing a Saildrone Explorer. The American seamen told the Iranians that the drone was U.S. government property and asked for it back. With a U.S. Fifth Fleet MH-60S Sea Hawk helicopter hovering above, the Iranians disconnected the towing line they had attached to that drone and eventually departed the area, according to a U.S. Navy release at the time. The Navy resumed operations "without incident," the release said. The United States and Iran often tangle in the Persian Gulf -- which the United States calls the Arabian Gulf -- and in the Red Sea and the Arabian Sea.

The Biden administration is working to limit U.S. exports of certain semiconductors and equipment because of fears that Chinese companies could use them for military purposes. Graphics processors like the kind that Nvidia and Advanced Micro Devices make are well suited for artificial intelligence applications that could include weapons development, facial recognition and other military uses. The H100 is Nvidia's upcoming enterprise AI chip that was previously expected to ship by the end of the year. Part of its development takes place in China. The A100 is an older model that has been shipping for three years. They are both graphics processors that can be used for supercomputing and artificial intelligence.

Iran's navy has released two American surface drones hours after seizing them in the Red Sea, accusing the unmanned vessels of jeopardising maritime safety, Iranian state television reports, in the second such incident this week. "The [Iranian navy] frigate Jamaran seized the two vessels on Thursday to prevent any possible accident after issuing warnings to the US fleet. After international shipping lanes were secured, the two vessels were released in a safe area," the state TV reported on Friday. Footage appeared to show more than a dozen Iranian navy personnel pushing two drones into the sea from the deck of their vessel – the latest maritime incident involving the United States Navy's new drone fleet in the Middle East as negotiations over Tehran's nuclear deal with the world powers hang in the balance. The state TV said an Iranian naval flotilla found "several unmanned spying vessels abandoned in the international maritime routes" and "after warning an American destroyer twice, seized the two drone vessels to prevent possible accidents".

In a Securities and Exchange Commission filing last Friday, Nvidia reported that US government officials have ordered restrictions on sales of its top AI chips to China and Russia. The new restrictions (in the form of licensing requirements, subject to approval by the US government) include the powerful A100 Tensor Core GPU, the upcoming H100, and any chips of equivalent power or systems that incorporate them. The goal is to "address the risk that the covered products may be used in, or diverted to, a'military end use' or'military end user' in China and Russia," according to Nvidia, which notes that the firm already does not sell products to customers in Russia. Reuters reports that the Department of Commerce intends for the new policy to "keep advanced technologies out of the wrong hands." The US has also restricted sales of AMD's MI250 Accelerator AI chip to China.

NVIDIA has revealed that it's subject to new laws restricting the export of AI chips to China and Russia. In an SEC filing, NVIDIA says the US government has informed the chipmaker of a new license requirement that impacts two of its GPUs designed to speed up machine learning tasks: the current A100, and the upcoming H100. "The license requirement also includes any future NVIDIA integrated circuit achieving both peak performance and chip-to-chip I/O performance equal to or greater than thresholds that are roughly equivalent to the A100, as well as any system that includes those circuits," adds NVIDIA. The US government has reportedly told NVIDIA that the new rules are geared at addressing the risk of the affected products being used for military purposes. "While we are not in a position to outline specific policy changes at this time, we are taking a comprehensive approach to implement additional actions necessary related to technologies, end-uses, and end-users to protect US national security and foreign policy interests," said a US Department of Commerce spokesperson.