The United States has imposed sanctions on an Iranian company it accused of coordinating military flights to transport Iranian drones to Russia and three other companies it said were involved in the production of Iranian drones. The United States accuses Iran of supplying drones to Russia for use in its war in Ukraine, which Tehran has denied. The US Treasury Department, in a statement on Thursday, said it designated Tehran-based Safiran Airport Services, accusing it of coordinating Russian military flights between Iran and Russia, including those associated with transporting drones, personnel and related equipment. The Treasury also designated Paravar Pars Company, Design and Manufacturing of Aircraft Engines and Baharestan Kish Company, accusing them of being involved in the research, development, production and procurement of Iranian drones. The Treasury singled out Paravar Pars Company for involvement in the reverse engineering of US and Israeli-made drones, without specifying which models.

Taipei, Taiwan – China is facing a steeper climb to overtake the United States and its allies in semiconductors as Washington ramps up measures to restrict Beijing's ability to produce advanced chips and secure dominance over the strategic technology. On Wednesday, Washington restricted the sale to China of select Nvidia and AMD advanced graphic processor units (GPUs) used in artificial intelligence applications and supercomputers. The move followed the US Commerce Department's announcement last month of a ban on exports to China of electronic design automation (EDA) software used in the production of next-generation chips. Meanwhile, Washington has been nudging East Asian partners Taiwan, South Korea, and Japan to form a "Chip 4" industry alliance to isolate China from the international tech ecosystem, and bolstered efforts to develop its homegrown industry with the passage of the CHIPS Act, offering $52bn in subsidies to firms that make chips on US soil. "The US is trying to reinforce its central role in the world's semiconductor ecosystem and ensure that China is unable to produce the most cutting edge chips," Chris Miller, author of the upcoming book Chip War: The Fight for the World's Most Critical Technology, told Al Jazeera.

Sign up for dot.LA's daily newsletter for the latest news on Southern California's tech, startup and venture capital scene. Computers can now write poems, paint portraits and produce music better than many humans. The case will now head to federal court as the AI program's owner, Stephen Thaler, plans to file an appeal, according to Ryan Abbott, a Los Angeles-based attorney representing Thaler. The case arrives as artists are increasingly using AI to help generate artwork, including works produced by autonomous machines. Abbott, a partner at L.A.-based law firm Brown, Neri, Smith & Khan, noted that AI-produced artwork is creating significant commercial value, such as an AI-authored painting that sold for $432,000 at auction in 2018.

A widely established set of unsupervised node embedding methods can be interpreted as consisting of two distinctive steps: i) the definition of a similarity matrix based on the graph of interest followed by ii) an explicit or implicit factorization of such matrix. Inspired by this viewpoint, we propose improvements in both steps of the framework. On the one hand, we propose to encode node similarities based on the free energy distance, which interpolates between the shortest path and the commute time distances, thus, providing an additional degree of flexibility. On the other hand, we propose a matrix factorization method based on a loss function that generalizes that of the skip-gram model with negative sampling to arbitrary similarity matrices. Compared with factorizations based on the widely used $\ell_2$ loss, the proposed method can better preserve node pairs associated with higher similarity scores. Moreover, it can be easily implemented using advanced automatic differentiation toolkits and computed efficiently by leveraging GPU resources. Node clustering, node classification, and link prediction experiments on real-world datasets demonstrate the effectiveness of incorporating free-energy-based similarities as well as the proposed matrix factorization compared with state-of-the-art alternatives.

We introduce a method for explaining the results of various linear and hierarchical multi-criteria decision-making (MCDM) techniques such as WSM and AHP. The two key ideas are (A) to maintain a fine-grained representation of the values manipulated by these techniques and (B) to derive explanations from these representations through merging, filtering, and aggregating operations. An explanation in our model presents a high-level comparison of two alternatives in an MCDM problem, presumably an optimal and a non-optimal one, illuminating why one alternative was preferred over the other one. We show the usefulness of our techniques by generating explanations for two well-known examples from the MCDM literature. Finally, we show their efficacy by performing computational experiments.

The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment with reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies for adopting the Metaverse for healthcare, the potential applications and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.

Most proposals in the anomaly detection field focus exclusively on the detection stage, specially in the recent deep learning approaches. While providing highly accurate predictions, these models often lack transparency, acting as "black boxes". This criticism has grown to the point that explanation is now considered very relevant in terms of acceptability and reliability. In this paper, we addressed this issue by inspecting the ADMNC (Anomaly Detection on Mixed Numerical and Categorical Spaces) model, an existing very accurate although opaque anomaly detector capable to operate with both numerical and categorical inputs. This work presents the extension EADMNC (Explainable Anomaly Detection on Mixed Numerical and Categorical spaces), which adds explainability to the predictions obtained with the original model. We preserved the scalability of the original method thanks to the Apache Spark framework. EADMNC leverages the formulation of the previous ADMNC model to offer pre hoc and post hoc explainability, while maintaining the accuracy of the original architecture. We present a pre hoc model that globally explains the outputs by segmenting input data into homogeneous groups, described with only a few variables. We designed a graphical representation based on regression trees, which supervisors can inspect to understand the differences between normal and anomalous data. Our post hoc explanations consist of a text-based template method that locally provides textual arguments supporting each detection. We report experimental results on extensive real-world data, particularly in the domain of network intrusion detection. The usefulness of the explanations is assessed by theory analysis using expert knowledge in the network intrusion domain.

The unmanned aerial vehicles (UAVs) technology, which is moving towards full autonomous flight, requires operation under uncertainties due to dynamic environments, interaction with humans, system faults, and even malicious cyber attacks. Ensuring security and safety is the first step to making the solutions using such systems certifiable and scalable. In this paper, we introduce an autopilot framework called "Multi-level Adaptive Safety Control" (MASC) for the resilient control of autonomous UAVs under large uncertainties and employ it for engine-out automatic landing under severe weather conditions. A. MASC Architecture In 2009, an Airbus A320 passenger plane (US Airways flight 1549) lost both engines minutes after take-off from LaGuardia airport in New York City due to severe bird strikes [1]. Captain Sullenberger safely landed the plane in the nearby Hudson River. Inspired by this story, we aim to equip UAVs with the capability of human pilots to determine if the current mission is still possible after a severe system failure. If not, the mission is re-planned so that it can be accomplished using the remaining capabilities. This is achieved by the proposed autopilot framework, MASC, which is capable of performing safe maneuvers that are traditionally reserved for human pilots.

Machine learning methods must be trusted to make appropriate decisions in real-world environments, even when faced with out-of-distribution (OOD) samples. Many current approaches simply aim to detect OOD examples and alert the user when an unrecognized input is given. However, when the OOD sample significantly overlaps with the training data, a binary anomaly detection is not interpretable or explainable, and provides little information to the user. We propose a new model for OOD detection that makes predictions at varying levels of granularity as the inputs become more ambiguous, the model predictions become coarser and more conservative. Consider an animal classifier that encounters an unknown bird species and a car. Both cases are OOD, but the user gains more information if the classifier recognizes that its uncertainty over the particular species is too large and predicts bird instead of detecting it as OOD. Furthermore, we diagnose the classifiers performance at each level of the hierarchy improving the explainability and interpretability of the models predictions. We demonstrate the effectiveness of hierarchical classifiers for both fine- and coarse-grained OOD tasks.

In this paper, we present a manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied access to testing, claimed cures and preventions. We designed slot-filling questions for each event type and annotated a total of 31 fine-grained slots, such as the location of events, recent travel, and close contacts. We show that our corpus can support fine-tuning BERT-based classifiers to automatically extract publicly reported events and help track the spread of a new disease. We also demonstrate that, by aggregating events extracted from millions of tweets, we achieve surprisingly high precision when answering complex queries, such as "Which organizations have employees that tested positive in Philadelphia?" We will release our corpus (with user-information removed), automatic extraction models, and the corresponding knowledge base to the research community.