Such processes are visible in many applications ranging from geoscience, bioscience, and engineering to computer vision. In particular, deep learning algorithms, such as neural network parameterized normalizing flows, neural ordinary differential equations, diffusion models, and generative adversarial networks, have shown remarkable advances in learning and enabling rapid sampling from these stochastic processes. Such advances are further pronounced for very high-dimensional systems where classical methods are seen to saturate their effectiveness. However, the effective use of deep learning is frequently hampered by difficulties associated with computational cost as well as optimal hyperparameter selection. In this article, we propose a novel approach based on projection-pursuit optimal transport, which learns to sample from the densities of time-varying stochastic processes. It is competitive (both in terms of computational cost and accuracy) with a state-of-the-art deep learning algorithm (given by the neural spline flow). Crucially, our proposed method requires few hyperparameter choices by the user in contrast with most neural network-based methodologies. Thus, our main contributions to this work are as follows: 1. We implement a projection-pursuit optimal transport-based method to learn maps between time-varying densities from snapshots of particles sampled from these densities.

FOX News' Jennifer Griffin reports the latest on the Israel-Hamas war. Sirens are sounding off in northern Israel on Wednesday and residents are being told to shelter in place amid reports of an incoming "large-scale" drone attack. Israeli Defense Forces said it received a report of a "suspected infiltration" from Lebanon into Israeli airspace. "All residents in the areas where the warning was sounded are asked to enter the protected areas and stay in them until further notice," IDF said. "Israelis throughout the country were ordered to shelter in place amid a large-scale attack involving drones coming from the direction of the northern border on Wednesday evening," the Jerusalem Post reported.

For decades, the Department of Commerce has maintained a little-known list of technologies that, on grounds of national security, are prohibited from being sold freely to foreign countries. Any company that wants to sell such a technology overseas must apply for permission, giving the department oversight and control over what is being exported and to whom. These export controls are now inflaming tensions between the United States and China. They have become the primary way for the U.S. to throttle China's development of artificial intelligence: The department last year limited China's access to the computer chips needed to power AI and is in discussions now to expand them. A semiconductor analyst told The New York Times that the strategy amounts to a kind of economic warfare.

The United Nations' main internet governance body looks set to host its next international forum in Riyadh, Saudi Arabia. In 2025, the UN may take its discussions on the future of an open internet to Russia. Holding the Internet Governance Forum (IGF), back to back, in authoritarian countries notorious for their surveillance and censorship of the internet risks making "a joke of the whole system," one advocate says. While the UN has yet to formally announce the host countries for either meeting, Saudi Arabia's minister of communications and information technology, Abdullah Alswaha, seemed to let the news slip at this year's forum in Tokyo, Japan, which began on Sunday and ends Thursday. In a short speech before the plenary, Alswaha ran through some key issues facing the IGF, including generative artificial intelligence and the digital divide.

An automated Veterans Affairs system meant to help accelerate claims decisions actually helped contribute to inaccurate ratings on 27% of high blood pressure claims. A VA Office of the Inspector General (OIG) report published last week found that more than a quarter of the 60 reviewed high blood pressure claims that were handled by the Automated Benefits Delivery System resulted in wrongful claims decisions for veterans, according to a report from Military.com. The system was introduced in December 2021, ahead of what the VA believed was going to be a "flood" of disability applications as a result of the PACT Act, with Vietnam-era veterans filing high blood pressure claims under the act after their exposure to Agent Orange, an exposure linked to hypertension. 'WE'RE HUMAN': DELTA FORCE VETERAN REFLECTS ON BATTLE OF MOGADISHU 30 YEARS LATER The automated system was designed to pull blood pressure readings and other high blood pressure data from VA treatment recons and create a summary that is reviewed by VA staff, who make the final decision on the claim. But incomplete data compiled by the system led to several incorrect decisions, the IG's office found in its review, which recommended that the VA make improvements to the technology and the quality assurance process.

While reliable data-driven decision-making hinges on high-quality labeled data, the acquisition of quality labels often involves laborious human annotations or slow and expensive scientific measurements. Machine learning is becoming an appealing alternative as sophisticated predictive techniques are being used to quickly and cheaply produce large amounts of predicted labels; e.g., predicted protein structures are used to supplement experimentally derived structures, predictions of socioeconomic indicators from satellite imagery are used to supplement accurate survey data, and so on. Since predictions are imperfect and potentially biased, this practice brings into question the validity of downstream inferences. We introduce cross-prediction: a method for valid inference powered by machine learning. With a small labeled dataset and a large unlabeled dataset, cross-prediction imputes the missing labels via machine learning and applies a form of debiasing to remedy the prediction inaccuracies. The resulting inferences achieve the desired error probability and are more powerful than those that only leverage the labeled data. Closely related is the recent proposal of prediction-powered inference, which assumes that a good pre-trained model is already available. We show that cross-prediction is consistently more powerful than an adaptation of prediction-powered inference in which a fraction of the labeled data is split off and used to train the model. Finally, we observe that cross-prediction gives more stable conclusions than its competitors; its confidence intervals typically have significantly lower variability.

The rapid increase in the number of cyber-attacks in recent years raises the need for principled methods for defending networks against malicious actors. Deep reinforcement learning (DRL) has emerged as a promising approach for mitigating these attacks. However, while DRL has shown much potential for cyber-defence, numerous challenges must be overcome before DRL can be applied to autonomous cyber-operations (ACO) at scale. Principled methods are required for environments that confront learners with very high-dimensional state spaces, large multi-discrete action spaces, and adversarial learning. Recent works have reported success in solving these problems individually. There have also been impressive engineering efforts towards solving all three for real-time strategy games. However, applying DRL to the full ACO problem remains an open challenge. Here, we survey the relevant DRL literature and conceptualize an idealised ACO-DRL agent. We provide: i.) A summary of the domain properties that define the ACO problem; ii.) A comprehensive evaluation of the extent to which domains used for benchmarking DRL approaches are comparable to ACO; iii.) An overview of state-of-the-art approaches for scaling DRL to domains that confront learners with the curse of dimensionality, and; iv.) A survey and critique of current methods for limiting the exploitability of agents within adversarial settings from the perspective of ACO. We conclude with open research questions that we hope will motivate future directions for researchers and practitioners working on ACO.

Current privacy research on large language models (LLMs) primarily focuses on the issue of extracting memorized training data. At the same time, models' inference capabilities have increased drastically. This raises the key question of whether current LLMs could violate individuals' privacy by inferring personal attributes from text given at inference time. In this work, we present the first comprehensive study on the capabilities of pretrained LLMs to infer personal attributes from text. We construct a dataset consisting of real Reddit profiles, and show that current LLMs can infer a wide range of personal attributes (e.g., location, income, sex), achieving up to $85\%$ top-1 and $95.8\%$ top-3 accuracy at a fraction of the cost ($100\times$) and time ($240\times$) required by humans. As people increasingly interact with LLM-powered chatbots across all aspects of life, we also explore the emerging threat of privacy-invasive chatbots trying to extract personal information through seemingly benign questions. Finally, we show that common mitigations, i.e., text anonymization and model alignment, are currently ineffective at protecting user privacy against LLM inference. Our findings highlight that current LLMs can infer personal data at a previously unattainable scale. In the absence of working defenses, we advocate for a broader discussion around LLM privacy implications beyond memorization, striving for a wider privacy protection.

The accurate and efficient vessel draft reading (VDR) is an important component of intelligent maritime surveillance, which could be exploited to assist in judging whether the vessel is normally loaded or overloaded. The computer vision technique with an excellent price-to-performance ratio has become a popular medium to estimate vessel draft depth. However, the traditional estimation methods easily suffer from several limitations, such as sensitivity to low-quality images, high computational cost, etc. In this work, we propose a multi-task learning-enabled computational method (termed MTL-VDR) for generating highly reliable VDR. In particular, our MTL-VDR mainly consists of four components, i.e., draft mark detection, draft scale recognition, vessel/water segmentation, and final draft depth estimation. We first construct a benchmark dataset related to draft mark detection and employ a powerful and efficient convolutional neural network to accurately perform the detection task. The multi-task learning method is then proposed for simultaneous draft scale recognition and vessel/water segmentation. To obtain more robust VDR under complex conditions (e.g., damaged and stained scales, etc.), the accurate draft scales are generated by an automatic correction method, which is presented based on the spatial distribution rules of draft scales. Finally, an adaptive computational method is exploited to yield an accurate and robust draft depth. Extensive experiments have been implemented on the realistic dataset to compare our MTL-VDR with state-of-the-art methods. The results have demonstrated its superior performance in terms of accuracy, robustness, and efficiency. The computational speed exceeds 40 FPS, which satisfies the requirements of real-time maritime surveillance to guarantee vessel traffic safety.

The detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge for the research community, which has significant implications beyond the online realm. To address this challenge, we propose a new AI-based solution that identifies troll accounts solely through behavioral cues associated with their sequences of sharing activity, encompassing both their actions and the feedback they receive from others. Our approach does not incorporate any textual content shared and consists of two steps: First, we leverage an LSTM-based classifier to determine whether account sequences belong to a state-sponsored troll or an organic, legitimate user. Second, we employ the classified sequences to calculate a metric named the "Troll Score", quantifying the degree to which an account exhibits troll-like behavior. To assess the effectiveness of our method, we examine its performance in the context of the 2016 Russian interference campaign during the U.S. Presidential election. Our experiments yield compelling results, demonstrating that our approach can identify account sequences with an AUC close to 99% and accurately differentiate between Russian trolls and organic users with an AUC of 91%. Notably, our behavioral-based approach holds a significant advantage in the ever-evolving landscape, where textual and linguistic properties can be easily mimicked by Large Language Models (LLMs): In contrast to existing language-based techniques, it relies on more challenging-to-replicate behavioral cues, ensuring greater resilience in identifying influence campaigns, especially given the potential increase in the usage of LLMs for generating inauthentic content. Finally, we assessed the generalizability of our solution to various entities driving different information operations and found promising results that will guide future research.