Accurate and efficient prediction of extreme ship responses continues to be a challenging problem in ship hydrodynamics. Probabilistic frameworks in conjunction with computationally efficient numerical hydrodynamic tools have been developed that allow researchers and designers to better understand extremes. However, the ability of these hydrodynamic tools to represent the physics quantitatively during extreme events is limited. Previous research successfully implemented the critical wave groups (CWG) probabilistic method with computational fluid dynamics (CFD). Although the CWG method allows for less simulation time than a Monte Carlo approach, the large quantity of simulations required is cost prohibitive. The objective of the present paper is to reduce the computational cost of implementing CWG with CFD, through the construction of long short-term memory (LSTM) neural networks. After training the models with a limited quantity of simulations, the models can provide a larger quantity of predictions to calculate the probability. The new framework is demonstrated with a 2-D midship section of the Office of Naval Research Tumblehome (ONRT) hull in Sea State 7 and beam seas at zero speed. The new framework is able to produce predictions that are representative of a purely CFD-driven CWG framework, with two orders of magnitude of computational cost savings.

This study proposes an approach for predicting the impacts of scenarios on food security and demonstrates its application in a case study. The approach involves two main steps: (1) scenario definition, in which the end user specifies the assumptions and impacts of the scenario using a scenario template, and (2) scenario evaluation, in which a Vector Autoregression (VAR) model is used in combination with Monte Carlo simulation to generate predictions for the impacts of the scenario based on the defined assumptions and impacts. The case study is based on a proprietary time series food security database created using data from the Food and Agriculture Organization of the United Nations (FAOSTAT), the World Bank, and the United States Department of Agriculture (USDA). The database contains a wide range of data on various indicators of food security, such as production, trade, consumption, prices, availability, access, and nutritional value. The results show that the proposed approach can be used to predict the potential impacts of scenarios on food security and that the proprietary time series food security database can be used to support this approach. The study provides specific insights on how this approach can inform decision-making processes related to food security such as food prices and availability in the case study region.

With the growth of online news over the past decade, empirical studies on political discourse and news consumption have focused on the phenomenon of filter bubbles and echo chambers. Yet recently, scholars have revealed limited evidence around the impact of such phenomenon, leading some to argue that partisan segregation across news audiences cannot be fully explained by online news consumption alone and that the role of traditional legacy media may be as salient in polarizing public discourse around current events. In this work, we expand the scope of analysis to include both online and more traditional media by investigating the relationship between broadcast news media language and social media discourse. By analyzing a decade's worth of closed captions (2 million speaker turns) from CNN and Fox News along with topically corresponding discourse from Twitter, we provide a novel framework for measuring semantic polarization between America's two major broadcast networks to demonstrate how semantic polarization between these outlets has evolved (Study 1), peaked (Study 2) and influenced partisan discussions on Twitter (Study 3) across the last decade. Our results demonstrate a sharp increase in polarization in how topically important keywords are discussed between the two channels, especially after 2016, with overall highest peaks occurring in 2020. The two stations discuss identical topics in drastically distinct contexts in 2020, to the extent that there is barely any linguistic overlap in how identical keywords are contextually discussed. Further, we demonstrate at scale, how such partisan division in broadcast media language significantly shapes semantic polarity trends on Twitter (and vice-versa), empirically linking for the first time, how online discussions are influenced by televised media.

Artificial intelligence (AI) is not "just around the corner" but here today and proceeding rapidly to change much about how we live and operate in a digital world. Like it or not ... it is here to stay! I got the following guest piece on the impacts of AI to cybersecurity and wanted to share it with you. One thing not mentioned in the piece is how AI will significantly reduce your workforce shortage of cybersecurity technicians. They will be needed, as is pointed out in the summary below, but not in the numbers they are projected to be needed in the coming years. Here's the piece -- which is a summary of an article by the author: Monica Oravcova, COO and co-founder of cybersecurity firm Naoris Protocol, on how AI affects cybersecurity.

A U.S. congressman has begun advocating for a federal department to regulate the use of artificial intelligence, postulating a dystopian future where AIs will make key decisions and autonomous weapons roam America. Rep. Ted Lieu (D-CA) authored an opinion piece in The New York Times on Monday, arguing that AI has emerged as a powerful tool that can be used to benefit humanity -- or deceive it, and worse. In fact, the example he cited, reproduced above, wasn't written by Lieu, but by ChatGPT, the AI chatbot developed by OpenAI. Lieu, who earned a B.S. degree in Computer Science from Stanford, noted that AI is now present in everything from smart speakers to Google Maps. But where AI fails, people can be hurt: The editorial points out that a driver blamed Tesla's self-driving mode for an eight-car pileup on the San Francisco Bay Bridge.

Stroke patients in the US could soon take advantage of cutting-edge robotics during the recovery process. The Food and Drug Administration has cleared Wandercraft's Atalante exoskeleton for use in stroke rehabilitation. The machine can help with intensive gait training, particularly for people with limited upper body mobility that might prevent using other methods. The current-generation Atalante is a self-balancing, battery-powered device with an adjustable gait that can help with early steps through to more natural walking later in therapy. While the hardware still needs to be used in a clinical setting with help from a therapist, its hands-free use lets patients reestablish their gait whether or not they can use their arms. Wandercraft plans to deliver its first exoskeletons to the US during the first quarter of the year, though it didn't name initial customers.

Migration was quite a big deal, Brexit-wise, but six years on, and two after the end of free movement, what has been the impact? The level of net migration certainly hasn't fallen, but a new report from Jonathan Portes and John Springford argues that if you focus on workers, the end of freedom of movement has left about 330,000 fewer in Britain (460,000 fewer Europeans, but 130,000 more from elsewhere). That's a reduction of roughly 1% of the labour force, prompting many to say that a lack of migration drove recent economy-wide labour shortages. This is overstated (hiring difficulties have been common across Europe), but fewer available workers will have contributed to the hiring challenges in lower-paying sectors that were previously reliant on EU workers (and where the authors show the workforce reduction is concentrated). The important question is what happens next.

Many computational argumentation tasks, like stance classification, are topic-dependent: the effectiveness of approaches to these tasks significantly depends on whether the approaches were trained on arguments from the same topics as those they are tested on. So, which are these topics that researchers train approaches on? This paper contributes the first comprehensive survey of topic coverage, assessing 45 argument corpora. For the assessment, we take the first step towards building an argument topic ontology, consulting three diverse authoritative sources: the World Economic Forum, the Wikipedia list of controversial topics, and Debatepedia. Comparing the topic sets between the authoritative sources and corpora, our analysis shows that the corpora topics-which are mostly those frequently discussed in public online fora - are covered well by the sources. However, other topics from the sources are less extensively covered by the corpora of today, revealing interesting future directions for corpus construction.

Actionable Knowledge Discovery (AKD) is a crucial aspect of data mining that is gaining popularity and being applied in a wide range of domains. This is because AKD can extract valuable insights and information, also known as knowledge, from large datasets. The goal of this paper is to examine different research studies that focus on various domains and have different objectives. The paper will review and discuss the methods used in these studies in detail. AKD is a process of identifying and extracting actionable insights from data, which can be used to make informed decisions and improve business outcomes. It is a powerful tool for uncovering patterns and trends in data that can be used for various applications such as customer relationship management, marketing, and fraud detection. The research studies reviewed in this paper will explore different techniques and approaches for AKD in different domains, such as healthcare, finance, and telecommunications. The paper will provide a thorough analysis of the current state of AKD in the field and will review the main methods used by various research studies. Additionally, the paper will evaluate the advantages and disadvantages of each method and will discuss any novel or new solutions presented in the field. Overall, this paper aims to provide a comprehensive overview of the methods and techniques used in AKD and the impact they have on different domains.

We propose an adversarial, time-varying test-synthesis procedure for safety-critical systems without requiring specific knowledge of the underlying controller steering the system. From a broader test and evaluation context, determination of difficult tests of system behavior is important as these tests would elucidate problematic system phenomena before these mistakes can engender problematic outcomes, e.g. loss of human life in autonomous cars, costly failures for airplane systems, etc. Our approach builds on existing, simulation-based work in the test and evaluation literature by offering a controller-agnostic test-synthesis procedure that provides a series of benchmark tests with which to determine controller reliability. To achieve this, our approach codifies the system objective as a timed reach-avoid specification. Then, by coupling control barrier functions with this class of specifications, we construct an instantaneous difficulty metric whose minimizer corresponds to the most difficult test at that system state. We use this instantaneous difficulty metric in a game-theoretic fashion, to produce an adversarial, time-varying test-synthesis procedure that does not require specific knowledge of the system's controller, but can still provably identify realizable and maximally difficult tests of system behavior. Finally, we develop this test-synthesis procedure for both continuous and discrete-time systems and showcase our test-synthesis procedure on simulated and hardware examples.