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.

The El Ni\~no Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.

But when it came to visual datasets, the field immediately stepped towards the algorithmic side. One of the fundamental reasons was the amount of information needed to translate from an image. But with the introduction of convolutional networks and transfer learning [1], [2], [3], it is possible to convert an image or a visual object into feature vectors without losing too much information about the entity under concern. It defined a way to use feature maps to compare and distinguish one visual object from another [4]. There has been a lot of work in using these feature vector conversions in systems like content-based image retrievals [5], using feature vectors as representations of different scenarios [6], [7]. It is critical to understand that there is a difference between the way a machine looks at the data and the way we do. There are scenarios in which the interpretation through features is a little different from the interpretation of humans. DSI is there to bridge the gap and understand the machine's perspective before molding it to shape better architectures, in turn, better model performances. I think two concepts could be linked together to understand a machine's viewpoint while working with visual

Earthquake prediction has been a challenging research area for many decades, where the future occurrence of this highly uncertain calamity is predicted. In this paper, several parametric and non-parametric features were calculated, where the non-parametric features were calculated using the parametric features. $8$ seismic features were calculated using Gutenberg-Richter law, the total recurrence, and the seismic energy release. Additionally, criterions such as Maximum Relevance and Maximum Redundancy were applied to choose the pertinent features. These features along with others were used as input for an Extreme Learning Machine (ELM) Regression Model. Magnitude and time data of $5$ decades from the Assam-Guwahati region were used to create this model for magnitude prediction. The Testing Accuracy and Testing Speed were computed taking the Root Mean Squared Error (RMSE) as the parameter for evaluating the mode. As confirmed by the results, ELM shows better scalability with much faster training and testing speed (up to a thousand times faster) than traditional Support Vector Machines. The testing RMSE came out to be around $0.097$. To further test the model's robustness -- magnitude-time data from California was used to calculate the seismic indicators which were then fed into an ELM and then tested on the Assam-Guwahati region. The model proves to be robust and can be implemented in early warning systems as it continues to be a major part of Disaster Response and management.

In today's multilingual lexical databases, the majority of the world's languages are under-represented. Beyond a mere issue of resource incompleteness, we show that existing lexical databases have structural limitations that result in a reduced expressivity on culturally-specific words and in mapping them across languages. In particular, the lexical meaning space of dominant languages, such as English, is represented more accurately while linguistically or culturally diverse languages are mapped in an approximate manner. Our paper assesses state-of-the-art multilingual lexical databases and evaluates their strengths and limitations with respect to their expressivity on lexical phenomena of linguistic diversity.

A novel topological-data-analytical (TDA) method is proposed to distinguish, from noise, small holes surrounded by high-density regions of a probability density function. The proposed method is robust against additive noise and outliers. Traditional TDA tools, like those based on the distance filtration, often struggle to distinguish small features from noise, because both have short persistences. An alternative filtration, called the Robust Density-Aware Distance (RDAD) filtration, is proposed to prolong the persistences of small holes of high-density regions. This is achieved by weighting the distance function by the density in the sense of Bell et al. The concept of distance-to-measure is incorporated to enhance stability and mitigate noise. The persistence-prolonging property and robustness of the proposed filtration are rigorously established, and numerical experiments are presented to demonstrate the proposed filtration's utility in identifying small holes.

If business wants to use AI at scale, adhering to the technical guidelines for responsible AI development isn't enough. It must obtain society's explicit approval to deploy the technology. Six years ago, in March 2016, Microsoft Corporation launched an experimental AI-based chatbot, TayTweets, whose Twitter handle was @TayandYou. Tay, an acronym for "thinking about you," mimicked a 19-year-old American girl online, so the digital giant could showcase the speed at which AI can learn when it interacts with human beings. Living up to its description as "AI with zero chill," Tay started off replying cheekily to Twitter users and turning photographs into memes. Some topics were off limits, though; Microsoft had trained Tay not to comment on societal issues such as Black Lives Matter. Soon enough, a group of Twitter users targeted Tay with a barrage of tweets about controversial issues such as the Holocaust and Gamergate. They goaded the chatbot into replying with racist and sexually charged responses, exploiting its repeat-after-me capability. Realizing that Tay was reacting like IBM's Watson, which started using profanity after perusing the online Urban Dictionary, Microsoft was quick to delete the first inflammatory tweets. Less than 16 hours and more than 100,000 tweets later, the digital giant shut down Tay.

In one experiment, the team used these methods to determine if radar signals from Earth's Global Navigation Satellite System (GNSS), which were reflected over the ocean and received by GNSS receivers located at towns offshore in the Gulf of Mexico, could be used to track hurricane evolution by measuring rising sea levels after landfall. Between 2020 and 2021, the team studied how seven storms, such as Hurricane Hana and Hurricane Delta, affected coastal sea levels before they made landfall in the Gulf of Mexico. By monitoring these complex changes, they found a positive correlation between higher sea levels and how intense the storm surges were.

Annapolis, MD – Chesapeake Conservancy's data science team developed an artificial intelligence deep learning model for mapping wetlands, which resulted in 94% accuracy. Supported by EPRI, an independent, non-profit energy research and development institute; Lincoln Electric System; and the Grayce B. Kerr Fund, Inc., this method for wetland mapping could deliver important outcomes for protecting and conserving wetlands. The results are published in the peer-reviewed journal Science of the Total Environment. The team trained a machine learning (convolutional neural network) model for high-resolution (1m) wetland mapping with freely available data from three areas: Mille Lacs County, Minnesota; Kent County, Delaware; and St. Lawrence County, New York. The full model, which requires local training data provided by state wetlands data and the National Wetlands Inventory (NWI), mapped wetlands with 94% accuracy.

Water current prediction is essential for understanding ecosystems, and to shed light on the role of the ocean in the global climate context. Solutions vary from physical modeling, and long-term observations, to short-term measurements. In this paper, we consider a common approach for water current prediction that uses Lagrangian floaters for water current prediction by interpolating the trajectory of the elements to reflect the velocity field. Here, an important aspect that has not been addressed before is where to initially deploy the drifting elements such that the acquired velocity field would efficiently represent the water current. To that end, we use a clustering approach that relies on a physical model of the velocity field. Our method segments the modeled map and determines the deployment locations as those that will lead the floaters to 'visit' the center of the different segments. This way, we validate that the area covered by the floaters will capture the in-homogeneously in the velocity field. Exploration over a dataset of velocity field maps that span over a year demonstrates the applicability of our approach, and shows a considerable improvement over the common approach of uniformly randomly choosing the initial deployment sites. Finally, our implementation code can be found in [1].