The code is available here.

The code is available here.

Florida man Jonathan Rinderknecht faces federal arson charges after allegedly starting California's Palisades Fire, then asking ChatGPT if he was responsible.

Bayesian inference is widely used in many different fields to test hypotheses against observations. In most such applications, an assumption is made of precise input values to produce a precise output value. However, this is unrealistic for real-world applications. Often the best available information from subject matter experts (SMEs) in a given field is interval range estimates of the input probabilities involved in Bayes Theorem. This paper provides two key contributions to extend Bayes Theorem to an interval type-2 (IT2) version. First, we develop an IT2 version of Bayes Theorem that uses a novel and conservative method to avoid potential inconsistencies in the input IT2 MFs that otherwise might produce invalid output results. We then describe a novel and flexible algorithm for encoding SME-provided intervals into IT2 fuzzy membership functions (MFs), which we can use to specify the input probabilities in Bayes Theorem. Our algorithm generalizes and extends previous work on this problem that primarily addressed the encoding of intervals into word MFs for Computing with Words applications.

Data-driven learning is rapidly evolving and places a new perspective on realizing state-space dynamical systems. However, dynamical systems derived from nonlinear ordinary differential equations (ODEs) suffer from limitations in computational efficiency. Thus, this paper stems from data-driven learning to advance states of dynamical systems utilizing a structured neural network (StNN). The proposed learning technique also seeks to identify an optimal, low-complexity operator to solve dynamical systems, the so-called Hankel operator, derived from time-delay measurements. Thus, we utilize the StNN based on the Hankel operator to solve dynamical systems as an alternative to existing data-driven techniques. We show that the proposed StNN reduces the number of parameters and computational complexity compared with the conventional neural networks and also with the classical data-driven techniques, such as Sparse Identification of Nonlinear Dynamics (SINDy) and Hankel Alternative view of Koopman (HAVOK), which is commonly known as delay-Dynamic Mode Decomposition(DMD) or Hankel-DMD. More specifically, we present numerical simulations to solve dynamical systems utilizing the StNN based on the Hankel operator beginning from the fundamental Lotka-Volterra model, where we compare the StNN with the LEarning Across Dynamical Systems (LEADS), and extend our analysis to highly nonlinear and chaotic Lorenz systems, comparing the StNN with conventional neural networks, SINDy, and HAVOK. Hence, we show that the proposed StNN paves the way for realizing state-space dynamical systems with a low-complexity learning algorithm, enabling prediction and understanding of future states.

--True-time-delay (TTD) beamformers can produce wideband, squint-free beams in both analog and digital signal domains, unlike frequency-dependent FFT beams. Our previous work showed that TTD beamformers can be efficiently realized using the elements of delay V andermonde matrix (DVM), answering the longstanding beam-squint problem. Thus, building on our work on classical algorithms based on DVM, we propose neural network (NN) architecture to realize wideband multi-beam beamformers using structure-imposed weight matrices and submatrices. The structure and sparsity of the weight matrices and submatrices are shown to reduce the space and computational complexities of the NN greatly. L) complexity, where M is the number of nodes in each layer of the network, p is the number of submatrices per layer, and M >> p . We will show numerical simulations in the 24 GHz to 32 GHz range to demonstrate the numerical feasibility of realizing wideband multi-beam beamformers using the proposed neural architecture. We also show the complexity reduction of the proposed NN and compare that with fully connected NNs, to show the efficiency of the proposed architecture without sacrificing accuracy. The accuracy of the proposed NN architecture was shown using the mean squared error, which is based on an objective function of the weight matrices and beamformed signals of antenna arrays, while also normalizing nodes. The proposed NN architecture shows a low-complexity NN realizing wideband multi-beam beamformers in real-time for low-complexity intelligent systems. H. Aluvihare is with the Department of Mathematics, Embry-Riddle Aeronautical University, Daytona Beach, FL, 32703 USA email:aluvihah@my.erau.edu S. Sivasankar is with the Department of Electrical and Computer Engineering, Florida International University, Miami, FL, 33174 USA email:ssiva011@fiu.edu X. Li is with the Department of Mathematics & Systems Engineering, Florida Institute of Technology, Melbourne, FL 32901, USA e-mail: xli@fit.edu

This paper introduces a novel strategy aimed at enhancing productivity and minimizing non-productive movements within container terminals, specifically focusing on container yards. It advocates for the implementation of a digital twin-based methodology to streamline the operations of stacking cranes (SCs) responsible for container handling. The proposed approach entails the creation of a virtual container yard that mirrors the physical yard within a digital twin system, facilitating real-time observation and validation. In addition, this article demonstrates the effectiveness of using a digital twin to reduce unproductive movements and improve productivity through simulation. It defines various operational strategies and takes into account different yard contexts, providing a comprehensive understanding of optimisation possibilities. By exploiting the capabilities of the digital twin, managers and operators are provided with crucial information on operational dynamics, enabling them to identify areas for improvement. This visualisation helps decision-makers to make informed choices about their stacking strategies, thereby improving the efficiency of overall container terminal operations. Overall, this paper present a digital twin solution in container terminal operations, offering a powerful tool for optimising productivity and minimising inefficiencies.