Solitary waves and nonlinear Klein-Gordon Equations

We analytically study the kink-antikink (K-K) collisions in the classical one spatial dimension and time phi-fourth field theory as an example of inelastic collisions between solitary waves. We use the linear eigenvalue collective coordinate approach to describe the system in terms of the separation distance between the kink and the antikink and the amplitude of shape vibrations generated on each kink as a result of the collision. By calculating the energy given to the shape vibrations as a function of the incoming velocity, we find the critical value of the initial velocity above which the two colliding kinks always separate after the collision. A model previously proposed to explain the two-bounce collisions in terms of a resonant energy exchange between the orbital frequency of the bound K-K pair and the frequency of shape vibrations is modified using our analytical results. We derive a (data-free) formula that predicts the values of the initial velocities for which resonance occurs. A generalized version of this modified model is shown to give good results when it is applied to K-K collisions in other similar field theories. In the Appendices Nonlinear Klein Gordon equations with solitary (travelling) wave solutions are reviewed and solved for particular cases. The solutions are related to soliton solutions of the sine-Gordon equation. Also the phi-fourth equation perturbed with a constant force and dissipation is solved, and finally, we present new kink-bearing integro-differential and nonlinear differential equations

Virtual power plants with electric vehicles

The benefits of integrating aggregated Electric Vehicles (EV) within the Virtual Power Plant (VPP) concept, are addressed. Two types of EV aggregators are identified: i) Electric Vehicle Residential Aggregator (EVRA), which is responsible for the management of dispersed and clustered EVs in a residential area and ii) Electric Vehicle Commercial Aggregator (EVCA), which is responsible for the management of EVs clustered in a single car park. A case study of a workplace EVCA is presented, providing an insight on its operation and service capabilities

VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Bias

Multimedia content has become ubiquitous on social media platforms, leading to the rise of multimodal misinformation (MM) and the urgent need for effective strategies to detect and prevent its spread. In recent years, the challenge of multimodal misinformation detection (MMD) has garnered significant attention by researchers and has mainly involved the creation of annotated, weakly annotated, or synthetically generated training datasets, along with the development of various deep learning MMD models. However, the problem of unimodal bias in MMD benchmarks -- where biased or unimodal methods outperform their multimodal counterparts on an inherently multimodal task -- has been overlooked. In this study, we systematically investigate and identify the presence of unimodal bias in widely-used MMD benchmarks (VMU-Twitter, COSMOS), raising concerns about their suitability for reliable evaluation. To address this issue, we introduce the "VERification of Image-TExtpairs" (VERITE) benchmark for MMD which incorporates real-world data, excludes "asymmetric multimodal misinformation" and utilizes "modality balancing". We conduct an extensive comparative study with a Transformer-based architecture that shows the ability of VERITE to effectively address unimodal bias, rendering it a robust evaluation framework for MMD. Furthermore, we introduce a new method -- termed Crossmodal HArd Synthetic MisAlignment (CHASMA) -- for generating realistic synthetic training data that preserve crossmodal relations between legitimate images and false human-written captions. By leveraging CHASMA in the training process, we observe consistent and notable improvements in predictive performance on VERITE; with a 9.2% increase in accuracy. We release our code at: https://github.com/stevejpapad/image-text-verificatio