The stable periodic patterns present in time series data serve as the foundation for conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly modeling this periodicity to enhance the performance of models in long-term time series forecasting (LTSF) tasks. Specifically, we introduce the Residual Cycle Forecasting (RCF) technique, which utilizes learnable recurrent cycles to model the inherent periodic patterns within sequences, and then performs predictions on the residual components of the modeled cycles. Combining RCF with a Linear layer or a shallow MLP forms the simple yet powerful method proposed in this paper, called CycleNet. CycleNet achieves state-of-the-art prediction accuracy in multiple domains including electricity, weather, and energy, while offering significant efficiency advantages by reducing over 90% of the required parameter quantity. Furthermore, as a novel plug-and-play technique, the RCF can also significantly improve the prediction accuracy of existing models, including PatchTST and iTransformer. The source code is available at: https://github.com/ACAT-SCUT/CycleNet.

Variational Autoencoders (VAEs) are a powerful framework for learning compact latent representations, while NeuralODEs excel in learning transient system dynamics. This work combines the strengths of both to create fast surrogate models with adjustable complexity. By leveraging the VAE's dimensionality reduction using a non-hierarchical prior, our method adaptively assigns stochastic noise, naturally complementing known NeuralODE training enhancements and enabling probabilistic time series modeling. We show that standard Latent ODEs struggle with dimensionality reduction in systems with time-varying inputs. Our approach mitigates this by continuously propagating variational parameters through time, establishing fixed information channels in latent space. This results in a flexible and robust method that can learn different system complexities, e.g. deep neural networks or linear matrices. Hereby, it enables efficient approximation of the Koopman operator without the need for predefining its dimensionality. As our method balances dimensionality reduction and reconstruction accuracy, we call it Balanced Neural ODE (B-NODE). We demonstrate the effectiveness of this method on academic test cases and apply it to a real-world example of a thermal power plant.

Thanks to low corporation tax and government incentives, Dublin has hosted the European Headquarters of many large US technology companies--Google, Meta, LinkedIn and Microsoft all have offices in the city's Silicon Docks. "The big US companies operated independently of the startup world for many years," explains Will Prendergast, partner at Frontline Ventures. "But in the last five years, US technology companies have been building product and engineering functions here, and that talent is starting to spill out, driving startup creation." Government support via Enterprise Ireland's Pre-Seed Start Fund, designed to accelerate early stage startups, and hubs such as Dogpatch Labs are supporting this wave of new talent. "Ireland does have a capital issue," says employee benefits startup Kota co-founder Luke Mackey.

Why is Stockholm, a capital city with a population less than one million, home to global brands such as Skype, Spotify, Klarna and Minecraft? "I think it has to do with the Swedish creed," says Ben Eliass, CEO of bodycare brand Estrid. "It's a nation which put emphasis on high-quality education and invested heavily in telecoms infrastructure in the nineties, so we all grew up with high-speed internet." "It allows people to take high risk and start companies, not needing to be too afraid of the downsides," says Max Junestrand, CEO of legaltech startup Leya. Indeed, Sweden has now produced more unicorns per capita than any other country in Europe, except for Estonia, earning a reputation as the Silicon Valley of Europe"Stockholm has a truly unique ecosystem where you can stand on the shoulders of giants," says Colin Treseler, CEO of Supernormal.

The 2023 Atomico's State of European Tech Report reveals Netherlands to be a standout success, cementing its position as a star player in the startup ecosystem. In terms of capital invested in its private tech companies, for instance, it's risen back into the top five countries with a projected 2.1 billion. And while the UK has seen the share of its European capital invested drop by almost three per cent within the last three years, the Netherlands comes out top, capturing the biggest gains in Europe at almost 2 per cent. The hub of the Netherlands' startup ecosystem is Amsterdam, which hosts around 4,000 startups, including unicorns like Mollie, Mambu and Backbase. Known for its international focus, collaborative ecosystem, and diverse and skilled workforce, it's also dedicated to tackling urgent societal issues.

German innovation is not limited to the country's capital. In fact, some of this year's most prolific startups are based hundreds of miles away. The AI startup Alpha Alpha hails from Heidelberg. Helsing, which sells AI to Europe's militaries, was set up in Munich. Yet both companies operate Berlin offices.

In the "Startup-up, Scale-up" review report published last year, chancellor Rachel Reeves promised to make Britain the "high growth, start-up hub of the world". Now, almost six months into the new government, entrepreneurs remain encouraged by the promises made in the Labour manifesto. "The ambition embodied in Great British Energy and the 2030 decarbonization targets is precisely what we need and deserve," says Shilpika Gautam, CEO of greentech startup Opna, about Labour's energy policies. "It's high time the UK caught up with the policy and financing innovations in other countries, such as the Inflation Reduction Act in the US." Amit Gudka, founder of Field, agrees: "We welcome Labour's plans to double onshore wind, triple solar and quadruple offshore wind by 2030. These plans are ambitious, but not unrealistic, provided the Government continues to make clear policy decisions and create a stable policy and regulatory environment."

Ultra-short-period exoplanets like K2-141 b likely have magma oceans on their dayside, which play a critical role in redistributing heat within the planet. This could lead to a warm nightside surface, measurable by the James Webb Space Telescope, offering insights into the planet's structure. Accurate models of properties like viscosity, which can vary by orders of magnitude, are essential for such studies. We present a new model for predicting molten magma viscosity, applicable in diverse scenarios, including magma oceans on lava planets. Using a database of 28,898 viscosity measurements on phospho-alumino-silicate melts, spanning superliquidus to undercooled temperatures and pressures up to 30 GPa, we trained a greybox artificial neural network, refined by a Gaussian process. This model achieves high predictive accuracy (RMSE $\approx 0.4 \log_{10}$ Pa$\cdot$s) and can handle compositions from SiO$_2$ to multicomponent magmatic and industrial glasses, accounting for pressure effects up to 30 GPa for compositions such as peridotite. Applying this model, we calculated the viscosity of K2-141 b's magma ocean under different compositions. Phase diagram calculations suggest that the dayside is fully molten, with extreme temperatures primarily controlling viscosity. A tenuous atmosphere (0.1 bar) might exist around a 40{\deg} radius from the substellar point. At higher longitudes, atmospheric pressure drops, and by 90{\deg}, magma viscosity rapidly increases as solidification occurs. The nightside surface is likely solid, but previously estimated surface temperatures above 400 K imply a partly molten mantle, feeding geothermal flux through vertical convection.

Point cloud completion networks are conventionally trained to minimize the disparities between the completed point cloud and the ground-truth counterpart. However, an incomplete object-level point cloud can have multiple valid completion solutions when it is examined in isolation. This one-to-many mapping issue can cause contradictory supervision signals to the network because the loss function may produce different values for identical input-output pairs of the network. In many cases, this issue could adversely affect the network optimization process. In this work, we propose to enhance the conventional learning objective using a novel completion consistency loss to mitigate the one-to-many mapping problem. Specifically, the proposed consistency loss ensure that a point cloud completion network generates a coherent completion solution for incomplete objects originating from the same source point cloud. Experimental results across multiple well-established datasets and benchmarks demonstrated the proposed completion consistency loss have excellent capability to enhance the completion performance of various existing networks without any modification to the design of the networks. The proposed consistency loss enhances the performance of the point completion network without affecting the inference speed, thereby increasing the accuracy of point cloud completion. Notably, a state-of-the-art point completion network trained with the proposed consistency loss can achieve state-of-the-art accuracy on the challenging new MVP dataset. The code and result of experiment various point completion models using proposed consistency loss will be available at: https://github.com/kaist-avelab/ConsistencyLoss .

Autonomous underwater vehicles (AUVs) are underwater robotic platforms used in a variety of applications. An AUV's navigation solution relies heavily on the fusion of inertial sensors and Doppler velocity logs (DVL), where the latter delivers accurate velocity updates. To ensure accurate navigation, a DVL calibration is undertaken before the mission begins to estimate its error terms. During calibration, the AUV follows a complex trajectory and employs nonlinear estimation filters to estimate error terms. In this paper, we introduce DCNet, a data-driven framework that utilizes a two-dimensional convolution kernel in an innovative way. Using DCNet and our proposed DVL error model, we offer a rapid calibration procedure. This can be applied to a trajectory with a nearly constant velocity. To train and test our proposed approach a dataset of 276 minutes long with real DVL recorded measurements was used. We demonstrated an average improvement of 70% in accuracy and 80% improvement in calibration time, compared to the baseline approach, with a low-performance DVL. As a result of those improvements, an AUV employing a low-cost DVL, can achieve higher accuracy, shorter calibration time, and apply a simple nearly constant velocity calibration trajectory. Our results also open up new applications for marine robotics utilizing low-cost, high-accurate DVLs.