Excessive Volatility is Also a Feature of Individual Level Forecasts

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During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and ...

Motion forecasting is crucial in enabling autonomous vehicles to anticipate the future trajectories of surrounding agents. To do so, it requires solving mapping, detection, tracking, and then forecasting problems, in a multi-step pipeline. In this complex ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

Primordial features, in particular oscillatory signals, imprinted in the primordial power spectrum of density perturbations represent a clear window of opportunity for detecting new physics at high-energy scales. Future spectroscopic and photometric measur ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

Accurate forecasting of photovoltaic (PV) power production is crucial for the integration of more renewable energy sources into the power grid. PV power production is highly intermittent, due to the stochastic cloud behaviour and cloud dynamics. Previous w ...

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited ...

Context. The Euclid mission of the European Space Agency will perform a survey of weak lensing cosmic shear and galaxy clustering in order to constrain cosmological models and fundamental physics. Aims. We expand and adjust the mock Euclid likelihoods of t ...

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

In this thesis we address various factors that contribute both theoretically and practically to mitigating supply demand mismatches. The thesis is composed of three chapters, where each chapter is an independent scientific paper. In the first paper, we dev ...

Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

On average every 2 years, the stratospheric polar vortex exhibits extreme perturbations known as sudden stratospheric warmings (SSWs). The impact of these events is not limited to the stratosphere: but they can also influence the weather at the surface of ...

In multi-agent reinforcement learning, multiple agents learn simultaneously while interacting with a common environment and each other. Since the agents adapt their policies during learning, not only the behavior of a single agent becomes non-stationary, b ...

In the context of SARS-CoV-2 pandemic, mathematical modelling has played a funda-mental role for making forecasts, simulating scenarios and evaluating the impact of pre-ventive political, social and pharmaceutical measures. Optimal control theory represent ...

The Nancy Grace Roman Space Telescope, NASA's next flagship observatory, will redefine deep-field galaxy survey with a field of view two orders of magnitude larger than Hubble and an angular resolution of matching quality. These future deep-wide galaxy sur ...

The surface mass balance (SMB) of large polar ice sheets and of snow and ice surfaces in general are incompletely understood because of the complexity of processes involved. One such process, drifting and blowing snow, has only been considered in a very si ...

The governing hydrological processes are expected to shift under climate change in the alpine regions of Switzerland. This raises the need for more adaptive and accurate methods to estimate river flow. In high-altitude catchments influenced by snow and gla ...

Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriating a common-pool resource (which diminishes future stock, and thus harvest, for everyone) ...

In this work, we systematically evaluate the accuracy in band gap prediction of range-separated hybrid functionals on a large set of semiconducting and insulating materials and carry out comparisons with the performance of their global counterparts. We obs ...

Demand forecasting is becoming increasingly important as firms launch new products with short life cycles more frequently. This paper provides a framework based on state-of-the-art techniques that enables firms to use quantitative methods to forecast sales ...

Excessive Volatility is Also a Feature of Individual Level Forecasts

Chattez avec Graph Search

Challenges of COVID-19 Case Forecasting in the US, 2020-2021

Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Euclid: The search for primordial features

Machine Learning for Modeling Stock Returns

Network time series forecasting in photovoltaics power production

Toward Reliable Human Pose Forecasting With Uncertainty

Euclid: Validation of the MontePython forecasting tools

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Optimizing Sales Forecasting, Inventory, Pricing and Sourcing Decisions

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

Improved extended-range prediction of persistent stratospheric perturbations using machine learning

Multi-agent actor-critic with time dynamical opponent model

Optimized numerical solutions of SIRDVW multiage model controlling SARS-CoV-2 vaccine roll out: An application to the Italian scenario

Semi-analytic forecasts for Roman - the beginning of a new era of deep-wide galaxy surveys

CRYOWRF—Model Evaluation and the Effect of Blowing Snow on the Antarctic Surface Mass Balance

Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network

AI-driven Prices for Externalities and Sustainability in Production Markets

Range-separated hybrid functionals for accurate prediction of band gaps of extended systems

A Machine Learning-based Framework for Forecasting Sales of New Products With Short Life Cycles Using Deep Neural Networks