The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems.

We include all our annotations and extracted landmarks. This ensures that we uphold the highest standards of ethical data usage. In Table A1, we summarize the severity label distribution in Y ouTubePD. We also summarize the demographic distribution in Y ouTubePD, split between PD-positive and healthy control (HC), or PD-negative, subjects. This decision is based on the clinician's suggestion, since an accurate UPDRS facial expression rating would require more This strategy also allows for a finer classification.

The healthcare and AI communities have witnessed a growing interest in the development of AI-assisted systems for automated diagnosis of Parkinson's Disease

Learning the Koopman operator and its spectral decomposition from data is enabled by a number of algorithms.

Why is the US Fed chair criminal probe causing alarm? Venezuela's defence minister has accused the United States of using the country as a "weapons laboratory" during the abduction of President Nicolas Maduro and his wife, Cilia Flores, on January 3. Vladimir Padrino Lopez said last week that the US had used Venezuela as a testing ground for "advanced military technologies" that rely on artificial intelligence and weaponry never used before, according to the Venezuelan newspaper El Universal. On Sunday, US President Donald Trump told the New York Post that US forces had indeed used a weapon he referred to as "the discombobulator". "I'm not allowed to talk about it," he said, adding that the weapon "made equipment not work" during the operation. Details of the US military mission to abduct Maduro have not been made public, but the US has been known to use weapons to disorient soldiers and guards or disable equipment and infrastructure in the past.

How much is US support for Israel costing Trump? What is a Palestinian without olives? Why are Gaza's homes collapsing in winter? 'The end of the world as we know it': Is the rules-based order finished? Canadian Prime Minister Mark Carney said the quiet part out loud at the World Economic Forum: what many call the global rules-based order was either collapsing or had collapsed already.

Granger causality (GC) [15] is a time series causal discovery framework that uses predictive modeling to identify the underlying causal structure of a time series system. Relying on the assumption that cause precedes effect, GC assesses whether including the lagged information from one time series in the autoregressive model of a second time series enhances its predictions. This improvement indicates a predictive relationship between the time series variables, where one time series provides supplemental information about the future of another time series, thereby signifying the presence of a (Granger) causal relationship. GC requires only observational data, and has been used for time series causal discovery across diverse domains, including climate science [33], political and social sciences [17], econometrics [4], and biological systems studies [13]. The original formulation of GC requires several assumptions to be satisfied for causal identifiability. In regards to the candidate time series system, it is assumed that the time series variables are stationary, and that all variables are observed (absence of latent confounders). GC was initially proposed for bivariate time series systems, but was generalised for the multivariate setting to accommodate the assumption that all relevant variables are included in the analysis [15]. Additional assumptions are made with regard to the types of causal relationships that can be identified within the time series system. GC cannot estimate a causal relationship between time series at an instantaneous time point, relying on the relationship between the lags and predicted values to determine a GC relationship.

Two years ago, companies like Meta and OpenAI were united against military use of their tools. Now all of that has changed. At the start of 2024, Anthropic, Google, Meta, and OpenAI were united against military use of their AI tools. But over the next 12 months, something changed. In January, OpenAI quietly rescinded its ban on using AI for "military and warfare" purposes, and soon after it was reported to be working on "a number of projects" with the Pentagon. In November, in the same week that Donald Trump was reelected US president, Meta announced that the United States and select allies would be able to employ Llama for defense uses.

Topological methods have the potential of exploring data clouds without making assumptions on their the structure. Here we propose a hierarchical topological clustering algorithm that can be implemented with any distance choice. The persistence of outliers and clusters of arbitrary shape is inferred from the resulting hierarchy. We demonstrate the potential of the algorithm on selected datasets in which outliers play relevant roles, consisting of images, medical and economic data. These methods can provide meaningful clusters in situations in which other techniques fail to do so.