The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descriptors of these trajectories are affected by the robot environment as well as by the robot state, with respect to maintenance operations. Topological Data Analysis will be used for extracting the trajectory descriptors, based on homology persistence. Then, appropriate metrics will be applied in order to compare that topological representation of the trajectories, for classifying them or for making efficient pattern recognition.

From movie suggestions to self-driving vehicles, machine learning has revolutionised modern life. Experts are now using it to help solve one of humanity's biggest problems: climate change. With machine learning, we can use our abundance of historical climate data and observations to improve predictions of Earth's future climate. And these predictions will have a major role in lessening our climate impact in the years ahead. Machine learning is a branch of artificial intelligence.

Machine learning (ML) is one approach for businesses to improve how they use large data to better understand their consumers' behaviour, happiness and loyalty. ML can look for patterns and abnormalities that users wouldn't think to look for on their own. Some machine learning algorithms are pre-programmed to specialise in a certain task, but in this article, we'll focus on machine learning tools that allow users to create their own machine learning methods for any data they have. Now, let's get down to the top 10 machine learning tools of 2021. Shogun toolbox, often known as Shogun, is a machine learning tool library that is independent and accessible to use.

Pinterest has launched a new search feature that could make it easier for Black, Brown, Indigenous, Latinx and other POC users to find hair inspiration that would suit their hair types. The visual discovery website has introduced hair pattern search, it said, with BIPOC users in mind. This new feature uses computer vision-powered object detection to enable users to refine their searches by six different hair patterns: protective, coily, curly, wavy, straight and shaved/bald. Now, after users search for broader terms like "summer hairstyles," "glam hair" or "short hair," they'll find new hair pattern buttons that will narrow down the results. The feature is now live in the US, UK, Ireland, Canada, Australia and New Zealand on desktop, as well as on iOS and Android. It will roll out to more locations over the coming months.

In the present time slowing down climate change should be everyone's priority. Because if the matter gets ignored any further, people might face more extensive crises than they experienced during the global COVID-19 pandemic. No doubt that climate change is the biggest challenge the world is facing right now and it will need every solution possible, including technology like artificial intelligence. This technology is capable of finding solutions faster and it could effectively power climate change strategy. As per the climate update issued by the World Meteorological Organization (WMO), there is a 40% chance of the annual average global temperature temporarily reaching 1.5 C above the pre-industrial level in the next couple of years.

As third party cookies are leaving our life, Google offers marketers Federated Learning of Cohorts (FLoC) so they can keep optimizing their ad spend. FLoC is a new tracking technology that is planned to be rolled out as third party cookies are banned by Google in 2023. FLoC uses federated learning principles. FLoC creates groups or "cohorts" by using browser historical data of users. Google has just started to test FLoC in 2021 in some countries.

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

We study the problem of controlling a partially observed Markov decision process (POMDP) to either aid or hinder the estimation of its state trajectory by optimising the conditional entropy of the state trajectory given measurements and controls, a quantity we dub the smoother entropy. Our consideration of the smoother entropy contrasts with previous active state estimation and obfuscation approaches that instead resort to measures of marginal (or instantaneous) state uncertainty due to tractability concerns. By establishing novel expressions of the smoother entropy in terms of the usual POMDP belief state, we show that our active estimation and obfuscation problems can be reformulated as Markov decision processes (MDPs) that are fully observed in the belief state. Surprisingly, we identify belief-state MDP reformulations of both active estimation and obfuscation with concave cost and cost-to-go functions, which enables the use of standard POMDP techniques to construct tractable bounded-error (approximate) solutions. We show in simulations that optimisation of the smoother entropy leads to superior trajectory estimation and obfuscation compared to alternative approaches. Index Terms Partially observed Markov decision process (POMDP), entropy, estimation, directed information. The problem of controlling a stochastic dynamical system to either aid or hinder the estimation of its time-varying state arises across numerous applications in automatic control, signal processing, and robotics.

Electrocardiography (ECG) signal is a highly applied measurement for individual heart condition, and much effort have been endeavored towards automatic heart arrhythmia diagnosis based on machine learning. However, traditional machine learning models require large investment of time and effort for raw data preprocessing and feature extraction, as well as challenged by poor classification performance. Here, we propose a novel deep learning model, named Attention-Based Convolutional Neural Networks (ABCNN) that taking advantage of CNN and multi-head attention, to directly work on the raw ECG signals and automatically extract the informative dependencies for accurate arrhythmia detection. To evaluate the proposed approach, we conduct extensive experiments over a benchmark ECG dataset. Our main task is to find the arrhythmia from normal heartbeats and, at the meantime, accurately recognize the heart diseases from five arrhythmia types. We also provide convergence analysis of ABCNN and intuitively show the meaningfulness of extracted representation through visualization. The experimental results show that the proposed ABCNN outperforms the widely used baselines, which puts one step closer to intelligent heart disease diagnosis system.

Nonlinear stochastic modeling is useful for describing complex engineering systems. Meanwhile, neuromorphic (brain-inspired) computing paradigms are developing to tackle tasks that are challenging and resource intensive on digital computers. An emerging scheme is reservoir computing which exploits nonlinear dynamical systems for temporal information processing. This paper introduces reservoir computers with output feedback as stationary and ergodic infinite-order nonlinear autoregressive models. We highlight the versatility of this approach by employing classical and quantum reservoir computers to model synthetic and real data sets, further exploring their potential for control applications.