Understanding and quantifying chaos from data remains challenging. We present a data-driven method for estimating the largest Lyapunov exponent (LLE) from one-dimensional chaotic time series using machine learning. A predictor is trained to produce out-of-sample, multi-horizon forecasts; the LLE is then inferred from the exponential growth of the geometrically averaged forecast error (GMAE) across the horizon, which serves as a proxy for trajectory divergence. We validate the approach on four canonical 1D maps-logistic, sine, cubic, and Chebyshev-achieving R2pos > 0.99 against reference LLE curves with series as short as M = 450. Among baselines, KNN yields the closest fits (KNN-R comparable; RF larger deviations). By design the estimator targets positive exponents: in periodic/stable regimes it returns values indistinguishable from zero. Noise robustness is assessed by adding zero-mean white measurement noise and summarizing performance versus the average SNR over parameter sweeps: accuracy saturates for SNRm > 30 dB and collapses below 27 dB, a conservative sensor-level benchmark. The method is simple, computationally efficient, and model-agnostic, requiring only stationarity and the presence of a dominant positive exponent. It offers a practical route to LLE estimation in experimental settings where only scalar time-series measurements are available, with extensions to higher-dimensional and irregularly sampled data left for future work.

We address the identification of grain-corresponding Laue reflections in energy dispersive Laue diffraction (EDLD) experiments by formulating it as a clustering problem solvable through unsupervised machine learning (ML). To achieve reliable and efficient identification of grains in a Laue pattern, we employ a combination of clustering algorithms, namely hierarchical clustering (HC) and K-means. These algorithms allow us to group together similar Laue reflections, revealing the underlying grain structure in the diffraction pattern. Additionally, we utilise the elbow method to determine the optimal number of clusters, ensuring accurate results. To evaluate the performance of our proposed method, we conducted experiments using both simulated and experimental datasets obtained from nickel wires. The simulated datasets were generated to mimic the characteristics of real-world EDLD experiments, while the experimental datasets were obtained from actual measurements.

In the last two years, Microsoft has invested a lot of money in the ChatGPT inventor Open AI and in its own AI developments. Since then, Microsoft has been equipping more and more programs with artificial intelligence, above all the Edge browser and Office programs. But AI is also finding its way into Windows. In this article, we present the new AI functions in Windows, the Windows apps, and Microsoft 365. Microsoft's AI engine Copilot has been part of the operating system since Windows 11 23H2 for U.S. users, and is available via the Copilot app in the Microsoft Store for others.

SUMMARY: The paper proposes an algorithm that combines imitation learning with tree search, which results in an apprentice learning from an ever-improving expert. A DQN is trained to learn a policy derived from an MCTS agent, with the DQN providing generalisation to unseen states. It is also then used as feedback to improve the expert, which can then be used to retrain the DQN, and so on. The paper makes two contributions: (1) a new target for imitation learning, which is empirically shown to outperform a previously-suggested target and which results in the apprentice learning a policy of equal strength to the expert. COMMENTS: I found the paper generally well-written, clear and easy to follow, barring Section 6.

Microsoft is currently focusing significant financial and human resources on the development of its AI assistant Copilot and its integration into Windows and Microsoft 365 applications. The company sees this as an opportunity to set itself apart from the competition of Libre Office and Google. Today, users have several alternatives for AI support in Office. This is because ChatGPT from OpenAI, the software that triggered the AI hype, is also suitable for office tasks in conjunction with Word, Excel, and others. Independent developers provide add-ons that allow you to integrate ChatGPT directly into Word so that you always have it at hand. At the same time, there are AI systems, especially from American providers, that help you create presentations online. These presentations can then be downloaded and in many cases converted into PowerPoint format PPTX.

We study reinforcement learning (RL) for a class of continuous-time linear-quadratic (LQ) control problems for diffusions where volatility of the state processes depends on both state and control variables. We apply a model-free approach that relies neither on knowledge of model parameters nor on their estimations, and devise an actor-critic algorithm to learn the optimal policy parameter directly. Our main contributions include the introduction of a novel exploration schedule and a regret analysis of the proposed algorithm. We provide the convergence rate of the policy parameter to the optimal one, and prove that the algorithm achieves a regret bound of $O(N^{\frac{3}{4}})$ up to a logarithmic factor. We conduct a simulation study to validate the theoretical results and demonstrate the effectiveness and reliability of the proposed algorithm. We also perform numerical comparisons between our method and those of the recent model-based stochastic LQ RL studies adapted to the state- and control-dependent volatility setting, demonstrating a better performance of the former in terms of regret bounds.

Copilot Pro delivers the power of the AI chat tool Chat-GPT directly to your Microsoft Office applications and the Windows sidebar. The tool summarizes texts, replies to emails, creates new texts, analyzes Excel spreadsheets, and creates presentations. A miracle machine that does all your Office tasks for you automatically? We took a look at the tool. To be able to use Copilot Pro, a few requirements must be met. First, you need a paid subscription to Microsoft 365 Single or Family (from 7 per month). Then you need to take out a subscription to Copilot Pro with the same Microsoft account for 20 per month. Both subscriptions can be cancelled monthly.

The technological revolution taking place in the fields of robotics and artificial intelligence seems to indicate a future shift in our human-centred social paradigm towards a greater inclusion of artificial cognitive agents in our everyday environments. This means that collaborative scenarios between humans and robots will become more frequent and will have a deeper impact on everyday life. In this setting, research regarding trust in human–robot interactions (HRI) assumes a major importance in order to ensure the highest quality of the interaction itself, as trust directly affects the willingness of people to accept information produced by a robot and to cooperate with it. Many studies have already explored trust that humans give to robots and how this can be enhanced by tuning both the design and the behaviour of the machine, but not so much research has focused on the opposite scenario, that is the trust that artificial agents can assign to people. Despite this, the latter is a critical factor in joint tasks where humans and robots depend on each other's effort to achieve a shared goal: whereas a robot can fail, so can a person. For an artificial agent to know when to trust or distrust somebody and adapt its plans to this prediction can make all the difference in the success or failure of the task. Our work is centred on the design and development of an artificial cognitive architecture for a humanoid autonomous robot that incorporates trust, theory of mind (ToM) and episodic memory, as we believe these are the three key factors for the purpose of estimating the trustworthiness of others. We have tested our architecture on an established developmental psychology experiment [1] and the results we obtained confirm that our approach successfully models trust mechanisms and dynamics in cognitive robots. Trust is a fundamental, unavoidable component of social interactions that can be defined as the willingness of a party (the trustor) to rely on the actions of another party (the trustee), with the former having no control over the latter [2].

We study the sequential resource allocation problem where a decision maker repeatedly allocates budgets between resources. Motivating examples include allocating limited computing time or wireless spectrum bands to multiple users (i.e., resources). At each timestep, the decision maker should distribute its available budgets among different resources to maximize the expected reward, or equivalently to minimize the cumulative regret. In doing so, the decision maker should learn the value of the resources allocated for each user from feedback on each user's received reward. For example, users may send messages of different urgency over wireless spectrum bands; the reward generated by allocating spectrum to a user then depends on the message's urgency. We assume each user's reward follows a random process that is initially unknown. We design combinatorial multi-armed bandit algorithms to solve this problem with discrete or continuous budgets. We prove the proposed algorithms achieve logarithmic regrets under semi-bandit feedback.

Blizzard Entertainment made true to its promise to host an online version of BlizzCon after the company was forced to cancel its in-person convention due to the pandemic caused by COVID-19. BlizzCon 2020 was supposed to take place later this year, but with the events that caused a worldwide dilemma has urged organizers to think of ways to still push through the annual event while observing guidelines set by health agencies and the government to curb the spread of the virus. "We're talking about how we might be able to channel the BlizzCon spirit and connect with you in some way online, far less impacted by the state of health and safety protocols for mass in-person gatherings," said BlizzCon Executive Producer Saralyn Smith in a May 26, 2020 blog post. Blizzard Entertainment's eSports World Championship competitions for "Hearthstone," "Heroes of the Storm," "World of Warcraft" and "StarCraft 2" will be held over an entire week later this year. Windows 10 has come on board as an official BlizzCon 2015 sponsor, with the opening ceremony streaming on Xbox One for the first time.