Climate events arise from intricate, multivariate dynamics governed by global-scale drivers, profoundly impacting food, energy, and infrastructure. Yet, accurate weather prediction remains elusive due to physical processes unfolding across diverse spatio-temporal scales, which fixed-resolution methods cannot capture. Hierarchical Graph Neural Networks (HGNNs) offer a multiscale representation, but nonlinear downward mappings often erase global trends, weakening the integration of physics into forecasts. We introduce HiFlowCast and its ensemble variant HiAntFlow, HGNNs that embed physics within a multiscale prediction framework. Two innovations underpin their design: a Latent-Memory-Retention mechanism that preserves global trends during downward traversal, and a Latent-to-Physics branch that integrates PDE solution fields across diverse scales. Our Flow models cut errors by over 5% at 13-day lead times and by 5-8% under 1st and 99th quantile extremes, improving reliability for rare events. Leveraging pretrained model weights, they converge within a single epoch, reducing training cost and their carbon footprint. Such efficiency is vital as the growing scale of machine learning challenges sustainability and limits research accessibility. Code and model weights are in the supplementary materials.

Information popularity prediction is important yet challenging in various domains, including viral marketing and news recommendations. The key to accurately predicting information popularity lies in subtly modeling the underlying temporal information diffusion process behind observed events of an information cascade, such as the retweets of a tweet. To this end, most existing methods either adopt recurrent networks to capture the temporal dynamics from the first to the last observed event or develop a statistical model based on self-exciting point processes to make predictions. However, information diffusion is intrinsically a complex continuous-time process with irregularly observed discrete events, which is oversimplified using recurrent networks as they fail to capture the irregular time intervals between events, or using self-exciting point processes as they lack flexibility to capture the complex diffusion process. Against this background, we propose ConCat, modeling the Continuous-time dynamics of Cascades for information popularity prediction. On the one hand, it leverages neural Ordinary Differential Equations (ODEs) to model irregular events of a cascade in continuous time based on the cascade graph and sequential event information. On the other hand, it considers cascade events as neural temporal point processes (TPPs) parameterized by a conditional intensity function which can also benefit the popularity prediction task. We conduct extensive experiments to evaluate ConCat on three real-world datasets. Results show that ConCat achieves superior performance compared to state-of-the-art baselines, yielding a 2.3%-33.2% improvement over the best-performing baselines across the three datasets.

This paper describes a family of seasonal and non-seasonal time series models that can be viewed as generalisations of additive and multiplicative exponential smoothing models, to model series that grow faster than linear but slower than exponential. Their development is motivated by fast-growing, volatile time series. In particular, our models have a global trend that can smoothly change from additive to multiplicative, and is combined with a linear local trend. Seasonality when used is multiplicative in our models, and the error is always additive but is heteroscedastic and can grow through a parameter sigma. We leverage state-of-the-art Bayesian fitting techniques to accurately fit these models that are more complex and flexible than standard exponential smoothing models. When applied to the M3 competition data set, our models outperform the best algorithms in the competition as well as other benchmarks, thus achieving to the best of our knowledge the best results of per-series univariate methods on this dataset in the literature. An open-source software package of our method is available.

In this work, we propose a novel framework for large-scale Gaussian process (GP) modeling. Contrary to the global, and local approximations proposed in the literature to address the computational bottleneck with exact GP modeling, we employ a combined global-local approach in building the approximation. Our framework uses a subset-of-data approach where the subset is a union of a set of global points designed to capture the global trend in the data, and a set of local points specific to a given testing location to capture the local trend around the testing location. The correlation function is also modeled as a combination of a global, and a local kernel. The performance of our framework, which we refer to as TwinGP, is on par or better than the state-of-the-art GP modeling methods at a fraction of their computational cost.

If we see the present outsourcing market is changing faster than expected. Despite all the challenges faced globally, this industry is growing faster in the past couple of years. Also, outsourcing gas is very well adjusted to any sort of business disruptions such as the COVID-19 pandemic, tight labor market, supply issues, or any sort of climate changes, and global power shifts. Now outsourcing has become a proven solution to reduce risk, maintain productivity, navigate the labor market, and various challenges. The latest trends are enabling businesses to focus on core competencies and manage tasks efficiently.

The Artificial Intelligence in Healthcare Market reports gives a far reaching review of the worldwide market size and global trends with values. Artificial Intelligence in Healthcare Market reports additionally give a multi-year pre-memorable for the segment and remember information for financial information of worldwide. Key partners can think about measurements, tables and figures referenced in this report for vital arranging which lead to achievement of the association. Artificial Intelligence in Healthcare market detailed by definitions, orders, applications and market outline; product determinations; producing forms; cost structures, crude materials, etc. At that point it investigated the world's principle locale economic situations, including the product value, benefit, limit, creation, gracefully, request and market development rate and conjecture and other.

Trusted Business Insights presents an updated and Latest Study on Machine Learning Market Market 2019-2029.

By now, you'll have interacted with chatbots whether it's doing banking, buying online, getting help for a technical issue or searching for a place to stay through a booking site. It might have been a satisfactory experience or quite frustrating but it's the norm now. The big question is how much more advanced will chatbot technology get and will chatbots eventually replace humans in the workforce? Basically, chatbots are virtual robots that are always at work, never go on leave, never get sick and cost a lot less to have on board than their human counterparts. But, just like the Tin Man in the Wizard of Oz, they don't have a heart and it's highly unlikely that for a long time to come they'll be able to adequately replace the personal touch of a living, breathing human.

Therefore, agribusiness corporations adopt artificial intelligence technologies in terms of predictive analytics-based solutions.

The research report on artificial intelligence market, in substance, presents an exclusive understanding of the vast expanse of the business space in question. The report comprises a gist of the industry by means of providing an executive summary, industry insights, industry ecosystem analysis, market segmentation, and global trends. Furthermore, the study also provides deliverables pertaining to the regulatory and competitive landscapes and the strategic perspectives of various industry contenders with respect to the artificial intelligence indutry . However, the major challenges faced by industry players are the low return on investment and the complexity involved in the creation of AI mechanisms and models. Lack of energy-efficient and cost-effective hardware restricts the adoption of such technology in small and medium enterprises, thereby restricting the artificial intelligence market growth during the forecast timeline.