Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The unavailability of specific drugs and ready-to-use vaccines to prevent most of these epidemics makes the situation worse. These force public health officials and policymakers to rely on early warning systems generated by reliable and accurate forecasts of epidemics. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyse a wide variety of epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it EWNet model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with several statistical, machine learning, and deep learning models. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods.

The discussion of Artificial Intelligence (AI) has become so prevalent that every sector is now informed of its existence. Over the years, technology has been a major driving force in growing and developing human work states and engagements. With various examples of newly developed AI, the adoption of this digital revolution has become so paramount to everyone. However, an important question I have amid all these is, "how will AI help developing Third World countries?" It is important to first examine what third-world countries are before delving into the ways in which AI can help them better.

New AI systems such as ChatGPT, the overhauled Microsoft Bing search engine, and the reportedly soon-to-arrive GPT-4 have utterly captured the public imagination. ChatGPT is the fastest-growing online application, ever, and it's no wonder why. Type in some text, and instead of getting back web links, you get well-formed, conversational responses on whatever topic you selected--an undeniably seductive vision. But the public, and the tech giants, aren't the only ones who have become enthralled with the Big Data–driven technology known as the large language model. Bad actors have taken note of the technology as well. At the extreme end, there's Andrew Torba, the CEO of the far-right social network Gab, who said recently that his company is actively developing AI tools to "uphold a Christian worldview" and fight "the censorship tools of the Regime."

From South Juba to Entebbe, from Marrakesh to Accra, on the cusp of technology in Africa, the need for responsible AI development and ethical data practices has never been more pressing. As technology continues to advance and shape the global economy, Africa is taking steps toward positioning itself as a leader in Artificial Intelligence (AI). Investments and innovations in AI are on the rise across the continent, with a growing number of countries beginning to develop policies and strategies to harness the power of this transformative technology. Although only a few countries have officially adopted AI strategies and policies, many more are actively working towards defining their AI policies. As philosopher and economist Amartya Sen noted, 'Development requires the removal of major sources of unfreedom that leave people with little choice and little opportunity of exercising their reasoned agency.'

There was a young lady called Aisha who lived in the wide and varied continent of Africa. Aisha dreamt of opening her jewelry store one day when she and her family resided in a tiny town. Yet she quickly saw that the absence of financial services in her town impeded her ability to accomplish her aspirations. Aisha and her family often encountered difficult and expensive processes even when they could visit banks despite traveling great distances. But suddenly, a miraculous event took place.

Strolling down the picturesque paths surrounding Felixstowe, a port town on the heath-speckled coast of southern England, it's hard to imagine that such a peaceful-looking place has played host to events that disrupted the global logistics industry. The connected and interdependent nature of our modern economy means that when port workers in Felixstowe went on an eight-day strike in September 2022, it caused major issues around the world. What's more, "once-in-a-generation" events are becoming the new normal, producing even greater upheaval and begging the question: "Can supply chain technology come to the rescue?" The last three years have redefined what global supply chain disruption means. The COVID-19 pandemic, Suez Canal blockage and port congestion have brought chaos to many companies' logistics operations.

Many planning and decision activities in logistics and supply chain management are based on forecasts of multiple time dependent factors. Therefore, the quality of planning depends on the quality of the forecasts. We compare various forecasting methods in terms of out of the box forecasting performance on a broad set of simulated time series. We simulate various linear and non-linear time series and look at the one step forecast performance of statistical learning methods.

Deep neural networks have been successful in many reinforcement learning settings. However, compared to human learners they are overly data hungry. To build a sample-efficient world model, we apply a transformer to real-world episodes in an autoregressive manner: not only the compact latent states and the taken actions but also the experienced or predicted rewards are fed into the transformer, so that it can attend flexibly to all three modalities at different time steps. The transformer allows our world model to access previous states directly, instead of viewing them through a compressed recurrent state. By utilizing the Transformer-XL architecture, it is able to learn long-term dependencies while staying computationally efficient. Our transformer-based world model (TWM) generates meaningful, new experience, which is used to train a policy that outperforms previous model-free and model-based reinforcement learning algorithms on the Atari 100k benchmark.

Sparse tensors are prevalent in many data-intensive applications, yet existing differentiable programming frameworks are tailored towards dense tensors. This presents a significant challenge for efficiently computing gradients through sparse tensor operations, as their irregular sparsity patterns can result in substantial memory and computational overheads. In this work, we introduce a novel framework that enables the efficient and automatic differentiation of sparse tensors, addressing this fundamental issue. Our experiments demonstrate the effectiveness of the proposed framework in terms of performance and scalability, outperforming state-of-the-art frameworks across a range of synthetic and real-world datasets. Our approach offers a promising direction for enabling efficient and scalable differentiable programming with sparse tensors, which has significant implications for numerous applications in machine learning, natural language processing, and scientific computing.

The field of natural language processing (NLP) has made significant strides in recent years, particularly in the development of large-scale vision-language models (VLMs). These models aim to bridge the gap between text and visual information, enabling a more comprehensive understanding of multimedia data. However, as these models become larger and more complex, they also become more challenging to train and deploy. One approach to addressing this challenge is the use of sparsely-gated mixture-of-experts (MoE) techniques, which divide the model into smaller, specialized sub-models that can jointly solve a task. In this paper, we explore the effectiveness of MoE in scaling vision-language models, demonstrating its potential to achieve state-of-the-art performance on a range of benchmarks over dense models of equivalent computational cost. Our research offers valuable insights into stabilizing the training of MoE models, understanding the impact of MoE on model interpretability, and balancing the trade-offs between compute performance when scaling VLMs. We hope our work will inspire further research into the use of MoE for scaling large-scale vision-language models and other multimodal machine learning applications.