Veeva [NYSE: VEEV] is the leader in cloud-based software for the global life sciences industry. Committed to innovation, product excellence, and customer success, our customers range from the world's largest pharmaceutical companies to emerging biotechs. Veeva's software helps our customers bring medicines and therapies to patients faster. We are the first public company to become a Public Benefit Corporation. As a PBC, we are committed to making the industries we serve more productive, and we are committed to creating high-quality employment opportunities.

Hierarchical forecasting problems arise when time series compose a group structure that naturally defines aggregation and disaggregation coherence constraints for the predictions. In this work, we explore a new forecast representation, the Poisson Mixture Mesh (PMM), that can produce probabilistic, coherent predictions; it is compatible with the neural forecasting innovations, and defines simple aggregation and disaggregation rules capable of accommodating hierarchical structures, unknown during its optimization. We performed an empirical evaluation to compare the PMM \ to other hierarchical forecasting methods on Australian domestic tourism data, where we obtain a 20 percent relative improvement.

With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over contradicting information to derive correct answers. This urgency gives rise to the need to make QA systems robust to misinformation, a topic previously unexplored. We study the risk of misinformation to QA models by investigating the behavior of the QA model under contradicting contexts that are mixed with both real and fake information. QA, which contains over 10K human-written and model-generated contradicting pairs of contexts. Experiments show that QA models are vulnerable under contradicting contexts brought by misinformation. To defend against such threat, we build a misinformation-aware QA system as a counter-measure that integrates question answering and misinformation detection in a joint fashion. A typical Question Answering (QA) system (Chen et al., 2017; Yang et al., 2019; Karpukhin et al., 2020; Lewis et al., 2020b) starts by retrieving a set of relevant context documents from the Web, which are then examined by a machine reader to identify the correct answer. Existing work equate Wikipedia as the web corpus. Therefore, all retrieved context documents are assumed to be clean and trustable. However, real-world QA faces a much noisier environment, where the web corpus is tainted with misinformation.

The catastrophic Fukushima nuclear disaster in 2011 caused an estimated 250,000 people to evacuate their homes, but scientists have determined certain wildlife species in the area are thriving, suggesting people could eventually return to the region, according to a new study. Researchers at Colorado State University, the University of Georgia and Fukushima University's Institute of Environmental Radioactivity have found that multiple generations of wild boar and rat snakes have not suffered from any significant adverse health effects. Multiple generations of animals have been exposed to radiation levels above the threshold for human occupancy, but have suffered no ill effects. That may be due to the fact that cesium-134, one of the major radioactive materials released during the accident, saw its levels decrease by almost 90 percent. The researchers looked at biomarkers of DNA damage and stress to determine that the boar and snakes were thriving in the area. The researchers looked at the wild boars and snakes between 2016 and 2018, or five to seven years after the earthquake and resulting tsunami destroyed the Fukushima Dai-ichi Nuclear Power Plant, releasing massive amounts of radioactive material in the environment.

Machine Learning refers to the process through which a computer learns and changes its operations based on patterns identified in vast quantities of data. When we think about machine learning, we think of a few well-known instances. For example, the way Amazon recommends products is remarkably similar to Google searches you've done. Machine learning's reach is far broader than what we are familiar with and observes in our daily lives. Because machine learning is such a young science, the boundaries of its applicability are continuously being pushed outside. Virtual personal assistants were once the stuff of fantasies, but now they can be found in every other home.

Veeva [NYSE: VEEV] is the leader in cloud-based software for the global life sciences industry. Committed to innovation, product excellence, and customer success, our customers range from the world's largest pharmaceutical companies to emerging biotechs. Veeva's software helps our customers bring medicines and therapies to patients faster. We are the first public company to become a Public Benefit Corporation. As a PBC, we are committed to making the industries we serve more productive, and we are committed to creating high-quality employment opportunities.

Veeva [NYSE: VEEV] is the leader in cloud-based software for the global life sciences industry. Committed to innovation, product excellence, and customer success, our customers range from the world's largest pharmaceutical companies to emerging biotechs. Veeva's software helps our customers bring medicines and therapies to patients faster. We are the first public company to become a Public Benefit Corporation. As a PBC, we are committed to making the industries we serve more productive, and we are committed to creating high-quality employment opportunities.

The next generation of wireless technology could affect a wide range of industries, from healthcare to financial services to retail. The technology enables faster data transfer speeds -- up to 10x faster than the speeds achievable with older standards -- lower latency, and greater network capacity. As a result, 5G creates a tremendous opportunity for numerous industries, but also sets the stage for large-scale disruption. Download the free report to understand what 5G is, the industries it's disrupting, and the drivers paving the way for its implementation. As of June 2021, commercial 5G services have already been deployed across more than 1,500 cities in 60 countries worldwide, according to Viavi Solutions. The number of IoT devices -- which will rely on 5G to transmit vast amounts of data in real time -- is projected to grow from 12B in 2020 to 30B in 2025, per IoT Analytics, more than 4 devices for every person on Earth. Executives across industries are already jostling to take advantage of 5G tech -- and avoid being disrupted by it. Earnings call mentions of 5G have soared in recent years. From enabling remote robotic surgery and autonomous cars to improving crop management, 5G is poised to transform many of the world's biggest industries. The impact of 5G on manufacturing could be huge. It's estimated that improved connectivity through 5G will create $13T in global economic value across industries by 2035, according to IHS Markit. A third of that total is projected to come from the manufacturing sector alone. This would enable manufacturers to build "smart factories" that rely on automation, augmented reality, and IoT. And with 5G powering large amounts of IoT devices and sensors around the factory, artificial intelligence can be integrated more deeply with operations. On fast-paced assembly lines, even microseconds of latency can cause costly disruptions for the manufacturer.

Most organizations – regardless of their size and industry – can benefit from the application of artificial intelligence. Charts and dashboards are useful tools, but they often struggle to analyze big and complex datasets. This is precisely when AI outperforms the traditional Business Intelligence approach! Correlation doesn't imply causation, and this is a significant limitation of BI. Artificial Intelligence can help a company in a variety of ways – it can be employed to build customer retention models, increase gross revenue by optimizing your selling price, find a way to minimize costs and optimize business processes, and ultimately – to run an organization more effectively.

As well as causing death and destruction, a study suggests natural disasters can put people at a greater risk of developing dementia. Using health and survey data on people living in the areas worst hit by a 2011 tsunami in Japan, experts from Harvard University explored the link between cognitive decline and natural disasters. The event, which killed 20,000 and saw 100,000 children uprooted from their homes, may also have contributed to cognitive decline in older adults, they found. More than 3,000 people with an average age of 73 were questioned about their cognitive state, finding those that lost a home in the disaster experienced an increase in their rate of cognitive decline, but losing a loved one had no impact. The team say this is linked to increased isolation, with the unmarried, lower educated and eldest at the greatest risk of increasing cognitive decline.