The document provides an overview of the industrial robotics sector including key investment trends, market details, exit outlook, notable investors, and recent news. It summarizes funding activity in the sector with over $4.1B invested in 1400+ companies. Healthcare, logistics, and manufacturing are highlighted as top markets. Notable companies include Intuitive Surgical, Kuka, Blue River Technology, and Universal Robots.
Tracxn Research - Industrial Robotics Landscape, February 2017Tracxn
A number of investments in 2016 were made by CVCs such as GE Ventures, Caterpillar, Medtronic, and Mitsubishi UFJ Capital, who envision robotic technology to be implemented in their area of expertise.
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Tracxn - Southeast Asia Tech Monthly Funding ReportTracxn
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Tracxn - Top Business Models in Sustainability Tech - 26 Oct 2023Tracxn
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Tracxn - United States Tech Monthly Funding ReportTracxn
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Tracxn - Top Business Models in High Tech - 27 Oct 2023Tracxn
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Tracxn - Top Business Models in Vietnam Tech - 20 Oct 2023Tracxn
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Tracxn - Top Business Models in Singapore Tech - 19 Oct 2023Tracxn
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Tracxn - Top Business Models in United Kingdom Tech - 18 Oct 2023Tracxn
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Tracxn -Top Business Models in Transportation and Logistics Tech - 17 Oct 2023Tracxn
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Tracxn - United Kingdom Tech Monthly Funding ReportTracxn
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Tracxn - Top Business Models in Sustainability Tech - 26 Oct 2023Tracxn
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Tracxn - United States Tech Monthly Funding ReportTracxn
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Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Join 4000+ people taking sharper decisions with Tracxn
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