News by Subject Mathematics

A novel system developed at MIT uses RFID tags to help robots home in on moving objects with unprecedented speed and accuracy. The system could enable greater collaboration and precision by robots working on packaging and assembly, and by swarms of drones carrying out search-and-rescue missions.

Rafael Polanía and his team of ETH researchers have developed a computer model capable of predicting certain human decisions. With it, researchers can predict for example which food someone in a supermarket will choose to buy -- valuable information for marketing and health.

One of the makers of the light detector to be used in the Deep Underground Neutrino Experiment (DUNE) presented a new version, X-Arapuca, that will make the capture of photons even more efficient, at FAPESP Week London.

Despite rapid advances in targeted therapies for cancer, tumors commonly develop resistance to treatment. When resistance emerges, tumor cells continue to grow unchecked, despite all attempts to slow cancer progression. While mutations in cancer cells significantly affect drug sensitivity, it is increasingly recognized that ecological interactions between cells can also play a role. Jacob Scott, M.D., D.Phil., a physician-scientist at Cleveland Clinic, is interested in learning how cancer cells develop and maintain drug resistance from an eco-evolutionary perspective.

Debbie Leung, a fellow in CIFAR's Quantum Information Science program and a faculty member at the University of Waterloo's Institute for Quantum Computing, will discuss the challenges of scaling quantum computing at the AAAS meeting on Feb. 16.

Rice University statistician Genevera Allen is cautioning fellow scientists at the 2019 AAAS Annual Meeting in Washington, D.C., not to make assumptions about the accuracy, uncertainty or reproducibility of scientific discoveries made with today's machine learning models.

Like going from a pinhole camera to a Polaroid, a significant mathematical update to the formula for a popular bioinformatics data visualization method will allow researchers to develop snapshots of single-cell gene expression not only several times faster but also at much higher-resolution. Published in Nature Methods, this innovation by Yale mathematicians will reduce the rendering time of a million-point single-cell RNA-sequencing (scRNA-seq) data set from over three hours down to just fifteen minutes.