Extracting frequent words from a collection of texts is commonly performed in many subjects. However, as useful as it is to obtain a collection of commonly occurring words from texts, there is a need for more specific information to be obtained from texts in the form of most commonly occurring phrases. Despite this need, extracting frequent phrases is not commonly done due to inherent complications, the most significant being double-counting. Double-counting occurs when words or phrases are counted when they appear inside longer phrases that themselves are also counted, resulting in a selection of mostly meaningless phrases that are frequent only because they occur inside frequent super phrases. Several papers have been written on phrase mining that describe solutions to this issue; however, they either require a list of so-called quality phrases to be available to the extracting process, or they require human interaction to identify those quality phrases during the process. We present here a method that eliminates double-counting via a unique rectification process that does not require lists of quality phrases. In the context of a set of texts, we define a principal phrase as a phrase that does not cross punctuation marks, does not start with a stop word, with the exception of the stop words "not" and "no", does not end with a stop word, is frequent within those texts without being double counted, and is meaningful to the user. Our method identifies such principal phrases independently without human input, and enables their extraction from any texts within a reasonable amount of time.

It normally takes a user three to five minutes to go through the authentication process, but she is faster because the store, Opulent Habits, in Madison, N.J., has been using the app since 2018. "I can do it in less than a minute at this point," Ms. Matthaei says. A look at how innovation and technology are transforming the way we live, work and play. Increasingly, the role of spotting counterfeits is being filled by artificial-intelligence algorithms that have studied every angle of tens of thousands of bags, shoes and other items that are often knocked off. Inc. are developing machine-learning tools to help protect shoppers.

When Olivia Matthaei, a consignment store sales clerk, needs to check whether a designer handbag is authentic, she knows the drill. She grabs a custom camera with a microscope lens provided by Entrupy, a New York-based artificial-intelligence startup. The shape of a bulky battery pack, it pops onto an iPhone or iPod. She opens the Entrupy app and selects a brand from a list. It normally takes a user three to five minutes to go through the authentication process, but she is faster because the store, Opulent Habits, in Madison, N.J., has been using the app since 2018.

Origami Surgical last week filed an SEC Form D to confirm the sale of more than $2.2 million in an equity offering.The Madison, N.J.-based company, founded this year, filed a new notice for the sale of equity on March 23, 2020, with the intention of the offering lasting less than a year. According to the Form D filing, Origami Surgical's offering is not being made in connection with a business combination transaction, such as a merger, acquisition or exchange offer. Two investors contributed to the sale of $2,224,998 in the equity offering that is set to bring in $2,499,996, leaving $274, 998 left to be sold. Origami Surgical did not list an intended use of proceeds. The company develops the StitchKit, which it touts as the first suture system designed to improve endoscopic robotic surgery outcomes by increasing efficiency, autonomy and safety, according to the company website.

JERSEY CITY, NEW JERSEY – With the push of a button, a perfectly healthy 34-year-old museum-goer named Ugo Dumont was transformed into a confused 85-year-old man with cataracts, glaucoma and a ringing in his ears known as tinnitus. Dumont had volunteered at Liberty Science Center on Tuesday to don a computer-controlled exoskeleton that can be remotely manipulated to debilitate joints, vision and hearing and shared with the crowd what aging feels like decades before his time. Headphones muffled his hearing while goggles left him with only peripheral vision due to macular degeneration while the suit's joints were adjusted to simulate the stiffness of rheumatoid arthritis. The 40-pound (18 kg) suit also gave Dumont a taste of the weight gain people typically experience as they age. "Wow," Dumont gasped as he struggled to walk on a treadmill facing a video titled "Walk on the Beach."

We introduce a new wavelet transform suitable for analyzing functions on point clouds and graphs. Our construction is based on a generalization of the average interpolating refinement scheme of Donoho. The most important ingredient of the original scheme that needs to be altered is the choice of the interpolant. Here, we define the interpolant as the minimizer of a smoothness functional, namely a generalization of the Laplacian energy, subject to the averaging constraints. In the continuous setting, we derive a formula for the optimal solution in terms of the poly-harmonic Green's function. The form of this solution is used to motivate our construction in the setting of graphs and point clouds. We highlight the empirical convergence of our refinement scheme and the potential applications of the resulting wavelet transform through experiments on a number of data stets.