The reigning MVP is attempting an unfathomable amount of 3-pointers just to keep the Rockets in the playoff picture. He may have created the latest positional archetype in the process.
With his surgically repaired torn Achilles propped up behind him, veteran wide receiver Demaryius Thomas is looking at an uncertain future.
Although the 31-year-old five-time Pro Bowl selection would like to be back with the Texans, it will take him roughly six months to rehab the injury. And his contract would need to be adjusted as he’s due a nonguaranteed $14 million base salary next year.
Whether the Texans would need Thomas with the healthy return of Will Fuller and Keke Coutee working in tandem with All-Pro DeAndre Hopkins is another question.
Professional swimmer and former Stanford student-athlete Katie Ledecky spent the last week training with her former teammates at the U.S. Olympic Training Center in Colorado Springs, Colorado. [video, 1:16]
There’s nowhere Frederik Andersen wants to be more than back in the Maple Leafs’ crease. But he’ll have to wait at least three more days before that happens.
Despite Andersen taking part in his second full-team practice on Sunday, Mike Babcock already ruled him out for Monday’s game against Nashville by revealing Michael Hutchinson will get his third consecutive start for Toronto. Andersen’s next opportunity to play won’t come until Thursday in New Jersey.
“The idea is to be ready to play. It goes without saying,” Andersen said. “Until then, we take care of what we can control and make sure the right treatment is being given.”
Intrinsic cardiorespiratory fitness (CRF) is defined as the level of CRF in the sedentary state. There are large individual differences in intrinsic CRF among sedentary adults. The physiology of variability in CRF has received much attention, but little is known about the genetic and molecular mechanisms that impact intrinsic CRF. These issues were explored in the present study by interrogating intrinsic CRF-associated DNA sequence variation and skeletal muscle gene expression data from the HERITAGE Family Study through an integrative bioinformatics guided approach. A combined analytic strategy involving genetic association, pathway enrichment, tissue-specific network structure, cis-regulatory genome effects, and expression quantitative trait loci was used to select and rank genes through a variation-adjusted weighted ranking scheme. Prioritized genes were further interrogated for corroborative evidence from knockout mouse phenotypes and relevant physiological traits from the HERITAGE cohort. The mean intrinsic VO2max was 33.1 mL O2/kg/min (SD = 8.8) for the sample of 493 sedentary adults. Suggestive evidence was found for gene loci related to cardiovascular physiology (ATE1, CASQ2, NOTO, SGCG), hematopoiesis (PICALM, SSB, CA9, CASQ2), skeletal muscle phenotypes (SGCG, DMRT2, ADARB1, CASQ2), and metabolism (ATE1, PICALM, RAB11FIP5, GBA2, SGCG, PRADC1, ARL6IP5, CASQ2). Supportive evidence for a role of several of these loci were uncovered via association between DNA variants and muscle gene expression levels with exercise cardiovascular and muscle physiological traits. This initial effort to define the underlying molecular substrates of intrinsic CRF warrants further studies based on appropriate cohorts and study designs, complemented by functional investigations.
In the companion study, we report a new approach for analyzing gait complexity and the structure of gait variability (12). Previous studies show that the fractal scaling index (FSI) determined using detrended fluctuation analysis (DFA) is reduced by neuromuscular disorders (6) as well as low back
pain, fatigue, injury and overtraining (2, 5, 8, 10), indicating a less healthy gait (1). On the other hand, physical training increases DFA (9).
The First Day of the Gregg Berhalter-era of the U.S. national team is in the books and the new manager spoke to the media after training in Chula Vista on Monday. While no conclusions can be drawn after a single day, the New Jersey native was eager to talk about his vision.
“One of the main objectives of this camp is team building,” Berhalter said. “It’s an intense period but it’s a focused period. I think we’re really going to get quality time together as a team.”
The venue is not new to Berhalter as it was also the site where the U.S. team trained in one of its final times in the United States before heading to Europe for the 1998 World Cup. While that venture was not successful 21 years ago, Berhalter is aiming to kick off something positive in Southern California.
We propose a radical change in design from experts designing for people to people designing for themselves. In the traditional approach, experts study, design, and implement solutions for the people of the world. Instead, we propose that we leverage the creativity within the communities of the world to solve their own problems: This is community-driven design, taking full advantage of the fact that it is the people in communities who best understand their problems and the impediments and affordances that impede and support change. Experts become facilitators, by mentoring and providing tools, toolkits, workshops, and support.
Valencell, the leading innovator in biometric sensor technology, today announces the next generation of its high-performance, low-power Benchmark biometric sensor series for hearables. Designed in collaboration with strategic partner, Sonion A/S, Benchmark BE5.0 is designed to advance the metrics that can be accurately measured with a PPG sensor, while reducing power consumption to improve overall battery life. BE5.0 will also provide the platform to deliver cutting edge features in the future including motion-tolerant R-R interval, blood pressure and motion-tolerant multi-wavelength applications in one module. BE5.0 will be on display at the Consumer Electronics Show (CES) 2019 Tuesday, January 8 through Friday, January 11 in the Sands Expo, Level 2, Halls A-D, in Booth #44006.
“Since the company’s founding, Valencell has chosen not to compete with silicon providers but rather to collaborate with them by breathing life into sensor and processor components with our technology and expertise,” said Dr. Steven LeBoeuf, co-founder and president of Valencell. “That decision has continued to pay off as we focus on what we do best – providing the marketplace with complete biometric sensor systems for wearables with the highest levels of accuracy to enable long sought-after use cases.”
… Every major sports league is counting on data to revolutionize how athletes train and recover — and how coaches evaluate and prepare for games. But the analytics boom has also produced some thorny questions. Should a player’s privacy factor in? Should the data be used in contract negotiations? And who should share the spoils if broadcasters and sports-gambling companies pay for the information?
To resolve these questions, research firm Sports Innovation Lab formed a 16-person advisory board with executives from the major sports leagues, unions, tech companies and gambling houses. The board will meet four times — starting this week — with the goal of producing standards and best practices by the end of 2019.
‘Over and Over’
“We see these questions over and over again,” said Angela Ruggiero, a four-time Olympic medalist for USA hockey and co-founder of Sports Innovation Lab. “Everyone is trying to solve them in their own unique, siloed way. Our plan is to accelerate that conversation.”
The US economy has undergone a number of puzzling changes in recent decades. Large firms now account for a greater share of economic activity, new firms are being created at a slower rate, and workers are getting paid a smaller share of GDP. This paper shows that changes in population growth provide a unified quantitative explanation for these long-term changes. The mechanism goes through firm entry rates. A decrease in population growth lowers firm entry rates, shifting the firm-age distribution towards older firms. Heterogeneity across firm age groups combined with an aging firm distribution replicates the observed trends. Micro data show that an aging firm distribution fully explains i) the concentration of employment in large firms, ii) and trends in average firm size and exit rates, key determinants of the firm entry rate. An aging firm distribution also explains the decline in labor’s share of GDP. In our model, older firms have lower labor shares because of lower overhead labor to employment ratios. Consistent with our mechanism, we find that the ratio of nonproduction workers to total employment has declined in the US.
Reuters Investigates, Tom Bergin and Cassell Bryan-Low i from
The Portuguese agent Jorge Mendes joined forces with a wealthy investor from Shanghai and planned to cash in on a lucrative but controversial corner of European football: buying and selling athletes. “Football Leaks” documents give an insight into the scale of their ambitions.
Summary: Cognitive biases change the way that we recall past events. The peak–end rule focuses our memories around the most intense moments of an experience and the way an experience ends.
Harvard Business School, HBS Working Knowledge, Roberta Holland from
Whether it’s booking a hotel, renting a movie, or buying a car, many of us consult multiple reviews before deciding. It’s called aggregating opinions, and we do it without even thinking about it.
Crowdsourcing works so well, in fact, says Harvard Business School visiting associate professor Yael Grushka-Cockayne, that executives should adopt a similar approach when it comes to using probability forecasts of business-critical issues; for example, the likelihood that product demand will increase by a given percentage next quarter.
“The whole notion of using crowds is very popular in many different fields,” says Grushka-Cockayne, whose research is on data science, forecasting, project management, and behavioral decision-making. “Our work is focused on using crowds for prediction and for forecasting something that is unknown.”
… today’s paper choice is “The tradeoffs of large scale learning,” which won the ‘test of time’ award at NeurIPS last month.
this seminal work investigated the interplay between data and computation in ML, showing that if one is limited by computing power but can make use of a large dataset, it is more efficient to perform a small amount of computation on many individual training examples rather than to perform extensive computation on a subset of the data. [Google AI blog: The NeurIPS 2018 Test of Time Award].
For a given time/computation budget, are we better off performing a computationally cheaper (e.g., approximate) computation over lots of data, or a more accurate computation over less data? In order to get a handle on this question, we need some model to help us think about how good a given solution is.